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My AI Forecasts--Past, Present, and Future (Main Post)

1/4/2017

11 Comments

 
I have a long-standing interest in understanding how predictable AI progress is, and occasionally make my own forecasts. In this post, I’ll review some of my previous forecasts, make new ones for 2017, and suggest ways that I and others could make better predictions in the future.  The purpose of this post is to gather all of the forecasts in one place, keep myself honest/accountable regarding AI forecasting (which was part of the point of making the forecasts in the first place), see what if anything can be learned so far, and encourage others to do more of the above.

For those most interested in the stuff on Atari (my more quantitative forecasts) and my new predictions, and less interested in how my other miscellaneous forecasts fared, just read this blog post. If you want to know more about the process I went through to review all of my other forecasts, and to see how I did on predicting non-Atari things, see this supplemental page.


Atari Forecasts

I’m focusing on these because they’re forecasts about which I’ve thought a lot more than the ones linked to above, and about which I've made more specific data-based forecasts. I also have a lot of data on Atari performance, which will be made public soon.

In early 2016, I made a simple extrapolation of trends in mean and median Atari performance (that is, the best single algorithm’s mean and median score across several dozens games). For both mean and median performance, I made a linear extrapolation and an exponential extrapolation.




Extrapolation of mean/median Atari trends (https://t.co/UYzh8bleNF) - to do seriously, would include error bars... pic.twitter.com/LIZR53MMDI

— Miles Brundage (@Miles_Brundage) April 23, 2016
I also said:

Sidenote: if Atari AI prog. is fairly predictable, median score in the no-op condition will be in the vicinity of 190-250% by end of year.

— Miles Brundage (@Miles_Brundage) May 19, 2016
As the use of the word “sidenote” suggests, this wasn’t that rigorous of a forecast. I just took the available data and assumed the trend was either linear or exponential, and that the future data would be between those two lines. I’ll mention some ways I could have done this better later in this post. But it turned out to be fairly accurate, which I find interesting because it’s often claimed that progress in general AI is nonexistent or impossible to predict. In contrast, I think that general game playing is one of the better (though still partial) measures of cross-domain learning and control we have, and it’s fairly steady over time.

Here is the same plot, with two recent scores added, along with the range I forecasted. The light blue ovals are the data I had as of April, the lines are the same as those I plotted in April, the dark blue stars are very recent scores, and the red oval is the range I forecasted before. Two of DeepMind’s ICLR submissions are roughly at the bottom and top of the range I expected based on eyeballing the graph in April. Obviously, this could have been done more rigorously, but it seems to have been about right.
Picture
Note that there is eventually some upper bound due to the way evaluation is done (with a fixed amount of time per game), but it may not be reached for a while. And other metrics can be developed (e.g. learning speed, perhaps explored in a future post) which allow for other measures of progress to be projected, even if final scores max out, so I don't see any reason why we couldn't keep making short-term forecasts of benchmarks like this.

Based on the recent data, I think that that we might be seeing an exponential improvement in median scores. The range I gave before was agnostic regarding linear vs. exponential, and recent data points were in the ballpark of both of those lines, but only the higher one really counts since we’re interested in the highest reported score. Using the same sort of simple extrapolation I used before, I pretty strongly (80% confidence) expect that median scores will be between 300 and 500% at the end of 2017 (a range that covers linear progress and a certain speed of exponential progress), and not quite as strongly (60%) expect it to be at the higher end of this, that is, 400-500%, reflecting exponential progress before some eventual asymptote well above human performance.

For mean scores (which I didn’t make a prediction for before), here is what the most recent data (the PGQ paper submitted for ICLR) looks like when added to the graph from April.
Picture
It turns out that mean scores could have been more accurately predicted by an exponential curve—and more specifically, a faster exponential curve than I had come up with. It makes sense that mean scores would grow faster than median scores, but I’m somewhat surprised by how fast mean progress has been. I didn’t make a forecast in April, though, so I’ll rectify that now: by the end of the year, I weakly (60% confidence) expect mean scores to be between 1500% and 4000%. Obviously, that’s a pretty wide range, reflecting uncertainty about the exponent, but even at the low end, it’d be a lot higher than where we are today (877.23%).

Finally, note that these are pretty simple extrapolations and it might turn out that scores asymptote at some level before the end of the year. It seems plausible to me that you could figure out a rough upper bound based on detailed knowledge of the 57 games in question, but I haven't done this.


Conclusion re: Past Forecasts

Overall, I think my forecasts for Atari and the other domains covered (see the supplemental post for more examples of forecasts) were decent and reasonably well-calibrated, but I’m perhaps biased in my interpretation. I haven’t calculated a Brier score for my previous forecasts, but this would be an interesting exercise. Among other things, to do this, I’d have to quantify my implicit levels of confidence in earlier predictions. Perhaps I could have others assign these numbers in order to reduce bias. Since I’m giving confidence levels for my forecasts below, it will be easier to calculate the Brier score for my 2017 predictions.

Also, I think that the success of the median Atari forecast, and the plausibility that the mean forecast could have been better via e.g. error bars, suggests that there may be high marginal returns on efforts to quantify and extrapolate AI progress over the short-term.

Finally, it was a pain to find all of my old forecasts, so in the future, I’ll be putting them in blog posts or using a specific hashtag to make them more easily discoverable in the future.

Present Forecasts

Below are forecasts that I’ve either thought about a lot already, or just came up with in a few minutes for the purpose of this post. These are labeled “present” forecasts because while they’re about the future, they’re relatively weak and shoddy compared to what I or others might do in the future, e.g. with theoretically well-motivated error bars, more rigorous data collection, a wider range of tasks/domains covered, etc. I’ll say a bit about such future forecasts later, but for now I’ll just list a bunch of present forecasts.

First, I’ll just repeat what I said above about Atari.

Best median Atari score between 300 and 500% at end of 2017

Confidence level: 80%

Best mean Atari score between 1500% and 4000% at end of 2017

Confidence level: 60%

No human-level or superintelligent AI

By the end of 2017, there will still be no broadly human-level AI. No leader of a major AI lab will claim to have developed such a thing, there will be recognized deficiencies in common sense reasoning (among other things) in existing AI systems, fluent all-purpose natural language will still not have been achieved, etc.

Confidence level: 95%

Superhuman Montezuma’s Revenge AI

I don’t think this is that provocative to those who follow Atari AI super closely, versus how it may seem to those who are casual observers and have heard that Montezuma’s Revenge is hard for AIs, but I think by the end of the year, there will be algorithms that achieve significantly greater than DeepMind’s “human-level” threshold for performance on Montezuma’s Revenge (75% of a professional game tester’s score). Already, there are scores in that ballpark. By superhuman, let’s say that the score will be over 120%.

Confidence level: 70%

Superhuman Labyrinth performance

Labyrinth is another environment that DeepMind uses for AI evaluation, and which affords human-normalized performance evaluation. Already, the UNREAL agent tests at 92% median and 87% mean. So I’ll use the same metric as above for Montezuma’s Revenge (superhuman=120%) and say that both mean and median will be superhuman for the tasks DeepMind has historically used. I’m not as familiar with Labyrinth as Atari, so am not as confident in this.

Confidence level: 60%

Impressive transfer learning

Something really impressive in transfer learning will be achieved in 2017, possibly involving some of the domains above, possibly involving Universe. Sufficient measures of “really impressive” include Science or Nature papers, keynote talks at ICML or ICLR on the achievement, widespread tech media coverage, or 7 out of 10 experts (chosen by someone other than me) agreeing that it’s really impressive.

Confidence level: 70%

I also weakly predict that progressive neural networks and/or elastic weight consolidation (thanks to Jonathan Yan for suggesting the latter to me) will help with this (60%).

Speech recognition essentially solved

I think progress in speech recognition is very fast, and think that by the end of 2017, that for most recognized benchmarks (say, 7 out of 10 of those suggested by asking relevant experts), greater than human results will have been achieved. This doesn’t imply perfect speech recognition, but better than the average human, and competitive with teams of humans.

Confidence level: 60%

No defeat of AlphaGo by human

It has been announced that there will be something new happening related to AlphaGo in the future, and I’m not sure what that looks like. But I’d be surprised if anything very similar to the Seoul version of AlphaGo (that is, one trained with expert data and then self-play—as opposed to one that only uses self-play which may be harder), using similar amounts of hardware, is ever defeated in a 5 game match by a human.

Confidence level: 90%

StarCraft progress via deep learning

Early results from researchers at Alberta suggest that deep learning can help with StarCraft, though historically this hasn’t played much of if any role in StarCraft competitions. I expect this will change: in the annual StarCraft competition, I expect one of the 3 top performing bots to use deep learning in some way.

Confidence level: 60%

Professional StarCraft player beaten by AI system

I don’t know what the best metric for this is, as there are many ways such a match could occur. I’m also not that confident it will happen next year, but I think I’d be less surprised by it than some people. So partly because I think it’s plausible, and partly because it’s a more interesting prediction than some of the others here, I’ll say that it’ll happen by the end of 2018. I think it is plausible that such an achievement could happen through a combination of deep RL, recent advances in hierarchical learning, scaling up of hardware and researcher effort, and other factors soon-ish, but it's also plausible that other big, longer-term breakthroughs are needed.

Confidence level: 50%

More efficient Atari learning

I haven’t looked super closely at the data on this, but I think there’s pretty fast progress in Atari learning happening with less computational resources. See e.g. this graph of several papers hardware type-adjusted score efficiency (how many points produced per day of CPU, with GPUs counting as 5 units of CPU).


Picture
The big jump is from A3C, which learned relatively quickly using CPUs, vs. days of GPUs on earlier systems. Moreover, the UNREAL agent learns approximately 10x faster than A3C. So by the end of 2017, I’ll say that learning efficiency will be twice as good as that: an agent will be able to get A3C’s score using 5% as much data as A3C. Considering how big a jump happened with just one paper (UNREAL), this seems conservative, but as with the mean score forecast above, it’s still a big jump over what exists today so is arguably a non-trivial prediction.

Confidence level: 70%

Future Forecasts


There is a lot of room for improvement in the methodology and scale of AI forecasting.

One can use error bars based on the variance of technological progress rates and the number of data points available, as suggested by Farmer and Lafond (that paper is included in the list of resources below).

There are also many more tasks for which one could gather data and make forecasts. For example, one area that I think is worth looking at is progress in continuous control. It’s an area of real-world importance (specifically, robotics for manufacturing and service applications), and there’s a lot of data available for tasks in MuJoCo in terms of scores, data efficiency, etc.  That’s a case where further research and forecasting/subsequent evaluation of forecasts could be valuable not only for improving our knowledge of AI’s predictability, but also our early warning system for economic impacts of AI. Likewise for some NLP tasks, possibly, but I’m less familiar with the nature of those tasks.

A lot of my forecasts are about technical things rather than the social impact of AI, and the latter is also ripe for well-grounded forecasting. Right now, the people making forecasts of AI adoption are people like Forrester Research, who sell $500 reports, and aren’t transparent about methods (or at least, I don’t know how transparent they are since I can’t afford their reports). It might be useful to have better vetted, and/or crowdsourced, free alternatives to such analyses. Topics on which one could make forecasts include AI adoption, publication rates, relative performance of labs/companies/countries, dataset sizes, job displacement scales/types, etc.

The literature on AI forecasting is pretty sparse at the moment, though there are many resources to draw on (listed below). A lot of things can be improved. But in the future, besides growing this literature on its own terms, I think it’d be good for there to be stronger connections between AI forecasts and the literature on technological change in general. For example, Bloom et al. had a very interesting paper recently called “Are Ideas Getting Harder to Find?” which suggested that fast technological improvement has occurred alongside fast growth in the inputs to that improvement (researcher hours). One could ask the question of how much AI progress we’re getting for a given amount of input, how much those inputs (researchers, data, hardware, etc.) are growing, and why/under what conditions AI progress is predictable at all.

Recommended Reading:

Stuart Armstrong et al., “The errors, insights and lessons of famous AI predictions – and what they mean for the future”:  www.fhi.ox.ac.uk/wp-content/uploads/FAIC.pdf

Miles Brundage, “Modeling Progress in AI”: https://arxiv.org/abs/1512.05849

Jose Hernandez-Orallo, The Measure of All Minds: Evaluating Natural and Artificial Intelligence: https://www.amazon.com/Measure-All-Minds-Evaluating-Intelligence/dp/1107153018

Doyne Farmer and Francois Lafond, “How predictable is technological progress?”: http://www.sciencedirect.com/science/article/pii/S0048733315001699

Katja Grace and Paul Christiano et al., AI Impacts (blog on various topics related to the future of AI):  http://aiimpacts.org/

Anthony Aguirre et al., Metaculus, website for aggregating forecasts, with a growing number of AI events to be forecasted: http://www.metaculus.com/questions/#?show-welcome=true

Luke Muehlhauser, “What should we learn from past AI forecasts?”: http://www.openphilanthropy.org/focus/global-catastrophic-risks/potential-risks-advanced-artificial-intelligence/what-should-we-learn-past-ai-forecasts

Alan Porter et al., Forecasting and Management of Technology (second edition): https://www.amazon.com/Forecasting-Management-Technology-Alan-Porter/dp/0470440902

Tom Schaul et al., “Measuring Intelligence through Games”: https://arxiv.org/abs/1109.1314

Acknowledgments: Thanks to various commenters on Twitter for suggesting different considerations for new forecasts, various people for encouraging me to keep doing AI forecasting and writing it up (sorry it took so long to make this post!), and Allan Dafoe for comments on an earlier version of this post.
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My AI Forecasts--Past, Present, and Future (Supplement)

1/3/2017

1 Comment

 
Warning: less well-written than main post

Methodology for Past Forecast Review

I downloaded a CSV file with all of my tweets and searched for all tweets with the strings forecast*, predict*, extrapolat*, state of the art*, SOTA*, and expect*.  This may have missed a few predictions, and there are some forecasts that I’ve made in places other than Twitter, but this method has probably covered the vast majority of predictions, as I’m pretty tweet-prone.  

It turns out that there were a lot more than I thought (I forgot about a lot of the less rigorous ones), and the forecasts have different implicit (and sometimes explicit) confidence levels and focuses (e.g. quantifiable technical achievements vs. social adoption of/responses to AI).   

For each of the forecasts below, which are arranged in chronological order, I’ll reproduce the text of the tweet, and then say something about how it fared. I didn’t reproduce every single forecast-y tweet here because some are extremely vague or otherwise uninteresting.

Annotated List of Forecasts 

I expected that CMU, a NASA-related team, and the Institute for Human and Machine Cognition (IHMC) would do well in the first (virtual) round of the DARPA Robotics Challenge:

Looking forward to the first DARPA Robotics Challenge results on Thursday. My bet is CMU, one of the NASA-related teams, and IHMC do well.

— Miles Brundage (@Miles_Brundage) June 26, 2013
This was a decent forecast (much better than chance under some interpretations of what I meant, though I was pretty vague), as IHMC got first place out of 28 teams and a JPL-related team got fifth place out of 28. This is definitely better than a random prediction. The DARPA Robotics Challenge website is no longer live, so I am having trouble verifying how CMU did in this round. I assume they weren’t in the top six based on what I later tweeted:

I was two out of three with my DARPA Robotics Challenge predictions...IHMC did the best - not surprised.

— Miles Brundage (@Miles_Brundage) June 27, 2013
I had previously done an internship at IHMC and had personally seen that they were putting a lot of effort into the DRC, so I probably don’t deserve much credit for this forecast. I also didn’t put much work into making it.

Later, I doubled down on this IHMC-boosterism:

If you're in Florida, consider checking out the DARPA Robotics Challenge live on Dec. 20-21 http://t.co/L41D4Wi5A4 My money is on IHMC!

— Miles Brundage (@Miles_Brundage) December 9, 2013

DARPA Robotics Challenge is Friday and Saturday! My bet is still on IHMC. Anyone else have a favorite?

— Miles Brundage (@Miles_Brundage) December 19, 2013
They got second place, but due to events I did not predict (SCHAFT, the winner, being bought by Google and dropping out), this retroactively improved:

So now that Google-SCHAFT is out of the DRC, my prediction of IHMC doing well has retroactively improved, they won rounds 1 and 2! ;-)

— Miles Brundage (@Miles_Brundage) June 26, 2014
Again, I don’t think I get much credit for this.

In early 2015, I said some things about DeepMind's likely work in 2015:

​

In 2015 I think DeepMind will prob demo some sort of mind blowing learning thing in a 3D world or at least much-richer-than-Atari 2D world.

— Miles Brundage (@Miles_Brundage) January 1, 2015
I don’t know what evidence I based this on, if any, or what counts as a “mind blowing learning thing in a 3D world,” but I think this basically happened a bit later than I expected: the A3C paper showing early impressive results in Labyrinth came out in early 2016. Fortunately, this was within my vague confidence interval:

On error bars: wouldn't be stunned if what I said re: DeepMind demo happened in 2016 not 2015, but if not in 2016 then my model is v. wrong.

— Miles Brundage (@Miles_Brundage) January 1, 2015
In early 2015, I had a vague and pretty incorrect model of what DeepMind and others were trying to do with games – roughly, move forward through time/game complexity space (with newer games generally being harder for AI) and show impressive learning across a wide variety of games for that point in time/complexity space. Based on this, I said:

Mode prediction for where in videogame chronology/complexity space DeepMind will have impressively dominated many hard games in 2016 is 2000

— Miles Brundage (@Miles_Brundage) January 1, 2015
This model of what DeepMind and others are up to turned out to be a bit misguided, since they’re still publishing a lot of results with (old) Atari games, and making brand new environments that don’t easily map onto the metric above (since the environments have highly variable difficulty/complexity). I was wrong for thinking that they’d try to move on before having more definitively solved Atari and games that are not well captured in that metric (e.g. Go – thousands of years old, but still pretty hard). Nevertheless, if you wanted to be generous and take “DeepMind” to refer to the broader AI community, you could say that OpenAI’s Universe covers a lot of Flash games from the early 2000s, some of which deep reinforcement learning (RL) works pretty well on. But overall, I’d say this was a misguided and vague forecast.  I did caveat it a bit:

DeepMind *could* focus on playing higher fraction of old games w/o input, but they're also simultaneously moving forward in time game-wise.

— Miles Brundage (@Miles_Brundage) January 6, 2015
Regarding non-game stuff, I said:

DeepMind will prob someday (if they haven't already) do non-game stuff, but for now that's their metric, with some reason - it's very hard!

— Miles Brundage (@Miles_Brundage) January 1, 2015
DeepMind has since applied deep RL to data center energy management and deep learning to healthcare. They have also used non-game domains for benchmarks in research (e.g. MuJoCo). But this was a pretty uninteresting/banal prediction (it’s pretty obvious they would have done something non-game-related eventually).

Anti-prediction for DeepMind 2015-2016: them playing Destiny or other current video game. Way too hard/not worth their time except for fun.

— Miles Brundage (@Miles_Brundage) January 6, 2015
As far as I know, this was correct, unless you count StarCraft 2 as a “current” video game.

Another key pt on DeepMind's near-term game stuff: suspect some of the impressive results they show will *not* be fully autonomous learners.

— Miles Brundage (@Miles_Brundage) January 6, 2015
Arguably, this was ultimately true of AlphaGo – its learning was kickstarted with a dataset of human play, though they have said they’ll explore learning from scratch in the future.

Elaboration on previous 2015-2016 DeepMind predictions: simultaneous to video game stuff, they will prob make some big progress on Go. (1/2)

— Miles Brundage (@Miles_Brundage) January 6, 2015
This was based on the early results from Maddison et al. (including some DeepMind authors) in late 2014 that seemed to suggest to me that they might work more on it in the future and that deep learning could help a lot.

My money is on IHMC doing well in, if not winning, DARPA Robotics Challenge finals. Will be v. interesting to see how the Chinese team does.

— Miles Brundage (@Miles_Brundage) March 21, 2015
IHMC got second (same number of points as the winner, KAIST, but with a slower time) and the Chinese team did poorly.

Regarding speech recognition, in late 2015, i said:

2. Think 2016 will be year in which it's pretty clear that speech recognition is now of broad utility. Also, note role hardware played in...

— Miles Brundage (@Miles_Brundage) December 17, 2015
There wasn’t a clear metric for this. There was a lot of coverage of speech recognition in the tech press, and some impressive (nearly) human-level results, but I’m not sure whether 2016 represented any sort of shift in terms of wide adoption. Anecdotally, it seems more widely used in Beijing than in Western countries, but I don’t know for sure.

As part of a longer rant in 2016, I said:

6. And I no longer think massive progress in AI in, say, 10 years is implausible - now seems plausible enough to plan for possibility of it.

— Miles Brundage (@Miles_Brundage) January 8, 2016
And:

8. I expect enough prog that "human-level AI" will be more clearly revealed as a problematic threshold, and in many domains, long surpassed.

— Miles Brundage (@Miles_Brundage) January 8, 2016

10. access to the Internet is allowed, a la https://t.co/Vs9KX98v3p

— Miles Brundage (@Miles_Brundage) January 8, 2016
This was pretty vague, and the timeline in question is still ongoing, so I can’t evaluate it yet.

Regarding hardware and neural network training speeds, I said:

2. This would affect, as prior hardware improvements have affected, three things: attainable performance, speed thereof, and iteration pace.

— Miles Brundage (@Miles_Brundage) January 15, 2016

3. And that's all just from hardware - algorithmic advances have also been rapid in recent years, though I haven't yet quantified that rate.

— Miles Brundage (@Miles_Brundage) January 15, 2016

4. Seems like a not too crazy projection is that in, say, 3 years, neural nets will be 100x faster to train, w/ big impacts on applications.

— Miles Brundage (@Miles_Brundage) January 15, 2016
(sorry for the bad formatting here)

6. These are just rough ideas currently - may do more rigorous calculation with error bars at some point. Point is, expect much NN progress.

— Miles Brundage (@Miles_Brundage) January 15, 2016
I’m still pretty confident that hardware is speeding up and will speed up neural net training a lot, but we’ll have to wait until early 2019 to evaluate the 100x thing. I’ll try to specify it a bit better now: for a multiple expert-suggested set of 10 benchmarks in image recognition and NLP, you will be able to achieve the same the same performance, using new hardware and algorithms, in 100x less training time (wall time) vs. results reported in early 2016 on at least 8 of those benchmarks. This is a rough, intuitive guess, so I have less confidence in it than some of my more quantitative extrapolations of Atari results discussed below.

Regarding AlphaGo’s success against Lee Sedol, I said in the middle of the match:

Predicted AlphaGo victory w/ 65% confidence and 4-1/5-0 for whichever victor w/ 90% confidence, so not too late for me to be very wrong.. :)

— Miles Brundage (@Miles_Brundage) March 12, 2016
This reference to a prior prediction was based on a Facebook comment I made before the match began, which in turn elaborated on views expressed in a blog post I wrote. The comment (not publicly linkable, unfortunately), on March 1, said:
Picture
 I think my reasoning at the time was essentially correct, deep RL is in fact very effective and scalable for well-defined zero-sum games, and that my conclusions in the aforementioned blog post (about the importance of hardware in this case and humans occupying a small band in the space of possible intelligence levels) are still correct. But lots of people thought AlphaGo would win, and I wasn’t extremely confident, so don’t get much credit for this.

Regarding dialogue systems:

In few years, I expect impressive (by today's standards, though maybe not future revised ones) limited dialogue AIs from Goog, IBM, FB, etc.

— Miles Brundage (@Miles_Brundage) March 12, 2016
I still believe this, but “a few years” haven’t yet passed so don’t have much more to say about this right now, other than that it is probably too vague.

Regarding Google’s business model for AI:

@samim my expectation is that they will gradually, over next 10 years, introduce more, better, and more integrated cognition-as-a-service.

— Miles Brundage (@Miles_Brundage) March 24, 2016
Again, it’s early for this, but this seems pretty plausible to me.

Atari Forecasts

See main blog post.




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