Intelligent beings learn to interact with the world. Artificial intelligence researchers have adopted a similar strategy to teach virtual agents new skills.
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In 2009, Fei-Fei Li, then a computer scientist at Princeton University, created a database that would change the history of artificial intelligence. A database known as ImageNet contains millions of labeled images that can train sophisticated machine learning models to recognize something in a photo. In 2015, machines surpass human cognitive abilities. Soon after, Li began searching for another “north star” that would give AI another push toward intelligence.
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Its inspiration goes back 530 million years to the Cambrian explosion, where many animal species appeared for the first time. One influential theory says that the explosion of new species was caused in part by the appearance of eyes that were able to see the world around them for the first time. According to Li, seeing in animals is not something that happens by chance, but “embedded in a full body that has to live, explore, survive, manipulate and change in a rapidly changing environment.” “So it was very natural for me to go to a more advanced view [of AI].”
Li’s work today focuses on AI agents that can move and interact with their environment in a three-dimensional virtual world simulation, rather than simply collecting static images of a data set.
This is a broad goal in the new field known as implementation AI, and Li is not alone in its acceptance. It overlaps with robots because robots can be the physical equivalent of AI agents implemented in the real world. Reinforcement learning has often taught communication agents to learn using long-term rewards as incentives. However, Li and others believe that implementation AI can transform a simple task such as image recognition from machine learning to learning to perform human-like tasks in several steps, such as making pancakes.
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“Obviously we got more excited and said, ‘Okay, how about building an intelligence agent?’ said Jitendra Malik, a computer scientist at UC Berkeley, CA.
Fei-Fei Li, who created the ImageNet dataset, created a set of virtual tasks to help evaluate the progress of these learning machines.
AI functions performed today include any agent capable of inspecting and modifying its own environment. In robotics, AI agents still live in robotic bodies, but today’s agents in real-world simulations can have virtual bodies or even see the world through moving camera views that can interact with their environment. . “The meaning of appearance is not the body itself, but the general need and ability to interact with the environment and perform tasks,” Li said.
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These conversations provide agents with a new (and often better) way to learn. It is the difference between seeing a possible relationship between two things and experience and abandoning the relationship. Armed with this new understanding and as your thoughts progress, greater understanding will follow. And as a series of new virtual worlds are running, the AI agents implemented are beginning to understand this ability and have made significant progress in the new environment.
“Currently there is no scientific evidence that we have not learned to interact with the world,” said Viviane Clay, an AI researcher associated with the University of Osnabrück in Germany.
Researchers have wanted to create virtual worlds for AI agents to explore, but it’s only been in the last five years or so that construction has begun. This power comes from the graphical advancements made by the film and video game industries. In 2017, AI agents are actually real, but they can be at home in the first virtual world to represent the reality of indoor spaces at home. Created by the Allen Institute’s computer science department for AI, a prototype called AI2-Thor allows agents to navigate natural kitchens, bathrooms, living rooms and bedrooms. Agents study three-dimensional views that change as they move, revealing new angles when they decide to look further.
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This new world also gives representatives the opportunity to reflect on a new dimension: change in time. “It’s a big difference,” says Manolis Savva, a computer graphics researcher at Simon Fraser University who has built many virtual worlds. “In a deployed AI system … you have this temporary flow of information and you have control. .”
These simulation opportunities are sufficient to train agents to perform completely new tasks. Instead of just knowing things, you can interact with them, move them and explore them. Although this may seem like a small step, it is an important step for any agent to understand their environment. And in 2020, virtual agents go beyond vision to hear what they do, providing another way to learn about things and how they work in the world.
Artificial AI agents that can operate in a virtual world, such as the ManipulaTHOR environment shown here, learn differently and may be better suited for more complex, human-like tasks.
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Just because the job is done doesn’t mean it’s done. “It’s much less realistic than the real world, even the best simulators,” said Daniel Yamins, a computer scientist at Stanford University. Yamins, together with colleagues from MIT and IBM, developed 3DWorld, which focuses on simulating real physics in a virtual world. For example, how water behaves, so that some objects can be soft in one area and soft in another.
A simple way to measure the progress of AI made so far is to compare the performance of an implementation agent with an algorithm built on a simple static image task. The researchers note that these comparisons are far from perfect, but the first results show that the implemented AI agents learn differently, and sometimes better, than their ancestors.
In a recent paper, researchers found that related AI agents were more accurate in detecting specific objects, an improvement of nearly 12% over traditional approaches. “It took the voice search community more than three years to achieve this level of progress,” said Roozbeh Mottaghi, a computer scientist and co-author at the Allen Institute for AI. I can,” he said.
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Other papers have shown that among the previously written algorithms, object detection can be implemented as an implementation, which allows a simultaneous virtual space or progress when moving to obtain multiple views of an object.
The researchers also found that the implemented algorithms and existing algorithms learn in a different way. For proof, consider neural networks, the key ingredient behind the learning power of all implementations and many non-intrusive algorithms. A neural network is a type of algorithm that consists of many layers of interconnected nodes of artificial neurons that seamlessly model the network of the human brain. In a paper led by Clay and two separate papers led by New York University professor Grace Lindsay, researchers found that a neural network of representation implemented in a few neurons is activated in response to sights. explanation. Optionally choose what to respond to. A refined network is less efficient and requires more neurons to operate most of the time. Lindsay’s team also compared implemented and unimplemented neural networks with neural activity in the living brain (visual cortex of rats), and found that the implemented version was the closest.
Lindsay is quick to point out that this doesn’t mean the implementation version is better. It’s just different. Unlike the voice search literature, Clay and Lindsay’s research compares the basic differences of the same neural network, making agents perform completely different tasks, so it may require neural networks that behave in a different way to achieve success. their goals.
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However, while comparing implemented networks to non-implemented networks is a measure of progress, researchers are not really interested in improving the performance of agents implemented in their current work. This line of work will continue separately using AI of custom learning. The real goal is to learn the most difficult and human tasks, and especially in the tasks of discovery, researchers are most eager to see impressive signs of progress. Here, the agent must plan to reach the destination without getting lost or bumping into things, keeping in mind the long-term goals of the destination.
Over the years, a team led by Dhruv Batra, head of research at Meta AI and a computer scientist at the Georgia Institute of Technology, has made rapid progress for a specific type of navigation task called spatial navigation. Here, the agent is transported to a completely new environment and must move to the destination coordinates relative to its initial location.
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