A Deep Learning Alternative Can Help AI Agents Gameplay the Real World

A new machine The learning approach is able to adopt a number of simple video games with a number of simple video games, which are seen as a model of the human brain and a number of simple video games in the world.

The new system called AXIOM offers an alternative to artificial neural networks, which are dominant in modern AI. AxIom, developed by a program company AYSE AI, is equipped with predictive knowledge of those who interact with each other in the world of the game. It then uses an algorithm to model how the game will be in order to respond to a process based on a process based on how the game observes.

The approach is inspired by a theory of free energy principle, mathematics, physics and information, as well as biology, as well as the theory trying to explain intelligence using biology. Free energy principle “Bilishcel Computing” was developed by the famous neurocientist Karl Friston, the Main Scientist.

Friston told me that video from his home in London could be especially important to create an AI agents of approach. “They must support the type of cognition we see in real brains,” he said. “It’s not just the ability to learn products, it really requires an idea to learn how you moved around the world.”

The conventional approach to learn to play the game is known as deep reinforcement learning or recognized by tweaking and tweaking in response to positive or negative feedback. The approach can prepare algorithms playing superhuman games, but requires a lot of experience for work. AxIom, a fewer example and smaller computing power, simplified versions of popular video games, masters, bounce, hunt and jumping.

“The common goals of the approach are infected with what is seen as the most important problems focusing on achieving the main features of the approach. Chollet is also investigating Roman approaches to the learning of the bench and use the former examples to test the models.

“The work is hitting me like a very original,” he says. “We need more people to test new ideas away from the beaten path of large language models and thinking language models.”

The modern EU relies on artificial neural networks that work nearly inspiring, but radically inspired by the wires of the brain. In the last ten years and a slightly deep learning, an approach using the nervous networks, the transcription of computers, including the transcription speech, was able to recognize and create pictures. The latest, of course, has caused great language models that are deep learning, strong and increasingly capable of conversation.

Akiom promises a more efficient approach to building the AI ​​from scratch. It can be especially effective for the creation of agents that need to learn more effectively, the Director General Gabe René says tokens. René says that a financial company began to experience the company’s technology as a market modeling. “It is a new architecture for AI agents, which can learn in real time and is more accurate, more efficient and smaller, says René says. “They are as if it’s as a digital brain.”

In a slight mock, this axioma, an alternative to modern AI and deep learning, was affected by the work of the English Canadian computer scientist first Geoffrey HintonAwarding both TURING AWARD The Nobel Prize is awarded to work on his deep learning. Hinton was a colleague of Fristo in London University College for many years.

Recommended for more information about Friston and free energy principle This 2018 Wired feature article. Fristo’s work also affected Sensitive New TheoryDescribed in a book reviewed in 2021.