State Of AI, December 2022

This week NeurIPS 2022 happens, one of the most important events of the year where the latest in AI research is presented. The topics covered by the keynotes are a great sample of the key preoccupations of the AI research community today: From whether large language models are sentient to the forward-looking alternative to backpropagation.

Sentient or not, MineDojo got Nvidia’s researchers the NeurIPS 2022 Outstanding Datasets and Benchmarks Paper Award, demonstrating their belief that large language models in the future will be “embodied agents that proactively take actions, endlessly explore the world, and continuously self-improve.” The current version of this AI agent learned Minecraft’s flexible gameplay using a massive online database of more than 7,000 wiki pages, millions of Reddit threads and 300,000 hours of recorded gameplay. (Here’s a Twitter thread summarizing all 15 Outstanding papers, written by Nvidia’s Jim Fan).

More than 59 zettabytes (59 trillion gigabytes) of data will be created, captured, copied, and consumed in the world this year, says IDC. The explosion of online, easily-available data leads to a constant stream of new applications of large language models.

Meta’s AI agent Cicero integrates a language model with planning and reinforcement learning algorithms by inferring players’ beliefs and intentions from its conversations and generating dialogue in pursuit of its plans. Across 40 games of an anonymous online Diplomacy league, Cicero achieved more than double the average score of the human players and ranked in the top 10% of participants who played more than one game.

Meta’s Yann LeCun proudly announced galactica.ai, “A Large Language Model trained on scientific papers. Type a text and galactica.ai will generate a paper with relevant references, formulas, and everything. Amazing work by @MetaAI.” To which Gary Marcus replied: “Pitch perfect and utterly bogus imitations of science and math, presented as the real thing. Is this really what AI has come to, automatically mixing reality with bullshit so finely we can no longer recognize the difference?” After three days of similar criticisms, Meta took down the public demo.

Indeed, there’s more work to be done, says Andrew Ng: “Some engineers (including the Galactica’s team) have proposed that LLMs could be an alternative to search engines. For example, instead of using search to find out the distance to the Moon, why not pose the question as a prompt to a language model and let it answer? Unfortunately, the maximum-likelihood objective is not well aligned with the goal of providing factually accurate information. To make LLMs better at conveying facts, research remains to be done on alternative training objectives or, more likely, model architectures that optimize for factual accuracy rather than likelihood.”[…]

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