Machine learning researchers and engineers further
Feb 14, 2024 1:30:20 GMT -5
Post by sabbirislam258 on Feb 14, 2024 1:30:20 GMT -5
Looking at the body of research done on animal cognition can inspire AI researchers to consider problems from different angles. As deep reinforcement learning has become more powerful and sophisticated, AI researchers specializing in this field are exploring new ways to test the cognitive abilities of reinforcement learning agents. In the research paper, the research team cites a variety of experiments conducted with primates and birds, noting that their goal is to design systems capable of accomplishing similar tasks. According to the authors of the paper, AI as a kind of " intelligence ", they "advocate an approach in which RL agents, perhaps with as-yet-underdeveloped architectures, achieve what the rich virtual environment required through extended interaction with."
As reported by VentureBeat. AI researchers argue that intelligence is not Russia Telemarketing Data unique to humans and depends on an understanding of basic properties of the physical world, such as how an object occupies space and space, what constraints an object has. . motions, and definition of cause and effect. Animals show these traits in laboratory studies. For example, crows understand that objects are permanent objects, because they are able to retrieve seeds even when the seed is hidden from them, hidden by something else. To empower learning systems with these features, researchers argue, they will need to create tasks that, when paired with the right architecture, enable agents to transfer learned principles to other tasks. will The researchers argue that training such a model should include techniques that require the agent to gain understanding of a concept after only being exposed to a few examples, known as few-shot training.
This is in contrast to the traditional hundreds or thousands of trials that typically go into the trial-and-error training of an RL agent. The research team explains that although some advanced RL agents can learn to solve multiple tasks, some of which require basic transfer of learned rules, it is not clear that RL agents can understand a concept. "Common sense" can be learned as abstract. . If an agent is potentially capable of learning such a concept, they would need tests capable of determining whether an RL agent understands the concept of a container. DeepMind is particularly excited to engage in new and different ways of developing and testing reinforcement learning agents. Recently, at the Stanford HAI conference in early October, DeepMind's head of neuroscience research, Matthew Botwink, emphasized.
As reported by VentureBeat. AI researchers argue that intelligence is not Russia Telemarketing Data unique to humans and depends on an understanding of basic properties of the physical world, such as how an object occupies space and space, what constraints an object has. . motions, and definition of cause and effect. Animals show these traits in laboratory studies. For example, crows understand that objects are permanent objects, because they are able to retrieve seeds even when the seed is hidden from them, hidden by something else. To empower learning systems with these features, researchers argue, they will need to create tasks that, when paired with the right architecture, enable agents to transfer learned principles to other tasks. will The researchers argue that training such a model should include techniques that require the agent to gain understanding of a concept after only being exposed to a few examples, known as few-shot training.
This is in contrast to the traditional hundreds or thousands of trials that typically go into the trial-and-error training of an RL agent. The research team explains that although some advanced RL agents can learn to solve multiple tasks, some of which require basic transfer of learned rules, it is not clear that RL agents can understand a concept. "Common sense" can be learned as abstract. . If an agent is potentially capable of learning such a concept, they would need tests capable of determining whether an RL agent understands the concept of a container. DeepMind is particularly excited to engage in new and different ways of developing and testing reinforcement learning agents. Recently, at the Stanford HAI conference in early October, DeepMind's head of neuroscience research, Matthew Botwink, emphasized.