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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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From Classical Breeding to Modern Biotechnological Advancement in Horticultural Crops — Trait Improvement and Stress Resilience, volume II

This study puts forward a machine vision-based prediction method to solve the problem regarding the measurement of traits in shiitake mushroom caps during th…

Neanderthal DNA could be the cause of some modern brain malformations

If you regularly experience headaches, dizziness, balance problems and blurred vision, our Neanderthal cousins could be to blame.

These are common symptoms of Chiari malformations, structural defects in which the lower part of the brain extends into the spinal cord. People with this condition have skulls shaped like those of our ancient relatives, leading to a hypothesis (known as the Archaic Homo Introgression Hypothesis) that it may be a genetic legacy from interbreeding between and Neanderthals.

To investigate this, Kimberly Plomp of the University of the Philippines Diliman and colleagues zeroed in on Chiari 1, the mildest form of the condition, which affects around 1 in 100 people.

Measuring individual radioactive decays enables faster detection method for nuclear applications

Researchers at the National Institute of Standards and Technology (NIST) have demonstrated a new and faster method for detecting and measuring the radioactivity of minuscule amounts of radioactive material. The innovative technique, known as cryogenic decay energy spectrometry (DES), could have far-reaching impacts, from improving cancer treatments to ensuring the safety of nuclear waste cleanup.

The NIST team has published its results in Metrologia.

The key to this novel technique is a transition-edge sensor (TES), a high-tech device widely used to measure radiation signatures. TES provides a revolutionary capability to record individual radioactive decay events, in which an unstable atom releases one or more particles. By building up data from many individual decays, researchers can then identify which unstable atoms, known as radionuclides, produce the events.

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.

Physicists observe image rotation in plasma

Light sometimes appears to be “dragged” by the motion of the medium through which it is traveling. This phenomenon, referred to as “light dragging,” is typically imperceptible when light is traveling in most widely available materials, as the movement is significantly slower than the speed of light. So far, it has thus proved difficult to observe in experimental settings.

Researchers at the University of Toulouse, University of California-Los Angeles (UCLA), University of Paris-Saclay and Princeton University recently observed a specific type of dragging known as image rotation in a plasma-based system.

Their observation, outlined in a paper published in Physical Review Letters, was made using magnetohydrodynamic (MHD) that propagate in a magnetized plasma, known as Alfvén waves.

Targeting MXenes for sustainable ammonia production

In a hunt for more sustainable technologies, researchers are looking further into enabling two-dimensional materials in renewable energy that could lead to sustainable production of chemicals such as ammonia, which is used in fertilizer.

This next generation of low-dimensional materials, called MXenes, catalyzes the production of air into ammonia for foods and transportation for high-efficiency energy fertilizers.

MXenes has a wide range of possibilities that allow for highly flexible chemical compositions, offering significant control over their properties.

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.