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Category: robotics/AI – Page 9

What It’s Like Using a Brain Implant With ChatGPT

The potential of chat gpt and neural link is limitless. Really chat gpt with agi would automate even an entire world and even do all work by itself basically taking the forever mental labor of work forever scenario away from humans so we can sit and drink tea or other leisure activities. Then if we miniaturize even chat gpt, neural link, and agi all in one whether it is in the neural link or even on a smartphone it could allow for near infinite money 💵 with little to no effort which takes away mental labor forever because we could solve anything or do all jobs with no need for even training it would be like an everything calculator for an eternity of work so no humans need suffer the dole of forever mental labor which can evolve earths civilization into complete abundance.

We spoke to two people pioneering ChatGPT’s integration with Synchron’s brain-computer-interface to learn what it’s like to use and where this technology is headed.

Read more on CNET: How This Brain Implant Is Using ChatGPT https://bit.ly/3y5lFkD

0:00 Intro.
0:25 Meet Trial Participant Mark.
0:48 What Synchron’s BCI is for.
1:25 What it’s like to use.
1:51 Why work with ChatGPT?
3:05 How Synchron’s BCI works.
3:46 Synchron’s next steps.
4:27 Final Thoughts.

Never miss a deal again! See CNET’s browser extension 👉 https://bit.ly/3lO7sOU

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Spin as an input parameter: Machine learning predicts magnetic properties of materials

Magnetic materials are in high demand. They’re essential to the energy storage innovations on which electrification depends and to the robotics systems powering automation. They’re also inside more familiar products, from consumer electronics to magnetic resonance imaging (MRI) machines.

Current sources and supply chains won’t be able to keep up as demand continues to grow. We need to design new , and quickly.

A collaboration between Carnegie Mellon University, Lawrence Berkeley National Laboratory, and the Fritz-Haber-Institut der Max-Planck-Gesellschaft is broadening capabilities to screen potential new materials with machine learning models.

Hybrid model reveals people act less rationally in complex games, more predictably in simple ones

Throughout their everyday lives, humans are typically required to make a wide range of decisions, which can impact their well-being, health, social connections, and finances. Understanding the human decision-making processes is a key objective of many behavioral science studies, as this could in turn help to devise interventions aimed at encouraging people to make better choices.

Researchers at Princeton University, Boston University and other institutes used machine learning to predict the strategic decisions of humans in various games. Their paper, published in Nature Human Behavior, shows that a trained on human decisions could predict the strategic choices of players with high levels of accuracy.

“Our main motivation is to use modern computational tools to uncover the cognitive mechanisms that drive how people behave in strategic situations,” Jian-Qiao Zhu, first author of the paper, told Phys.org.

Can ChatGPT actually ‘see’ red? New study results are nuanced

ChatGPT works by analyzing vast amounts of text, identifying patterns and synthesizing them to generate responses to users’ prompts. Color metaphors like “feeling blue” and “seeing red” are commonplace throughout the English language, and therefore comprise part of the dataset on which ChatGPT is trained.

But while ChatGPT has “read” billions of words about what it might mean to feel blue or see red, it has never actually seen a blue sky or a red apple in the ways that humans have. This begs the questions: Do embodied experiences—the capacity of the human visual system to perceive color—allow people to understand colorful language beyond the textual ways ChatGPT does? Or is language alone, for both AI and humans, sufficient to understand color metaphors?

New results from a study published in Cognitive Science led by Professor Lisa Aziz-Zadeh and a team of university and industry researchers offer some insights into those questions, and raise even more.

Scientists unlock key manufacturing challenge for next-generation optical chips

Researchers at the University of Strathclyde have developed a new method for assembling ultra-small, light-controlling devices, paving the way for scalable manufacturing of advanced optical systems used in quantum technologies, telecommunications and sensing.

The study, published in Nature Communications, centers on photonic crystal cavities (PhCCs), micron-scale structures that trap and manipulate light with extraordinary precision. These are essential components for high-performance technologies ranging from quantum computing to photonic artificial intelligence.

Until now, the creation of large arrays of PhCCs has been severely limited by the tiny variations introduced during fabrication. Even nanometer-scale imperfections can drastically shift each device’s optical properties, making it impossible to build arrays of identical units directly on-chip.

Microrobots shaped and steered by metal patches could aid drug delivery and pollution cleanup

Researchers at the University of Colorado Boulder have created a new way to build and control tiny particles that can move and work like microscopic robots, offering a powerful tool with applications in biomedical and environmental research.

The study, published in Nature Communications, describes a new method of fabrication that combines high-precision 3D printing, called two-photon lithography, with a microstenciling technique. The team prints both the particle and its stencil together, then deposits a thin layer of metal—such as gold, platinum or cobalt—through the stencil’s openings. When the stencil is removed, a metal patch remains on the particle.

The particles, invisible to the naked eye, can be made in almost any shape and patterned with surface patches as small as 0.2 microns—more than 500 times thinner than a human hair. The metal patches guide how the particles move when exposed to electric or magnetic fields, or chemical gradients.

Pretrained jet foundation model successfully utilized for tau reconstruction

Simulating data in particle physics is expensive and not perfectly accurate. To get around this, researchers are now exploring the use of foundation models—large AI models trained in a general, task-agnostic way on large amounts of data.

Just like how language models can be pretrained on the full dataset of internet text before being fine-tuned for specific tasks, these models can learn from large datasets of particle jets, even without labels.

After the pretraining, they can be fine-tuned to solve specific problems using much less data than traditional approaches.

AI reveals astrocytes play a ‘starring’ role in dynamic brain function

Long overlooked and underestimated, glial cells—non-neuronal cells that support, protect and communicate with neurons—are finally stepping into the neuroscience spotlight. A new Florida Atlantic University study highlights the surprising influence of a particular glial cell, revealing that it plays a much more active and dynamic role in brain function than previously thought.

Using sophisticated computational modeling and , researchers discovered how astrocytes, a “star” shaped glial cell, subtly—but significantly—modulate communication between neurons, especially during highly coordinated, synchronous brain activity.

“Clearly, are significantly implicated in several brain functions, making identifying their presence among neurons an appealing and important problem,” said Rodrigo Pena, Ph.D., senior author, an assistant professor of biological sciences within FAU’s Charles E. Schmidt College of Science on the John D. MacArthur Campus in Jupiter, and a member of the FAU Stiles-Nicholson Brain Institute.