Does the breakthrough to general AI need more data and computing power above all else? Yann LeCun, Chief AI Scientist at Metaon the recent debate about scaling sparked by Deepmind’s Gato.

The recent successes of large AI models such as OpenAI’s DALL-E 2, Google’s PaLM and Deepmind’s Flamingo have sparked a debate about their significance for progress towards general AI. Deepmind’s Gato has recently given a particular boost to the debate, which has been conducted publicly, especially on Twitter.

Gato is a Transformer model trained with numerous data modalities, including images, text, proprioception or joint moments. All training data is processed by Gato in a token sequence similar to those of large language models. Thanks to the versatile training, Gato can text, describe images, play video games or control robotic arms. Deepmind tested the AI model with over 600 benchmarks.

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DeepMind has released what it calls a “generalist” AI called Gato, which can play Atari games, accurately caption images, chat naturally with a human and stack coloured blocks with a robot arm, among 600 other tasks. But is Gato truly intelligent – having artificial general intelligence – or is it just an AI model with a few extra tricks up its sleeve?

What is artificial general intelligence (AGI)?

Outside science fiction, AI is limited to niche tasks. It has seen plenty of success recently in solving a huge range of problems, from writing software to protein folding and even creating beer recipes, but individual AI models have limited, specific abilities. A model trained for one task is of little use for another.