Although this is still an emerging area of research, a new study has announced a leap. Researchers from the Center for Neuromorphic Engineering at the Korea Institute of Science and Technology (KIST) have implemented an integrated hardware system consisting of artificial neurons and synaptic devices using hexagonal boron nitride (hBN) material.
They aimed to construct building blocks of neuron-synapse-neuron structures that can be stacked to develop large-scale artificial neural networks.
“Artificial neural network hardware systems can be used to efficiently process vast amounts of data generated in real-life applications such as smart cities, healthcare, next-generation communications, weather forecasting, and autonomous vehicles,” said KIST’s Dr. Joon Young Kwak, one of the study’s authors, in a press release.
Autonomous and AI-enabled systems increasingly rely on optical and radio frequency sensors and significant computer power. They face growing vulnerabilities from directed-energy laser and microwave weapons.
In May the U.S. secretary of the Air Force flew in an F-16 that engaged in a mock dogfight over the California desert while controlled by artificial intelligence. Carmakers from San Francisco to Boston are jousting to deliver driverless cars. In Norway a crewless cargo ship carries fertilizer from port to port. On the land, sea and in the air, we face the coming of such autonomous platforms—some envisioned to benefit humanity, and others meant for destruction—available to everyone, to governments, businesses and criminals.
Large language models (LLMs), artificial neural networks-based architectures that can process, generate and manipulate texts in various human languages, have recently become increasingly widespread. These models are now being used in a wide range of settings, to rapidly find answers to queries, produce content for specific purposes and interpret complex texts.
I found this on NewsBreak:#Publichealth #Computerscience #AI
AI holds the potential to help doctors find early markers of disease and policymakers to avoid decisions that lead to war. But a growing body of evidence has revealed deep flaws in how machine learning is used in science, a problem that has swept through dozens of fields and implicated thousands of erroneous papers.
Go to https://hensonshaving.com/isaacarthur and enter “isaacarthur” at checkout to get 100 free blades with your purchase. We think very highly of the human brain, after all, it’s what lets us think about anything in the first place, but Nature is vast, and our primate brains are not the end-all and be-all of neural engineering. Join this channel to get access to perks: / @isaacarthursfia Visit our Website: http://www.isaacarthur.net Join Nebula: https://go.nebula.tv/isaacarthur Support us on Patreon:
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/ discord Credits: Alternative Intelligence: The Other A.I. Episode 448; May 23, 2024 Produced & Narrated by: Isaac Arthur Written by: Erik Eldritch & Isaac Arthur Editor: Darius Said Music Courtesy of Epidemic Sound http://epidemicsound.com/creator Stellardrone, “Red Giant”, “Ultra Deep Field”, “Cosmic Sunrise” Sergey Cheremisinov, “Labyrinth”, “Forgotten Stars” Taras Harkavyi, “Alpha and…” Miguel Johnson, “So Many Stars”
Scientists put their heads together for an insane medical breakthrough.
Neuroscience and biomedical engineering startup BrainBridge announced that it has created an AI-mechanized system for performing head transplants.
The procedure would graft a head onto the body of a brain-dead donor, maintaining the memories, cognitive abilities and consciousness of the transplanted individual.
I found this on NewsBreak: #Design
Leveraging advanced computational techniques in physical sciences has become vital for accelerating scientific discovery. This involves integrating large language models (LLMs) and simulations to enhance hypothesis generation, experimental design, and data analysis. Automating these processes aims to streamline and democratize access to cutting-edge research tools, pushing the boundaries of scientific knowledge and improving efficiency across various scientific domains.
Researchers face a significant challenge in effectively simulating observational feedback and integrating it with theoretical models in physical sciences. Traditional methods often need a universal approach that can be applied across various scientific fields, leading to inefficiencies and limiting the potential for innovative discoveries. The need for a more comprehensive and adaptable framework is evident to address this issue and advance scientific inquiry.
All sensations—hunger, feeling pain, seeing red, falling in love—are the result of physiological states that an LLM simply doesn’t have. Consequently we know that an LLM cannot have subjective experiences of those states. In other words, it cannot be sentient.
An LLM is a mathematical model coded on silicon chips. It is not an embodied being like humans. It does not have a “life” that needs to eat, drink, reproduce, experience emotion, get sick, and eventually die.
It is important to understand the profound difference between how humans generate sequences of words and how an LLM generates those same sequences. When I say “I am hungry,” I am reporting on my sensed physiological states. When an LLM generates the sequence “I am hungry,” it is simply generating the most probable completion of the sequence of words in its current prompt. It is doing exactly the same thing as when, with a different prompt, it generates “I am not hungry,” or with yet another prompt, “The moon is made of green cheese.” None of these are reports of its (nonexistent) physiological states. They are simply probabilistic completions.