DeepMind’s new AI system can perform over 600 tasks
The ultimate achievement to some in the AI industry is creating a system with artificial general intelligence (AGI), or the ability to understand and learn any task that a human can. Long relegated to the domain of science fiction, it’s been suggested that AGI would bring about systems with the ability to reason, plan, learn, represent knowledge, and communicate in natural language.
Not every expert is convinced that AGI is a realistic goal — or even possible. But it could be argued that DeepMind, the Alphabet-backed research lab, took a toward it this week with the release of an AI system called Gato,
Gato is what DeepMind describes as a “general-purpose” system, a system that can be taught to perform many different types of tasks. Researchers at DeepMind trained Gato to complete 604, to be exact, including captioning images, engaging in dialogue, stacking blocks with a real robot arm, and playing Atari games.
Jack Hessel, a research scientist at the Allen Institute for AI, points out that a single AI system that can solve many tasks isn’t new. For example, Google recently began using a system in Google Search called multitask unified model, or MUM, which can handle text, images, and videos to perform tasks from finding interlingual variations in the spelling of a word to relating a search query to an image. But what is potentially newer, here, Hessel says, is the diversity of the tasks that are tackled and the training method.
“We’ve seen evidence previously that single models can handle surprisingly diverse sets of inputs,” Hessel told TechCrunch via email. “In my view, the core question when it comes to multitask learning … is whether the tasks complement each other or not. You could envision a more boring case if the model implicitly separates the tasks before solving them, e.g., ‘If I detect task A as an input, I will use subnetwork A. If I instead detect task B, I will use different subnetwork B.’ For that null hypothesis, similar performance could be attained by training A and B separately, which is underwhelming. In contrast, if training A and B jointly leads to improvements for either (or both!), then things are more exciting.”
Like all AI systems, Gato learned by example, ingesting billions of words, images from real-world and simulated environments, button presses, joint torques, and more in the form of tokens. These tokens served to represent data in a way Gato could understand, enabling the system to — for example — tease out the mechanics of Breakout, or which combination of words in a sentence might make grammatical sense.
Gato doesn’t necessarily do these tasks well. For example, when chatting with a person, the system often responds with a superficial or factually incorrect reply (e.g., “Marseille” in response to “What is the capital of France?”). In captioning pictures, Gato misgenders people. And the system correctly stacks blocks using a real-world robot only 60% of the time.
