In surveys of AI “experts” on when we are going to get to human level intelligence in our AI systems, I am usually an outlier, predicting it will take ten or twenty times longer than the second most pessimistic person surveyed. Others have a hard time believing that it is not right around the corner given how much action we have seen in AI over the last decade.

Could I be completely wrong?  I don’t think so (surprise!), and I have come up with an analogy that justifies my beliefs.  Note, I started with the beliefs, and then found an analogy that works.  But I think it actually captures why I am uneasy with the predictions of just two, or three, or seven decades, that abound for getting to human level intelligence.  It’s a more sophisticated and detailed version of the story about how building longer and longer ladders will not get us to the Moon.

The analogy is to heavier than air flight.

All the language that follows is expressing that analogy.

Starting in 1956 our AI research got humans up in the air in hot air balloons, in tethered kites, and in gliders. About a decade ago Deep Learning cracked the power to weight ratio of engines that let us get to heavier than air powered flight.

If we look at the arc of the 20^th century, heavier than air flight transformed our world in major ways.

It had a major impact on war in just its first two decades, and four decades in it completely transformed how wars were fought, and continued that transformation for the rest of the century.

From the earliest days it changed supply chains for goods with high ratios of value to mass.  First it was with mail–the speed of sending long messages (telegraphs were good only for short messages), and then later the speed of getting bank instructions and letters of credit, and receipts, all paper based, around the globe transforming commerce into a global enterprise. Later it could be used for the supply chains of intrinsically high value goods, including digital devices, and even bluefin tuna.

It also transformed human mobility, giving rise to global business even in the PZ¹ era. But it also gave the richest parts of the world a new level of personal mobility and a new sort of leisure for them. Airplanes caused overcrowding in Incan Citadels and palaces of divine kings, steadily getting worse as more countries and larger populations reached the wealth threshold of personal air travel. Who knew?  I doubt that this implication was on the mind of either Wilbur or Orville.

Note that for the bulk of the 20^th century most heavier than air flight used a human pilot on board, and even today that is still true for the majority of flying vehicles.  The same for AI.  Humans will still be in the loop for AI applications, at one level or another, for many decades.

This analogy between AI and heavier than air flight embodies two things that I firmly believe about AI.

First, there is tremendous potential for our current version of AI. It will have enormous economic, cultural, and geopolitical impact. It will change life for ordinary people over the next many decades. It will create great riches for some. It will lead to different world views for many.  Like heavier than air flight it will transform our world in ways in which we can not yet guess.

Second, we are not yet done with the technology by a long shot. The airplanes of 1910 (and AI of today) in hindsight look incredibly dangerous, their use was really only for daredevils without too much regard for fatal consequences, and no one but a complete nutcase flies a 1910 technology airplane any more. But the airplanes did get better, and the technology underwent radical changes. No modern airplane engine works at all like those of 1903 or even 1920.  Deep Learning, too, will be replaced over time. It will seem quaint, and barely viable in retrospect.  And woefully energy inefficient.

But what about human level intelligence?  Well, my friends, I am afraid that in this analogy that lies on the Moon. No matter how much we improve on our basic architecture of our current AI over the next 100 years, we are just not going to get there. We’re going to need to invent something else. Rockets. To be sure, the technologies used in airplanes and rockets are not completely disjoint. But rockets are not just over developed heavier than air flying machines. They use a different set of principles, equations, and methodologies.

And now the real kicker for this analogy.  We don’t yet know how deep is our gravity well.  We don’t yet know how to build human level intelligence.  Our current approaches to AI (and neuroscience) might be so far off that in retrospect it will seen to have been so wacky that it will be classified as “not even wrong”. (For those who do not know the reference that is a severe diss.)

Our gravity well?  For a fixed density of its innards the surface gravity of a planet is proportional to its radius or diameter. If our Earth was twice its actual diameter, gravity would be twice what it is for us, and today’s chemical rockets probably wouldn’t have enough oomph to get people into orbit. Our space faring aspirations would be much harder to achieve, even at the tiny deployed level we have so far, 117 years after our first manned heavier than air flights. But could it be worse than two times? Earth with a 32,000 mile diameter, rather than our 8,000 mile diameter and gosh, chemical rockets might never be enough (and we would probably be much shorter and squatter…).  That is how little we know today.  We don’t know what the technology of human level intelligence is going to look like, so we can’t estimate how hard it is going to be to achieve. Two hundred years ago no one could have given a convincing argument on whether chemical powered rockets would get us to the Moon or not.  We didn’t know the relationships between chemical reactions and Earth’s gravity that now give us the answer.

That human level intelligence up there on the Moon is going to be out of reach for a long, long time. We just do not know for how long, but we surely know that we are not going to fly there on winged aircraft.

¹ Pre Zoom.

This post is about how much things can change in the world over a lifetime. I’m going to restrict my attention to science, though there are many parallels in technology, human rights, and social justice.

I was born in late 1954 so I am 65 years old. I figure I have another 30 years, with some luck, of active intellectual life. But if I look backward and forward within my family, I knew as an adult some of my grandparents who were born late in the nineteenth century, and I expect that both I will know some of my own grandchildren when they are adults, and that they will certainly live into the twenty second century. My adult to adult interactions with members of my own direct genetic line will span five generations and well over two hundred years from the beginning to the end of their collective lives, from the nineteenth to the twenty second century.

I’m going to show how shocking the changes have been in science throughout just my lifetime, how even more shocking the changes have been since my grandparents were born, and by induction speculate on how much more shock there will be during my grandchildren’s lifetimes. All people who I have known.

Not everything will change, but certainly some things that we treat as truth and obvious today will no longer seem that way by early next century. We can’t know exactly which of them will be discarded, but I will put up quite a few candidates. I would be shocked if at least some of them have not fallen by the wayside a century from now.

How Has Science Changed Since Relatives I knew were born?

My oldest grandparent was born before the Michelson-Morley experiment of 1887 that established that the universe was not filled with aether, but rather with vacuum.

Even when all four of my grandparents were first alive neither relativity nor quantum mechanics had been thought of.  Atoms with a nucleus of protons and neutrons, surrounded by a cloud of electrons were unknown. X rays and other radiation had not been detected.

The Earth was thought to be just 20 to 40 million years old, and it wasn’t until after I was born that the current estimates were first published.

It was two months after my father was born that Edwin Hubble described galaxies and declared that we lived in one of many, in our case the Milky Way. While my father was young the possibility that some elements might be fissile was discovered, along with the idea that some might be fusible. He was an adult back from the war when the big bang theory of the universe was first proposed, and it is only in the last 30 years that alternatives were largely shouted down.

In my lifetime we started out with nine planets, went down to eight, and now have observed thousands of them in nearby star systems. Plate tectonics, which first revealed that continents were not historically statically in place and explained both earthquakes and volcanoes were first hypothesized after I was born.

Crick and Watson determined the structure of DNA just the year before I was born. I was a toddler when Crick hypothesized the DNA to RNA translation and transcription mechanism, in school when the first experimental results showing how it might work came in, and in college before it was mostly figured out. Then it was realized that most animal and plant DNA does not code for proteins, and so it was labeled as junk DNA. Gradually over time other functions for that DNA have been discovered and it is now called  non-coding DNA. All its functions have still not been worked out.

I was in graduate school when it was figured out that the split of all life on Earth into prokaryotes (cells without a nucleus) and eukaryotes (cells with a nucleus) was inadequate. All animals, plants, and fungi, belong to the latter class. But in fact there are two very distinct sorts of prokaryotes, both single celled, the bacteria and the archaea. The latter were completely unknown until the first ones were found in 1977. Now the tree of life has archaea and eukaryotes branching off from a common point on a different branch than the bacteria. We are more closely related to the unknown archaea than we are to bacteria. We had completely missed a major type of living organism; the archaea on Earth have a combined mass of more than three times that of all animals. We were just plain unaware of them–they are predominantly located in deep subsurface environments, so admittedly they were not hiding in plain sight.

In just the last few years we have realized that human bodies contain ten times more cells that are bacteria, than they contain cells that have our DNA in them, though the bacterial mass is only about 3% of our body weight. Before that we thought we were mostly us.

The physical structure of neurons and the way they connect with each other was first discovered when my grandparents were already teenagers and young adults. The rough way that neurons operate was elucidated in the decade before my birth, but the first paper that laid out a functional explanation of how neurons could operate in an ensemble did not occur until I was already in kindergarten (in the “What the Frog’s Eye Tells the Frog’s Brain paper”). Half the cells in brains, the glia, were long thought to be physical support and suppliers of nutrients to the neurons, playing no direct role in what neural systems did. In the second half of the twentieth century we have come to understand that they play a role in neurotransmission, and modulate all sorts of behavior of the neurons. There is still much to be learned. More recently the role of small molecules diffusing locally in the brain have been shown to also affect how neurons operate.

What is known in science about cosmology, physics, the mechanisms of life, and neuroscience had changed drastically since my grand parents were born, and has continued to change right up until today. Our scientific beliefs have not been static, and have constantly evolved.

Will science continue to change?

It seems entirely unlikely to me that my grandchildren will one day be able to say that up until they were born scientific ideas came and went with accepted truth regularly changing, but since they were born science has been very stable in its set of accepted truths.

Things will continue to change. Below I have put a few things that I think could change from now into the beginning of the next century. I am not saying that any particular one of these will be what changes. And I would be very surprised if more than half of these will be adopted. But I have selected the ideas that currently gnaw at me and do not feel as solid as some other ideas in science. Some will no doubt become more solid. But it will not surprise me so much if any individual one of these turns into accepted wisdom.

Cosmology:

• There is no dark matter.
• The Universe is not expanding.
• The big bang was wrong.

Physics:

• There is a big additional part of quantum mechanics to be understood.
• String theory is bogus.
• The many worlds interpretation is decided to be confused and discarded.

Life:

• We discover that there is a common ancestor to archaea, bacteria, and eukarya, a fourth domain of life, that still exists in some places on Earth–and it is clearly a predecessor to the three that we know about now in that it does not have the full modern DNA version of genetics, but instead is a mixture of DNA and RNA based genetics, or purely RNA, or perhaps purely PNA, and has a simplified ancestral coding scheme.
• We discover life elsewhere in the solar system and it is clearly not related to life on Earth. It is different, with different components and mechanisms, and its abiogenesis was clearly independent of the one on Earth.
• We detect life on a planet that we can observe in a nearby solar system.
• We detect an unambiguously artificial signal from much further away.

Neuroscience:

• We discover the principles of a rich control system in plants that is not based on neurons, but nevertheless explains the complex behaviors of plants that we can observe when we speed up videos of them operating in the world. So much for electro-centrism!
• We move away from computational neuroscience with a new set of metaphors that turn out to have both better explanatory power and provide tools that explain otherwise obtuse aspects of neural systems.
• We find not just a different metaphor, but actual new mechanisms in neural systems of which we have not previously been aware, and they become dominant in our understanding of the brain.

How to have impact in the world

If you want to be a famous scientist then figure one of these out. You will redirect major intellectual pursuits of mankind. But, it will be a long lonely road.

This blog is not peer reviewed at all.  I write it, I put it out there, and people read it or not. It is my little megaphone that I alone control.

But I don’t think anyone, or at least I hope that no-one, thinks that I am publishing scientific papers here.  They are my opinion pieces, and only worthwhile if there are people who have found my previous opinions to have turned out to be right in some way.

There has been a lot of discussion recently about peer review. This post is to share some of my experiences with peer review, both as an author and as an editor, from three decades ago.

In my opinion peer review is far from perfect. But with determination new and revolutionary ideas can get through the peer review process, though it may take some years. The problem is, of course, that most revolutionary ideas are wrong, so peer review tends to stomp hard on all of them. The alternative is to have everyone self publish and that is what is happening with the arXiv distribution service. Papers are getting posted there with no intent of ever undergoing peer review, and so they are effectively getting published with no review. This can be seen as part of the problem of populism where all self proclaimed experts are listened to with equal authority, and so there is no longer any expertise.

My Experience with Peer Review as an Author

I have been struggling with a discomfort about where the herd has been headed in both Artificial Intelligence (AI) and neuroscience since the summer of 1984. This was a time between my first faculty job at Stanford and my long term faculty position at MIT. I am still concerned and I am busy writing a longish technical book on the subject–publishing something as a book gets around the need for full peer review, by the way…

When I got to MIT in the fall of 1984 I shifted my research based on my concerns. A year later I was ready to talk about the what I was doing, and submitted a journal paper describing the technical idea and an initial implementation. Here is one of the two reviews.

It was encouraging, but both it and a second review recommended that the paper not be published. That would have been my first rejection.  However, the editor, George Bekey, decided to publish it anyway, and it appeared as:

Brooks, R. A. “A Robust Layered Control System for a Mobile Robot“, IEEE Journal of Robotics and Automation, Vol. 2, No. 1, March 1986, pp. 14–23; also MIT AI Memo 864, September 1985.

Google Scholar reports just under 12,000 citations of this paper, my most cited paper ever. The approach to controlling robots, the subsumption architecture that it proposed led directly to the Roomba, a robot vacuum cleaner, which with over 30 million sold is the most produced robot ever. Furthermore the control architecture was formalized over the years by a series of researchers, and its descendant, behavior trees, is now the basis for most video games. (Both Unity and Unreal use behavior trees to specify behavior.) The paper still has multi billion dollar impact every year.

Most researchers who stray, believing the herd is wrong, end up heading off in their own wrong direction. I was extraordinarily lucky to choose a direction that has had incredible practical impact.

However, I was worried at a deeper intellectual level, and so almost simultaneously started writing about the philosophical underpinnings of research in AI, and how my approach differed. There the reviews were more brutal, as is shown in a review here:

This was a a review of lab memo AIM-899, Achieving Artificial Intelligence through Building Robots which I had submitted to a conference.This paper was the first place that I talked about the possibility of robot vacuum cleaners as an example of how the philosophical approach I was advocating could lead to new practical results.

The review may be a little hard to read in the image above. It says:

This paper is an extended, wandering complaint that the world does not view the author’s work as the salvation of mankind.

There is no scientific content here; little in the way of reasoned argument, as opposed to petulant assertions and non-sequiturs; and ample evidence of ignorance of the literature on these questions. The only philosopher cited is Dreyfus–but many of the issues raised have been treated more intelligibly by others (the chair definition problem etc. by Wittgenstein and many successors; the interpreted toy proscription by Searle; the modularity question by Fodor; the multiple behaviors ideas by Tinbergen; and the constructivist approach by Glymour (who calls it computational positivism). The argument about evolution leaks all over, and the discussion on abstraction indicates the author has little understanding of analytic thought and scientific investigation.

Ouch! This was like waving a red flag at a bull. I posted this and other negative reviews on my office door where they stayed for many years. By June of the next year I had added to it substantially, and removed the vacuum cleaner idea, but kept in all the things that the reviewer did not like, and provocatively retitled it Intelligence Without Representation. I submitted the paper to journals and got further rejections–more posts for my door. Eventually its fame had spread to the point that the Artificial Intelligence Journal, the mainstream journal of the field, published it unchanged (Artificial Intelligence Journal (47), 1991, pp. 139–159) and it now has 6,900 citations. I outlasted the criticism and got published.

That same year at the major international conference IJCAI: International Joint Conference on Artificial Intelligence I was honored to win the Computers and Thought award, quite a surprise to me, and I think to just about every one else. With that honor came an invitation to have a paper in the proceedings without the six page limit that applied to everyone else, and without the peer review process that applied to everyone else. My article was twenty seven pages long, double column, a critical review article of the history of AI, also with a provocative and complementary title, Intelligence Without Reason, (Proceedings of 12th Int. Joint Conf. on Artificial Intelligence, Sydney, Australia, August 1991, pp. 569–595). It now has over 3,100 citations.

My three most cited papers were either rejected under peer review or accepted with no peer review.  So I am not exactly a poster child for peer reviewed papers.

My Experience With Peer Review As an Editor

In 1987 I co-founded a journal, the International Journal of Computer Vision. It was published by Kluwer as a hardcopy journal for many years, but now it is run by Springer and is totally online. It is now in its 128^th volume, and has had many hundreds of issues. I co-edited the first seven volumes which together had a total of twenty eight issues.

The journal has a very strong reputation and consistently ranks in the top handful of places to publish in computer vision, itself a very hot topic of research today.

As an editor I soon learned a lot of things.

1. If a paper was purely theoretical with lots of equations and no experiments involving processing an image it was much more likely to get accepted than a paper which did have experimental results. I attributed this to people being unduly impressed by mathematics (I had a degree in pure mathematics and was not as easily impressed by equations and complex notation). I suspected that many times the reviewers did not fully read and understand the mathematics as many of them had very few comments about the contents of such papers. If, however, a paper had experiments with real images (and back then computers were so slow it was rarely more than a handful of images that had been processed), the same reviewers would pick apart the output, faulting it for not being as good as they thought it should be.
2. I soon learned that one particular reviewer would always read the mathematics in detail, and would always find things to critique about the more mathematical papers. This seemed good. Real peer review. But soon I realized that he would always recommend rejection. No paper was ever up to his standard. Reject! There were other frequent rejecters, but none as dogmatic as this particular one.
3. Likewise I found certain reviewers would always say accept. Now it was just a matter of me picking the right three referees for almost any paper and I could know whether the majority of reviewers would recommend acceptance or rejection before I had even sent the paper off to be reviewed. Not so good.
4. I came to realize that the editor’s job was real, and it required me to deeply understand the topic of the paper, and the biases of the reviewers, and not to treat the referees as having the right to determine the fate of the paper themselves. As an editor I had to add judgement to the process at many steps along the way, and to strive for the process to improve the papers, but also to let in ideas that were new. I now came to understand George Bekey and his role in my paper from just a couple of years before.

Peer reviewing and editing is a lot more like the process of one on one teaching than it is of processing the results of a multiple choice exam. When done right it is about coaxing the best out of scientists, and encouraging new ideas to flourish and the field to proceed.

The UPSHOT?

Those who think that peer review is inherently fair and accurate are wrong. Those who think that peer review necessarily suppresses their brilliant new ideas are wrong. It is much more than those two simple opposing tendencies.

Peer review grew up in a world where there were many fewer people engaging in science than today. Typically an editor would know everyone in the world who had contributed to the field in the past, and would have enough time to understand the ideas of each new entrant to the field as they started to submit papers. It relied on personal connections and deep and thoughtful understanding.

That has changed just due to the scale of the scientific endeavor today, and is no longer possible in that form.

There is a clamor for double blind anonymous review, in the belief that that produces a level playing field. While in some sense that is true, it also reduces the capacity for the nurturing of new ideas. Clamorers need to be careful what they wish for–metaphorically it reduces them to competing in a speed trial, rather than being appreciated for virtuosity. What they get in return for zeroing the risk of being rejected on the basis of their past history or which institution they are from is that they are condemned to forever aiming their papers at the middle of a field of mediocrity, with little chance for achieving greatness.

Another factor is that the number of new journals has changed. Institutions, and sometimes whole countries, decide that the way for them to get a better name for themselves is to have a scientific journal, or thirty. They set them up and put one of their local people who has no real understanding of the flow of ideas in the particular field at the global scale, as editor. Now editing becomes a mechanical process, with no understanding of the content of the paper or the qualifications of who they ask to do the reviews. I know this to be true as I regularly get asked to review papers in fields in which I have absolutely no knowledge, by journal editors that I have never heard of, nor of their journal, nor its history. I have been invited to submit a review that can not possibly be a good review. I must induce that other reviews may also not be very good.

I don’t have a solution, but I hope my observations here might be interesting to some.

Again and again in human history networks spanning physical geography have both enabled and been enabled by the very same innovations. Networks are the catalysts for the innovations and the innovations are the catalysts for the networks. This is autocatalysis at human civilization scale.

The Roman empire brought for people within its expanding borders long distance trade, communication, peace and stability. Key to this was the network of roads, many of which survive as the routes of modern transportation systems, and ports. And the stability of that network was made possible by the things that the empire brought.

The Silk Road, a network of trade routes, enabled many civilizations that themselves supported the continued existence of those trade routes.

In the eighteenth century England’s network of canals enabled both the delivery of raw materials, coal for power, and access to ports for the finished goods, enabling the industrial revolution with the invention of factories. The canals were built on the wealth of the factory owners who formed syndicates to build those canals.

Later the train network enhanced and replaced the canal network in England. And building a train network in the United States enabled large scale farming in the mid-west to have access to markets on the east coast, and later to ports on both coasts to make the US a major source of food. At the same time, a second network, the telegraph, was overlaid on the same physical route system, first to operate the train network itself and later to form the basis of new forms of communications.

As the later telephone networks were built they ushered in the world of commerce and general business became the principal industry instead of farming. And as business grew the need for more extensive telephone networks with more available lines grew with it.

When Henry Ford started mass producing automobiles he realized that a network of roads was necessary for the masses to have somewhere to drive. And as there were more and more roads the demand for automobiles increased. As a side effect the roads came to replace much of the rail network for moving goods around the country.

The personal computer of the 1980’s was not ubiquitous in ordinary households until it was coupled to the second generation data packet network that had started out as a reliable communications network for the military and for sharing scarce computer resources in academia. The pull on network bandwidth lead to rapid growth of the Internet, and that enabled the World Wide Web, a network of information overlaid on the data packet network, that gave a real impetus for more people to own their own computer.

As commerce started to be carried out on the Web, demand rose even more, and ultimately large data centers needed to be built as the backend of that commerce system. Then those data centers got offered to other businesses and cloud computing became a network of computational resources, on top of what had been a network for moving data from place to place.

Cloud computing enabled the large scale training needed for deep networks, a computational technique very vaguely inspired by the network of neurons in the brain. Deep networks are what many people call AI today. Those networks and their demands for computation for training are driving the growth of the cloud computing network, and a world wide network of low paid piece workers who label data needed to drive the training, using the substrate of the Web network to move the data around, and to get paid.

Are we at the end game for AI driving networks? Or when we can get past the very narrow capabilities of deep networks to new AI technologies will there be new networks that arise and are autocatalytic with the new AI?

And what about robotics?

The disruptions to world supply chains from COVID-19 are only just beginning to be seen–there will be turbulence in many areas later in 2020. The exceptionally lean supply chains we have lived with over the last few years (relying on a network of shipping routes that rely on the standardization of shipping containers to grow and interact) are likely to feel pressure to get a little fatter. That is likely to increase the demand for robotics and automation in those supply chains, a phenomenon that we have already see starting over the last few years.

Another lesson which may be drawn from the current pandemic is that more automation is needed in healthcare, as trained medical professions have been pushed to their limits of endurance, besides being in personal mortal peril at times.

So what might be the new networks that arise over the next few years, demanded by the way we change automation, and supported by that very change?

Here are a few ideas, none of which seem particularly compelling at the moment, certainly not in comparison to Roman roads or to the Internet itself:

A commerce network of data sets, and of sets of weights for networks trained on large data sets.

A physical network of supply points, down to the city or county level, for major robot components; mobile bases for indoors or outdoors, legged tracked and wheeled; various sensor packages; human-robot interaction displays and sensors; and all sorts of arms with different characteristics. These can be assembled plug and play to produced appropriate robots as needed to respond to all sorts of emergency needs.

A network of smart sensors embedded in almost everything in our lives, which lives on top of the current Internet–this is already getting built and is called IoT (Internet of Things).

A new supply network of both partially finished goods (e.g., standard embedded processor boards) and materials (seventy eight different sorts of raw stock for 3D printers a generation or two out) so that much more manufacturing can be done closer to end customers, using automation and robots.

An automated distribution network down to the street corner level in cities, with short term storage units on the sidewalk (probably a little bigger than the green storage units that the United States Postal Service has on street corners throughout US cities). Automated vehicles would supply these, perhaps at off peak traffic times, and then smaller neighborhood sidewalk robots would distribute to individual houses, or people could come to pick up.

I’m not particularly proud of or happy with any of these ideas. But based on history over the last 2,000 plus years I am confident that some sort of new networks will soon arise. Please do not let my lack of imagination dissuade you from the idea that new forms of networks will be coming.

[Phillip Alvelda is an old friend from MIT, and CEO of Brainworks.]

Pondering how to close what seems to be a rapidly widening empathy gap here in the U.S. and globally.

I used to just be resigned to the fact that many of my white friends who had never felt, or experienced discrimination directed at themselves seem incapable of seeing or recognizing implicit, or even explicit, bias directed at others. I didn’t used to think of these people as mean or racist…just oblivious through lack of direct experience.

But now, with a nation inflamed by our own government inciting and validating hatred and bigotry, with brown asylum seekers and children dying in mass US internment camps, and LGBTQ and women’s’ rights under mounting assault, the discrimination has literally turned lethal. And the empathy gap is enabling these crimes against humanity to continue and grow in the US now, just like the silent majority in Weimar Germany allowed the Jewish genocide to advance.

I’ve come to see supporters of this corrupt and criminal administration as increasingly complicit in the ongoing crimes. It is no longer just a matter of not seeing discrimination that doesn’t impact your family directly.

Trump supporters and anyone who supports any of his Republican enablers must now find some way to look past the growing reports of discrimination, minority voter suppression and gerrymandering, hate crimes, repression, the roll back of women’s and LGBTQ rights, a measurable biased justice system, mass internment camps, and now even the murder of the weak and vulnerable kidnapped children that commit no crime other than to follow our own ancestors to seek freedom and opportunity in the US….. This growing mass of willfully blind conservatives have abandoned fair morality, and are direct enablers of evil.

We are now in an era I never thought to see in the US, when government manufactured propaganda is purposely driving the dehumanization of women, LGBTQ people, and people of color. The US empathy gap is widening rapidly. How can we fight these dark divisive forces and narrow the gap, when our polarized society can’t even agree on measurable objective realities like the climate crisis?

Otherwise, I fear the U.S. is on a path to dissolve into at least two countries, divided along a border between those states who value empathy and seek an inclusive and pluralistic future society, and those who seek to retreat to tribal protectionism of historical rights for a shrinking privileged majority.

That this struggle rises now really baffles me. Consider the world’s obviously increasing wealth and abundance, with declining poverty and starvation and increasing access to virtually unlimited renewable energy. The need for tribal dominance to horde resources is dissapearing. The need for borders to protect resources that are no longer scarce, is vanishing.

Just imagine if all of our military and arms spending, all of the money we spend enforcing borders and limiting access to food and medicine and energy and education were instead directed towards sharing this abundance!

Pluralism and empathy are clearly the answer. How can we get more people to realize this despite the onslaught of vitriol and tribal Incitement from the likes of Fox News?

A very recent article follows in the footsteps of many others talking about how the promise of autonomous cars on roads is a little further off than many pundits have been predicting for the last few years. Readers of this blog will know that I have been saying this for over two years now. Such skepticism is now becoming the common wisdom.

In this new article at The Ringer, from May 16^th, the author Victor Luckerson, reports:

For Elon Musk, the driverless car is always right around the corner. At an investor day event last month focused on Tesla’s autonomous driving technology, the CEO predicted that his company would have a million cars on the road next year with self-driving hardware “at a reliability level that we would consider that no one needs to pay attention.” That means Level 5 autonomy, per the Society of Automotive Engineers, or a vehicle that can travel on any road at any time without human intervention. It’s a level of technological advancement I once compared to the Batmobile.

Musk has made these kinds of claims before. In 2015 he predicted that Teslas would have “complete autonomy” by 2017 and a regulatory green light a year later. In 2016 he said that a Tesla would be able to drive itself from Los Angeles to New York by 2017, a feat that still hasn’t happened. In 2017 he said people would be able to safely sleep in their fully autonomous Teslas in about two years. The future is now, but napping in the driver’s seat of a moving vehicle remains extremely dangerous.

When I saw someone tweeting that Musk’s comments meant that a million autonomous taxis would be on the road by 2020, I tweeted out the following:

Let’s count how many truly autonomous (no human safety driver) Tesla taxis (public chooses destination & pays) on regular streets (unrestricted human driven cars on the same streets) on December 31, 2020. It will not be a million. My prediction: zero. Count & retweet this then.

I think these three criteria need to be met before someone can say that we have autonomous taxis on the road.

The first challenge, no human safety driver, has not been met by a single experimental deployment of autonomous vehicles on public roads anywhere in the world. They all have safety humans in the vehicle. A few weeks ago I saw an autonomous shuttle trial along the paved beachside public walkways at the beach on which I grew up, in Glenelg, South Australia, where there were two “two onboard stewards to ensure everything runs smoothly” along with eight passengers. Today’s demonstrations are just not autonomous. In fact in the article above Luckerson points out that Uber’s target is to have their safety drivers intervene only once every 13 miles, but they are way off that capability at this time. Again, hardly autonomous, even if they were to meet that goal. Imagine having a breakdown of your car that you are driving once every 13 miles–we expect better.

And if normal human beings can’t simply use these services (in Waymo’s Phoenix trial only 400 pre-approved people are allowed to try them out) and go anywhere that they can go in a current day taxi, then really the things deployed will not be autonomous taxis. They will be something else. Calling them taxis would be redefining what a taxi is. And if you can just redefine words on a whim there is really not much value to your words.

I am clearly skeptical about seeing autonomous cars on our roads in the next few years. In the long term I am enthusiastic. But I think it is going to take longer than most people think.

In response to my tweet above, Kai-Fu Lee, a very strong enthusiast about the potential for AI, and a large investor in Chinese AI companies, replied with:

If there are a million Tesla robo-taxis functioning on the road in 2020, I will eat them. Perhaps @rodneyabrooks will eat half with me?

I readily replied that I would be happy to share the feast!

Luckerson talks about how executives, in general, are backing off from their previous predictions about how close we might be to having truly autonomous vehicles on our roads.  Most interestingly he quotes Chris Urmson:

Chris Urmson, the former leader of Google’s self-driving car project, once hoped that his son wouldn’t need a driver’s license because driverless cars would be so plentiful by 2020. Now the CEO of the self-driving startup Aurora, Urmson says that driverless cars will be slowly integrated onto our roads “over the next 30 to 50 years.”

Now let’s take note of this. Chris Urmson was the leader of Google’s self-driving car project, which became Waymo around the time he left, and is the CEO of a very well funded self-driving start up. He says “30 to 50 years”. Chris Urmson has been a leader in the autonomous car world since before it entered mainstream consciousness. He has lived and breathed autonomous vehicles for over ten years. No grumpy old professor is he. He is a doer and a striver. If he says it is hard then we know that it is hard.

I happen to agree, but I want to use this reality check for another thread.

If we were to have AGI, Artificial General Intelligence, with human level capabilities, then certainly it ought to be able to drive a car, just like a person, if not better. Now a self driving car does not need to have general human level intelligence, but a self driving car is certainly a lower bound on human level intelligence.  Urmson, a strong proponent of self driving cars says 30 to 50 years.

So what does that say about predictions that AGI is just around the corner? And what does it say about it being an existential threat to humanity any time soon. We have plenty of existential threats to humanity lining up to bash us in the short term, including climate change, plastics in the oceans, and a demographic inversion. If AGI is a long way off then we can not say anything sensible today about what promises or threats it might provide as we need to completely re-engineer our world long before it shows up, and when it does show up it will be in a world that we can not yet predict.

Do people really say that AGI is just around the corner? Yes, they do…

Here is a press report on a conference on “Human Level AI” that was held in 2018. It reports that of respondents to a survey at that conference said they expected human level AI to be around in 5 to 10 years. Now, I must say that looking through the conference site I see more large hats than cattle, but these are mostly people with paying corporate or academic jobs, and of them think this.

Ray Kurzweil still maintains, in Martin Ford’s recent book that we will see a human level intelligence by 2029–in the past he has claimed that we will have a singularity by then as the intelligent machines will be so superior to human level intelligence that they will exponentially improve themselves (see my comments on belief in magic as one of the seven deadly sins in predicting the future of AI). Mercifully the average prediction of the 18 respondents for this particular survey was that AGI would show up around 2099.  I may have skewed that average a little as I was an outlier amongst the 18 people at the year 2200. In retrospect I wish I had said 2300 and that is the year I have been using in my recent talks.

And a survey taken by the Future of Life Institute (warning: that institute has a very dour view of the future of human life, worse than my concerns of a few paragraphs ago) says were are going to get AGI around 2050.

But that is the low end of when Urmson thinks we will have autonomous cars deployed. Suppose he is right about his range. And suppose I am right that  autonomous driving is a lower bound on AGI, and I believe it is a very low bound. With these very defensible assumptions then the seemingly sober experts in Martin Ford’s new book are on average wildly optimistic about when AGI is going to show up.

AGI has been delayed.