MIT researchers developed an efficient approach for training more reliable reinforcement learning models, focusing on complex tasks that involve variability. This could enable the leverage of reinforcement learning across a wide range of applications.
We’ll break down the key points of the patents and make them as understandable as possible. This new patent is likely how Tesla will implement FSD on non-Tesla vehicles, Optimus, and other devices.
Decision Making
Imagine a neural network as a decision-making machine. But building one also requires making a series of decisions about its structure and data processing methods. Think of it like choosing the right ingredients and cooking techniques for a complex recipe. These choices, called “decision points,” play a crucial role in how well the neural network performs on a given hardware platform.
The company has agreed to train its flagship generative AI models primarily on Amazon Web Services (AWS), Amazon’s cloud computing division.
The biggest battleground in the robotaxi race may be winning public trust.
Autonomous vehicles are already clocking up millions of miles on public roads, but they face an uphill battle to convince people to climb in to enjoy the ride.
Continue reading “How robotaxis are trying to win passengers’ trust” »
Developers from Amazon, Meta, and more discuss how AI adoption is changing the industry and which skills developers will need to stay competitive.
OpenScholar, an innovative AI system by Allen Institute for AI and University of Washington, revolutionizes scientific research by processing 45 million papers instantly, offering researchers citation-backed answers and challenging proprietary AI systems.
An MRI scan shows a Chiari type-1 malformation, in which the cerebellum extends beyond the gap in the skull where it connects to the spinal cord.
Artificial intelligence identified 3 subtypes of Chiari type-1 malformations, could improve medical decision making.
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How can scientific discoveries based on large volumes of experimental data be accelerated by artificial intelligence (AI)? This can be achieved in heterogeneous catalysis, according to a recent study led by Prof. Weixue Li from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences, published in Science.
The researchers developed a comprehensive theory of metal-support interaction (MSI), a key aspect of catalysis, by combining interpretable AI with domain knowledge, experimental data, and first-principles simulations.
Supported metal catalysts are widely used in industrial chemical production, petrochemical refining, and environmental control systems like exhaust catalysts. MSI influences interfacial activities, such as charge transfer, chemical composition, perimeter sites, particle shape, and suboxide encapsulation, in addition to stabilizing dispersed catalysts. As a result, modifying MSI is one of the few ways to enhance catalyst performance.
