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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: DeepMind’s generalist AI, Gato: A non-technical explainer, published by frances lorenz on May 16, 2022 on The Effective Altruism Forum. Summary DeepMind’s recent paper, A Generalist Agent, catalyzed a wave of discourse regarding the speed at which current artificial intelligence systems are improving and the risks posed by these increasingly advanced systems. We aim to make this paper accessible to non-technical folks by: (i) providing a non-technical summary, and (ii) discussing the relevant implications related to existential risk and AI policy. Introduction DeepMind has just introduced its new agent, Gato: the most general machine learning (ML) model to date. If you’re familiar with arguments for the potential risks posed by advanced AI systems, you’ll know the term general carries strong implications. Today’s ML systems are advancing quickly; however, even the best systems we see are narrow in the tasks they can accomplish. For example, DALL-E impressively generates images that rival human creativity; however, it doesn’t do anything else. Similarly, large language models like GPT-3 perform well on certain text-based tasks, like sentence completion, but poorly on others, such as arithmetic (Figure 1). If future AI systems are to exhibit human-like intelligence, they’ll need to use various skills and information to complete diverse tasks across different contexts. In other words, they’ll need to exhibit general intelligence in the same way humans do—a type of system broadly referred to as artificial general intelligence (AGI). While AGI systems could lead to hugely positive innovations, they also have the potential to surpass human intelligence and become “superintelligent”. If a superintelligent system were unaligned, it could be difficult or even impossible to control for and predict its behavior, leaving humans vulnerable. Figure 1: An attempt to teach GPT-3 addition. The letter ‘Q’ denotes human input while ‘A’ denotes GPT-3’s response (from Peter Wildeford’s tweet) So what exactly has DeepMind created? Gato is a single neural network capable of performing hundreds of distinct tasks. According to DeepMind, it can, “play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens.” It’s not currently analogous to human-like intelligence; however, it does exhibit general capabilities. In the rest of this post, we’ll provide a non-technical summary of DeepMind’s paper and explore: (i) what this means for potential future existential risks posed by advanced AI and (ii) some relevant AI policy considerations. A Summary of Gato How was Gato built? The technique used to train Gato is slightly different from other famous AI agents. For example, AlphaGo, the AI system that defeated world champion Go player Lee Sedol in 2016, was trained largely using a sophisticated form of trial and error called reinforcement learning (RL). While the initial training process involved some demonstrations from expert Go players, the next iteration named AlphaGo Zero removed these entirely, mastering games solely by playing itself. By contrast, Gato was trained to imitate examples of “good” behavior in 604 distinct tasks. These tasks include: Simulated control tasks, where Gato has to control a virtual body in a simulated environment. Vision and language tasks, like labeling images with corresponding text captions. Robotics, specifically the common RL task of stacking blocks. Examples of good behavior were collected in a few different ways. For simulated control and robotics, examples were collected from other, more specialized AI agents trained using RL. For vision and language tasks, “behavior” took the form of text and images generated by humans, largely scraped from ...