Posts

  • Carbon Footprint Comparison for Gas and Electric Cars

    My car’s dead. It went through a long series of death throes, almost making it all the way through the pandemic, but now it’s dead, and the price of fixing it is too high.

    I still need a car! You can get away without one in SF, but I live in South Bay, and South Bay is still a sprawling suburban hellscape if you don’t have a car.

    I also want to consider the carbon footprint of my decision. What should I do?

    * * *

    First off - electric, hybrid, or gas? This is supposed to be obvious, but maybe it’s not. In high school, one of my teachers claimed that a new Toyota Prius was worse for the environment than a new Hummer over the course of its lifetime, because the CO2 emitted during production time was much higher and the gas savings didn’t make up the difference. I never checked this claim, and it was 10 years ago. Let’s see if it still holds up with the advances in battery production.

    The European Parliament has an infographic for lifetime CO2 emissions from different kinds of cars, from 2014.

    Chart of lifetime CO2 emissions

    The green bars are the share from vehicle production. The top bar is a gasoline car, the second bar is a diesel car, and the last 3 are electric cars under different assumptions of clean power. Lifetime CO2 emissions are measured in g/km, assuming a 150,000 km mileage. This is about 93,000 miles for the Americans out there. Unfortunately I wasn’t able to find the primary source for this chart, but at a glance electric cars do pay a higher up-front cost in CO2 emissions. It’s 62.5g CO2/km versus 50g CO2/km, a 25% increase. However, this is later offset by the decreased emissions from power generation. The exact difference depends on how clean your electricity is. At the extreme ends, an electric car powered by electricity from coal is worse than a gasoline car! On average though, it looks good for the electric car, 170 g/km compared to 220 g/km for a gas car.

    A 2018 brief from the International Council on Clean Transportation found similar conclusions. They compared an average conventional car, an efficient internal combustion car (the 2017 Peugeot 208, which gets 65.7 mpg), and an electric vehicle (the 2017 Nissan Leaf). The Nissan Leaf won out.

    ICCT 2018 brief

    Electric vehicles should have larger gains than the ones shown here, because these numbers are based on a lifetime mileage of 93,000 miles. Most EVs I’ve looked at come with a 8 year, 100,000 mile warranty on their batteries. Power grid electricity is greener than burning gasoline, so underestimates of mileage are worse for electric cars when comparing their emissions.

    Interestingly, for Germans, an electric car is only on par with an efficient gas car, since their power grid is more carbon heavy. For the French and Norwegians, it’s amazing, and this is part of the argument for electric vehicles I find most compelling: gas vehicles are forever locked into using gasoline, whereas electric vehicles will become greener over time as power grids move towards renewable energy. Electricity is inherently more fungible, it doesn’t matter where it comes from, and if more of the transportation network moves to electric, it reduces lock in of suboptimal technology.

    If you live in the US and are curious, the Alternative Fuels Data Center (AFDC) from the Department of Energy has a tool that lets you estimate annual emissions for different kinds of vehicles, with breakdowns by state. Their US average says a gas car produces 11,435 pounds of CO2 annually, a hybrid produces 6,258 pounds of CO2 annually, and an electric car produces 4,091 pounds of CO2 annually. In California, the electric car produces 1,960 pounds of CO2 annually, less than half the national average, thanks to heavier usage of solar and hydropower.

    * * *

    We’ve now established that the obvious answer is the correct one: electric cars produce less CO2 over their lifetime. Now, used or new?

    Consider the chart from before. Around \(50/220 \approx 25\%\) of the lifetime emissions for a gas car come from manufacturing. For electric cars, its \(62.5/170 \approx 35\%\). As added confirmation, I checked the gas car numbers against other sources. This 2010 Guardian article finds that producing a medium-sized car produces 17 tonnes of CO2. The EPA greenhouse gas guidelines from 2020 estimates gas cars emit 4.6 tonnes of CO2 per year. The average age of cars in the US is 11.9 years, an all-time high. Using those numbers gives \(17 / (17 + 4.6 \cdot 11.9) = 23.7\%\) for gas cars, which is close enough to \(25\%\).

    The three Rs go reduce, reuse, recycle, and that’s the order of priority. If you don’t need a car, that’s still the best, but reusing an old car offsets producing 1 new car. That immediately cuts your environmental impact by 25%-35%. Right?

    Well, it depends how far you want to carry out the consequentialist chain. Say you buy a used car directly from someone else. That person likely needs a replacement car. If they replace it with a new car, then you haven’t changed anything. You still caused 1 new car to be manufactured, along with all the emissions that entails. In reality, not everyone will replace their car, so buying a used car is equivalent to producing some fraction of a new car. If that fraction is \(p\), then you save \(p\cdot 30\%\) of the emissions. But what’s \(p\)? Intuitively, \(p\) is probably close to 100%, since people need transportation, but is there a way we can estimate it?

    As a simple model, let’s assume that everyone owns 0 or 1 car, and everyone acts identically. After someone sells their car, they have a XX% chance of not replacing it, a YY% chance of buying a new car, and a ZZ% chance of buying a used car from a third person. If the last case happens, that third person no longer has a car, and has the same choice of if and how they want to replace it. We can define the fraction of new cars (\(p\)) in a recursive way, where the first two cases are base cases.

    \[p = x \cdot 0 + y \cdot 1 + z \cdot p\]

    Solving for \(p\) gives \(p = y/(1-z) = y / (x+y)\). In other words, it’s the number of new car sales, divided by (new cars + people who don’t replace their car). Counting new car sales is easy, because it directly affects revenue of automakers, and anything that affects revenue gets measured by everybody. Projections put it at 17.3 million new cars in 2018. Counting people who don’t replace their car is harder, but we can use numbers from the Bureau of Transportation Statistics. There are fluctuations between each year, but if we consider the 2013-2018 time span, the number of vehicle registrations increased by 18 million. So let’s say 3.6 million vehicle registrations per year. This is the net increase, so \(17.3-3.6 = 13.7\) million vehicles leave the road each year. Let’s treat 13.7 million as the number of cars that don’t get replaced. Then we get

    \[p = 17.3 / (17.3 + 13.7) = 0.558\]

    This is a lot smaller than I expected, I thought it would be closer to 0.8 than 0.5. The Transportation Statistics numbers include aircraft and boats in their vehicle registrations, so I’m likely overestimating the denominator, meaning I’m underestimating \(p\). Let’s round up and say buying a used car leads to about 0.6 new cars. This saves 0.4 new cars of production, and you can expect a \(0.4 \cdot 30 = 12\%\) cut to environmental impact.

    * * *

    Combining it all together, even if you use the most efficient gas vehicle, and buy it used, you will struggle to do as good for the environment as buying a new electric vehicle. The ICCT brief estimates a 60+ mpg conventional car at 180 g CO2/km. Buying it used gives a 12% cut, to 158.4 g CO2/km. The same ICCT brief estimates electric vehicles at 130 g CO2/km, while the EU parliament infographic estimates them at 170 g CO2/km. Perhaps you’ll do better, but it won’t be by much.

    California residents can expect their electric cars to be much better for the environment, thanks to more investment in green power. Other states may get smaller gains, but I estimate a 25%-50% reduction in lifetime CO2 emissions compared to a conventional car.

    If you’re concerned about how regularly you can charge your car (like me), then you could consider a plug-in hybrid. These cars come with a smaller battery that lets them use electric power for short drives, then switch to hybrid mode (using gas) once that runs out. The CO2 emissions will depend on how diligent you are about charging the battery, but if your commute is short, it’ll be almost equivalent to a pure electric car with the option to fallback on gasoline.

    ICCT 2018 brief, with plug-in comparison

    Comments
  • My AI Timelines Have Sped Up

    For this post, I’m going to take artificial general intelligence (AGI) to mean an AI system that matches or exceeds humans at almost all (95%+) economically valuable work. I prefer this definition because it focuses on what causes the most societal change, rather than how we get there.

    In 2015, I made the following forecasts about when AGI could happen.

    • 10% chance by 2045
    • 50% chance by 2050
    • 90% chance by 2070

    Now that it’s 2020, I’m updating my forecast to:

    • 10% chance by 2035
    • 50% chance by 2045
    • 90% chance by 2070

    I’m keeping the 90% line the same, but shifting everything else to be faster. Now, if you’re looking for an argument of why I picked these particular years, and why I shifted by 10 years instead of 5 or 15, you’re going to be disappointed. Both are driven by a gut feeling. What’s important is why parts of my thinking have changed - you can choose your own timeline adjustment based on that.

    Let’s start with the easy part first.

    I Should Have Been More Uncertain

    It would be incredibly weird if I was never surprised by machine learning (ML) research. Historically, it’s very hard to predict the trajectory a research field will take, and if I were never surprised, I’d take that as a personal failing to not consider large enough ideas.

    At the same time, when I think back on the past 5 years, I believe I was surprised more often than average. It wasn’t all in a positive direction. Unsupervised learning got better way faster than I expected. Deep reinforcement learning got better a little faster than I expected. Transfer learning has been slower than expected. Combined, I’ve decided I should widen the distribution of outcomes, so now I’m allocating 35 years to the 10%-90% interval instead of 25 years.

    I also noticed that my 2015 prediction placed 10% to 50% in a 5 year range, and 50% to 90% in a 20 year range. AGI is a long-tailed event, and there’s a real possibility it’s never viable, but a 5-20 split is absurdly skewed. I’m adjusting accordingly.

    Now we’re at the hard part. Why did I choose to shift the 10% and 50% lines closer to present day?

    I Didn’t Account for Better Tools

    Three years ago, I was talking to someone who mentioned that there was no fire alarm for AGI. I told them I knew Eliezer Yudkowsky had written another post about AGI, and I’d seen it shared among Facebook friends, but I hadn’t gotten around to reading it. They summarized it as, “It will never be obvious when AGI is going to occur. Even a few years before it happens, it will be possible to argue AGI is far away. By the time it’s common knowledge that AI safety is the most important problem in the world, it’ll be too late.”

    And my reaction was, “Okay, that matches what I’ve gotten from my Facebook timeline. I already know the story of Fermi predicting a nuclear chain reaction was very likely to be impossible, only a few years before he worked on the Manhattan Project. More recently, we had Rémi Coulom state that superhuman Go was about 10 years away, one year before the first signs it could happen, and two years before AlphaGo made it official. I also already know the common knowledge arguments for AI safety.” I decided it wasn’t worth my time to read it.

    (If you haven’t heard the common knowledge arguments, here’s the quick version: it’s possible for the majority to believe AI safety is worthwhile, even if no one says so publicly, because each individual could be afraid everyone else will call them crazy if they argue for drastic action. This can happen even if literally everyone agrees, because they don’t know that everyone agrees.)

    I read the post several years later out of boredom, and I now need to retroactively complain to all my Facebook friends who only shared the historical events and common knowledge arguments. Although that post summary is correct, the ideas I found useful were all outside that summary. I trusted you, filter bubble! How could you let me down like this?

    Part of the fire alarm post proposes hypotheses for why people claim AGI is impossible. One of the hypotheses is that researchers pay too much attention to the difficulty of getting something working with their current tools, extrapolate that difficulty to the future, and conclude we could never create AGI because the available tools aren’t good enough. This is a bad argument, because your extrapolation needs to account for research tools also improving over time.

    What “tool” means is a bit fuzzy. One clear example is our coding libraries. People used to write neural nets in Caffe, MATLAB, and Theano. Now it’s mostly TensorFlow and PyTorch. A less obvious example is feature engineering for computer vision. When was the last time anyone talked about SIFT features for computer vision? Ages ago, they’re obsolete. But feature engineering didn’t disappear, it just turned into convolutional neural net architecture tuning instead. For a computer vision researcher, SIFT features were the old tool, convolutional neural nets are the new tool, and computer vision is the application that’s been supercharged by the better tool.

    Whereas for me, I’m not a computer vision person. I think ML for control is a much more interesting problem. However, you have to do computer vision to do control in image-based environments, and if you want to handle the real world, image-based inputs are the way to go. So for me, computer vision is the tool, robotics is the application, and the improvements in computer vision have driven many promising robot learning results.

    AlexNet conv filters

    (Filters automatically learned by AlexNet, which has itself been obsoleted by the better tool, ResNets.)

    I’m a big advocate for research tools. I think on average, people underestimate their impact. So after reading the hypothesis that people don’t forecast tool improvement properly, I thought for a bit, and decided I hadn’t properly accounted for it either. That deserved shaving off a few years.

    In the more empirical sides of ML, the obvious components of progress are your ideas and computational budget, but there are less obvious ones too, like your coding and debugging skills, and your ability to utilize your compute. It doesn’t matter how many processors you have per machine, if your code doesn’t use all the processors available. There are a surprising number of ML applications where the main value-add comes from better data management and data summarizing, because those tools free up decision making time for everything else.

    In general, everyone’s research tools are deficient in some way. Research is about doing something new, which naturally leads to discovering new problems, and it’s highly unlikely someone’s already made the perfect tool for a problem that didn’t exist three months ago. So, your current research tools will always feel janky, and you shouldn’t be using that to argue anything about timelines.

    The research stack has lots of parts, improvements continually happen across that entire stack, and most of these improvements have multiplicative benefits. Multiplicative factors can be very powerful. One simple example is that to get 10x better results, you can either make one thing 10x better with a paradigm shift, or you can make ten different things 1.26x better, and they’ll combine to a 10x total improvement. The latter is just as transformative, but can be much easier, especially if you get 10 experts with different skill sets to work together on a common goal. This is how corporations become a thing.

    Tiny gains graph

    (From JamesClear.com)

    Semi-Supervised and Unsupervised Learning are Getting Better

    Historically, unsupervised learning has been in this weird position where it is obviously the right way to do learning, and also a complete waste of time if you want something to work ASAP.

    On the one hand, humans don’t have labels for most things they learn, so ML systems shouldn’t need labels either. On the other hand, the deep learning boom of 2015 was mostly powered by supervised learning on large, labeled datasets. Richard Socher made a notable tweet at the time:

    I wouldn’t say unsupervised learning has always been useless. In 2010, it was common wisdom that deep networks should go through an unsupervised pre-training step before starting supervised learning. See (Erhan et al, JMLR 2010). In 2015, self-supervised word vectors like GloVe and word2vec were automatically learning interesting relationships between words. As someone who started ML around 2015, these unsupervised successes felt like exceptions to the rule. Most other applications relied on labels. Pretrained ImageNet features were the closest thing to general behavior, and those features were learned from scratch through only supervised learning.

    I’ve long agreed that unsupervised learning is the future, and the right way to do things, as soon as we figure out how to do so. But man, we have spent a long time trying to do so. That’s made me pretty impressed with the semi-supervised and unsupervised learning papers from the past few months. Momentum Contrast from (He et al, CVPR 2020) was quite nice, SimCLR from (Chen et al, ICML 2020) improved on that, and Bootstrap Your Own Latent (Grill, Strub, Altché, Tallec, Richemond et al, 2020) has improved on that. And then there’s GPT-3, but I’ll get to that later.

    When I was thinking through what made ML hard, the trend lines were pointing to larger models and larger labeled datasets. They’re still pointing that way now. I concluded that future ML progress would be bottlenecked by labeling requirements. Defining a 10x bigger model is easy. Training a 10x bigger model is harder, but it doesn’t need 10x as many people to work on it. Getting 10x as many labels does. Yes, data labeling tools are getting better, Amazon Mechanical Turk is very popular, and there are even startups whose missions are to provide fast data labeling as a service. But labels are fundamentally a question about human preferences, and that makes it hard to escape human labor.

    Reward functions in reinforcement learning have a similar issue. In principle, the model figures out a solution after you define what success looks like. In practice, you need a human to check the model isn’t hacking the reward, or your reward function is implicitly defined by human raters, which just turns into the same labeling problem.

    Large labeled datasets don’t appear out of nowhere. They take deliberate, sustained effort to generate. There’s a reason ImageNet won the Test of Time award at CVPR 2019 - the authors of that paper went out and did the work. If ML needed ever larger labeled datasets to push performance, and models kept growing by orders of magnitude, then you’d hit a point where the amount of human supervision needed to make progress would be insane.

    (This isn’t even getting into the problem of labels being imperfect. We’ve found that many labeled datasets used in popular benchmarks contain lots of bias. That isn’t surprising, but now that it’s closer to common knowledge, building a large dataset with a laissez-faire labeling system isn’t going to fly anymore.)

    Okay. Well, if 10x labels is a problem, are there ways around that problem? One way is if you don’t need 10x as many labels to train a 10x larger model. The messaging on that is mixed. One scaling law paper, (Hestness et al, 2017), recommends a model size that grows sublinearly with dataset size.

    We expect that number of model parameters to fit a data set should follow \(s(m) \propto \alpha m^{\beta_p}\), where \(s(m)\) is the required model size to fit a training set of size \(m\).

    (From Section 2.2)

    Different problem settings have different coefficients. Image classification followed a \(\beta_p=0.573\) power law, while language modeling followed a \(\beta_p \approx 0.72\) line.

    Scaling law lines

    Trend lines for image classification (left) and language modeling (right) from (Hestness et al, 2017)

    Inverting this suggests dataset size should grow superlinearly with model size - a 10x larger image classification model should use \(10^{1/0.573} = 55.6\)x times as much data! That’s awful news!

    But, the (Kaplan and Candlish, 2020) paper suggests the inverse relationship - that dataset size should grow sublinearly with model size. They only examine language modeling, but state in Section 6.3 that

    To keep overfitting under control, the results of Section 4 imply we should scale the dataset size as \(D \propto N^{0.74}\), [where \(D\) is dataset size and \(N\) is model size].

    This is strange when compared to the Hestness result of \(D \propto N^{1/0.72}\) . Should the dataset grow faster or slower than the model?

    The difference between the two numbers happens because the Kaplan result is derived assuming a fixed computational budget. One of the key results they found was that it was more efficient to train a very large model for a short amount of time, rather than train a smaller model to convergence. Meanwhile, as far as I could tell, the Hestness results always use models trained to convergence.

    Kaplan compute graph

    Figure 2 of (Kaplan and Candlish, 2020)

    That was a bit of a digression, but after plugging the numbers in, we get that every 10x increase in model size should require between a 4x and 50x increase in dataset size. Let’s assume the 4x side to be generous. A 4x factor for label needs is definitely way better than a 10x factor, but it’s still a lot.

    Enter unsupervised learning. These methods are getting better, and what “label” means is shifting towards something easier to obtain. GPT-3 is trained on a bunch of web crawling data, and although some input processing was required, it didn’t need a human to verify every sentence of text before it went into model training. At sufficient scale, it’s looking like it’s okay for your labels to be noisy and your data to be messy.

    There’s a lot of potential here. If you have \(N\) unsupervised examples, then yes, \(N\) labeled examples will be better, but remember that labels take effort. The size of your labeled dataset is limited by the supervision you can afford, and you can get much more unlabeled data for the same amount of effort.

    A lot of Big Data hype was driven by plots showing data was getting created faster than Moore’s Law. Much of the hype fizzled out because uninformed executives didn’t understand that having data is not the same as having useful data for machine learning. The true amount of usable data was much smaller. The research community had a big laugh, but the joke will be on us if unsupervised learning gets better and even junk data becomes marginally useful.

    Is unsupervised learning already good enough? Definitely not. 100% not. It is closer than I expected it to be. I expect to see more papers use data sources that aren’t relevant to their target task, and more “ImageNet moments” where applications are built by standing on the shoulders of someone else’s GPU time.

    GPT-3 Results are Qualitatively Better than I Expected

    I had already updated my timeline estimates before people started toying with GPT-3, but GPT-3 was what motivated me to write this blog post explaining why.

    What we’re seeing with GPT-3 is that language is an incredibly flexible input space. People have known this for a while. I know an NLP professor who said language understanding is an AI-Complete task, because a hypothetical machine that perfectly understands and replies to all questions might as well be the same as a person. People have also argued that compression is a proxy for intelligence. As argued on the Hutter Prize website, to compress data, you must recognize patterns in that data, and if you view pattern recognition as a key component of intelligence, then better compressors should be more intelligent.

    To clarify: these are nowhere near universal NLP opinions! There’s lively debate over what language understanding even means. I mention them because these opinions are held by serious people, and the GPT-3 results support them.

    GPT-3 is many things, but its core is a system that uses lots of training time to compress a very large corpus of text into a smaller set of Transformer weights. The end result demonstrates a surprisingly wide breadth of knowledge, that can be narrowed into many different tasks, as long as you can turn that task into a prompt of text to seed the model’s output. It has flaws, but the breadth of tech demos is kind of absurd. It’s also remarkable that most of this behavior is emergent from getting good at predicting the next token of text.

    This success is a concrete example of the previous section (better unsupervised learning), and it’s a sign of the first section (better tooling). Although there’s a lot of fun stuff in story generation, I’m most interested in the code generation demonstrations. They look like early signs of a “Do What I Mean” programming interface.

    If the existing tech demos could be made 5x better, I wouldn’t be surprised if they turned into critical productivity boosters for nuts-and-bolts programming. Systems design, code verification, and debugging will likely stick to humans for now, but a lot of programming is just coloring inside the lines. Even low levels of capability could be a game changer, in the same way as pre-2000 search engines. AltaVista was the 11th most visited website in 1998, and it’s certainly worse than what Google/Bing/DuckDuckGo can do now.

    One specific way I could see code generation being useful is for ML for ML efforts, like neural architecture search and black-box hyperparameter optimization. One of the common arguments around AGI is intelligence explosion, and that class of black-box methods has been viewed as a potential intelligence explosion mechanism. However, they’ve long had a key limitation: even if you assume infinite compute, someone has to implement the code that provides a clean API from experiment parameters to final performance. The explorable search space is fundamentally limited by what dimensions of the search space humans think of. If you don’t envision part of the search space, machine learning can’t explore it.

    Domain randomization in robot learning has the same problem. This was my main criticism of the OpenAI Rubik’s Cube result. The paper read like a year long discovery of the Rubik’s Cube domain randomization search space, rather than any generalizable robot learning lesson. The end result is based on a model learning to generalize from lots of random simulations, but that model only got there because of the human effort spent determining which randomizations were worth implementing.

    Now imagine that whenever you discovered a new unknown unknown in your simulator, you could very quickly implement the code changes that add it to your domain randomization search space. Well, those methods sure look more promising!

    There are certainly problems with GPT-3. It has a fixed attention window. It doesn’t have a way to learn anything it hasn’t already learned from trying to predict the next character of text. Determining what it does know requires learning how to prompt GPT-3 to give the outputs you want, and not all simple prompts work. Finally, it has no notion of intent or agency. It’s a next-word predictor. That’s all it is, and I’d guess that trying to change its training loss to add intent or agency would be much, much more difficult than it sounds. (And it already sounds quite difficult to me! Never underestimate the inertia of a working ML research project.)

    But, again, this reminds me a lot of early search engines. As a kid, I was taught ways to structure my search queries to make good results appear more often. Avoid short words, place important key words first, don’t enter full sentences. We dealt with it because the gains were worth it. GPT-3 could be similar.

    I don’t know where this leads, but there’s something here.

    I Now Expect Compute to Play a Larger Role, and See Room for Models to Grow

    For reasons I don’t want to get into in this post, I don’t like arguments where people make up a compute estimate of the human brain, take a Moore’s Law curve, extrapolate the two out, and declare that AGI will happen when the two lines intersect. I believe they oversimplify the discussion.

    However, it’s undeniable that compute plays a role in ML progress. But how much are AI capabilities driven by better hardware letting us scale existing models, and how much is driven by new ML ideas? This is a complicated question, especially because the two are not independent. New ideas enable better usage of hardware, and more hardware lets you try more ideas. My 2015 guess to the horrid simplification was that 50% of AGI progress would come from compute, and 50% would come from better algorithms. There were several things missing between 2015 models, and something that put the “general” in artificial general intelligence. I was not convinced more compute would fix that.

    Since then, there have been many successes powered by scaling up models, and I now think the balance is more like 65% compute, 35% algorithms. I suspect that many human-like learning behaviors could just be emergent properties of larger models. I also suspect that many things humans view as “intelligent” or “intentional” are neither. We just want to think we’re intelligent and intentional. We’re not, and the bar ML models need to cross is not as high as we think.

    If compute plays a larger role, that speeds up timelines. ML ideas are bottlenecked by the size and growth of the ML community, whereas faster hardware is powered by worldwide consumer demand for hardware. The latter is a much stronger force.

    Let’s go back to GPT-3 for a moment. GPT-3 is not the largest Transformer you could build, and there are reasons to build a larger one. If the performance of large Transformers scaled for 2 orders of magnitude (1.5B params for GPT-2, 175B params for GPT-3), then it wouldn’t be too weird if they scaled for another 2 orders of magnitude. Of course, it might not. The (Kaplan et al, 2020) scaling laws are supposed to start contradicting each other starting around \(10^{12}\) parameters. which is less than 1 order of magnitude away from GPT-3. That doesn’t mean the model will stop improving though. It just means it’ll improve at a different rate. I don’t see a good argument why we should be confident a 100x model would not be qualitatively different.

    This is especially true if you move towards multi-modal learning. Focusing on GPT-3’s text generation is missing the main plot thread. If you believe the rumors, OpenAI has been working towards incorporating audio and visual data into their large models. So far, their research output is consistent with that. MuseNet was a generative model for audio, based on large Transformers. The recent Image GPT was a generative model for images, also based on large transformers.

    Was MuseNet state-of-the-art at audio synthesis when it came out? No. Is Image GPT state-of-the-art for image generation? Also no. Model architectures designed specifically for audio and image generation do better than both MuseNet and Image GPT. Focusing on that is missing the point OpenAI is making: a large enough Transformer is not state-of-the-art, but it does well enough on these very different data formats. There’s better things than MuseNet, but it’s still good enough to power some silly yet maybe useful audio completions.

    If you’ve got proof that a large Transformer can handle audio, image, and text in isolation, why not try doing so on all three simultaneously? Presumably this multi-modal learning will be easier if all the modalities go through a similar neural net architecture, and their research implies Transformers are good-enough job to be that architecture.

    It helps that OpenAI can leverage any intuition they already have about very large Transformers. Once you add in other data streams, there should definitely be enough data to train much larger unsupervised models. Sure, you could use just text, but you could also use all that web text and all the videos and all the audio. There shouldn’t be a trade-off, as long as you can scale large enough.

    Are large Transformers the last model architecture we’ll use? No, probably not, some of their current weaknesses seem hard to address. But I do see room for them to do more than they’ve done so far. Model architectures are only going to get better, so the capabilities of scaling up current models must be a lower bound on what could be possible 10 or 20 years from now, with scaled up versions of stronger model architectures. What’s possible right now is already interesting and slightly worrying.

    The Big Picture

    In “You and Your Research”, Richard Hamming has a famous piece of advice: “what are the important problems in your field, and why aren’t you working on them?” Surely AGI is one of the most important problems for machine learning.

    So, for machine learning, the natural version of this question is, “what problems need to be solved to get to artificial general intelligence?” What waypoints do you expect the field to hit on the road to get there, and how much uncertainty is there about the path between those waypoints?

    I feel like more of those waypoints are coming into focus. If you asked 2015-me how we’d build AGI, I’d tell you I have no earthly idea. I didn’t feel like we had meaningful in-roads on any of the challenges I’d associate with human-level intelligence. If you ask 2020-me how we’d build AGI, I still see a lot of gaps, but I have some idea how it could happen, assuming you get lucky. That’s been the biggest shift for me.

    There have always been disagreements over what large-scale statisical ML means for AI. The deep learning detractors can’t deny large statisical ML models have been very useful, but deep learning advocates can’t deny they’ve been very expensive. There’s a grand tradition of pointing out how much compute goes into state-of-the-art models. See this image that made the rounds on Twitter during the Lee Se-dol match:

    Compute comparison

    (By @samim)

    Arguments like this are good at driving discussion to places models fall short compared to humans, and poking at ways our existing models may be fundamentally flawed, but I feel these arguments are too human-centered. Our understanding of how humans learn is still incomplete, but we still took over the planet. Similarly, we don’t need to have fine-grained agreement on what “understanding” or “knowledge” means for AI systems to have far-reaching impacts on the world. We also don’t have to build AI systems that learn like humans do. If they’re capable of doing most human-level tasks, economics is going to do the rest, whether or not those systems are made in our own image.

    Trying Hard To Say No

    The AGI debate is always a bit of a mess, because people have wildly divergent beliefs over what matters. One useful exercise is to assume AGI is possible in the short term, determine what could be true in that hypothetical future, then evaluate whether it sounds reasonable.

    This is crucially very different from coming up with reasons why AGI can’t happen, because there are tons of arguments why it can’t happen. There are also tons of arguments why it can happen. This exercise is about putting more effort into the latter, and seeing how hard it is to say “no” to all of them. This helps you focus on the arguments that are actually important.

    Let me take a shot at it. If AGI is possible soon, how might that happen? Well, it would require not needing many more new ideas. It would likely be based on scaling existing models, because I don’t think there’s much time for the field to do a full-scale paradigm shift. And, it’s going to need lots of funding, because it needs to be based on scaling, and scaling needs funding.

    Perhaps someone develops an app or tool, using a model of GPT-3’s size or larger, that’s a huge productivity multiplier. Imagine the first computers, Lotus Notes, or Microsoft Excel taking over the business world. Remember, tools drive progress! If you code 2x faster, that’s probably 1.5x as much research output. Shift up or down depending on how often you’re bottlenecked by implementation.

    If that productivity boost is valuable enough to make the economics work out, and you can earn net profit once you account for inference and training costs, then you’re in business - literally. Big businesses pay for your tool. Paying customers drives more funding and investment, which pays for more hardware, which enables even larger training runs. In cloud computing, you buy excess hardware to anticipate spikes in consumer demand, then sell access to the extra hardware to earn money. In this scenario, you buy excess hardware to anticipate spikes in consumer inference needs, then give excess compute capacity to research to see what they come up with.

    This mechanism is already playing out. You might recognize the chip below.

    Picture of first TPU

    It’s a picture of the first TPU, and as explained in a Google blog post,

    Although Google considered building an Application-Specific Integrated Circuit (ASIC) for neural networks as early as 2006, the situation became urgent in 2013. That’s when we realized that the fast-growing computational demands of neural networks could require us to double the number of data centers we operate.

    Google needed to run more neural nets in production. This drove more hardware investment. A few years later, and we’re now on TPUv3, with rumors that Facebook is hiring hardware people to build custom silicon for AR technology. So the story for hardware demand seems not just plausible, but likely to be true. If you can scale to do something impractically, that sparks research and demand into making it practical.

    On top of this, let’s assume cross-modality learning turns out to be easier than expected at scale. Similar emergent properties as GPT-3 show up. Object tracking and intuitive physics turn out to be naturally occurring phenomena that are learnable just from images, without direct environment interaction or embodiment. With more tweaks, even larger models, and even more data, you end up with a rich feature space for images, text, and audio. It quickly becomes unthinkable to train anything from scratch. Why would you?

    Much of the prior work in several fields gets obsoleted, going the way of SIFT features for vision, parse trees for machine translation, and phoneme decoding steps for speech recognition. Deep learning has already killed these methods. People who don’t know any of those techniques are working on neural nets that achieve state-of-the-art results in all three domains. That’s faintly sad, because some of the obsolete ideas are really cool decompositions of how we understand language and speech, but it is what it is.

    As models grow larger, and continue to demonstrate improved performance, research coalesces around a small pool of methods that have been shown to scale with compute. Again, that happened and is still happening with deep learning. When lots of fields use the same set of techniques, you get more knowledge sharing, and that drives better research. CNNs have heavy priors towards considering nearby values. They were first useful for image recognition, but now have implications for genomics (Nature Genetics, 2019), as well as music generation (van den Oord et al, 2016). Transformers are a sequence model that were first used for language modeling. They were later applied to video understanding (Sun et al, 2019). This trend is likely to continue. Machine learning has hit a point where describing something as “deep learning” is practically meaningless, since multilayer perceptions have integrated with enough of the field that you’re no longer specifying anything. Maybe five years from now, we’ll have a new buzzword that takes deep learning’s place.

    If this model is good at language, speech, and visual data, what sensor inputs do humans have that this doesn’t? It’s just the sensors tied to physical embodiment, like taste and touch. Can we claim intelligence is bottlenecked on those stimuli? Sure, but I don’t think it is. You arguably only need text to pretend to be human.

    A lot has to go right in this scenario above. Multi-modal learning has to work. Behaviors need to continue to emerge out of scaling, because your researcher timer is mostly going into ideas that help you scale, rather than inductive priors. Hardware efficiency has to match pace, which includes clean energy generation and fixing your ever-increasing hardware fleet. Overall, the number of things that have to go right makes me think it’s unlikely, but still a possibility worth taking seriously.

    The most likely problem I see with my story is that unsupervised learning could be way harder for anything outside of language. Remember, in 2015, unsupervised learning gave us word vectors for language, and nothing great for images. One reasonable hypothesis is that the compositional properties of language make it well suited to unsupervised learning, in a way that isn’t true for other input modalities. If that’s true, I could be overestimating research by paying too much attention to the successes.

    It’s for those reasons that I’m only adjusting my estimates by a few years. I don’t think GPT-3, by itself, is a reason to radically adjust what I believe to be possible. I think transfer learning being harder than anticipated is also a damper on things. But on net, I’ve mostly seen reasons to speed up my estimates, rather than slow them down.

    Thanks to all the people who gave feedback on earlier drafts, including: Michael Andregg, James Bradbury, Ethan Caballero, Ajeya Cotra, William Fedus, Nolan Kent, David Krueger, Simon Ramstedt, and Alex Ray.

    Comments
  • Five Years Later

    Sorta Insightful turns five years old today! That feels kinda weird, because it’s the longest I’ve spent sustaining one thing. Elementary school was five years, middle school was three years, high school was four years. Undergrad was four years, and I’ve since been working at Google for another four years.

    Thanks for reading. If you haven’t read my previous anniversary posts, I do a meta-post every year about blogging. This year’s meta-post is, once again, a bit rushed, because I’ve been working on another post about AI timelines, which I’m also releasing today.

    Statistics

    Word Count

    Last year, I wrote 21,878 words. This year, I wrote 32,161 words.

     1,751 2019-08-18-four-years.markdown  
     3,215 2019-10-30-openai-rubiks.markdown  
       709 2019-11-18-alphastar-update.markdown  
     1,187 2019-12-25-neurips-2019.markdown  
     1,819 2020-01-17-berkeley-back-pay.markdown  
     1,461 2020-01-22-mh-2020.markdown  
     7,434 2020-02-27-mh-2020-part2.markdown  
     5,281 2020-03-16-puzzlehunt-tech.markdown  
       912 2020-03-22-spring-cleaning.markdown  
     2,875 2020-04-16-contact-tracing.markdown  
     1,379 2020-05-07-rl-potpourri.markdown  
     2,080 2020-05-17-corona-chernobyl.markdown  
     2,058 2020-06-24-openai-lp.markdown  
    32,161 total  

    I’m pleasantly surprised to see it’s so much larger. I suspect it’s because of the very long posts I wrote about puzzlehunts. Due to running a hunt this year, I had spent a lot of time thinking about puzzlehunt design, which made those posts relatively easy to write.

    Traditionally, I count posts written on August 18 for the next year’s count, so the AI timelines post is not included and I’ll have a good start for next year.

    Post length is a bad measure of output, for the same reason that lines of code changed is a bad software engineering metric. As the Mark Twain quote goes, “If I had more time, I would have written a shorter letter.” I do think it’s a reasonable measure though, as long as I stay aware of how much time I spent editing each post.

    (I just noticed I shared a Woodrow Wilson quote with the same sentiment last year. Incredible. Amazing. Next year I’ll find a new person to cite for this. I put odds on using either Blaise Pascal, or Benjamin Franklin.)

    I wrote 13 posts this year, counting the Mystery Hunt post which was split into 2 parts. This is just over my trend of 1 post per month. Again, pleasantly surprised by that. I can’t even credit the coronavirus for this. In general I have been much less productive during the pandemic, due to having my goof-off space and work space so close to one another. The Bay Area shelter in place order started March 17. It has been 5 months since that order, 5 posts were written in those five months and 8 posts were written in the seven preceding months. So it looks like my blog writing was less productive as well, but not by much.

    View Counts

    These are the view counts from August 18, 2019 to today, for the posts I’ve written this year.

      471 2019-08-18-four-years.markdown  
    1,639 2019-10-30-openai-rubiks.markdown  
      273 2019-11-18-alphastar-update.markdown  
      375 2019-12-25-neurips-2019.markdown  
      402 2020-01-17-berkeley-back-pay.markdown  
      738 2020-01-22-mh-2020.markdown  
      322 2020-02-27-mh-2020-part2.markdown  
      298 2020-03-16-puzzlehunt-tech.markdown  
      134 2020-03-22-spring-cleaning.markdown  
      207 2020-04-16-contact-tracing.markdown  
      481 2020-05-07-rl-potpourri.markdown  
      295 2020-05-17-corona-chernobyl.markdown  
      262 2020-06-24-openai-lp.markdown  

    This is a big drop in view count compared to last year, about 1/3rd the views from last year. Many of my posts were about more obscure topics, so it makes sense. I’m a bit bummed my contact tracing post got so few views though, I put a lot of work into it and felt it could have been one of my more important posts if it changed someone’s mind about contact tracing. However, the US response has been so dysfunctional that we don’t have the centralized contact tracing app I envisioned. We instead of a lot of independent efforts with low install rates, and country wide reopenings that are happening for economic reasons, even though the \(R_0\) is above 1. What a mess.

    Time Spent Writing

    Excluding time spent on this post, but including time spent on the AI timelines post, I spent 116 hours, 36 minutes writing for my blog this year. This is more than last year but less than 2 years ago. That feels about right.

    (Side note #2: On a reread of last year’s meta-post, I found that not only did I quote Woodrow Wilson / Mark Twain / whoever last year, I also made the exact same “quote attribution unclear” joke, with the exact same link! What the heck, am I really that predictable? I really should remove both this side note and the previous one, but you know what, I’m keeping it, the fact my mind is so repetitive is pretty interesting.)

    Posts in Limbo

    I still want to write a Gunnerkrigg Court post. But I want to write it in the sense that I like the idea of writing it. That isn’t the same as actually doing it.

    (Me, last year)

    I think it’s literally been four years since I talked about writing that Gunnerkrigg Court post. I’m no longer surprised I haven’t gotten around to it. Hopefully it will not be an ongoing meme.

    Just as a quick review of last year’s predicted post topics…

    Post about measurement: 20% odds of writing before end of 2019 (I didn’t), 95% odds of writing eventually.

    New odds of writing eventually: 65%. I’m starting to think that the ideas I wanted to express are already known to the people that care about it, which makes me less motivated to write this. I still believe it’s an important topic though!

    Post about Gunnerkrigg Court: 25% odds of writing before end of 2019 (I didn’t), 70% odds of writing eventually.

    New odds of writing eventually: 50%

    Post about My Little Pony: 50% odds of writing before end of 2019 (I didn’t), 85% odds of writing eventually.

    New odds of writing eventually: 90%. It’s coming I swear. I thought I would be done with My Little Pony by now, but every time I think it’s over, they pull me back in.

    Post about Dominion Online: 35% odds of writing before end of 2019 (I didn’t), 85% odds of writing eventually.

    New odds of writing eventually: 65%. The draft is untouched since last year, but I can see myself visiting it again.

    Based on me going 0 of 4 on my “write by end of 2019” predictions, you should probably downgrade all those numbers a bit. My bloging rate is fairly steady. My calibration on what I write about is awful.

    Anyways, what are you doing reading this meta-post? Go read the AI timelines post instead! Go tell me why I’m an idiot or why I’m right.

    Comments