Most current AIs are task-oriented, trained with neural networks for a specific purpose, such as creation. Or . With Gado, Took the opposite approach and created a single AI that could perform many different tasks.
According to the authors, ” You can run the same network adari console with the same weight, identify images content, chat, and stack volumes with real hands.Even more so “.AI uses the environment to determine what form its answers should be given. In total, it can perform 604 tasks with the same model, which is a real achievement.
AI beats experts, but not all the time
A transformer type used neural network, commonly used in language processing. Gado has trained in large numbers Contains images, text and the agent’s experience in the real world or in simulated contexts.
The problem is, AI failed to do these tasks properly all the time. For example, answers may be incorrect during a discussion. Gato noted that Marseille was the capital of France. For three-quarters of tasks (450 out of 604), AI indicates that it performs better half the time than an expert. So we have a success rate of over a third.
A common model develops with technological advancement
However, there is a good reason why Deep Mind operates in a general-purpose organization capable of performing such a variety of tasks. This exam follows the results of many experts in the field. , One of the founders of Reinforcement Learning said that general purpose computational methods are very efficient. According to him, most researchers are based on the notion that available computing power does not exist. Conversely, rather than increasing human knowledge in the field, a steady increase in available power will be a more important factor in the development of AI.
Someone’s commentary. My opinion: It’s about size now! Game over! These models are zoom, secure, computational, quick to sample, better memory, more modes, innovative data, on / offline,… 1 / N https://t.co/UJxSLZGc71
– Nando de Freitas ???? ️ ???? (NandoDF) May 14, 2022
Reply to an article on the site Next web Pessimistic about Gato’s ability to evolve into strong AI, DeepMind researcher Nando de Feitas clarified the company’s goals on Twitter. For him, the path to robust AI is now a question of scale. He emphasizes that there is no need to work anymore on the philosophy of codes that there will be no problem in creating and manipulating large networks. Henceforth, with more intelligent memory, larger, more efficient, faster models need to be developed, and research should go in this direction.
Compared to 170 billion, Gato is actually less complex than many specialized AIs with 1.2 billion parameters. . The explanation is that they wanted Kado to be able to command a robot arm in real time. With a more sophisticated system, they believe AI can do any task.