[This is the second part of a four part essay–here is Part I.]

As we (eventually…) start to plot out how to build Artificial General Intelligence there is going to be a bifurcation in the path ahead.  Some will say that we must choose which direction to take. I argue that the question is complex and it may be felicitous to make a Shrödingerian compromise.

I get people making two arguments to me about analogies between AGI and heavier than air flight.  And the arguments are made to me with about equal frequency. As I write these sentences I can recall at least one of the two arguments being made to me in each of the last six weeks.

One argument is that we should not need to take into account how humans come to be intelligent, nor try to emulate them, as heavier than air flight does not emulate birds. That is only partially true as there were multiple influences on the Wright brothers from bird flight¹. Certainly today the appearance of heavier than air flight is very different from that of birds or insects, though the continued study of the flight of those creatures continues to inform airplane design. This is why over the last twenty years or so jet aircraft have sprouted winglets at the ends of primary wings.

Airplanes can fly us faster and further than something that more resembled birds would. On the other hand our airplanes have not solved the problem of personal flight. We can no more fly up from the ground and perch in a tall tree than we could before the Wright brothers. And we are not able to take off and land wherever we want without large and extremely noisy machines. A little more bird would not be all bad.

I accept the point that to build a human level intelligence it may well not need to be much at all like humans in how it achieves that. However, for now at least, it is the only model we have and there is most likely a lot to learn still from studying how it is that people are intelligent. Furthermore, as we will see below, having a lot of commonality between humans and intelligent agents will let them be much more understandable partners.

This is the compromise that I am willing to make. I am willing to believe that we do not need to do everything like humans do, but I am also convinced that we can learn a lot from humans and human intelligence.

The second argument that I get is largely trying to cast me as a grumpy old guy, and that may well be fair!

People like to point out that very late in the nineteenth century, less than a decade before the Wright brothers’ first flight, Lord Kelvin and many others had declared that heavier than air flight was impossible. Kelvin, and others, could not have meant that literally as they knew that birds and insects were both heavier than air and could fly. So they were clearly referring to something more specific. If they were talking about human powered heavier than air flight, they were sort of right as it was more than sixty years away², and then only for super trained athletes.

More likely, they were discouraged by the failures of so many attempts with what had seemed like enough aerodynamic performance and engine power to fly. What they were missing understanding was that it was the control of flight which needed to be developed, and that is what made the Wright brothers successful.

So… are we, by analogy, just one change in perspective away from getting to AGI? And really, is deep learning that change in perspective?  And I don’t know that it is already solved, and the train is accelerating down the track?

I, old(ish) guy for sure, grumpy or not, think that the analogy breaks down. I think it is not just an analogy for flight of “control” that we are missing, but that in fact there are at least a hundred such things we are currently missing. Intelligence is a much bigger deal than flight. A bird brain does not cut it.

So now, with the caveat of the second argument perhaps identifying a personal failing, I want to proclaim that there is no need to come down on one side or the other, airplane vs bird, doing it a completely different way AI vs emulating how a human does it.

Instead I think we should use the strengths wherever we can find them, in engineering, or in biology. BUT, if we do not make our AI systems understandable to us, if they can not relate to the quirks that make us human, and if they are so foreign that we can not treat them with empathy, then they will not be adopted into our lives.

Underneath the shiny exterior they may be not be flesh and blood, but instead may be silicon chips and wires, and yes, even the occasional deep learning network, but if they are to be successful in our world we are going to have to like them.

TWO INSTANCES OF AGI, AS AGENTS THAT DO REAL WORK

Just what a AGI agent or robot must be able to do to qualify as being generally intelligent is I think a murky question. And my goal of endowing them with human level intelligence is even murkier to define. To try to give it a little coherence I am going to choose two specific applications for AGI agents, and then discuss what this means in terms of research that awaits in order to achieve that goal. I could just as well have chosen different applications for them, but these two will make things concrete. And since we are talking about Artificial GENERAL Intelligence, I will push these application agents to be as good as one could expect a person to be in similar circumstances.

The first AGI agent will be a physically embodied robot that works in a person’s home providing them care as they age. I am not talking about companionship but rather physical assistance that will enable someone to live with dignity and independence as they age in place in their own home. For brevity’s sake we will refer to this robot, an eldercare worker, as ECW for the rest of this post.

ECW may come with lots of pre-knowledge about the general task of helping an elderly person in their home, and a lot of fundamental knowledge of what a home is, the sorts of things that will be found there, and all sorts of knowledge about people, both the elderly in general, and a good range of what to expect of family members and family dynamics, along with all sorts of knowledge about the sorts of people that might come into the home be it to deliver goods, or to carry out maintenance on the home. But ECW will also need to quickly adapt to the peculiarities of the particular elderly person and their extended social universe, the particular details of the house, and be able to adapt over time as the person ages.

The second AGI agent we will consider need not be embodied for the purposes of this discussion.  It will be an agent that can be assigned a complex planning task that will involve a workplace, humans as workers, machines that have critical roles, and humans as customers to get services at this workplace. For instance, and this is the example we will work through here, it might one particular day be asked to plan out all the details of a dialysis ward, specifying the physical layout, the machines that will be needed, the skillsets and the work flow for the people or robots to be employed there, and to consider how to make the experience for patients one that they will rank highly. We will refer to this services logistics planner as SLP.

Again SLP may come to this task with lots of prior knowledge about all sorts of things in the world, but there will be peculiarities about the particular hospital building, its geographical location and connection to transportation networks, the insurance profiles of the expected patient cohort, and dozens of other details. Although there have been many dialysis wards already designed throughout the world, for the purpose of this blog post we are going to assume that SLP has to design the very first one. Thus a dialysis ward, as used here, is a proxy for some task entirely new to humanity. This sort of logistical planning is not at all uncommon, and senior military officers, below the flag level, can assume that they will be handed assignments of this magnitude in times of unexpected conflicts. If we are going to be able to build an AGI agent that can work at human level, we surely should expect it to be able to handle this task.

We will see that both these vocations are quite complex and require much subtlety in order to be successful. I believe that the same is true of most human occupations, so I could equally well have chosen other AGI based agents, and the arguments below would be very similar.

My goal here is to replace the so-called “Turing Test” with something which test the intelligence of a system much more broadly. Stupid chat bots have managed to do well at the Turing Test. Being an effective ECW or SLP will be a much more solid test, and will guarantee a more general competence than what is required for the Turing Test.

Some who read this might argue that all the detailed problems and areas crying out for research that I give below will be irrelevant for a “Super Intelligence”, as it will do things its own way and that it will be beyond our understanding.  But such an argument would be falling into one of the traps that I identified in my blog post about seven common forms of mistake that are made in predicting the future of Artificial Intelligence. In this case it is the mistake of attributing magic to a sufficiently advanced technology. Super Intelligence is so super that we can not begin to understand it so there is no point making any rational arguments about it. Well, I am not prepared to sit back and idly wait for magic to appear. I prefer to be actively working to make magic happen, and that is what this post is about.

Oh, and one last meta-point on this rhetorical device that I have chosen to make my arguments. Yes, both EW and SLP will have plenty of opportunity to kill people should they want. I made sure of that so that the insatiable cravings of the Super Intelligence alarmists will have a place for expression concerning the wickedness that is surely on its way.

What Does ECW (ELDER CARE WORKER) Need To Do?

Let us suppose that the Elder Care Worker (ECW) robot is permanently assigned to work in a house with an older man, named Rodney. We will talk about the interactions between ECW and Rodney, and how Rodney may change over time as he gets older.

There are some basic things that almost go without saying. ECW should not harm Rodney, or guests at his house. It should protect Rodney from all things bad, should do what Rodney asks it to do, unless in some circumstances it should not (see below), etc.

One might think that Asimov’s three laws will cover this base necessity. In a 1942 short story titled “Runaround” the science fiction writer Isaac Asimov stated them as:

1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.

2. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.

3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

Then in subsequent stories the intricate details of how these rules might be in conflict, or have inherent ambiguity in how they should be interpreted, became a vehicle for new story after new story for Asimov.

It turns out that no robot today has a remote chance of successfully obeying these rules, for reasons of the necessary perception being difficult, using common sense, and predicting how people are going to behave being beyond current AI capabilities. We will talk at more length about these challenges in Part III of this essay.

But somehow ECW should have a code of conduct concerning the sorts of things it should do and should not do. That might be explicitly represented and available for introspection by ECW, or it may be built in due to inherent limitations in the way ECW is built. For instance any dishwashing machine that you can buy today just inherently can not wander into the living room during a wash cycle and pour out its water on the floor!

But ECW does need to be a lot more proactive than a dishwasher. To do so it must know who Rodney is, have access to relevant medical data about Rodney and be able to use that information in all sorts of decisions it must make on a daily basis. At the very least it must be able to recognize Rodney when he comes home, and track his appearance as he ages. ECW needs to know who is Rodney, who are family members, and needs to track who is in the house and when. It also needs to know the difference between a family member and a non family member, and individually know each of those family members. It needs to know who is a friend, who is just an acquaintance, who is a one time visitor, who is a regular visitor but a service provider of some sort, when the person at the door is a delivery person, and when the person at the door is someone who should not be allowed in.

As I pointed out in my post titled What Is It Like to Be a Robot? our domestic robots in the coming decades will be able to have a much richer set of sensory inputs than do we humans. They will be able to detect the presence of humans in all sorts of ways that we can not, but they will still need to be very good at quickly recognizing who is who, and it will most likely need to be done visually. But it won’t do to demand a full frontal face on view. Instead they will, like a person, need to piece together little pieces of information quickly, and make inferences. For example, if some person leaves a room to go to another room full of people, and a minute later a person comes from that room, with similar clothes on, perhaps that will be enough of a clue. Without knowing who is who the ECW could get quite annoying. And it should not be annoying. Especially not to Rodney, who will have to live with the robot for ten or twenty years.

Here is an example of an annoying machine. I have had an account with a particular bank for over thirty years and I have used its ATM card to get cash for that whole time. Every single time I insert my card it asks me whether I won’t so communicate in Spanish today. I have never said yes. I will never say yes. But it asks me anew every single interaction.

ECW should not be that sort of annoying with any of the people that it interacts with. It will need to understand what sorts of things are constant about a person, what sorts of things change often about a person, and what sort of changes in circumstances might precipitate a previously unlikely way of interacting. On the other hand, if a known service person shows up after months of absence, and ECW did not summon that person, it would probably be reasonable of ECW to ask why they are there. A human care giver knows all these sorts of social interaction things about people. ECW will need to also, or otherwise it will fall into the same class as an annoying ATM.

ECW will need to model family dynamics in order to not be annoying and to not make any sort of social faux pas. As Rodney deteriorates it may need to step in to the social dynamic to smooth things out, as a good human caretaker would. ECW might need to whisper to Rodney’s daughter when she arrives some day that Rodney seems a little cranky today, and then explain why–perhaps a bad reaction to some regular medicine, or perhaps he has been raving about someone claiming that one of his 25 year old blog predictions about future technology was overly pessimistic and he doesn’t buy it, but sure is upset about it. Other details may not matter at all. ECW will need to give the appropriate amount of information to the appropriate person.

In order to do this ECW will need to have a model of who knows what, who should know what, how they might already have the right information, or how their information may be wrong or out of date.

Rodney will likely change over ECW’s tenure, getting frailer, and needing more help from ECW. ECW will need to adjust its services to match Rodney’s changing state. That may include changing who it listens to primarily for instructions.

While the adults around will stay adults Rodney’s grandchild may change from being a helpless baby to a being a college graduate or even a medical doctor. If ECW is going to be as seamless as a person would be in accommodating those changes in its relationship with the grandchild over time then it is going to need to understand a lot about children and their levels of competence and how it should change its interaction. A college graduate is not going to appreciate being interacted with as though a baby.

But what does ECW need to actually do?

As Rodney declines over time, ECW will need to take over more and more responsibility for the normal aspects of living. It will need to clean up the house, picking up things and putting them away. It will need to distinguish things that are on the floor, understanding that disposing of a used tissue is different from dealing with a sock found on the floor.

It will need to start reaching for the things that are stored high up in the kitchen and hand them to Rodney. It may need to start cooking meals and be able to judge what Rodney should be eating to stay healthy.

When a young child shows something to an adult they know to use motion cues to draw the adult’s attention to the right thing. They know to put it in a place where the adult’s line of sight will naturally intersect. They know to glance at the adult’s eyes to see where their attention lies, and they know to use words to draw the adult’s attention when the person has not yet focused on the object being shown.

In order to do this well, even with all its super senses, ECW will still need to be able to “imagine” how the human sees the world so that it can ascertain what cues will most help the human understand what it is trying to convey.

When ECW first starts to support Rodney in his home Rodney may well be able to speak as clearly to ECW as he does to an Amazon Echo or a Google Home, but over time his utterances will become less well organized. ECW will need to use all sorts of context and something akin to reasoning in order to make sense of what Rodney is trying to convey. It likely will be very Rodney-dependent and likely not something that can be learned from a general purpose large data set gathered across many elderly people.

As Rodney starts to have trouble with noun retrieval ECW will need to follow the convoluted ways that Rodney tries to get around missing words when he is trying to convey information to ECW. Even a phrase as simple sounding as “the red thing over there” may be complex for ECW to understand. Current Deep Learning vision systems are terrible at color constancy (we will talk about that in the what is hard section next). Color constancy is something that is critical to human based ontologies of the world, the formal naming systems any group that shares a spoken language assumes that all other speakers will understand. It turns out that “red” is actually quite complex in varied lighting conditions–humans handle it just fine, but it is one of many tens of sub-capabilties that we all have without even realizing it.

ECW will have to take turns with Rodney on some tasks, encouraging him to do things for himself as he gets older–it will be important for Rodney to remain active, and ECW will have to judge when pushing Rodney to do things is therapeutic  and when it is unsupportive.

Eventually ECW will need to help Rodney doing all the things that happen in bathrooms. At some point it will need to start going into the bathroom with him, observing whether he is unsteady or not and provide verbal coaching. Over time ECW will need to stick closer to Rodney in the bathroom providing physical support as needed. Eventually it may need to help him get on to and off of the oval office, perhaps eventually providing wiping assistance. Even before this, ECW may need to help Rodney get into and out of bed–once a person loses the ability to do that on their own they often need to leave their home and go into managed care. ECW will be able to stave off that day, perhaps for years, just by helping with this twice daily task.

Coming into contact with, and supplying support and even lifting a frail and unsteady human body, easily damaged, and under control of a perhaps not quite rational degraded human intelligence is a complex physical interaction. It can often be eased by verbal communication between the two participants. Over the years straightforward language communication will get more and more difficult in both directions, as split second decisions will need to be made just at the physical level alone on what motions are appropriate, effective, and non-threatening.

As ECW comes into contact with Rodney there will be more opportunities for diagnostics. A human caregiver helping Rodney out of bed would certainly notice if his pajamas were wet. Or if some worse accident had happened. Now we get to an ethical issue. Suppose Rodney notices that ECW noticed, and says, “please don’t tell my children/doctor”. Under what conditions should ECW honor Rodney’s request, and when will it be in Rodney’s better interest to disobey Rodney and violate his privacy?

Early versions of ECW, before such robots are truly intelligent, will most likely rely on a baked in set of rules which require only straightforward perceptual inputs in order make these decisions–they will appear rigid and sometimes inhuman. When we get ECW to the level of intelligence of a person we will all expect it to make such decisions in much more nuanced ways, and be able to explain how it weighed the competing factors arguing for the two possible outcomes–tell Rodney’s children or not.

All along ECW will need to manage its own resources, including its battery charge, its maintenance schedule, its software upgrades, its network access, its cloud accounts, and perhaps figuring out how to pay its support bills if Rodney’s finances are in disarray. There will be a plethora of ECW’s other cyber physical needs that will need to be met while working around Rodney’s own schedule. ECW will have to worry about its own ongoing existence and health and weigh that against its primary mission of providing support to Rodney.

What Does SLP (SERVICES LOGISTICS PLANNER) Need To Do?

The Services Logistics Planner (SLP) does not need to be embodied, but it will need to at least appear to be cerebral, even as it lives entirely in the cloud. It will need to be well grounded in what it is to be human, and what the human world is really like if it is to be able to do its task.

The clients, the people who want the facility planned, will communicate with SLP through speech as we all do with the Amazon Echo or Google Home, and through sending it documents (Word, Powerpoint, Excel, pdf’s, scanned images, movie files, etc.). SLP will reply with build out specifications, organizational instructions, lists of equipment that will be needed and where it will be placed, staffing needs, consumable analysis, and analysis of expected human behaviors within the planned facility. And then the client and other interested parties will engage in an interactive dialog with SLP,  asking for the rationale for various features, suggesting changes and modifying their requirements. If SLP is as good as an intelligent person then this dialog will appear natural and have give and take³.

Consider the task of designing a new dialysis ward for an existing hospital.

Most likely SLP will be given a fixed space within the floor plan, and that will determine some aspects of people flow in and out, utility conduits, etc.  SLP will need to design any layout changes of walls within the space, and submit petitions to the right hospital organizations to change entrances and exits and how they will affect people flow in other parts of the hospital. SLP will need to decide what equipment will be where in the newly laid out space, what staffing requirements there will be, what hours the ward should be accepting out patients, what flows there should be for patients who end up having problems during a visit and need to get transferred to other wards in the hospital, and the layout of the beds and chairs for dialysis (and even which should be used). SLP will need to decide, perhaps through consulting other policy documents, how many non-patients will be allowed to accompany a patient, where they will be allowed to sit while the patient is on dialysis, and what the layout of the waiting room will be.

SLP will need to consider boredom for people in the waiting areas. But it won’t be told this in the specification. It will have to know enough about people to consider the different sorts of boredom for patients and supporting friends and relatives, access to snacks (appropriate for support visitors and for pre and post dialysis patients), access to information on when someone’s turn will come, and when someone will be getting out of active dialysis, etc., etc. At some level this will require an understanding of humans, their fears, their expected range of emotions, and how people will react to stressful situations.

It will have to worry about the width of corridors, the way doors open, and the steps or slopes within the facility. It will need to consider these things from many different angles; how to get new equipment in and out, how the cleaning staff will work, what the access routes will be in emergencies for doctors and nurses, how patients will move around, how visitors will be isolated to appropriate areas without them feeling like they are locked in, etc., etc.

SLP will need to worry about break rooms, the design of check in facilities, the privacy that patients can and should expect, and how to make it so that the facility itself is calming, rather than a source of stress, for the staff, the patients, and the visitors.

Outside of the ward itself, SLP will need to look in to what the profile of the expected patients are for this ward, and how they will travel to and from the hospital, for this particular city, for their out patient visit. It will need to predict the likelihood of really bad travel days where multiple patients get unexpectedly delayed. It will need to determine both a policy that tries to push everyone to be on time, but also have a back up system in place as it will not be acceptable to tell such patients that it is just too bad that they missed this appointment through no fault of their own–this is a matter of life and death, and SLP will need to analyze what are the acceptable back up delays and risks to patients.

SLP will not be told any of these things when given the task to design the new dialysis ward. It will need to be smart enough about people to know that these will all be important issues. And it will need to be able to push back on the really important aspects during the “value engineering” (i.e., reduction in program) that the humans reviewing the task will be promoting. And remember that for the purposes of this demonstration of human level general intelligence we are going to assume that this is the very first dialysis ward ever designed.

There is a lot of knowledge that SLP will need to access. And a lot of things that will need to feed into every decision, and a lot of tradeoffs that will need to be made, as surely there will need to be many compromises to be made as in any human endeavor.

Most importantly SLP will need to be able to explain itself. An insurance company that is bidding on providing insurance services for the facility that SLP has just designed will want to ask specific questions about what considerations went into certain aspects of the design. Representatives from the company will push for models of all sorts of aspects of the proposed design in terms of throughput considerations, risks to people quite apart from their illness and their dialysis requirements, how it was determined that all aspects of the design meet fire code, what sort of safety certifications exist for the chosen materials, etc., etc., etc.

But “Wait!” the Deep Learning fan boy says. “It does not need to do all those humanny sorts of things. We’ll just show it thousands of examples of facilities throughout the world and it will learn! Then it will design a perfect system. It is not up to us mere humans to question the magic skill of the Super Intelligent AI.”

But that is precisely the point of this example. For humans to be happy with a system designed by SLP it must get details incredibly correct. And by making this the first dialysis ward ever designed  it means that there just will not be much in the way of data on which to train it. If something really is Super it will have to handle tasks à nouveau, since people have been doing that throughout history.

The Two New Tests

I said about that I was proposing these two test cases, ECW and SLP, as a replacement for the Turing Test. Some may be disappointed that I did not give a simple metric on them.

In other tests there is a metric. In the Turing Test it is what percentage of human judges it fools into making a wrong binary decision. In the ever popular robot soccer competitions it is which team wins. In the DARPA Grand Challenge it was how long it took an autonomous vehicle to finish the course.

ECW and SLP have much more nuanced tasks. Just as there is no single multiple choice test that one must pass to receive a PhD, we will only know if ECW or SLP are doing a good job by continuously challenging them and evaluating them over many years.

Welcome to the real world. That is where we want our Artificial General Intelligences and our Super Intelligences to live and work.

Next up: Part III, some things that are hard for AI today.

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¹ The Wright Brothers were very much inspired by the gliding experiments of Otto Lilienthal. He eventually died in an accident while gliding in 1896 after completing more than 2,000 flights in the previous five years in gliders of his own design. Lilienthal definitely studied and was inspired by birds. He had published a book in 1889 titled Der Vogelflug als Grundlage der Fliegekunst, which translates to Birdflight as the Basis of Aviation. According to Wikipedia. James Tobin, on page 70 of his 2004 book To Conquer The Air: The Wright Brothers and the Great Race for Flight, says something to the effect of “[o]n the basis of observation, Wilbur concluded that birds changed the angle of the ends of their wings to make their bodies roll right or left”.

² The first human powered heavier than air flight had to wait until 1961 in Southhampton, UK, and it was not until 1977 that the human powered heavier than air Gossamer Condor flew for a mile in a figure eight, showing both duration and controllability. In 1979 the Gossamer Albatross flew 22 miles across the English Channel, powered by Bryan Allen flying at an average height of 5 feet. And in 1988 MIT’s Daedalus powered by Kanellos Kannellopoulous flew a still record 72 miles from Crete to Santorini in a replay of ancient Greek mythology.

³ Humans are quite used to having conversations involving give and take where the two participants are not equals–parent and three year old, teacher and student, etc. Each can respect the other and be open to insights from the “lower ranked” individual while being respectful and each side letting the other feel like they belong in the conversation. Even when we get to Super Intelligence there is no reason to think we will immediately go to no respect for the humans. And if our earlier not quite Super systems start to get that way we will change them.

God created man in his own image.

Man created AI in his own image.

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Once again, with footnotes.

God created man in his¹ own image.²

Man³ created AI in his³ own image.

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At least that is how it started out. But figuring out what our selves are, as machines, is a really difficult task.

We may be stuck in some weird Gödel-like incompleteness world–perhaps we are creatures below some threshold of intelligence which stops us from ever understanding or building an artificial intelligence at our  level. I think most people would agree that that is true of all non-humans on planet Earth–we would be extraordinarily surprised to see a robot dolphin emerge from the ocean, one that had been completely designed and constructed by living dolphins. Like dolphins, and gorillas, and bonobos, we humans may be below the threshold as I discussed under “Hubris and Humility”.

Or, perhaps, as I tend to believe, it is just really hard, and will take a hundred years, or more, of concerted effort to get there. We still make new discoveries in chemistry, and people have tried to understand that, and turn it from a science into engineering, for thousands of years. Human level intelligence may be just as, or even more, challenging.

In my most recent blog post on the origins of Artificial Intelligence, I talked about how all the founders of AI, and most researchers since have really been motivated by producing human level intelligence. Recently a few different and small groups have tried to differentiate themselves by claiming that they alone (each of the different groups…) are interested in producing human level intelligence, and beyond, and have each adopted the name Artificial General Intelligence (AGI) to distinguish themselves from mainstream AI research. But mostly they talk about how great it is going to be, and how terrible it is going to be, and very often both messages at the same time coming out of just one mouth, once AGI is achieved. And really none of these groups have any idea how to get there. They are too much in love with the tingly feelings thinking about it to waste time actually doing it.

Others, who don’t necessarily claim to be AI researchers but merely claim to know all about AI and how it is proceeding (ahem…), talk about the exquisite dangers of what they call Super Intelligence, AI that is beyond human level. They claim it is coming any minute now, especially since as soon as we get AGI, or human level intelligence, it will be able to take over from us, using vast cloud computational resources, and accelerate  the development of AI even further. Thus there will be a runaway development of Super Intelligence. Under the logic of these hype-notists this super intelligence will naturally be way beyond our capabilities, though I do not know whether they believe it will supersede God…  In any case, they claim it will be dangerous, of course, and won’t care about us (humans) and our way of life, and will likely destroy us all. I guess they think a Super Intelligence is some sort of immigrant. But these heralds who have volunteered their clairvoyant services to us also have no idea about how AGI or Super Intelligence will be built. They just know that it is going to happen soon, if not already. And they do know, with all their hearts, that it is going to be bad. Really bad.

It does not help the lay perception at all that some companies claiming to have systems based on Artificial Intelligence are often using humans to handle the hard cases for their online systems, unbeknownst to the users. This can seriously confuse public perception of just where today’s Artificial Intelligence stands, not to mention the inherent lack of privacy since I think we humans are somehow more willing sometimes to share private thoughts and deeds with our machines than we are with other people.

In the interest of getting to the bottom of all this, I have been thinking about what research we need to do, what problems we need to solve, and how close we are to solving all of them in order to get to Artificial General Intelligence entities, or human intelligence level entities. We have been actively trying for 62 years, but apparently it is only just right now that we are about to make all the breakthroughs that will be necessary. That is what this blog is about, giving my best guess at all the things we still don’t know, that will be necessary to know for us to build AGI agents, and then how they will take us on to Super Intelligence. And thus the title of this post: Steps Toward Super Intelligence.

And yes, this title is an homage to Marvin Minsky’s Steps Toward Artificial Intelligence from 1961. I briefly reviewed that paper back in 1991 in my paper Intelligence Without Reason, where I pointed out the five main areas he identifies for research into Artificial Intelligence were search, three ways to control search (for pattern-recognition, learning, and planning), and a fifth topic, induction. Pattern recognition and learning have today clearly moved beyond search. Perhaps my prescriptions for research towards Super Intelligence will also turn out to be wrong before very long. But I am pretty confident that the things that will date my predictions are not yet known by most researchers, and certainly are not the hot topics of today.

This started out as a single long essay, but it got longer and longer. So I split it into four parts, but they are also all long. In any case, it is the penultimate essay in my series on the Future of Robotics and Artificial Intelligence.

BUT SURELY WE (YES, US HUMANS!) CAN DO IT SOON!

Earlier I said this endeavor may take a hundred years? For techno enthusiasts, of which I count myself as one, that sounds like a long time. Really, is it going to take us that long? Well, perhaps not, perhaps it is really going to take us two hundred, or five hundred. Or more.

Einstein predicted gravitational waves in 1916. It took ninety nine years of people looking before we first saw them in 2015. Rainer Weiss, who won the Nobel prize for it, sketched out the successful method after fifty one years in 1967. And by then the key technologies needed, laser and computers, were in wide spread commercial use. It just took a long time.

Controlled nuclear fusion has been forty years away for well over sixty years now.

Chemistry took millennia, despite the economic incentive of turning lead into gold (and it turns out we still can’t do that in any meaningful way).

P=NP? has been around in its current form for forty seven years and its solution would guarantee whoever did it to be feted as the greatest computer scientist in a generation, at least. No one in theoretical computer science is willing to guess when we might figure that one out. And it doesn’t require any engineering or production. Just thinking.

Some things just take a long time, and require lots of new technology, lots of time for ideas to ferment, and lots of Einstein and Weiss level contributors along the way.

I suspect that human level AI falls into this class. But that it is much more complex than detecting gravity waves, controlled fusion, or even chemistry, and that it will take hundreds of years.

Being filled with hubris about how tech (and the Valley) can do whatever they put their mind to may just not be nearly enough.

Four Previous Attempts at General AI

Referring again to my blog post of April on the origins of Artificial Intelligence people have been actively working on a subject, explicitly called “Artificial Intelligence” since the summer of 1956. There were precursor efforts for the previous twenty years, but that name had not yet been invented or assigned–and once again I point to my 1991 paper Intelligence Without Reason for a history of the prior work and the first 35 years of Artificial Intelligence.

I count at least four major approaches to Artificial Intelligence over the last sixty two years. There may well be others that some would want to include.

As I see it, the four main approaches have been, along with approximate start dates:

1. Symbolic (1956)
2. Neural networks (1954, 1960, 1969, 1986, 2006, …)
3. Traditional robotics (1968)
4. Behavior-based robotics (1985)

Before explaining the strengths and weakness of these four main approaches I will justify the dates that I given above.

For Symbolic I am using the 1956 date of the famous Dartmouth workshop on Artificial Intelligence

Neural networks have been investigated, abandoned, and taken up again and again. Marvin Minsky submitted his Ph.D. thesis in Princeton in 1954, titled Theory of Neural-Analog Reinforcement Systems and its Application to the Brain-Model Problem; two years later Minsky had abandoned this approach and was a leader in the symbolic approach at Dartmouth. Dead. In 1960 Frank Rosenblatt published results from his hardware Mark I Perceptron, a simple model of a single neuron, and tried to formalize what it was learning. In 1969 Marvin Minsky and Seymour Papert published a book, Perceptrons, analyzing what a single perceptron could and could not learn. This effectively killed the field for many years. Dead, again. After years of preliminary work by many different researchers, in 1986 David Rumelhart, Geoffrey Hinton, and Ronald Williams published a paper Learning Representations by Back-Propagating Errors, which re-established the field using a small number of layers of neuron models, each much like the Perceptron model. There was a great flurry of activity for the next decade until most researchers once again abandoned neural networks. Dead, again. Researchers here and there continued to work on neural networks, experimenting with more and more layers, and coining the term deep for those many more layers. They were unwieldy and hard to make learn well, and then in 2006 Geoffrey Hinton (again!) and Ruslan Salakhutdinov, published Reducing the Dimensionality of Data with Neural Networks, where an idea called clamping allowed the layers to be trained incrementally. This made neural networks undead once again, and in the last handful of years this deep learning approach has exploded into practicality of machine learning. Many people today know Artificial Intelligence only from this one technical innovation.

I trace Traditional Robotics, as an approach to Artificial Intelligence, to the work of Donald Pieper, The Kinematics of Manipulators Under Computer Control, at the Stanford Artificial Intelligence Laboratory (SAIL) in 1968.  In 1977 I joined what had by then become the “Hand-Eye” group at SAIL, working on the “eye” part of the problem for my PhD.

As for Behavior-based robotics, I track this to my own paper, A Robust Layered Control System for a Mobile Robot, which was written in 1985, but appeared in a journal in 1986⁴, when it was called the Subsumption Architecture. This later became the behavior-based approach, and eventually through technical innovations by others morphed into behavior trees. I am perhaps lacking a little humility in claiming this as one of the four approaches to AI. On the other hand it has lead to more than 20 million robots in people’s homes, numerically more robots by far than any other robots ever built, and behavior trees are now underneath the hood of two thirds of the world’s video games, and many physical robots from UAVs to robots in factories. So it has at least been a commercial success.

Now I attempt to give some cartoon level descriptions of these four approaches to Artificial Intelligence. I know that anyone who really knows Artificial Intelligence will feel that these descriptions are grossly inadequate. And they are. The point here is to give just a flavor for the approaches. These descriptions are not meant to be exhaustive in showing all the sub approaches, nor all the main milestones and realizations that have been made in each approach by thousands of contributors. That would require a book length treatment. And a very thick book at that. These descriptions are meant to give just a flavor.

Now to the four types of AI. Note that for the first two, there has usually been a human involved somewhere in the overall usage pattern. This puts a second intelligent agent into the system and that agent often handles ambiguity and error recovery.  Often, then, these sorts of AI systems have had to deliver much less reliability than autonomous systems will demand in the future.

1. Symbolic Artificial Intelligence

The key concept in this approach is one of symbols. In the straightforward (every approach to anything usually gets more complicated over a period of decades) symbolic approach to Artificial Intelligence a symbol is an atomic item which only has meaning from its relationship to other meanings. To make it easier to understand the symbols are often represented by a string of characters which correspond to a word (in English perhaps), such as  cat or animal. Then knowledge about the world can be encoded in relationships, such as instance of and is.

Usually the whole system would work just as well, and consistently if the words were replaced by, say g0537 and g0028. We will come back to that.

Meanwhile, here is some encoded knowledge:

• Every instance of a cat is an instance of a mammal.
• Fluffy is an instance of a cat.
• Now we can conclude that Fluffy is an instance of a mammal.
• Every instance of a mammal is an instance of an animal.
• Now we can conclude that every instance of a cat is an instance of an animal.
• Every instance of an animal can carry out the action of walking.
• Unless that instance of  animal is in the state of being dead.
• Every instance of an animal is either in the state of being alive or in the state of being dead — unless the time of now is before the time of (that instance of animal carrying out the action of birth).

While what we see here makes a lot of sense to us, we must remember that as far as an AI program that uses this sort of reasoning is concerned, it might as well have been:

• Every instance of a g0537 is an instance of a g0083.
• g2536 is an instance of a g0537.
• Now we can conclude that g2536 is an instance of a g0083.
• Every instance of a g0083 is an instance of an g0028.
• Now we can conclude that every instance of a g0537 is an instance of an g0028.
• Every instance of an g0028 can carry out the action of g0154.
• Unless that instance of  g0028 is in the state of being g0253.
• Every instance of an g0028 is either in the state of being g0252 or in the state of being g0253 — unless the value(the-computer-clock) < time of (that instance of g0028 carrying out the action of g0161).

In fact it is worse than this. Above the relationships are still described by English words. For an AI program that uses this sort of reasoning is concerned, it might as well have been.

• For every x where r0002(x, g0537) then r0002(x, g0083).
• r0002(g2536, g0537).
• Now we can conclude that r0002(g2536, g0083).
• For every x where r0002(x, g0083) then r0002(x, g0028).
• Now we can conclude that for every x where r0002(x, g0537) then r0002(x, g0028).
• For every x where r0002(x, g0028) then r0005(x, g0154).
• Unless r0007(x, g0253).
• For every x where r0002(x, g0028) then either r0007(x, g0252) or r0007(x, g2053) — unless the value(the-computer-clock) < p0043(a0027(g0028, g0161)).

Here the relationships like “is an instance of” have been replaced by anonymous symbols like r0002, and the symbol < replaces “before“, etc.  This is what it looks like inside an AI program, but even with this the AI program never looks at the names of the symbols, rather just when one symbol in an inference or statement is the same symbol as in another inference or statement. The names are only there for humans to interpret, so when g0537 and g0083 were cat and mammal, a human looking at the program⁵ or its input or ouput could put an interpretation on what the symbols might “mean”.

And this is the critical problem with symbolic Artificial Intelligence, how the symbols that it uses are grounded in the real world. This requires some sort of perception of the real world, some way to and from symbols that connects them to things and events in the real world.

For many applications it is the humans using the system that do the grounding. When we type in a query to a search engine it is we who choose the symbols to make our way into what the AI system knows about the world. It does some reasoning and inference, and then produces for us a list of Web pages that it has deduced match what we are looking for (without actually having any idea that we are something that corresponds to the symbol person that it has in its database). Then it is us who looks at the summaries that it has produced of the pages and clicks on the most promising one or two pages, and then we come up with some new or refined symbols for a new search if it was not what we wanted. We, the humans, are the symbol grounders for the AI system. One might argue that all the intelligence is really in our heads, and that really all the AI powered search engine provides us with is a fancy index and a fancy way to use it.

To drive home this point consider the following thought experiment.

Imagine you are a non Korean speaker, and that the AI program you are interacting with has all its input and output in Korean. Those symbols would not be much help. But suppose you had a Korean dictionary, with the definitions of Korean words written in Korean. Fortunately modern Korean has a finite alphabet and spaces between words (though the rules are slightly different from those of English text), so it will be possible to extract “symbols” from looking at the program output. And then you could look them up in the dictionary, and perhaps eventually infer Korean grammar.

Now it is just possible that you could use your extensive understanding of the human world, about which the Korean dictionary must be referring to for many entries, to guess at some of the meanings of the symbols. But if you were a Heptapod from the movie Arrival and it was before (uh-oh…) Heptapods had ever visited Earth then you would not even have this avenue for grounding these entirely alien symbols.

So it really is the knowledge in people’s heads that does the grounding in many situations. In order to get the knowledge into an AI program it needs to be able to relate the symbols to something outside its self consistent Korean dictionary (so to speak). Some hold out hope that our next character in the pantheon of pretenders to the throne of general Artificial Intelligence, neural networks, will play that role. Of course, people have been working on making that so for decades. We’re still a long way off.

To see the richness of sixty plus years of symbolic Artificial Intelligence work I recommend the AI Magazine, the quarterly publication of the Association for the Advancement of Artificial Intelligence. It is behind a paywall, but even without joining the association you can see the tables of contents of all the issues and that will give a flavor of the variety of work that goes on in symbolic AI. And occasionally there will also be an article there about neural networks, and other related types of machine learning.

2.0, 2.1. 2.2, 2.3, 2.4, … Neural networks

These are loosely, very loosely, based on a circa 1948 understanding of neurons in the brain. That is to say they do not bear very much resemblance at all to our current understand of the brain, but that does not stop the press talking about this approach as being inspired by biology. Be that as it may.

Here I am going to talk about just one particular kind of artificial neural network and how it is trained, namely a feed forward layered network under supervised learning. There are many variations, but this example gives an essential flavor.

The key element is an artificial neuron that has n inputs, flowing along which come n numbers, all between zero and one, namely x₁, x₂, … x_n. Each of these is multiplied by a weight, w₁, w₂, … w_n, and the results are summed, as illustrated in this diagram from Wikimedia Commons.

(And yes, that w₃ in the diagram should really be w_n.) These weights can have any value (though are practically limited to what numbers can be represented in the computer language in which the system is programmed) and are what get changed when the system learns (and recall that learn is a suitcase word as explained in my seven deadly sins post). We will return to this in just a few paragraphs.

The sum can be any sized number, but a second step compresses it down to a number between zero and one again by feeding it though a logistic or sigmoid function, a common one being:

I.e., the sum gets fed in as the argument to the function and the expression on the right is evaluated to produce a number that is strictly between zero and one, closer and closer to those extremes as the input gets extremely negative or extremely positive. Note that this function preserves the order between possible inputs fed to it. I.e., if then . Furthermore the function is symmetric about an input of zero, and an output of .

This particular function is very often used as it has the property that it is easy to compute its derivate for any given output value without having to invert the function to find the input value. In particular, if you work through the normal rules for derivatives and use algebraic simplification you can show that

This turns out to be very useful for the ways these artificial neurons started to be used in the 1980’s resurgence. They get linked together in regular larger networks such as the one below, where each large circle corresponds to one of the artificial neurons above. The outputs on the right are usually labelled with symbols, for instance cat, or car. The smaller circles on the left correspond to inputs to the network which come from some data source.

For instance, the source might be an image, there might be many thousand little patches of the image sampled on the left, perhaps with a little bit or processing of the local pixels to pick out local features in the image, such as corners, or bright spots. Once the network has been trained, the output labelled cat should put out a value close to one when there is a cat in the image and close to zero if there is no cat in the image, and the one labelled car should have a value similarly saying whether there is a car in the image. One can think of these numbers as the network saying what probability it assigned to there being a cat, a car, etc. This sort of network thus classifies its input into a finite number of output classes.

But how does the network get trained? Typically one would show it millions of images (yes, millions), for which there was ground truth known about which images contained what objects. When the output lines with their symbols did not get the correct result, the weights on the inputs of the offending output neuron would be adjusted up or down in order to next time produce a better result. The amount to update those weights depends on how much difference a change in weight can make to the output. Knowing the derivative  of where on the sigmoid function the output is coming from is thus critical. During training the proportional amount, or gain, of how much the weights are modified is reduced over time. And a 1980’s invention allowed the detected error at the output to be propagated backward through multiple layers of the network, usually just two or three layers at that time, so that the whole system could learn something from a single bad classification. This technique is known as back propagation.

One immediately sees that the most recent image will have a big impact on the weights, so it is necessary to show the network all the other images again, and gradually decrease how much weights are changed over time. Typically each image is shown to the network thousands, or even hundreds of thousands of times, interspersed amongst millions of other images also each being shown to the network hundreds of thousands of times.

That this sort of training works as well as it does is really a little fantastical. But it does work in many cases. Note that a human designs how many layers there are in the network, for each layer how the connections to the next layer of the network are arranged, and what the the inputs are for the network. And then the network is trained, using a schedule of what to show it when, and how to adjust the gains on the learning over time as chosen by the human designer. And if after lots of training the network has not learned well, the human may adjust the way the network is organized, and try again.

This process has been likened to alchemy, in contrast to the science of chemistry. Today’s alchemists who are good at it can command six or even seven figure salaries.

In the 1980’s when back propagation was first developed and multi-layered networks were first used, it was only practical from both a computational and algorithmic point of view to use two or three layers. Thirty years later the Deep Learning revolution of 2006 included algorithmic improvements, new incremental training techniques, of course lots more computer power, and enormous sets of training data harvested from the fifteen year old World Wide Web. Soon there were practical networks of twelve layers–that is where the word deep comes in–it refers to lots of layers in the network, and certainly not “deep introspection”…

Over the more than a decade since 2006 lots of practical systems have been built.

The biggest practical impact for most people recently, and likely over the next couple of decades is the impact on speech transliteration systems. In the last five years we have moved from speech systems over the phone that felt like “press or say `two’ for frustration”, to continuous speech transliteration of voice messages, and home appliances, starting with the Amazon Echo and Google Home, but now extending to our TV remotes, and more and more appliances built on top of the speech recognition cloud services of the large companies.

Getting the right words that people are saying depends on two capabilities. The first is detecting the phonemes, the sub pieces of words, with very different phonemes for different languages, and then partitioning a stream of those phonemes, some detected in error, into a stream of words in the target language. With out earlier neural networks the feature detectors that were applied to raw sound signals to provide low level clues for phonemes were programs that engineers had built by hand. With Deep Learning, techniques were developed where those earliest features were also learned by listening to massive amounts of speech from different speakers all talking in the target language. This is why today we are starting to think it natural to be able to talk to our machines. Just like Scotty did in Star Trek 4: The Voyage Home.

A new capability was unveiled to the world in a New York Times story on November 17, 2014 where the photo below appeared along with a caption that a Google program had automatically generated: “A group of young people playing a game of Frisbee”.

I think this is when people really started to take notice of Deep Learning. It seemed miraculous, even to AI researchers, and perhaps especially to researchers in symbolic AI, that a program could do this well. But I also think that people confused performance with competence (referring again to my seven deadly sins post). If a person had this level of performance, and could say this about that photo, then one would naturally expect that the person had enough competence in understanding the world, that they could probably answer each of the following questions:

• what is the shape of a Frisbee?
• roughly how far can a person throw a Frisbee?
• can a person eat a Frisbee?
• roughly how many people play Frisbee at once?
• can a 3 month old person play Frisbee?
• is today’s weather suitable for playing Frisbee?

But the Deep Learning neural network that produced the caption above can not answer these questions. It certainly has no idea what a question is, and can only output words, not take them in, but it doesn’t even have any of the knowledge that would be needed to answer these questions buried anywhere inside what it has learned. It has learned a mapping from colored pixels, with a tiny bit of spatial locality, to strings of words. And that is all. Those words only rise up a little beyond the anonymous symbols of traditional AI research, to have a sort of grounding, a grounding in the appearance of nearby pixels. But beyond that those words or symbols have no meanings that can be related to other things in the world.

Note that the medium in which learning happens here is selecting many hundreds of thousands, perhaps millions, of numbers or weights. The way that the network is connected to input data is designed by a human, the layout of the network is designed by a human, the labels, or symbols, for the outputs are selected by a human, and the set of training data has previously been labelled by a human (or thousands of humans) with these same symbols.

3. Traditional Robotics

In the very first decades of Artificial Intelligence, the AI of symbols, researchers sought to ground AI by building robots. Some were mobile robots that could move about and perhaps push things with their bodies, and some were robot arms fixed in place. It was just too hard then to have both, a mobile robot with an articulated arm.

The very earliest attempts at computer vision were then connected to these robots, where the goal was to, first, deduce the geometry of what was in the world, and then to have some simple mapping to symbols, sitting on top of that geometry.

In my post on the origins of AI I showed some examples of how perception was built up by looking for edges in images, and then working through rules on how edges might combine in real life to produce geometric models of what was in the world. I used this example of a complex scene with shadows:

In connecting cameras to computers and looking at the world, the lighting and the things allowed in the field of view often had to be constrained for the computer vision, the symbol grounding, to be successful. Below is a really fuzzy picture of the “copy-demo” at the MIT Artificial Intelligence Laboratory in 1970. Here the vision system looked at a stack of blocks and the robot tried to build a stack that looked the same.

At the same time a team at SRI International in Menlo Park, California, were building the robot Shakey, which operated in a room with large blocks, cubes and wedges, with each side painted in a different matte color, and with careful control of lighting.

 

By 1979 Hans Moravec at the Stanford Artificial Intelligence Lab had an outdoor capable robot, “The Cart”, in the center of the image here (a photograph that I took), which navigated around polyhedral objects, and other clutter. Since it took about 15 minutes to move one meter it did get a little confused by the high contrast moving shadows.

And here is the Freddy II robot at the Department of Artificial Intelligence at Edinburgh University in the mid 1970’s, stacking flat square and round blocks and inserting pegs into them.

These early experiments combined image to symbol mapping, along with extracting three dimensional geometry so that the robot could operate, using symbolic AI planning programs from end to end.

I think it is fair to say that those end to end goals have gotten a little lost over the years.  As the reality of complexities due to uncertainties when real objects are used have been realized, the tasks that AI robotics researchers focus on have been largely driven by a self defined research agenda, with proof of concept demonstrations as the goal.

And I want to be clear here. These AI based robotics systems are not used at all in industry. All the robots you see in factories (except those from my company, Rethink Robotics) are carefully programmed, in detail, to do exactly what they are doing, again, and again, and again. Although the lower levels of modeling robot dynamics and planning trajectories for a robot arm are shared with the AI robotics community, above that level it is very complete and precise scripting. The last forty years of AI research applied to factory robots has had almost no impact in practice.

On the other hand there has been one place from traditional robotics with AI that has had enormous impact. Starting with robots such as The Cart, above, people tried to build maps of the environment so that the system could deliberatively plan a route from one place to another which was both short in time to traverse and which would avoid obstacles or even rough terrain. So they started to build programs that took observations as the robot moved and tried to build up a map. They soon realized that because of uncertainties in how far the robot actually moved, and even more importantly what angle it turned when commanded, it was impossible to put the observations into a simple coordinate system with any certainty, and as the robot moved further and further the inaccuracies relative to the start of the journey just got worse and worse.

In late 1984 both Raja Chatila from Toulouse, and I, newly a professor at MIT, realized that if the robot could recognize when it saw a landmark a second time after wandering around for a while it could work backwards through the chain of observations made in between, and tighten up all their uncertainties. We did not need to see exactly the same scene as before, all we needed was to locate one of the things that we had earlier labeled with a symbol, and being sure that the new thing we saw labelled by the same symbol as in fact the same object in the world. This is now called “loop closing” and we independently published papers with this idea in March 1985 at a robotics conference held in St Louis (IEEE ICRA). But neither of us had very good statistical models, and mine was definitely worse than Raja’s.

By 1991 Hugh Durrant-Whyte and John Leonard, then both at Oxford, had come up with a much better formalization, which they originally called “Simultaneous Map Building and Localisation” (Oxford English spelling), which later turned into “Simultaneous Localisation and Mapping” or SLAM. Over the next fifteen years, hundreds, if not thousands, of researchers refined the early work, enabled by newly low cost and plentiful mobile robots (my company iRobot was supplying those robots as a major business during the 1990’s). With a formalized well defined problem, low cost robots, adequate computation, and researchers working all over the world, competing on performance, there was rapid progress. And before too long the researchers managed to get rid of symbolic descriptions of the world and do it all in geometry with statistical models of uncertainty.

The SLAM algorithms became part of the basis for self-driving cars, and subsystems derived from SLAM are used in all of these systems. Likewise the navigation and data collection from quadcopter drones is powered by SLAM (along with inputs from GPS).

4. Behavior-Based Robotics

By 1985 I had spent a decade working in computer vision, trying to extract symbolic descriptions of the world from images, and in traditional robotics, building planning systems for robots to operate in simulated or actual worlds.

I had become very frustrated.

Over the previous couple of years as I had tried to move from purely simulated demonstrations to getting actual robots to work in the real world, I had become more and more buried in mathematics that was all trying to estimate the uncertainty in what my programs knew about the real world. The programs were trying to measure the drift between the real world, and the perceptions that my robots were making of the world. We knew by this time that perception was difficult, and that neat mapping from perception to certainty was impossible. I was trying to accommodate that uncertainty and push it through my planning programs, using a mixture of traditional robotics and symbolic Artificial Intelligence. The hope was that by knowing how wide the uncertainty was the planners could accommodate all the possibilities in the actual physical world.

I will come back to the implicit underlying philosophical position that I was taking in the last major blog post in this series, to come out later this year.

But then I started to reflect on how well insects were able to navigate in the real world, and how they were doing so with very few neurons (certainly less that the number of artificial neurons in modern Deep Learning networks). In thinking about how this could be I realized that the evolutionary path that had lead to simple creatures probably had not started out by building a symbolic or three dimensional modeling system for the world. Rather it must have begun by very simple connections between perceptions and actions.

In the behavior-based approach that this thinking has lead to, there are many parallel behaviors running all at once, trying to make sense of little slices of perception, and using them to drive simple actions in the world. Often behaviors propose conflicting commands for the robot’s actuators and there has to be a some sort of conflict resolution. But not wanting to get stuck going back to the need for a full model of the world, the conflict resolution mechanism is necessarily heuristic in nature. Just as one might guess, the sort of thing that evolution would produce.

Behavior-based systems work because the demands of physics on a body embedded in the world force the ultimate conflict resolution between behaviors, and the interactions. Furthermore by being embedded in a physical world, as a system moves about it detects new physical constraints, or constraints from other agents in the world. For synthetic characters in video games under the control of behavior trees, the demands of physics are replaced by the demands of the simulated physics needed by the rendering engine, and other agents in the world are either the human player of yet more behavior-based synthetic characters.

Just in the last few weeks there has been a great example of this that has gotten a lot of press. Here is the original story about MIT Professor Sangbae Kim’s Cheetah 3 robot. The press was very taken with the robot blindly climbing stairs, but if you read the story you will see that the point of the research is not to produce a blind robot per se. Computer vision, even 3-D vision, is not completely accurate. So any robot that tries to climb rough terrain using vision, rather than feel, needs to be very slow, careful placing its feet one at a time, as it does not know exactly where the solid support in the world is. In this new work, Kim and his team have built a collection of low level behaviors which sense when things have gone wrong and quickly adapt individual legs. To prove the point, they made the robot completely blind–the performance of their robot will only increase as vision gives some high level direction to where the robot should aim its feet, but even so, having these reactive behaviors at the lowest levels make it much faster and more sure footed.

The behavior-based approach, which leaves the model out in the world rather than inside the agent, has allowed robots to proliferate in number. Unfortunately, I often get attacked by people outside the field, saying in effect, we were promised super intelligent robots and all you have given us is robot vacuum cleaners. Sorry, it is a work in progress. At least I gave you something practical…

Comparing The Four Approaches to AI

In my 1990 paper Elephants Don’t Play Chess, in the first paragraph of the second page I mentioned that the “holy grail” for both classical symbolic AI research, and my own research was “general purpose human level intelligence”–the youngsters today saying that the goal of AGI, or Artificial General Intelligence is a new thing are just plain wrong. All four of the approaches I outlined above have been motivated by eventually getting to human level intelligence, and perhaps beyond.

None of them are yet close by themselves, nor have any combinations of them turned into something that seems close. But each of the four approaches has somewhat unique strengths. And all of them have easily identifiable weaknesses.

The use of symbols in AI research allows one to use them as the currency of composition between different aspects of intelligence, passing the symbols from one reasoning component to another. For neural networks fairly weak symbols appear only as outputs and there is no way to feed them back in, or really to other networks. Traditional robotics trades on geometric relationships and coordinates, which means they are easy to compose, but they are very poor in semantic content. And behavior-based systems are sub-symbolic, although there are ways to have some sorts of proto symbols emerge.

Neural networks have been the most successful approach to getting meaningful symbols out of perceptual inputs. The other approaches either don’t try to do so (traditional robotics) or have not been particularly successful at it.

Hard local coordinate systems, with solid statistical relationships between them have become the evolved modern approach to traditional robotics. Both symbolic AI and behavior based systems are weak in having different parts of the systems relate to common, or even well understood relative, coordinate systems. And neural networks simply suck (yes, suck) at spatial understanding.

Of the four approaches to AI discussed here, only the behavior-based approach makes a commitment to an ongoing existence of the system, the others, especially neural networks are much more transactional in nature. And the behavior-based approach reacts to changes in the world on millisecond timescales, as it is embedded, and “living” in the real world. Or in the case of characters in video games, it is well embedded in the matrix. This ability to be part of the world, and to have agency within it is at some level an artificial sentience. A messy philosophical term to be sure. But I think all people who ever utter the phrase Artificial General Intelligence, or utter, or mutter, Super Intelligence, are expecting some sort of sentience. No matter how far from reality the sentience of behavior-based systems may be, it is the best we have got. By a long shot.

I have attempted to score the four approaches on where they are better and where worse. The scale is one to three with three being a real strength of the approach. Notice that besides the four different strengths I have added a column for how well the approaches deal with ambiguity.

These are very particular capabilities that one or the other of the four approaches does better at. But for general intelligence I think we need to talk about cognition. You can find many definitions of cognition but the all have to do with thinking. And the definitions talk about thinking variously in the context of attention, memory, language understanding, perception, problem solving and others. So my scores are going to be a little subjective, I admit.

If we think about a Super Intelligent AI entity, one might want it to act in the world with some sort of purpose. For symbolic AI and traditional robotics there have been a lot of work on planners, programs that look at the state of the world and try to work out a series of actions that will get the world (and the embedded AI system or robot) into a more desirable state. These planners, largely symbolic, and perhaps with a spatial component for robots, started out relying on full knowledge of the state of the world. In the last couple of decades that work has concentrated on finessing the impossibility of knowing the state of the world in detail. But such planners are quite deliberative in working out what is going to happen ahead of time. By contrast the behavior based approaches started out as purely reactive to how the world was changing. This has made them much more robust in the real world which is why the vast majority of deployed robots in the world are behavior-based. With the twenty year old innovation of behavior trees these systems can appear much more deliberative, though they lack the wholesale capability of dynamically re-planning that symbolic systems have. This table summarizes:

Note that neural nets are neither. There has been a relatively small amount of non-mainstream work of getting neural nets to control very simple robots, mostly in simulation only. The vast majority of work on neural networks has been to get them to classify data in some way or another. They have never been a complete system, and indeed all the recent successes of neural networks have had them embedded as part of symbolic AI systems or behavior-based systems.

To end Part I of our Steps Towards Super Intelligence, let’s go back to our comparison of the four approaches to Artificial Intelligence. Let’s see how well we are really doing (in my opinion) by comparing them to a human child.

Recall the scale here is one to three. I have added a column on the right on how well they do at cognition, and a row on the bottom on how well a human child does in comparison to each of the four AI approaches.  One to three.

Note that under this evaluation a human child scores six hundred points whereas the four AI approaches score a total of eight or nine points each. As usual, I think I may have grossly overestimated the current capabilities of AI systems.

Next up: Part II, beyond the Turing Test.

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¹ This pronoun is often capitalized in this quote, but in my version of the King James Bible, which was presented to my grandmother in 1908, it is just plain “his” without capitalization. Genesis 1:27.

² In the dedication of this 1973 PhD thesis at the MIT Artificial Intelligence Lab, to the Maharal of Prague–the creator of the best known Golem, Gerry Sussman points out that the Rabbi had noticed that this line was recursive. That observation has stayed with me since I first read it in 1979, and it inspired my first two lines of this blog post.

³ I am using the male form here only for stylistic purposes to resonate with the first sentence.

⁴ It appeared in the IEEE Journal of Robotics and Automation, Vol. 2, No. 1, March 1986, pp 14–23. Both reviewers for the paper recommended against publishing it, but the editor, Professor George Bekey of USC, used his discretion to override them and to go ahead and put it into print.

⁵ I chose this form, g0047, for anonymous symbols as that is exactly the form in which they are generated in the programming language Lisp, which is what most early work in AI was written in, and is still out there being used to do useful work.

Past is prologue¹.

I mean that both the ways people interpret Shakespeare’s meaning when he has Antonio utter the phrase in The Tempest.

In one interpretation it is that the past has predetermined the sequence which is about to unfold–and so I believe that how we have gotten to where we are in Artificial Intelligence will determine the directions we take next–so it is worth studying that past.

Another interpretation is that really the past was not much and the majority of necessary work lies ahead–that too, I believe. We have hardly even gotten started on Artificial Intelligence and there is lots of hard work ahead.

THE EARLY DAYS

It is generally agreed that John McCarthy coined the phrase “artificial intelligence” in the written proposal² for a 1956 Dartmouth workshop, dated August 31^st, 1955. It is authored by, in listed order, John McCarthy of Dartmouth, Marvin Minsky of Harvard, Nathaniel Rochester of IBM and Claude Shannon of Bell Laboratories. Later all but Rochester would serve on the faculty at MIT, although by early in the sixties McCarthy had left to join Stanford University. The nineteen page proposal has a title page and an introductory six pages (1 through 5a), followed by individually authored sections on proposed research by the four authors. It is presumed that McCarthy wrote those first six pages which include a budget to be provided by the Rockefeller Foundation to cover 10 researchers.

The title page says A PROPOSAL FOR THE DARTMOUTH SUMMER RESEARCH PROJECT ON ARTIFICIAL INTELLIGENCE. The first paragraph includes a sentence referencing “intelligence”:

The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.

And then the first sentence of the second paragraph starts out:

The following are some aspects of the artificial intelligence problem:

That’s it! No description of what human intelligence is, no argument about whether or not machines can do it (i.e., “do intelligence”), and no fanfare on the introduction of the term “artificial intelligence” (all lower case).

In the linked file above there are an additional four pages dated March 6^th, 1956, by Allen Newell and Herb Simon, at that time at the RAND Corporation and Carnegie Institute of Technology respectively (later both were giants at Carnegie Mellon University), on their proposed research contribution. They say that they are engaged in a series of forays into the area of complex information processing, and that a “large part of this activity comes under the heading of artificial intelligence”. It seems that the phrase “artificial intelligence” was easily and quickly adopted without any formal definition of what it might be.

In McCarthy’s introduction, and in the outlines of what the six named participants intend to research there is no lack of ambition.

The speeds and memory capacities of present computers may be insufficient to simulate many of the higher functions of the human brain, but the major obstacle is not lack of machine capacity, but our inability to write programs taking full advantage of what we have.

Some of the AI topics that McCarthy outlines in the introduction are how to get a computer to use human language, how to arrange “neuron nets” (they had been invented in 1943–a little while before today’s technology elite first heard about them and started getting over-excited) so that they can form concepts, how a machine can improve itself (i.e., learn or evolve), how machines could form abstractions from using its sensors to observe the world, and how to make computers think creatively. These topics are expanded upon in the individual work proposals by Shannon, Minsky, Rochester, and McCarthy. The addendum from Newell and Simon adds to the mix getting machines to play chess (including through learning), and prove mathematical theorems, along with developing theories on how machines might learn, and how they might solve problems similar to problems that humans can solve.

No lack of ambition! And recall that at this time there were only a handful of digital computers in the world, and none of them had more than at most a few tens of kilobytes of memory for running programs and data, and only punched cards or paper tape for long term storage.

McCarthy was certainly not the first person to talk about machines and “intelligence”, and in fact Alan Turing had written and published about it before this, but without the moniker of “artificial intelligence”. His best known foray is Computing Machinery and Intelligence³ which was published in October 1950. This is the paper where he introduces the “Imitation Game”, which has come to be called the “Turing Test”, where a person is to decide whether the entity they are conversing with via a 1950 version of instant messaging is a person or a computer. Turing estimates that in the year 2000 a computer with 128MB of memory (he states it as binary digits) will have a 70% chance of fooling a person.

Although the title of the paper has the word “Intelligence” in it, there is only one place where that word is used in the body of the paper (whereas “machine” appears at least 207 times), and that is to refer to the intelligence of a human who is trying to build a machine that can imitate an adult human. His aim however is clear. He believes that it will be possible to make a machine that can think as well as a human, and by the year 2000. He even estimates how many programmers will be needed (sixty is his answer, working for fifty years, so only 3,000 programmer years–a tiny number by the standards of many software systems today).

In a slightly earlier 1948 paper titled Intelligent Machinery but not published⁴ until 1970, long after his death,  Turing outlined the nature of “discrete controlling machines”, what we would today call “computers”, as he had essentially invented digital computers in a paper he had written in 1937. He then turns to making a a machine that fully imitates a person, even as he reasons, the brain part might be too big to be contained within the locomoting sensing part of the machine, and instead must operate it remotely. He points out that the sensors and motor systems of the day might not be up to it, so concludes that to begin with the parts of intelligence that may be best to investigate are games and cryptography, and to a less extent translation of languages and mathematics.

Again, no lack of ambition, but a bowing to the technological realities of the day.

When AI got started the clear inspiration was human level performance and human level intelligence. I think that goal has been what attracted most researchers into the field for the first sixty years. The fact that we do not have anything close to succeeding at those aspirations says not that researchers have not worked hard or have not been brilliant. It says that it is a very hard goal.

I wrote a (long) paper  Intelligence without Reason⁵ about the pre-history and early days of Artificial Intelligence in 1991, twenty seven years ago, and thirty five years into the endeavor. My current blog posts are trying to fill in details and to provide an update for a new generation to understand just what a long term project this is. To many it all seems so shiny and exciting and new. Of those, it is exciting only.

TOWARDS TODAY

In the early days of AI there were very few ways to connect sensors to digital computers or to let those computers control actuators in the world.

In the early 1960’s people wanting to work on computer vision algorithms had to take photographs on film, turn them into prints, attach the prints to a drum, then have that drum rotate and move up and down next to a single light brightness sensor to turn the photo into an array of intensities. By the late seventies, with twenty or thirty pounds of equipment, costing tens of thousands of dollars, a researcher could get a digital image directly from a camera into a computer. Things did not become simple-ish until the eighties and they have gotten progressively simply and cheaper over time.

Similar stories hold for every other sensor modality, and also for output–turning results of computer programs into physical actions in the world.

Thus, as Turing had reasoned, early work in Artificial Intelligence turned towards domains where there was little need for sensing or action. There was work on games, where human moves could easily be input and output to and from a computer via a keyboard and a printer, mathematical exercises such as calculus applied to symbolic algebra, or theorem proving in logic, and to understanding typed English sentences that were arithmetic word problems.

Writing programs that could play games quickly lead to the idea of “tree search” which was key to almost all of the early AI experiments in the other fields listed above, and indeed, is now a basic tool of much of computer science. Playing games early on also provided opportunities to explore Machine Learning and to invent a particular variant of it, Reinforcement Learning, which was at the heart of the recent success of the AlphaGo program. I described this early history in more detail in my August 2017 post Machine Learning Explained.

Before too long a domain known as blocks world was invented where all sorts of problems in intelligence could be explored. Perhaps the first PhD thesis on computer vision, by Larry Roberts at MIT in 1963, had shown that with a carefully lighted scene, all the edges of wooden block with planar surfaces could be recovered.

That validated the idea that it was OK to work on complex problems with blocks where the description of their location or their edges was the input to the program, as in principle the perception part of the problem could be solved. This then was a simulated world of perception and action, and it was the principal test bed for AI for decades.

Some people worked on problem solving in a two dimensional blocks world with an imagined robot that could pick up and put down blocks from the top of a stack, or on a simulated one dimensional table.

Others worked on recovering the geometry of the underlying three dimensional blocks from just the input lines, including with shadows, paving the way for future more complete vision systems than Roberts had demonstrated.

And yet others worked on complex natural language understanding, and all sorts of problem solving in worlds with complex three dimensional blocks.

No one worked in these blocks worlds because that was their ambition. Rather they worked in them because with the tools they had available they felt that they could make progress on problems that would be important for human level intelligence. At the same time they did not think that was just around the corner, one magic breakthrough away from all being understood, implemented, and deployed.

Over time may sub-disciplines in AI developed as people got deeper and deeper into the approaches to particular sub-problems that they had discovered. Before long there was enough new work coming out that no-one could keep up with the breadth of AI research. The names of the sub-disciplines included planning, problem solving, knowledge representation, natural language processing, search, game playing, expert systems, neural networks, machine inference, statistical machine learning, robotics, mobile robotics, simultaneous localization and mapping, computer vision, image understanding, and many others.

Breakoff Groups

Often, as a group of researchers found a common set of problems to work on they would break off from the mainstream and set up their own journals and conferences where reviewing of papers could all be done by people who understood the history and context of the particular problems.

I was involved in two such break off groups in the late 1980’s and early 1990’s, both of which still exist today; Artificial Life, and Simulation of Adaptive Behavior. The first of these looks at fundamental mechanisms of order from disorder and includes evolutionary processes. The second looks at how animal behaviors can be generated by the interaction of perception, action, and computation. Both of these groups and their journals are still active today.

Below is my complete set of the Artificial Life journal from when it was published on paper from 1993 through to 2014. It is still published online today, by the MIT Press.

There were other journals on Artificial Life, and since 1989 there have been international conferences on it. I ran the 1994 conference and there were many hundreds of participants and there were 56 carefully reviewed papers published in hard copy proceedings which I co-edited with Pattie Maes; all those papers are now available online.

And here is my collection of the Adaptive Behavior journal from when it was published on paper from 1992 through to 2013. It is still published online today, by Sage.

And there has always been a robust series of major conferences, called SAB, for Simulation of Adaptive Behavior with paper and now online proceedings.

The Artificial Life Conference will be in Tokyo this year in July, and the SAB conference will be in Frankfurt in August.  Each will attract hundreds of researchers. And the 20+ volumes of each of the journals above have 4 issues each, so close to 100 issues, with 4 to 10 papers each, so many hundreds of papers in the journal series. These communities are vibrant and the Artificial Life community has had some engineering impact in developing genetic algorithms which are in use in some number of application.

But neither the Artificial Life community nor the Simulation of Adaptive Behavior community have succeeded at their early goals.

We still do not know how living systems arise from non-living systems, and in fact still do not have good definitions of what life really is. We do not have generally available evolutionary simulations which let us computationally evolve better and better systems, despite the early promise when we first tried it. And we have not figured out how to evolve systems that have even the rudimentary components of a complete general intelligence, even for very simple creatures.

On the SAB side we can still not computationally simulate the behavior of the simplest creature that has been studied at length. That is the tiny worm C. elegans, which has 959 cells total of which 302 are neurons. We know its complete connectome (and even its 56 glial cells), but still we can’t simulate how they produce much of its behaviors.

I tell these particular stories not because they were uniquely special, but because they give an idea of how research in hard problems works, especially in academia. There were many, many (at least twenty or thirty) other AI subgroups with equally specialized domains that split off. They sometimes flourished and sometimes died off. All those subgroups gave themselves unique names, but were significant in size, in numbers of researchers and in active sharing and publication of ideas.

But all researchers in AI were, ultimately, interested in full scale general human intelligence. Often their particular results might seem narrow, and in application to real world problems were very narrow. But general intelligence has always been the goal.

I will finish this section with a story of a larger scale specialized research group, that of computer vision. That specialization has had real engineering impact. It has had four or more major conferences per year for thirty five plus years. It has half a dozen major journals. I cofounded one of them in 1987, with Takeo Kanade, the International Journal of Computer Vision, which has had 126 volumes (I only stayed as an editor for the first seven volumes) and 350 issues since then, with 2,080 individual articles. Remember, that is just one of the half dozen major journals in the field. The computer vision community is what a real large push looks like. This has been a sustained community of thousands of researchers world wide for decades.

CATCHY NAMES

I think the press, and those outside of the field have recently gotten confused by one particular spin off name, that calls itself AGI, or Artificial General Intelligence. And the really tricky part is that there a bunch of completely separate spin off groups that all call themselves AGI, but as far as I can see really have very little commonality of approach or measures of progress. This has gotten the press and people outside of AI very confused, thinking there is just now some real push for human level Artificial Intelligence, that did not exist before. They then get confused that if people are newly working on this goal then surely we are about to see new astounding progress. The bug in this line of thinking is that thousands of AI researchers have been working on this problem for 62 years. We are not at any sudden inflection point.

There is a journal of AGI, which you can find here. Since 2009 there have been a total of 14 issues, many with only a single paper, and only 47 papers in total over that ten year period. Some of the papers are predictions about AGI, but most are very theoretical, modest, papers about specific logical problems, or architectures for action selection. None talk about systems that have been built that display intelligence in any meaningful way.

There is also an annual conference for this disparate group, since 2008, with about 20 papers, plus or minus, per year, just a handful of which are online, at the authors’ own web sites. Again the papers range from risks of AGI to very theoretical specialized, and obscure, research topics. None of them are close to any sort of engineering.

So while there is an AGI community it is very small and not at all working on any sort of engineering issues that would result in any actual Artificial General Intelligence in the sense that the press means when it talks about AGI.

I dug a little deeper and looked at two groups that often get referenced by the press in talking about AGI.

One group, perhaps the most referenced group by the press, styles themselves as an East San Francisco Bay Research Institute working on the mathematics of making AGI safe for humans. Making safe human level intelligence is exactly the goal of almost all AI researchers. But most of them are sanguine enough to understand that that goal is a long way off.

This particular research group lists all their publications and conference presentations from 2001 through 2018 on their web site. This is admirable, and is a practice followed by most research groups in academia.

Since 2001 they have produced 10 archival journal papers (but see below), made 29 presentations at conferences, written 9 book chapters, and have 45 additional internal reports, for a total output of 93 things–about what one would expect from a single middle of the pack professor, plus students, at a research university. But 36 of those 93 outputs are simply predictions of when AGI will be “achieved”, so cut it down to 57 technical outputs, and then look at their content. All of them are very theoretical mathematical and logical arguments about representation and reasoning, with no practical algorithms, and no applications to the real world. Nothing they have produced in 18 years has been taken up and used by any one else in any application of demonstration any where.

And the 10 archival journal papers, the only ones that have a chance of being read by more than a handful of people? Every single one of them is about predicting when AGI will be achieved.

This particular group gets cited by the press and by AGI alarmists again and again. But when you look there with any sort of critical eye, you find they are not a major source of progress towards AGI.

Another group that often gets cited as a source for AGI, is a company in Eastern Europe that claims it will produce an Artificial General Intelligence within 10 years. It is only a company in the sense that one successful entrepreneur is plowing enough money into it to sustain it. Again let’s look at what its own web site tells us.

In this case they have been calling for proposals and ideas from outsiders, and they have distilled that input into the following aspiration for what they will do:

We plan to implement all these requirements into one universal algorithm that will be able to successfully learn all designed and derived abilities just by interacting with the environment and with a teacher.

Yeah, well, that is just what Turing suggested in 1948.  So this group has exactly the same aspiration that has been around for seventy years. And they admit it is their aspiration but so far they have no idea of how to actually do it. Turing, in 1948, at least had a few suggestions.

If you, as a journalist, or a commentator on AI, think that the AGI movement is large and vibrant and about to burst onto the scene with any engineered systems, you are confused. You are really, really confused.

Journalists, and general purpose prognosticators, please, please, do your homework. Look below the surface and get some real evaluation on whether groups that use the phrase AGI in their self descriptions are going to bring you human level Artificial Intelligence, or indeed whether they are making any measurable progress towards doing so. It is tempting to see the ones out on the extreme, who don’t have academic appointments, working valiantly, and telling stories of how they are different and will come up something new and unique, as the brilliant misfits. But in all probability they will not succeed in decades, just as  the Artificial Life and the Simulation of Adaptive Behavior groups that I was part of have still not succeeded in their goals of almost thirty years ago.

Just because someone says they are working on AGI, Artificial General Intelligence, that does not mean they know how to build it, how long it might take, or necessarily be making any progress at all. These lacks have been the historical norm. Certainly the founding researchers in Artificial Intelligence in the 1950’s and 1960’s thought that they were working on key components of general intelligence. But that does not mean they got close to their goal, even when they thought it was not so very far off.

So, journalists, don’t you dare, don’t you dare, come back to me in ten years and say where is that Artificial General Intelligence that we were promised? It isn’t coming any time soon.

And while we are on catchy names, let’s not forget “deep learning”. I suspect that the word “deep” in that name leads outsiders a little astray. Somehow it suggests that there is perhaps a deep level of understanding that a “deep learning” algorithm has when it learns something. In fact the learning is very shallow in that sense, and not at all what “deep” refers to. The “deep” in “deep learning” refers to the number of layers of units or “neurons” in the network.

When back propagation, the actual learning mechanism used in deep learning, was developed in the 1980’s most networks had only two or three layers. The revolutionary new networks are the same in structure as 30 years ago but have as many as 12 layers. That is what the “deep” is about, 12 versus 3. In order to make learning work on these “deep” networks there had to be lots more computer power (Moore’s Law took care of that over 30 years), a clever change to the activation function in each neuron, and a way to train the network in stages known as clamping. But not deep understanding.

WHY THIS ESSAY?

Why did I post this? I want to clear up some confusions about Artificial Intelligence, and the goals of people who do research in AI.

There have certainly been a million person-years of AI research carried out since 1956 (much more than the three thousand that Alan Turing thought it would take!), with an even larger number of person-years applied to AI development and deployment.

We are way off the early aspirations of how far along we would be in Artificial Intelligence by now, or by the year 2000 or the year 2001. We are not close to figuring it out. In my next blog post, hopefully in May of 2018 I will outline all the things we do not understand yet about how to build a full scale artificially intelligent entity.

My intent of that coming blog post is to:

1. Stop people worrying about imminent super intelligent AI (yet, I know, they will enjoy the guilty tingly feeling thinking about it, and will continue to irrationally hype it up…).
2. To suggest directions of research which can have real impact on the future of AI, and accelerate it.
3. To show just how much fun research remains to be done, and so to encourage people to work on the hard problems, and not just the flashy demos that are hype bait.

In closing, I would like to share Alan Turing’s last sentence from his paper “Computing Machinery and Intelligence”, just as valid today as it was 68 years ago:

We can only see a short distance ahead, but we can see plenty there that needs to be done.

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¹This whole post started out as a footnote to one of the two long essays in the FoR&AI series that I am working on. It clearly got too long to be a footnote, but is much somewhat shorter than my usual long essays.

²I have started collecting copies of hard to find historical documents and movies about AI in one place, as I find them in obscure nooks of the Web, where the links may change as someone reorganizes their personal page, or on a course page. Of course I can not guarantee that this link will work forever, but I will try to maintain it for as long as I am able. My web address has been stable for almost a decade and a half already.

³This version is the original full version as it appeared in the journal Mind, including the references.  Most of the versions that can be found on the Web are a later re-typesetting without references and with a figure deleted–and I have not fully checked them for errors that might have been introduced–I have noticed at least one place were has been substituted for . That is why I have tracked down the original version to share here.

⁴His boss at the National Physical Laboratory (NPL), Sir Charles Darwin, grandson of that Charles Darwin, did not approve of what he had written, and so the report was not allowed to be published. When it finally appeared in 1970 it was labelled as the “prologue” to the fifth volume of an annual series of volumes titled “Machine Intelligence”, produced in Britain, and in this case edited by Bernard Meltzer and Donald Michie, the latter a war time colleague of Turing at Bletchley Park. They too, used the past as prologue.

⁵This paper was written on the occasion of my being co-winner (with Martha Pollack, now President of Cornell University) in 1991 of the Computers and Thought award that is given at the bi-annual International Joint Conference on Artificial Intelligence (IJCAI) to a young researcher. There was some controversy over whether at age 36 I was still considered young and so the rules were subsequently tightened up in a way that guarantees that I will forever be the oldest recipient of this award. In any case I had been at odds with the conventional AI world for some time (I seem to remember a phrase including “angry young man”…) so I was very grateful to receive the award. The proceedings of the conference had a six page, double column, limit on contributed papers. As a winner of the award I was invited to contribute a paper with a relaxed page limit. I took them at their word and produced a paper which spanned twenty seven pages and was over 25,000 words long! It was my attempt at a scholarly deconstruction of the field of AI, along with the path forward as I saw it.

 

[An essay in my series on the Future of Robotics and Artificial Intelligence.]

We are surrounded by hysteria about the future of Artificial Intelligence and Robotics. There is hysteria about how powerful they will become how quickly, and there is hysteria about what they will do to jobs.

As I write these words on September 2^nd, 2017, I note just two news stories from the last 48 hours.

Yesterday, in the New York Times, Oren Etzioni, chief executive of the Allen Institute for Artificial Intelligence, wrote an opinion piece titled How to Regulate Artificial Intelligence where he does a good job of arguing against the hysteria that Artificial Intelligence is an existential threat to humanity. He proposes rather sensible ways of thinking about regulations for Artificial Intelligence deployment, rather than the chicken little “the sky is falling” calls for regulation of research and knowledge that we have seen from people who really, really, should know a little better.

Today, there is a story in Market Watch that robots will take half of today’s jobs in 10 to 20 years. It even has a graphic to prove the numbers.

The claims are ludicrous. [I try to maintain professional language, but sometimes…] For instance, it appears to say that we will go from 1 million grounds and maintenance workers in the US to only 50,000 in 10 to 20 years, because robots will take over those jobs. How many robots are currently operational in those jobs? ZERO. How many realistic demonstrations have  there been of robots working in this arena? ZERO. Similar stories apply to all the other job categories in this diagram where it is suggested that there will be massive disruptions of 90%, and even as much as 97%, in jobs that currently require physical presence at some particular job site.

Mistaken predictions lead to fear of things that are not going to happen. Why are people making mistakes in predictions about Artificial Intelligence and robotics, so that Oren Etzioni, I, and others, need to spend time pushing back on them?

Below I outline seven ways of thinking that lead to mistaken predictions about robotics and Artificial Intelligence. We find instances of these ways of thinking in many of the predictions about our AI future. I am going to first list the four such general topic areas of predictions that I notice, along with a brief assessment of where I think they currently stand.

A. Artificial General Intelligence. Research on AGI is an attempt to distinguish a thinking entity from current day AI technology such as Machine Learning. Here the idea is that we will build autonomous agents that operate much like beings in the world. This has always been my own motivation for working in robotics and AI, but the recent successes of AI are not at all like this.

Some people think that all AI is an instance of AGI, but as the word “general” would imply, AGI aspires to be much more general than current AI. Interpreting current AI as an instance of AGI makes it seem much more advanced and all encompassing that it really is.

Modern day AGI research is not doing at all well on being either general or getting to an independent entity with an ongoing existence. It mostly seems stuck on the same issues in reasoning and common sense that AI has had problems with for at least fifty years. Alternate areas such as Artificial Life, and Simulation of Adaptive Behavior did make some progress in getting full creatures in the eighties and nineties (these two areas and communities were where I spent my time during those years), but they have stalled.

My own opinion is that of course this is possible in principle. I would never have started working on Artificial Intelligence if I did not believe that. However perhaps we humans are just not smart enough to figure out how to do this–see my remarks on humility in my post on the current state of Artificial Intelligence suitable for deployment in robotics. Even if it is possible I  personally think we are far, far further away from understanding how to build AGI than many other pundits might say.

[Some people refer to “an AI”, as though all AI is about being an autonomous agent. I think that is confusing, and just as the natives of San Francisco do not refer to their city as “Frisco”, no serious researchers in AI refer to “an AI”.]

B. The Singularity. This refers to the idea that eventually an AI based intelligent entity, with goals and purposes, will be better at AI research than us humans are. Then, with an unending Moore’s law mixed in making computers faster and faster, Artificial Intelligence will take off by itself, and, as in speculative physics going through the singularity of a black hole, we have no idea what things will be like on the other side.

People who “believe” in the Singularity are happy to give post-Singularity AI incredible power, as what will happen afterwards is quite unpredictable. I put the word believe in scare quotes as belief in the singularity can often seem like a religious belief. For some it comes with an additional benefit of being able to upload their minds to an intelligent computer, and so get eternal life without the inconvenience of having to believe in a standard sort of supernatural God. The ever powerful technologically based AI is the new God for them. Techno religion!

Some people have very specific ideas about when the day of salvation will come–followers of one particular Singularity prophet believe that it will happen in the year 2029, as it has been written.

This particular error of prediction is very much driven by exponentialism, and I will address that as one of the seven common mistakes that people make.

Even if there is a lot of computer power around it does not mean we are close to having programs that can do research in Artificial Intelligence, and rewrite their own code to get better and better.

Here is where we are on programs that can understand computer code. We currently have no programs that can understand a one page program as well as a new student in computer science can understand such a program after just one month of taking their very first class in programming. That is a long way from AI systems being better at writing AI systems than humans are.

Here is where we are on simulating brains at the neural level, the other methodology that Singularity worshipers often refer to. For about thirty years we have known the full “wiring diagram” of the 302 neurons in the worm C. elegans, along with the 7,000 connections between them.  This has been incredibly useful for understanding how behavior and neurons are linked. But it has been a thirty years study with hundreds of people involved, all trying to understand just 302 neurons. And according to the OpenWorm project trying to simulate C. elegans bottom up, they are not yet half way there.  To simulate a human brain with 100 billion neurons and a vast number of connections is quite a way off. So if you are going to rely on the Singularity to upload yourself to a brain simulation I would try to hold off on dying for another couple of centuries.

Just in case I have not made my own position on the Singularity clear, I refer you to my comments in a regularly scheduled look at the event by the magazine IEEE Spectrum. Here is the the 2008 version, and in particular a chart of where the players stand and what they say. Here is the 2017 version, and in particular a set of boxes of where the players stand and what they say. And yes, I do admit to being a little snarky in 2017…

C. Misaligned Values. The third case is that the Artificial Intelligence based machines get really good at execution of tasks, so much so that they are super human at getting things done in a complex world. And they do not share human values and this leads to all sorts of problems.

I think there could be versions of this that are true–if I have recently bought an airline ticket to some city, suddenly all the web pages I browse that rely on advertisements for revenue start displaying ads for airline tickets to the same city. This is clearly dumb, but I don’t think it is a sign of super capable intelligence, rather it is a case of poorly designed evaluation functions in the algorithms that place advertisements.

But here is a quote from one of the proponents of this view (I will let him remain anonymous, as an act of generosity):

The well-known example of paper clips is a case in point: if the machine’s only goal is maximizing the number of paper clips, it may invent incredible technologies as it sets about converting all available mass in the reachable universe into paper clips; but its decisions are still just plain dumb.

Well, no. We would never get to a situation in any version of the real world where such a program could exist. One smart enough that it would be able to invent ways to subvert human society to achieve goals set for it by humans, without understanding the ways in which it was causing problems for those same humans. Thinking that technology might evolve this way is just plain dumb (nice turn of phrase…), and relies on making multiple errors among the seven that I discuss below.

This same author repeatedly (including in the piece from which I took this quote, but also at the big International Joint Conference on Artificial Intelligence (IJCAI) that was held just a couple of weeks ago in Melbourne, Australia) argues that we need research to come up with ways to mathematically prove that Artificial Intelligence systems have their goals aligned with humans.

I think this case C comes from researchers seeing an intellectually interesting research problem, and then throwing their well known voices promoting it as an urgent research question. Then AI hangers-on take it, run with it, and turn it into an existential problem for mankind.

By the way, I think mathematical provability is a vain hope. With multi-year large team efforts we can not prove that a 1,000 line program can not be breached by external hackers, so we certainly won’t be able to prove very much at all about large AI systems. The good news is that us humans were able to successfully co-exist with, and even use for our own purposes, horses, themselves autonomous agents with on going existences, desires, and super-human physical strength, for thousands of years. And we had not a single theorem about horses. Still don’t!

D. Really evil horrible nasty human-destroying Artificially Intelligent entities. The last category is like case C, but here the supposed Artificial Intelligence powered machines will take an active dislike to humans and decide to destroy them and get them out of the way.

This has been a popular fantasy in Hollywood since at least the late 1960’s with movies like 2001: A Space Odyssey (1968, but set in 2001), where the machine-wreaked havoc was confined to a single space ship, and Colossus: The Forbin Project (1970, and set in those times) where the havoc was at a planetary scale. The theme has continued over the years, and more recently with I, Robot (2004, set in 2035) where the evil AI computer VIKI takes over the world through the instrument of the new NS-5 humanoid robots. [By the way, that movie continues the bizarre convention from other science fiction movies that large complex machines are built with spaces that have multi hundred feet heights around them so that there can be great physical peril for the human heroes as they fight the good fight against the machines gone bad…]

This is even wronger than case C. I think it must make people feel tingly thinking about these terrible, terrible dangers…

In this blog, I am not going to address the issue of military killer robots–this often gets confused in the press with issue D above, and worse it often gets mashed together by people busy fear mongering about issue D. They are very separate issues. Furthermore I think that many of the arguments about such military robots are misguided. But it is a very different issue and will have to wait for another blog post.

Now, the seven mistakes I think people are making. All seven of them influence the assessments about timescales for and likelihood of each of scenarios A, B, C, and D, coming about. But some are more important I believe in the mis-estimations than others. I have labeled in the section headers for each of these seven errors where I think they do the most damage. The first one does some damage everywhere!

1. [A,B,C,D] Over and under estimating

Roy Amara was a futurist and the co-founder and President of the Institute For The Future in Palo Alto, home of Stanford University, countless venture capitalists, and the intellectual heart of Silicon Valley. He is best known for his adage, now referred to as Amara’s law:

We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.

There is actually a lot wrapped up in these 21 words which can easily fit into a tweet and allow room for attribution. An optimist can read it one way, and a pessimist can read it another. It should make the optimist somewhat pessimistic, and the pessimist somewhat optimistic, for a while at least, before each reverting to their norm.

A great example^⁠1 of the two sides of Amara’s law that we have seen unfold over the last thirty years concerns the US Global Positioning System. Starting in 1978 a constellation of 24 satellites (30 including spares) were placed in orbit. A ground station that can see 4 of them at once can compute the the latitude, longitude, and height above a version of sea level. An operations center at Schriever Air Force Base in Colorado constantly monitors the precise orbits of the satellites and the accuracy of their onboard atomic clocks and uploads minor and continuous adjustments to them. If those updates were to stop GPS would fail to have you on the correct road as you drive around town after only a week or two, and would have you in the wrong town after a couple of months.

The goal of GPS was to allow precise placement of bombs by the US military. That was the expectation for it. The first operational use in that regard was in 1991 during Desert Storm, and it was promising. But during the nineties there was still much distrust of GPS as it was not delivering on its early promise, and it was not until the early 2000’s that its utility was generally accepted in the US military. It had a hard time delivering on its early expectations and the whole program was nearly cancelled again and again.

Today GPS is in the long term, and the ways it is used were unimagined when it was first placed in orbit. My Series 2 Apple Watch uses GPS while I am out running to record my location accurately enough to see which side of the street I ran along. The tiny size and tiny price of the receiver would have been incomprehensible to the early GPS engineers. GPS is now used for so many things that the designers never considered. It synchronizes physics experiments across the globe and is now an intimate component of synchronizing the US electrical grid and keeping it running, and it even allows the high frequency traders who really control the stock market to mostly not fall into disastrous timing errors. It is used by all our airplanes, large and small to navigate, it is used to track people out of jail on parole, and it determines  which seed variant will be planted in which part of many fields across the globe. It tracks our fleets of trucks and reports on driver performance, and the bouncing signals on the ground are used to determine how much moisture there is in the ground, and so determine irrigation schedules.

GPS started out with one goal but it was a hard slog to get it working as well as was originally expected. Now it has seeped into so many aspects of our lives that we would not just be lost if it went away, but we would be cold, hungry, and quite possibly dead.

We see a similar pattern with other technologies over the last thirty years. A big promise up front, disappointment, and then slowly growing confidence, beyond where the original expectations were aimed. This is true of the blockchain (Bitcoin was the first application), sequencing individual human genomes, solar power, wind power, and even home delivery of groceries.

Perhaps the most blatant example is that of computation itself. When the first commercial computers were deployed in the 1950’s there was widespread fear that they would take over all jobs (see the movie Desk Set from 1957). But for the next 30 years computers were something that had little direct impact on people’s lives and even in 1987 there were hardly any microprocessors in consumer devices. That has all changed in the second wave over the subsequent 30 years and now we all walk around with our bodies adorned with computers, our cars full of them, and they are all over our houses.

To see how the long term influence of computers has consistently been underestimated one need just go back and look at portrayals of them in old science fiction movies or TV shows about the future. The three hundred year hence space ship computer in the 1966 Star Trek (TOS) was laughable just thirty years later, let alone three centuries later.  And in Star Trek The Next Generation, and Star Trek Deep Space Nine, whose production spanned 1986 to 1999, large files still needed to be carried by hand around the far future space ship or space station as they could not be sent over the network (like an AOL network of the time). And the databases available for people to search were completely anemic with their future interfaces which were pre-Web in design.

Most technologies are overestimated in the short term. They are the shiny new thing. Artificial Intelligence has the distinction of having been the shiny new thing and being overestimated again and again, in the 1960’s, in the 1980’s, and I believe again now. (Some of the marketing messages from large companies on their AI offerings are truly delusional, and may have very bad blowback for them in the not too distant future.)

Not all technologies get underestimated in the long term, but that is most likely the case for AI. The question is how long is the long term. The next six errors that I talk about help explain how the timing for the long term is being grossly underestimated for the future of AI.

2. [B,C,D] Imagining Magic

When I was a teenager, Arthur C. Clarke was one of the “big three” science fiction writers along with Robert Heinlein and Isaac Asimov. But Clarke was more than just a science fiction writer. He was also an inventor, a science writer, and a futurist.

In February 1945 he wrote a letter^⁠2 to Wireless World about the idea of geostationary satellites for research, and in October of that year he published a paper^⁠3 outlining how they could be used to provide world-wide radio coverage. In 1948 he wrote a short story The Sentinel which provided the kernel idea for Stanley Kubrick’s epic AI movie 2001: A Space Odyssey, with Clarke authoring a book of the same name as the film was being made, explaining much that had left the movie audience somewhat lost.

In the period from 1962 to 1973 Clarke formulated three adages, which have come to be known as Clarke’s three laws (he said that Newton only had three, so three were enough for him too):

1. When a distinguished but elderly scientist states that something is possible, he is almost certainly right. When he states that something is impossible, he is very probably wrong.
2. The only way of discovering the limits of the possible is to venture a little way past them into the impossible.
3. Any sufficiently advanced technology is indistinguishable from magic.

Personally I should probably be wary of the second sentence in his first law, as I am much more conservative than some others about how quickly AI will be ascendant. But for now I want to expound on Clarke’s third law.

Imagine we had a time machine (powerful magic in itself…) and we could transport Issac Newton from the late 17^th century to Trinity College Chapel in Cambridge University. That chapel was already 100 years old when he was there so perhaps it would not be too much of an immediate shock to find himself in it, not realizing the current date.

Now show Newton an Apple. Pull out an iPhone from your pocket, and turn it on so that the screen is glowing and full of icons and hand it to him. The person who revealed how white light is made from components of different colored light by pulling apart sunlight with a prism and then putting it back together again would no doubt be surprised at such a small object producing such vivid colors in the darkness of the chapel. Now play a movie of an English country scene, perhaps with some animals with which he would be familiar–nothing indicating the future in the content. Then play some church music with which he would be familiar. And then show him a web page with the 500 plus pages of his personally annotated copy of his masterpiece Principia, teaching him how to use the pinch gestures to zoom in on details.

Could Newton begin to explain how this small device did all that? Although he invented calculus and explained both optics and gravity, Newton was never able to sort out chemistry and alchemy. So I think he would be flummoxed, and unable to come up with even the barest coherent outline of what this device was. It would be no different to him than an embodiment of the occult–something which was of great interest to him when he was alive. For him it would be indistinguishable from magic. And remember, Newton was a really smart dude.

If something is magic it is hard to know the limitations it has. Suppose we further show Newton how it can illuminate the dark, how it can take photos and movies and record sound, how it can be used as a magnifying glass, and as a mirror. Then we show him how it can be used to carry out arithmetical computations at incredible speed and to many decimal places. And we show it counting his steps has he carries it.

What else might Newton conjecture that the device in front of him could do? Would he conjecture that he could use it to talk to people anywhere in the world immediately from right there in the chapel? Prisms work forever. Would he conjecture that the iPhone would work forever just as it is, neglecting to understand that it needed to be recharged (and recall that we nabbed him from a time 100 years before the birth of Michael Faraday, so the concept of electricity was not quite around)? If it can be a source of light without fire could it perhaps also transmute lead to gold?

This is a problem we all have with imagined future technology. If it is far enough away from the technology we have and understand today, then we do not know its limitations. It becomes indistinguishable from magic.

When a technology passes that magic line anything one says about it is no longer falsifiable, because it is magic.

This is a problem I regularly encounter when trying to debate with people about whether we should fear just plain AGI, let alone cases C or D from above. I am told that I do not understand how powerful it will be. That is not an argument. We have no idea whether it can even exist. All the evidence that I see says that we have no real idea yet how to build one. So its properties are completely unknown, so rhetorically it quickly becomes magical and super powerful. Without limit.

Nothing in the Universe is without limit. Not even magical future AI.

Watch out for arguments about future technology which is magical. It can never be refuted. It is a faith-based argument, not a scientific argument.

3. [A,B,C] Performance versus competence

One of the social skills that we all develop is an ability to estimate the capabilities of individual people with whom we interact. It is true that sometimes “out of tribe” issues tend to overwhelm and confuse our estimates, and such is the root of the perfidy of racism, sexism, classism, etc. In general, however, we use cues from how a person performs some particular task to estimate how well they might perform some different task. We are able to generalize from observing performance at one task to a guess at competence over a much bigger set of tasks. We understand intuitively how to generalize from the performance level of the person to their competence in related areas.

When in a foreign city we ask a stranger on the street for directions and they reply in the language we spoke to them with confidence and with directions that seem to make sense, we think it worth pushing our luck and asking them about what is the local system for paying when you want to take a bus somewhere in that city.

If our teenage child is able to configure their new game machine to talk to the household wifi we suspect that if sufficiently motivated they will be able to help us get our new tablet computer on to the same network.

If we notice that someone is able to drive a manual transmission car, we will be pretty confident that they will be able to drive one with an automatic transmission too. Though if the person is North American we might not expect it to work for the converse case.

If we ask an employee in a large hardware store where to find a particular item, a home electrical fitting say, that we are looking for and they send us to an aisle of garden tools, we will probably not go back and ask that very same person where to find a particular bathroom fixture. We will estimate that not only do they not know where the electrical fittings are, but that they really do not know the layout of goods within the store, and we will look for a different person to ask with our second question.

Now consider a case that is closer to some performances we see for some of today’s AI systems.

Suppose a person tells us that a particular photo is of people playing Frisbee in the park, then we naturally assume that they can answer questions like “what is the shape of a Frisbee?”, “roughly how far can a person throw a Frisbee?”, “can a person eat a Frisbee?”, “roughly how many people play Frisbee at once?”, “can a 3 month old person play Frisbee?”, “is today’s weather suitable for playing Frisbee?”; in contrast we would not expect a person from another culture who says they have no idea what is happening in the picture to be able to answer all those questions.  Today’s image labelling systems that routinely give correct labels, like “people playing Frisbee in a park” to online photos, have no chance of answering those questions.  Besides the fact that all they can do is label more images and can not answer questions at all, they have no idea what a person is, that parks are usually outside, that people have ages, that weather is anything more than how it makes a photo look, etc., etc.

This does not mean that these systems are useless however. They are of great value to search engine companies. Simply labelling images well lets the search engine bridge the gap from search for words to searching for images. Note too that search engines usually provide multiple answers to any query and let the person using the engine review the top few and decide which ones are actually relevant. Search engine companies strive to get the performance of their systems to get the best possible answer as one of the top five or so. But they rely on the cognitive abilities of the human user so that they do not have to get the best answer first, every time. If they only gave one answer, whether to a search for “great hotels in Paris”, or at an e-commerce site only gave one image selection for a “funky neck tie”, they would not be as useful as they are.

Here is what goes wrong. People hear that some robot or some AI system has performed some task. They then take the generalization from that performance to a general competence that a person performing that same task could be expected to have. And they apply that generalization to the robot or AI system.

Today’s robots and AI systems are incredibly narrow in what they can do. Human style generalizations just do not apply. People who do make these generalizations get things very, very wrong.

4. [A,B] Suitcase words

I spoke briefly about suitcase words (Marvin Minsky’s term⁴) in my post explaining how machine learning works. There I was discussing how the word learning can mean so many different types of learning when applied to humans. And as I said there, surely there are different mechanisms that humans use for different sorts of learning. Learning to use chopsticks is a very different experience from learning the tune of a new song. And learning to write code is a very different experience from learning your way around a particular city.

When people hear that Machine Learning is making great strides and they think about a machine learning in some new domain, they tend to use as a mental model the way in which a person would learn that new domain. However, Machine Learning is very brittle, and it requires lots of human preparation by researchers or engineers, special purpose coding for processing input data, special purpose sets of training data, and a custom learning structure for each new problem domain. Today’s Machine Learning by computers is not at all the sponge like learning that humans engage in, making rapid progress in a new domain without having to be surgically altered or purpose built.

Likewise when people hear that computers can now beat the world chess champion (in 1997) or the world Go champion (in 2016) they tend to think that it is “playing” the game just like a human would. Of course in reality those programs had no idea what a game actually was (again, see my post on machine learning), nor that they are playing. And as pointed out in this article in The Atlantic during the recent Go challenge the human player, Lee Sedol, was supported by 12 ounces of coffee, whereas the AI program, AlphaGo, was running on a whole bevy of machines as a distributed application, and was supported by a team of more than 100 scientists.

When a human plays a game a small change in rules does not throw them off–a good player can adapt. Not so for AlphaGo or Deep Blue, the program that beat Garry Kasparov back in 1997.

Suitcase words lead people astray in understanding how well machines are doing at tasks that people can do. AI researchers, on the other hand, and worse their institutional press offices, are eager to claim progress in their research in being an instance of what a suitcase word applies to for humans. The important phrase here is “an instance”. No matter how careful the researchers are, and unfortunately not all of them are so very careful, as soon as word of the research result gets to the press office and then out into the unwashed press, that detail soon gets lost. Headlines trumpet the suitcase word, and mis-set the general understanding of where AI is, and how close it is to accomplishing more.

And, we haven’t even gotten to saying many of Minsky’s suitcase words about AI systems; consciousness, experience, or thinking. For us humans it is hard to think about playing chess without being conscious, or having the experience of playing, or thinking about a move. So far, none of our AI systems have risen to an even elementary level where one of the many ways in which we use those words about humans apply. When we do, and I tend to think that we will, get to a point where we will start using some of those words about particular AI systems, the press, and most people, will over generalize again.

Even with a very narrow single aspect demonstration of one slice of these words I am afraid people will over generalize and think that machines are on the very door step of human-like capabilities in these aspects of being intelligent.

Words matter, but whenever we use a word to describe something about an AI system, where that can also be applied to humans, we find people overestimating what it means. So far most words that apply to humans when used for machines, are only a microscopically narrow conceit of what the word means when applied to humans.

Here are some of the verbs that have been applied to machines, and for which machines are totally unlike humans in their capabilities:

anticipate, beat, classify, describe, estimate, explain, hallucinate, hear, imagine, intend, learn, model, plan, play, recognize, read, reason, reflect, see, understand, walk, write

For all these words there have been research papers describing a narrow sliver of the rich meanings that these words imply when applied to humans. Unfortunately the use of these words suggests that there is much more there there than is there.

This leads people to misinterpret and then overestimate the capabilities of today’s Artificial Intelligence.

5. [A,B,B,B,…] Exponentials

Many people are suffering from a severe case of “exponentialism”.

Everyone has some idea about Moore’s Law, at least as much to sort of know that computers get better and better on a clockwork like schedule.

What Gordon Moore actually said was that the number of components that could fit on a microchip would double every year.  I published a blog post in February about this and how it is finally coming to an end after a solid fifty year run. Moore had made his predictions in 1965 with only four data points using this graph:

He only extrapolated for 10 years, but instead it has lasted 50 years, although the time constant for doubling has gradually lengthened from one year to over two years, and now it is finally coming to an end.

Double the components on a chip has lead to computers that keep getting twice as fast. And it has lead to memory chips that have four times as much memory every two years. It has also led to digital cameras which have had more and more resolution, and LCD screens with exponentially more pixels.

The reason Moore’s law worked is that it applied to a digital abstraction of true or false.  Was there an electrical charge or voltage there or not? And the answer to that yes/no question is the same when the number of electrons is halved, and halved again, and halved again. The answer remains consistent through all those halvings until we get down to so few electrons that quantum effects start to dominate, and that is where we are now with our silicon based chip technology.

Moore’s law, and exponential laws like Moore’s law can fail for three different reasons:

1. It gets down to a physical limit where the process of halving/doubling no longer works.
2. The market demand pull gets saturated so there is no longer an economic driver for the law to continue.
3. It may not have been an exponential process in the first place.

When people are suffering from exponentialism they may gloss over any of these three reasons and think that the exponentials that they use to justify an argument are going to continue apace.

Moore’s Law is now faltering under case (a), but it has been the presence of Moore’s Law for fifty years that has powered the relentless innovation of the technology industry and the rise of Silicon Valley, and Venture Capital, and the ride of the geeks to be amongst the richest people in the world, that has led too many people to think that everything in technology, including AI, is exponential.

It is well understood that many cases of exponential processes are really part of an “S-curve”, where at some point the hyper growth flattens out. Exponential growth of the number of users of a social platform such as Facebook or Twitter must turn into an S-curve eventually as there are only a finite number of humans alive to be new users, and so exponential growth can not continue forever. This is an example of case (b) above.

But there is more to this. Sometimes just the demand from individual users can look like an exponential pull for a while, but then it gets saturated.

Back in the first part of this century when I was running a very large laboratory at M.I.T. (CSAIL) and needed to help raise research money for over 90 different research groups, I tried to show sponsors how things were continuing to change very rapidly through the memory increase on iPods. Unlike Gordon Moore I had five data points! The data was how much storage one got for one’s music in an iPod for about $400.  I noted the dates of new models and for five years in a row, somewhere in the June to September time frame a new model would appear.  Here are the data:

Year	GigaBytes
2003	10
2004	20
2005	40
2006	80
2007	160

The data came out perfectly (Gregor Mendel would have been proud…) as an exponential. Then I would extrapolate a few years out and ask what we would do with all that memory in our pockets.

Extrapolating through to today we would expect a $400 iPod to have 160,000 GigaBytes of memory (or 160 TeraBytes). But the top of the line iPhone of today (which costs more than $400) only has 256 GigaBytes of memory, less than double the 2007 iPod, while the top of the line iPod (touch) has only 128 GigaBytes which ten years later is a decrease from the 2007 model.

This particular exponential collapsed very suddenly once the amount of memory got to the point where it was big enough to hold any reasonable person’s complete music library, in their hand. Exponentials can stop when the customers stop demanding.

Moving on, we have seen a sudden increase in performance of AI systems due to the success of deep learning, a form of Machine Learning. Many people seem to think that means that we will continue to have increases in AI performance of equal multiplying effect on a regular basis. But the deep learning success was thirty years in the making, and no one was able to predict it, nor saw it coming. It was an isolated event.

That does not mean that there will not be more isolated events, where backwaters of AI research suddenly fuel a rapid step increase in performance of many AI applications. But there is no “law” that says how often they will happen. There is no physical process, like halving the mass of material as in Moore’s Law, fueling the process of AI innovation. This is an example of case (c) from above.

So when you see exponential arguments as justification for what will happen with AI remember that not all so called exponentials are really exponentials in the first place, and those that are can collapse suddenly when a physical limit is hit, or there is no more economic impact to continue them.

6. [C,D] Hollywood scenarios

The plot for many Hollywood science fiction movies is that the world is just as it is today, except for one new twist. Certainly that is true for movies about aliens arriving on Earth. Everything is going along as usual, but then one day the aliens unexpectedly show up.

That sort of single change to the world makes logical sense for aliens but what about for a new technology? In real life lots of new technologies are always happening at the same time, more or less.

Sometimes there is a rational, within Hollywood reality, explanation for why there is a singular disruption of the fabric of humanity’s technological universe. The Terminator movies, for instance, had the super technology come from the future via time travel, so there was no need to have a build up to the super robot played by Arnold Schwarzenegger.

But in other movies it can seem a little silly.

In Bicentennial Man, Richard Martin, played by Sam Neill, sits down to breakfast being waited upon by a walking talking humanoid robot, played by Robin Williams. He picks up a newspaper to read over breakfast. A newspaper! Printed on paper. Not a tablet computer, not a podcast coming from an Amazon Echo like device, not a direct neural connection to the Internet.

In Blade Runner, as Tim Harford recently pointed out, detective Rick Deckard, played by Harrison Ford, wants to contact the robot Rachael, played by Sean Young. In the plot Rachael is essentially indistinguishable from a human being. How does Deckard connect to her? With a pay phone. With coins that you feed in to it. A technology that many of the readers of this blog may never had seen. (By the way, in that same post, Harford remarks: “Forecasting the future of technology has always been an entertaining but fruitless game.” A sensible insight.)

So there are two examples of Hollywood movies where the writers, directors, and producers, imagine a humanoid robot, able to see, hear, converse, and act in the world as a human–pretty much an AGI (Artificial General Intelligence). Never mind all the marvelous materials and mechanisms involved. But those creative people lack the imagination, or will, to consider how else the world may have changed as that amazing package of technology has been developed.

It turns out that many AI researchers and AI pundits, especially those pessimists who indulge in predictions C and D, are similarly imagination challenged.

Apart from the time scale for many C and D predictions being wrong, they ignore the fact that if we are able to eventually build such smart devices the world will have changed significantly from where we are. We will not suddenly be surprised by the existence of such super intelligences. They will evolve technologically over time, and our world will be different, populated by many other intelligences, and we will have lots of experience already.

For instance, in the case of D (evil super intelligences who want to get rid of us) long before we see such machines arising there will be the somewhat less intelligent and belligerent machines. Before that there will be the really grumpy machines. Before that the quite annoying machines. And before them the arrogant unpleasant machines.

We will change our world along the way, adjusting both the environment for new technologies and the new technologies themselves. I am not saying there may not be challenges. I am saying that they will not be as suddenly unexpected as many people think. Free running imagination about shock situations are not helpful–they will never be right, or even close.

“Hollywood scenarios” are a great rhetorical device for arguments, but they usually do not have any connection to future reality.

7. [B,C,D] SPEED OF Deployment

As the world has turned to software the deployment frequency of new versions has become very high in some industries. New features for platforms like Facebook are deployed almost hourly. For many new features, as long as they have passed integration testing, there is very little economic downside if a problem shows up in the field and the version needs to be pulled back–often I find that features I use on such platforms suddenly fail to work for an hour or so (this morning it was the pull down menu for Facebook notifications that was failing) and I assume these are deployment fails. For revenue producing components, like advertisement placement, more care is taken and changes may happen only on the scale of weeks.

This is a tempo that Silicon Valley and Web software developers have gotten used to. It works because the marginal cost of newly deploying code is very very close to zero.

Hardware on the other hand has significant marginal cost to deploy. We know that from our own lives. Many of the cars we are buying today, which are not self driving, and mostly are not software enabled, will likely still be on the road in the year 2040. This puts an inherent limit on how soon all our cars will be self driving. If we build a new home today, we can expect that it might be around for over 100 years. The building I live in was built in 1904 and it is not nearly the oldest building in my neighborhood.

Capital costs keep physical hardware around for a long time, even when there are high tech aspects to it, and even when it has an existential mission to play.

The US Air Force still flies the B-52H variant of the B-52 bomber. This version was introduced in 1961, making it 56 years old. The last one was built in 1963, a mere 54 years ago. Currently these planes are expected to keep flying until at least 2040, and perhaps longer–there is talk of extending their life out to 100 years. (cf. The Millennium Falcon!)

The US land-based Intercontinental Ballistic Missile (ICBM) force is all Minuteman-III variants, introduced in 1970. There are 450 of them. The launch system relies on eight inch floppy disk drives, and some of the digital communication for the launch procedure is carried out over analog wired phone lines.

I regularly see decades old equipment in factories around the world. I even see PCs running Windows 3.0 in factories–a software version released in 1990. The thinking is that “if it ain’t broke, don’t fix it”. Those PCs and their software have been running the same application doing the same task reliably for over two decades.

The principal control mechanisms in factories, including brand new ones in the US, Europe, Japan, Korea, and China, is based on Programmable Logic Controllers, or PLCs. These were introduced in 1968 to replace electromechanical relays. The “coil” is still the principal abstraction unit used today, and the way PLCs are programmed is as though they were a network of 24 volt electromechanical relays. Still.  Some of the direct wires have been replaced by Ethernet cables. They emulate older networks (themselves a big step up) based on the RS485 eight bit serial character protocol, which themselves carry information emulating 24 volt DC current switching.  And the Ethernet cables are not part of an open network, but instead individual cables are run point to point physically embodying the control flow in these brand new ancient automation controllers. When you want to change information flow, or control flow, in most factories around the world it takes weeks of consultants figuring out what is there, designing new reconfigurations, and then teams of tradespeople rewiring and reconfiguring hardware. One of the major manufacturers of this equipment recently told me that they aim for three software upgrades every twenty years.

In principle it could be done differently. In practice it is not. And I am not talking about just in technological backwaters. I just this minute looked on a jobs list and even today, this very day, Tesla is trying to hire full time PLC technicians at their Fremont factory. Electromagnetic relay emulation to automate the production of the most AI-software advanced automobile that exists.

A lot of AI researchers and pundits imagine that the world is already digital, and that simply introducing new AI systems will immediately trickle down to operational changes in the field, in the supply chain, on the factory floor, in the design of products.

Nothing could be further from the truth.

The impedance to reconfiguration in automation is shockingly mind-blowingly impervious to flexibility.

You can not give away a good idea in this field. It is really slow to change. The example of the AI system making paper clips deciding to co-opt all sorts of resources to manufacture more and more paper clips at the cost of other human needs is indeed a nutty fantasy. There will be people in the loop worrying about physical wiring for decades to come.

Almost all innovations in Robotics and AI take far, far, longer to get to be really widely deployed than people in the field and outside the field imagine. Self driving cars are an example. Suddenly everyone is aware of them and thinks they will soon be deployed. But it takes longer than imagined. It takes decades, not years. And if you think that is pessimistic you should be aware that it is already provably three decades from first on road demonstrations and we have no deployment. In 1987 Ernst Dickmanns and his team at the Bundeswehr University in Munich had their autonomous van drive at 90 kilometers per hour (56mph) for 20 kilometers (12 miles) on a public freeway. In July 1995 the first no hands on steering wheel, no feet on pedals, minivan from CMU’s team lead by Chuck Thorpe and Takeo Kanade drove coast to coast across the United States on public roads.  Google/Waymo has been working on self driving cars for eight years and there is still no path identified for large scale deployment. It could well be four or five or six decades from 1987 before we have real deployment of self driving cars.

New ideas in robotics and AI take a long long time to become real and deployed.

Epilog

When you see pundits warn about the forthcoming wonders or terrors of robotics and Artificial Intelligence I recommend carefully evaluating their arguments against these seven pitfalls. In my experience one can always find two or three or four of these problems with their arguments.

Predicting the future is really hard, especially ahead of time.

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¹ Pinpoint: How GPS is Changing Technology, Culture, and Our Minds, Greg Milner, W. W. Norton, 2016.

² “V2s for Ionosphere Research?”, A. C. Clarke, Wireless World, p. 45, February, 1945.

³ “Extra-Terrestrial Relays: Can Rocket Stations Give World-wide Radio Coverage”, Arthur C. Clarke, Wireless World, 305–308, October, 1945.

⁴ The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind, Marvin Minsky, Simon and Schuster, 2006.