Yan Lechun of Meta Publishes Autonomous AI Paper

Besides leading AI efforts at Meta, Yan Lechun is also a professor at New York University and has spent his storied career developing learning systems that many modern AI applications rely on today. In trying to give these machines better insight into how the world operates, he could arguably be hailed as the father of the next generation of AI. In 2013, he went on to found the Facebook AI Research (FAIR) group, Meta’s first foray in experimenting with AI research, before stepping down to become the company’s chief AI scientist a few years later.

In the nearly 70 years since AI was first introduced to the public, machine learning has exploded in popularity, and has since grown to reach dizzying heights. Yet despite how quickly we’ve come to rely on the power of computing, one question has haunted the field for almost as long as its inception: Could these superintelligent systems one day gain enough sentience to match, or even surpass humanity?

Instead, one of the biggest barriers to a robot overlord situation is the simple fact that compared to animals and humans, current AI and machine learning systems are lacking in reason.

Despite some dubious recent claims—for example, the ex-Google engineer who claimed a chatbot had gained sentience before being fired—we’re pretty far off from that reality. Instead, one of the biggest barriers to a robot overlord situation is the simple fact that compared to animals and humans, current AI and machine learning systems are lacking in reason, a concept essential to the development of “autonomous” machine intelligence systems—that is, AI that can learn on the fly, directly from observations of the real world, rather than lengthy training sessions to perform a specific task.

Now new research published earlier this month in Open Review.net by LeCun, proposes a way to fix this issue by training learning algorithms to learn more efficiently, as AI has proven that it isn’t very good at predicting and planning for changes in the real world. On the flip side, humans and our animal counterparts are able to gain enormous amounts of knowledge about how the world works through observation and with remarkably little physical interaction.

While the concept of common sense can pretty much be boiled down to having practical judgment, LeCun describes it in the paper as a collection of models that can help a living being infer the difference between what’s likely, what’s possible, and what’s impossible. Such a skill allows a person to explore their environment, fill in missing information, as well as imagine new solutions to unknown problems.

One of the most complex parts of the proposed architecture, the “world model module” would work to estimate the state of the world, as well as predict imagined actions and other world sequences, much like a simulator.

A Large Language Model trained on scientific papers.
Type a text and https://t.co/XKTkxs8Ae0 will generate a paper with relevant references, formulas, and everything.

Amazing work by @MetaAI / @paperswithcode https://t.co/IWGNAXiFeU

— Yann LeCun (@ylecun) November 15, 2022

Much like how varying sections of the brain are responsible for different functions of the body, LeCun suggests a model for spawning autonomous intelligence that would be composed of five separate, yet configurable modules. One of the most complex parts of the proposed architecture, the “world model module” would work to estimate the state of the world, as well as predict imagined actions and other world sequences, much like a simulator. But by using this single world model engine, knowledge about how the world operates can be easily shared across different tasks. In some ways, it might resemble memory.

Source: Meta’s AI Chief Publishes Paper on Creating ‘Autonomous’ Artificial Intelligence