Top AI institutions today

The ICLR conference (which is the top deep learning—specific conference (NIPS is larger, but more diffuse)) released their decisions for accepted/​rejected papers, and someone made some nice plots that show where the current deep learning /​ AI research happens at. It’s an imperfect measure because not every company might prioritize paper publications, but it’s indicative.

Here’s a plot that shows the total number of papers (broken down by oral/​poster/​workshop/​rejected) from any institution:

Long story short, Google is dominating with 83 paper submissions. The academic institutions (Berkeley /​ Stanford /​ CMU /​ MIT) are next, in 20-30 ranges each.

Just thought it was an interesting snapshot of where all the action is today. The full data is here: http://​​webia.lip6.fr/​​~pajot/​​dataviz.html

-Andrej

OpenAI is on a path of certain failure relative to Google. There obviously needs to be immediate and dramatic action or everyone except for Google will be consigned to irrelevance.

I have considered the ICO approach and will not support it. In my opinion, that would simply result in a massive loss of credibility for OpenAI and everyone associated with the ICO. If something seems too good to be true, it is. This was, in my opinion, an unwise diversion.

The only paths I can think of are a major expansion of OpenAI and a major expansion of Tesla AI. Perhaps both simultaneously. The former would require a major increase in funds donated and highly credible people joining our board. The current board situation is very weak.

I will set up a time for us to talk tomorrow. To be clear, I have a lot of respect for your abilities and accomplishments, but I am not happy with how things have been managed. That is why I have had trouble engaging with OpenAI in recent months. Either we fix things and my engagement increases a lot or we don’t and I will drop to near zero and publicly reduce my association. I will not be in a situation where the perception of my influence and time doesn’t match the reality.

fyi

What do you think makes sense? Happy to talk by phone if that’s better.

Hi Elon,

Thank you for the thoughtful note. I have always been impressed by your focus on the big picture, and agree completely we must change trajectory to achieve our goals. Let’s speak tomorrow, any time 4p or later will work.

My view is that the best future will come from a major expansion of OpenAI. Our goal and mission are fundamentally correct, and that will increasingly be a superpower as AGI grows near.

Fundraising

Our fundraising conversations show that:

• Ilya and I are able to convince reputable people that AGI can really happen in the next ≤10 years

• There’s appetite for donations from those people

• There’s very large appetite for investments from those people

I respect your decision on the ICO idea, which matches the evolution of our own thinking. Sam Altman has been working on a fundraising structure that does not rely on a public offering, and we will be curious to hear your feedback.

Of the people we’ve been talking to, the following people are currently my top suggestions for board members. Would also love suggestions for your top picks not on this list, and we can figure out how to approach them.

• [caviardé]

• [caviardé]

• [caviardé]

• [caviardé]

• [caviardé]

• [caviardé] (she heads Partnership on AI, originally created by Demis to steal OpenAI’s thunder – would bring a lot of outside credibility)

The next 3 years

Over the next 3 years, we must build 3 things:

• Custom AI hardware (such as [caviardé] computer)

• Massive AI data center (likely multiple revs thereof)

• Best software team, mixing between algorithm development, public demonstrations, and safety

We’ve talked the most about the custom AI hardware and AI data center. On the software front, we have a credible path (self-play in a competitive multiagent environment) which has been validated by Dota and AlphaGo. We also have identified a small but finite number of limitations in today’s deep learning which are barriers to learning from human levels of experience. And we believe we uniquely are on trajectory to solving safety (at least in broad strokes) in the next three years.

We would like to scale headcount in this way:

• Beginning of 2017: ~40

• End of 2018: 100

• End of 2019: 300

• End of 2020: 900

█████████████ █████████████████ ██████

Moral high ground

Our biggest tool is the moral high ground. To retain this, we must:

• Try our best to remain a non-profit. AI is going to shake up the fabric of society, and our fiduciary duty should be to humanity.

• Put increasing effort into the safety/​control problem, rather than the fig leaf you’ve noted in other institutions. It doesn’t matter who wins if everyone dies. Related to this, we need to communicate a “better red than dead” outlook — we’re trying to build safe AGI, and we’re not willing to destroy the world in a down-to-the-wire race to do so.

• Engage with government to provide trusted, unbiased policy advice — we often hear that they mistrust recommendations from companies such as ████████████.

• Be perceived as a place that provides public good to the research community, and keeps the other actors honest and open via leading by example.

The past 2 years

I would be curious to hear how you rate our execution over the past two years, relative to resources. In my view:

• Over the past five years, there have two major demonstrations of working systems: AlphaZero [DeepMind] and Dota 1v1 [OpenAI]. (There are a larger number of breakthroughs of “capabilities” popular among practitioners, the top of which I’d say are: ProgressiveGAN [NVIDIA], unsupervised translation [Facebook], WaveNet [DeepMind], Atari/​DQN [DeepMind], machine translation [Ilya at Google — now at OpenAI], generative adversarial network [Ian Goodfellow at grad school — now at Google], variational autoencoder [Durk at grad school — now at OpenAI], AlexNet [Ilya at grad school — now at OpenAI].) We benchmark well on this axis.

• We grew very rapidly in 2016, and in 2017 iterated to a working management structure. We’re now ready to scale massively, given the resources. We lose people on comp currently, but pretty much only on comp. I’ve been resuming the style of recruiting I did in the early days, and believe I can exceed those results.

• We have the most talent dense team in the field, and we have the reputation for it as well.

• We don’t encourage paper writing, and so paper acceptance isn’t a measure we optimize. For the ICLR chart Andrej sent, I’d expect our (accepted papers)/​(people submitting papers) to be the highest in the field.

- gdb

Working at the cutting edge of AI is unfortunately expensive. For example, DeepMind’s operating expenses in 2016 were at around $250M USD (does not include compute). With their growing team today it might be ~0.5B/​yr. But then Alphabet in 2016 reported ~20B net income so it’s still fairly cheap even if DeepMind had no revenue of its own. In addition to DeepMind, Google also has Google Brain, Research, and Cloud. And TensorFlow, TPUs, and they own about a third of all research (in fact, they hold their own AI conferences).

I also strongly suspect that compute horsepower will be necessary (and possibly even sufficient) to reach AGI. If historical trends are any indication, progress in AI is primarily driven by systems—compute, data, infrastructure. The core algorithms we use today have remained largely unchanged from the ~90s. Not only that, but any algorithmic advances published in a paper somewhere can be almost immediately re-implemented and incorporated. Conversely, algorithmic advances alone are inert without the scale to also make them scary.

It seems to me that OpenAI today is burning cash and that the funding model cannot reach the scale to seriously compete with Google (an 800B company). If you can’t seriously compete but continue to do research in open, you might in fact be making things worse and helping them out “for free”, because any advances are fairly easy for them to copy and immediately incorporate, at scale.

A for-profit pivot might create a more sustainable revenue stream over time and would, with the current team, likely bring in a lot of investment. However, building out a product from scratch would steal focus from AI research, it would take a long time and it’s unclear if a company could “catch up” to Google scale, and the investors might exert too much pressure in the wrong directions.

The most promising option I can think of, as I mentioned earlier, would be for OpenAI to attach to Tesla as its cash cow. I believe attachments to other large suspects (e.g. Apple? Amazon?) would fail due to an incompatible company DNA. Using a rocket analogy, Tesla already built the “first stage” of the rocket with the whole supply chain of Model 3 and its onboard computer and a persistent internet connection. The “second stage” would be a full self driving solution based on large-scale neural network training, which OpenAI expertise could significantly help accelerate. With a functioning full self-driving solution in ~2-3 years we could sell a lot of cars/​trucks. If we do this really well, the transportation industry is large enough that we could increase Tesla’s market cap to high O(~100K), and use that revenue to fund the AI work at the appropriate scale.

I cannot see anything else that has the potential to reach sustainable Google-scale capital within a decade.

-Andrej

[Forwarded previous message from Andrej]

Andrej is exactly right. We may wish it otherwise, but, in my and Andrej’s opinion, Tesla is the only path that could even hope to hold a candle to Google. Even then, the probability of being a counterweight to Google is small. It just isn’t zero.