On January 1st, 2018, I made predictions (here) about self driving cars, Artificial Intelligence and machine learning, and about progress in the space industry. Those predictions had dates attached to them for 32 years up through January 1st, 2050.
I made my predictions because at the time I saw an immense amount of hype about these three topics, and the general press and public drawing conclusions about all sorts of things they feared (e.g., truck driving jobs about to disappear, all manual labor of humans about to disappear) or desired (e.g., safe roads about to come into existence, a safe haven for humans on Mars about to start developing) being imminent. My predictions, with dates attached to them, were meant to slow down those expectations, and inject some reality into what I saw as irrational exuberance.
As part of self certifying the seriousness of my predictions I promised to review them, as made on January 1st, 2018, every following January 1st for 32 years, the span of the predictions, to see how accurate they were.
On January 1st, 2019, I posted my first annual self appraisal of how well I did. This post, today, January 1st, 2020, is my second annual self appraisal of how well I did–I have 30 more annual appraisals ahead of me. I think in the two years since my predictions, there has been a general acceptance that certain things are not as imminent or as inevitable as the majority believed just then. So some of my predictions now look more like “of course”, rather than “really, that long in the future?” as they did then.
This is a boring update. Despite lots of hoopla in the press about self driving cars, Artificial Intelligence and machine learning, and the space industry, this last year, 2019, was not actually a year of big milestones. Not much that will matter in the long run actually happened in 2019.
Furthermore, this year’s summary indicates that so far none of my predictions have turned out to be too pessimistic. Overall I am getting worried that I was perhaps too optimistic, and had bought into the hype too much. There is only one dated prediction of mine that I am currently worried may have been too pessimistic–I won’t name it here as perhaps I will turn out to be right after all.
Repeat of Last Year’s Explanation of Annotations
As I said last year, I am not going to edit my original post, linked above, at all, even though I see there are a few typos still lurking in it. Instead I have copied the three tables of predictions below from last year’s update post, and have simply added a total of six comments to the fourth column. As with last year I have highlighted dates in column two where the time they refer to has arrived.
I tag each comment in the fourth column with a cyan colored date tag in the form yyyymmdd such as 20190603 for June 3rd, 2019.
The entries that I put in the second column of each table, titled “Date” in each case, back on January 1st of 2018, have the following forms:
NIML meaning “Not In My Lifetime, i.e., not until beyond December 31st, 2049, the last day of the first half of the 21st century.
NET some date, meaning “No Earlier Than” that date.
BY some date, meaning “By” that date.
Sometimes I gave both a NET and a BY for a single prediction, establishing a window in which I believe it will happen.
For now I am coloring those statements when it can be determined already whether I was correct or not.
I have started using LawnGreen (#7cfc00) for those predictions which were entirely accurate. For instance a BY 2018 can be colored green if the predicted thing did happen in 2018, as can a NET 2019 if it did not happen in 2018 or earlier. There are five predictions now colored green, the same ones as last year, with no new ones in January 2020.
I will color dates Tomato (#ff6347) if I was too pessimistic about them. No Tomato dates yet. But if something happens that I said NIML, for instance, then it would go Tomato, or if in 2020 something already had happened that I said NET 2021, then that too would have gone Tomato.
If I was too optimistic about something, e.g., if I had said BY 2018, and it hadn’t yet happened, then I would color it DeepSkyBlue (#00bfff). None of these yet either. And eventually if there are NETs that went green, but years later have still not come to pass I may start coloring them LightSkyBlue (#87cefa).
In summary then: Green splashes mean I got things exactly right. Red means provably wrong and that I was too pessimistic. And blueness will mean that I was overly optimistic.
So now, here are the updated tables.
Self Driving Cars
No predictions have yet been relevant for self driving cars, but I have augmented one comment from last year in this first table. Also, see some comments right after this title.
[Self Driving Cars]
|A flying car can be purchased by any US resident if they have enough money.
|There is a real possibility that this will not happen at all by 2050.
|Flying cars reach 0.01% of US total cars.
|That would be about 26,000 flying cars given today's total.
|Flying cars reach 0.1% of US total cars.
|First dedicated lane where only cars in truly driverless mode are allowed on a public freeway.
|This is a bit like current day HOV lanes. My bet is the left most lane on 101 between SF and Silicon Valley (currently largely the domain of speeding Teslas in any case). People will have to have their hands on the wheel until the car is in the dedicated lane.
|Such a dedicated lane where the cars communicate and drive with reduced spacing at higher speed than people are allowed to drive
|First driverless "taxi" service in a major US city, with dedicated pick up and drop off points, and restrictions on weather and time of day.
|The pick up and drop off points will not be parking spots, but like bus stops they will be marked and restricted for that purpose only.
|20190101 Although a few such services have been announced every one of them operates with human safety drivers on board. And some operate on a fixed route and so do not count as a "taxi" service--they are shuttle buses. And those that are "taxi" services only let a very small number of carefully pre-approved people use them. We'll have more to argue about when any of these services do truly go driverless. That means no human driver in the vehicle, or even operating it remotely.
During 2019 Waymo started operating a 'taxi service' in Chandler, Arizona, with no human driver in the vehicles. While this is a big step forward see comments below for why this is not yet a driverless taxi service.
|Such "taxi" services where the cars are also used with drivers at other times and with extended geography, in 10 major US cities
|A key predictor here is when the sensors get cheap enough that using the car with a driver and not using those sensors still makes economic sense.
|Such "taxi" service as above in 50 of the 100 biggest US cities.
|It will be a very slow start and roll out. The designated pick up and drop off points may be used by multiple vendors, with communication between them in order to schedule cars in and out.
|Dedicated driverless package delivery vehicles in very restricted geographies of a major US city.
|The geographies will have to be where the roads are wide enough for other drivers to get around stopped vehicles.
|A (profitable) parking garage where certain brands of cars can be left and picked up at the entrance and they will go park themselves in a human free environment.
|The economic incentive is much higher parking density, and it will require communication between the cars and the garage infrastructure.
|A driverless "taxi" service in a major US city with arbitrary pick and drop off locations, even in a restricted geographical area.
|This is what Uber, Lyft, and conventional taxi services can do today.
|Driverless taxi services operating on all streets in Cambridgeport, MA, and Greenwich Village, NY.
|Unless parking and human drivers are banned from those areas before then.
|A major city bans parking and cars with drivers from a non-trivial portion of a city so that driverless cars have free reign in that area.
|This will be the starting point for a turning of the tide towards driverless cars.
|The majority of US cities have the majority of their downtown under such rules.
|Electric cars hit 30% of US car sales.
|Electric car sales in the US make up essentially 100% of the sales.
|Individually owned cars can go underground onto a pallet and be whisked underground to another location in a city at more than 100mph.
|There might be some small demonstration projects, but they will be just that, not real, viable mass market services.
|First time that a car equipped with some version of a solution for the trolley problem is involved in an accident where it is practically invoked.
|Recall that a variation of this was a key plot aspect in the movie "I, Robot", where a robot had rescued the Will Smith character after a car accident at the expense of letting a young girl die.
Chandler is a suburb of Phoenix and is itself the 84th largest city in the US. With apologies to residents of Chandler, I do not think that it comes to mind as a major US city for most Americans. Furthermore, the service has so far not been open to the public, but instead started with just a few hundred people (out of a population of about one quarter of a million residents) who had previously been approved to use the service when there was a human safety driver on board. These riders are banned from talking about when things go wrong so we really don’t know how well the systems works. Over 2019 the number of riders has grown to 1,500 monthly users, and a total of about 100,000 rides. Recently there has been an announcement that a phone app will make the service available to more users.
BUT, while there is no human driver in the taxi there is a remote human safety driver for all rides, as detailed in this story. While the humans can monitor more than one vehicle at a time, obviously there is a scaling issue, and the taxis are not truly autonomous. To make them so would be a big step. Also the taxis do not operate when it is raining. That would be the peak usage time for taxis in most cities. But they just don’t operate in the rain.
So… no self driving taxi service yet, even in a relatively small city with a population density many times less than that of major US cities.
The last twelve months have seen a real shakeout in expectations for deployment of self driving cars. Companies are realizing that it is much harder than the came to believe for a while, and that there are many issues beyond simply “driving”, that need to be addressed. I previously talked about a some of those issues in on this blog in January and June of 2017.
To illustrate how predictions have been slipping, here is a slide that I made for talks based on a snapshot of predictions about driverless cars from March 27, 2017. The web address still seems to give the same predictions with a couple more at the end that I couldn’t fit on my slide. In parentheses are the years the predictions were made, and in blue are the dates for when the innovation was predicted to happen.
Recently I had added some arrows to this slide. The skinny red arrows point to dates that have passed without the prediction coming to pass. The fatter orange arrows point to cases where company executives have since come out with updated predictions that are later than the ones given here. E.g., in the fourth line from the bottom, the Daimler chairman had said in 2014 that fully autonomous vehicles could be ready by 2025. In November of 2019 the chairman announced a reality check on self driving cars, as one can see in numerous online stories. Here is the first paragraph of one report on his remarks:
Mercedes-Benz parent Daimler has taken a “reality check” on self-driving cars. Making autonomous vehicles safe has proven harder than originally thought, and Daimler is now questioning their future earnings potential, CEO Ola Kaellenius told Reuters and other media.
None of the original predictions have come to pass, and those still standing are getting rather sparse.
At the same time, however, there have been more outrageously optimistic predictions made about fully self driving cars being just around the corner. I won’t name names, but on April 23rd of 2019, i.e., less than nine months ago, Elon Musk said that in 2020 Tesla would have “one million robo-taxis” on the road, and that they would be “significantly cheaper for riders than what Uber and Lyft cost today”. While I have no real opinion on the veracity these predictions, they are what is technically called bullshit. Kai-Fu Lee and I had a little exchange on Twitter where we agreed that together we would eat all such Tesla robo-taxis on the road at the end of this year, 2020.
Artificial Intelligence and Machine Learning
I had not predicted any big milestones for AI and machine learning for the current period, and indeed there were none achieved.
We have seen certain proponents be very proud of how much more compute they have, growing at many times what Moore’s Law at its best would provide. I think it is fair to say that the results of all that computing since 2012 are not very impressive when compared to what a single human brain, powered at just 20 Watts has been able to achieve in the same time frame — one just has to look at someone who’s 20th birthday is today, January 1st, 2020, and compare what they know now and what they can achieve now to what they could do in 2012.
And there has even been a little backlash about the carbon footprint that modern ML data sets cause in training. There are even tools and best practices for cutting down the carbon footprint of your ML research. People can argue about the details, but no one can make a case that the energy usage is not many orders of magnitude more than used by the meat machine inside people’s heads, and that human performance is way more impressive than any machine performance to date. People get fooled all the time by the slick marketing around each new achievement by the machine learning companies, but when you poke them you see that the achievements are rather pathetic compared to human performance.
Without any retraining make a Go playing program compete against a human on a 25 by 25 board, or even an 18 by 18 board. Or change all the colors of the pixels in a Quake Three Arena, or change the screen resolution, and humans will adapt seamlessly while the ML trained systems will have to start from zero again.
While ML conference attendance has gone up by a factor of 20 or so, the results are not so interestingly more powerful in terms of impact they have on the real world.
Right after the Artificial Intelligence and machine learning table I have some links to back up today’s assertion in it that there are more blog posts pushing back on DL as being all we will need to get to human level (whatever that might mean) Artificial Intelligence.
[AI and ML]
|Academic rumblings about the limits of Deep Learning
|Oh, this is already happening... the pace will pick up.
|20190101 There were plenty of papers published on limits of Deep Learning. I've provided links to some right below this table. 20200101
Go back to last year's update to see them.
|The technical press starts reporting about limits of Deep Learning, and limits of reinforcement learning of game play.
|20190101 Likewise some technical press stories are linked below. 20200101
Go back to last year's update to see them.
|The popular press starts having stories that the era of Deep Learning is over.
|20200101 We are seeing more and more opinion pieces by non-reporters saying this, but still not quite at the tipping point where reporters come at and say it. Axios and WIRED are getting close.
|VCs figure out that for an investment to pay off there needs to be something more than "X + Deep Learning".
|I am being a little cynical here, and of course there will be no way to know when things change exactly.
|Emergence of the generally agreed upon "next big thing" in AI beyond deep learning.
|Whatever this turns out to be, it will be something that someone is already working on, and there are already published papers about it. There will be many claims on this title earlier than 2023, but none of them will pan out.
|The press, and researchers, generally mature beyond the so-called "Turing Test" and Asimov's three laws as valid measures of progress in AI and ML.
|I wish, I really wish.
|Dexterous robot hands generally available.
BY 2040 (I hope!)
|Despite some impressive lab demonstrations we have not actually seen any improvement in widely deployed robotic hands or end effectors in the last 40 years.
|A robot that can navigate around just about any US home, with its steps, its clutter, its narrow pathways between furniture, etc.
|Lab demo: NET 2026
Expensive product: NET 2030
Affordable product: NET 2035
|What is easy for humans is still very, very hard for robots.
|A robot that can provide physical assistance to the elderly over multiple tasks (e.g., getting into and out of bed, washing, using the toilet, etc.) rather than just a point solution.
|There may be point solution robots before that. But soon the houses of the elderly will be cluttered with too many robots.
|A robot that can carry out the last 10 yards of delivery, getting from a vehicle into a house and putting the package inside the front door.
|Lab demo: NET 2025
Deployed systems: NET 2028
|A conversational agent that both carries long term context, and does not easily fall into recognizable and repeated patterns.
|Lab demo: NET 2023
Deployed systems: 2025
|Deployment platforms already exist (e.g., Google Home and Amazon Echo) so it will be a fast track from lab demo to wide spread deployment.
|An AI system with an ongoing existence (no day is the repeat of another day as it currently is for all AI systems) at the level of a mouse.
|I will need a whole new blog post to explain this...
|A robot that seems as intelligent, as attentive, and as faithful, as a dog.
|This is so much harder than most people imagine it to be--many think we are already there; I say we are not at all there.
|A robot that has any real idea about its own existence, or the existence of humans in the way that a six year old understands humans.
There are outlets now for non-journalists, perhaps practitioners in a scientific field, to write position papers that get widely referenced in social media. These position papers are often forerunners of what the popular press will soon start reporting.
During 2019 we saw many, many well informed such position papers/blogposts. We have seen explanations on how machine learning has limitations on when it makes sense to be used and that it may not be a universal silver bullet. There have been posts that deep learning may be hitting limits as it has no common sense. We have seen questions about the practical value of the results of deep learning on game playing as game playing is precisely where we have massive amounts of completely relevant data–problems in the real world more commonly have very little data and reasoning from other domains is imperative to figuring out how to make progress on the problem. And we have seen warnings that all the over-hype of machine and deep learning may lead to a new AI winter when those tens of thousands of jolly conference attendees will no longer have grants and contracts to pay for travel to and attendance at their fiestas.
I am very concerned about what will happen when the current machine/deep learning bubble bursts. We have seen the bursting of hype bubbles decimate AI research before. The self driving cars bubble and its bubble bursting having a potential negative impact in AI research also worries me.
There were no target dates that have been hit or missed in the last year in the space launch domain, but I have made a couple of update comments in the following table, and then follow it with details in the text below.
|Next launch of people (test pilots/engineers) on a sub-orbital flight by a private company.
|20190101 Virgin Galactic did this on December 13, 2018.
20200101 On February 22, 2019, Virgin Galactic had their second flight, this time with three humans on board, to space of their current vehicle. As far as I can tell that is the only sub-orbital flight of humans in 2019. Blue Origin's new Shepard flew three times in 2019, but with no people aboard as in all its flights so far.
|A few handfuls of customers, paying for those flights.
|A regular sub weekly cadence of such flights.
|Regular paying customer orbital flights.
|Russia offered paid flights to the ISS, but there were only 8 such flights (7 different tourists). They are now suspended indefinitely.
|Next launch of people into orbit on a US booster.
NET 2019BY 2021
BY 2022 (2 different companies)
|Current schedule says 2018.
|20190101 It didn't happen in 2018. Now both SpaceX and Boeing say they will do it in 2019.
20200101 Both Boeing and SpaceX had major failures with their systems during 2019, though no humans were aboard in either case. So this goal was not achieved in 2019. Both companies are optimistic of getting it done in 2020, as they were for 2019. I'm sure it will happen eventually for both companies.
|Two paying customers go on a loop around the Moon, launch on Falcon Heavy.
|The most recent prediction has been 4th quarter 2018. That is not going to happen.
|20190101 I'm calling this one now as SpaceX has revised their plans from a Falcon Heavy to their still developing BFR (or whatever it gets called), and predict 2023. I.e., it has slipped 5 years in the last year.
|Land cargo on Mars for humans to use at a later date
|SpaceX has said by 2022. I think 2026 is optimistic but it might be pushed to happen as a statement that it can be done, rather than for an pressing practical reason.
|Humans on Mars make use of cargo previously landed there.
|Sorry, it is just going to take longer than every one expects.
|First "permanent" human colony on Mars.
|It will be magical for the human race if this happens by then. It will truly inspire us all.
|Point to point transport on Earth in an hour or so (using a BF rocket).
|This will not happen without some major new breakthrough of which we currently have no inkling.
|Regular service of Hyperloop between two cities.
|I can't help but be reminded of when Chuck Yeager described the Mercury program as "Spam in a can".
During a ground test of the SpaceX Crewed Dragon capsule, on April 20th, 2019, it exploded catastrophically. This delayed the SpaceX program so that no manned test could be done in 2019. SpaceX traced the problem to a valve failure when starting up the capsule abort engines, needed during launch if the booster rocket is undergoing failure. They currently have a test scheduled for early 2020 where these engines will be ignited during a launch so that the capsule can safely fly away from the launch vehicle.
In December of 2019 Boeing had a major test of its CST-100 Starliner capsule, and ended up with both a failure and a success for the mission. It was supposed to be the final unmanned test of the vehicle, and was planned to dock with the International Space Station (ISS) and then do a soft landing on the ground. It launched on December 20th and achieved orbit, but due to software failures it was the wrong orbit and there was not enough fuel left to get it to the ISS. This was a major failure. On the other hand it achieved a major success in doing a soft landing in New Mexico on December 22nd.
Other Hype Magnets
I have not felt qualified to talk about the hype impact for both quantum computing and block chain. Just at the end of 2019 there was a very interesting blog post by Scott Aaronson, a true expert and theoretical contributor to the field of quantum computing, on how to read announcements about quantum computing results. I recommend it.