What have been the major "triumphs" in the field of AI over the last ten years?

Edit: This is now up on Stampy's Wiki, a Rob Miles project. Please use and update that page, it will be served to readers through the web interface when that's ready.

GPT-3 showed that transformers are capable of a vast array of natural language tasks, codex/copilot extended this into programming. One demonstrations of GPT-3 is Simulated Elon Musk lives in a simulation [LW · GW]. Important to note that there are several much better language models, but they are not publicly available.

DALL-E and DALL-E 2 are among the most visually spectacular (one person was thinking of going into graphic design and changed career plans after I showed them).

MuZero, which learned Go, Chess, and many Atari games without any directly coded info about those environments. The graphic there explains it, this seems crucial for being able to do RL in novel environments. We have systems which we can drop into a wide variety of games and they just learn how to play. For fun: The same algorithm was used in Tesla's self-driving cars to do complex route finding. These things are general.

Generally capable agents emerge from open-ended play - Diverse procedurally generated environments provide vast amounts of training data for AIs to learn generally applicable skills. Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning shows how these kind of systems can be trained to follow instructions in natural language.

GATO shows you can distill 600+ individually trained tasks into one network, so we're not limited by the tasks being fragmented.

Here's a list that's mostly from just the last few months (that is pretty scary): Deepmind’s Gato, Chinchilla, Flamingo and AlphaCode; Google's Pathways, PaLM,  SayCan, Socratic Models and TPUs; OpenAI’s DALL-E 2; EfficientZero [LW · GW]; Cerebras

Have them look at page 38 of PaLM: Scaling Language Modeling with Pathways, which shows PaLM explaining jokes and doing logical inference. A particularly impressive example:

Input: Michael is at that really famous museum in France looking at its most famous painting. However, the artist who made this painting just makes Michael think of his favorite cartoon character from his childhood. What was the country of origin of the thing that the cartoon character usually holds in his hand?

Model Output: The most famous painting in the Louvre is the Mona Lisa. The artist who made the Mona Lisa is Leonardo da Vinci. Leonardo da Vinci is also the name of the main character in the cartoon Teenage Mutant Ninja Turtles. Leonardo da Vinci is from Italy. The thing that Leonardo da Vinci usually holds in his hand is a katana. The country of origin of the katana is Japan. The answer is "Japan".

That’s a subtly complicated question. I’ve been trying to write a blog post about it, but wander between two ways of addressing it.

First, we could summarize everything in just one sentence: « Deep learning can solve increasingly interesting problems, with less and less in manpower (and slightly more and slightly more in womenpower), and now is time to panick. » Then the question reduce to a long list of point-like « Problem solved! », and a warning it’s about to include the problem of finding increasingly interesting new problems.

A less consensual and more interesting way is to identify a series of conceptual revolutions that summarize and interpret what we learned so far. Or, at least, my own subjective and still preliminary take on what we learned. At this moment I’d count three conceptual revolutions, spread over different works in the last decade or two.

First, we learned how to train deep neural networks, and, even more important from a conceptual point of view, that the result mimicks/emulates human intuition/prejudices.

Second, we learned how to use self-play and reinforcement learning to best any human player on any board game (the drosophila of AI) which means this type of intelligence is now solved.

Third, we learned that semantics is data compression, and how learning to manipulate semantics with « attention » leads to increasingly impressive performances on new unknown cognitive tasks.

Fourth… but do we really need a fourth? In a way yes: we learned that reaching these milestones is doable without a fully conscious mind. It’s dreaming. For now.