Lifeboat Foundation

Scientists at the National Institutes of Health (NIH) have uncovered a brain circuit in primates that rapidly detects faces. The findings help not only explain how primates sense and recognize faces, but could also have implications for understanding conditions such as autism, where face detection and recognition are often impaired from early childhood.

The newly discovered circuit first engages an evolutionarily ancient part of the brain called the superior colliculus, which can then trigger the eyes and head to turn for a better look. This better view enables different brain areas in the temporal cortex to engage in more complex facial recognition. The study was published in the journal Neuron.

“Quick recognition of faces is a key skill in humans and other primates,” said Richard Krauzlis, Ph.D., of NIH’s National Eye Institute (NEI) and senior author of the study.

Researchers have explained how the regularly structured topographic maps in the visual cortex of the brain could arise spontaneously to efficiently process visual information. This research provides a new framework for understanding functional architectures in the visual cortex during early developmental stages.

A KAIST research team led by Professor Se-Bum Paik from the Department of Bio and Brain Engineering has demonstrated that the orthogonal organization of retinal mosaics in the periphery is mirrored onto the primary visual cortex and initiates the clustered topography of higher visual areas in the brain.

This new finding provides advanced insights into the mechanisms underlying a biological strategy of brain circuitry for the efficient tiling of sensory modules. The study was published in Cell Reports on January 5.

Generative AI could saddle the planet with heaps more hazardous waste.

By Saima S. Iqbal

Every time generative artificial intelligence drafts an e-mail or conjures up an image, the planet pays for it. Making two images can consume as much energy as charging a smartphone; a single exchange with ChatGPT can heat up a server so much that it requires a bottle’s worth of water to cool. At scale, these costs soar. By 2027, the global AI sector could annually consume as much electricity as the Netherlands, according to one recent estimate. And a new study in Nature Computational Science identifies another concern: AI’s outsize contribution to the world’s mounting heap of electronic waste. The study found that generative AI applications alone could add 1.2 million to five million metric tons of this hazardous trash to the planet by 2030, depending on how quickly the industry grows.

AI-human collaboration could possibly achieve superhuman greatness in mathematics.

By Conor Purcell

Mathematicians explore ideas by proposing conjectures and proving them with theorems. For centuries, they built these proofs line by careful line, and most math researchers still work like that today. But artificial intelligence is poised to fundamentally change this process. AI assistants nicknamed “co-pilots” are beginning to help mathematicians develop proofs—with a real possibility this will one day let humans answer some problems that are currently beyond our mind’s reach.

NYU Langone performs the world’s first fully robotic double lung transplant, marking a breakthrough in minimally invasive surgical care.

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.

But it’s “urgent” to get to the next level, Jensen Huang tells The Economist.

The company has agreed to train its flagship generative AI models primarily on Amazon Web Services (AWS), Amazon’s cloud computing division.