Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI – Page 239

Irrespective of their personal, professional and social circumstances, different individuals can experience varying levels of life satisfaction, fulfillment and happiness. This general measure of life satisfaction, broadly referred to as “well-being,” has been the key focus of numerous psychological studies.

Better understanding the many factors contributing to well-being could help to devise personalized and targeted interventions aimed at improving people’s levels of fulfillment. While many past studies have tried to delineate these factors, few have done so leveraging the advanced machine learning models available today.

Machine learning models are designed to analyze large amounts of data, unveiling hidden patterns and making . Using these tools to analyze data collected in previous studies in neuroscience and psychology could help to shed light on the environmental and influencing well-being.

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Summary: A new study using artificial intelligence has provided novel insights into how the brain predicts future events and processes information. Researchers discovered that the brain’s spontaneous activity, even without external stimuli, plays a critical role in how we think and feel.

By analyzing local field potentials (LFPs), they uncovered how the brain remains active in anticipating possible scenarios, even in a resting state. These findings could lead to better diagnostic tools and treatments for neurological diseases.

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Related: Future moon astronauts may 3D-print their supplies using lunar minerals

“With the printing of the first metal 3D shape in space, ESA Exploration teams have achieved a significant milestone in establishing in-orbit manufacturing capabilities. This accomplishment, made possible by an international and multidisciplinary team, paves the way for long-distance and long-duration missions where creating spare parts, construction components, and tools on demand will be essential,” said Daniel Neuenschwander, director of Human and Robotic Exploration at ESA, in a statement.

This groundbreaking technology continues to expand its applications on Earth, revolutionizing fields such as medicine, fashion, art, construction, food production and manufacturing. In space, as long-duration missions to the moon and potentially Mars take shape, astronauts will need a means of independently repairing or creating tools or parts for machinery or structures that would be difficult to carry onboard a spacecraft, which have limited capacity.

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A team of researchers from Texas A&M University, Sandia National Lab — Livermore, and Stanford University are taking lessons from the brain to design materials for more efficient computing. The new class of materials discovered is the first of their kind – mimicking the behavior of an axon by spontaneously propagating an electrical signal as it travels along a transmission line. These findings could be critical to the future of computing and artificial intelligence.

This study was published in Nature (“Axon-like active signal transmission”).

Any electrical signal propagating in a metallic conductor loses amplitude due to the metal’s natural resistance. Modern computer processing (CPU) and graphic processing units can contain around 30 miles of fine copper wires moving electrical signals around within the chip. These losses quickly add up, requiring amplifiers to maintain the pulse integrity. These design constraints impact the performance of current interconnect-dense chips.

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AI may equal human intelligence without matching the true nature of our experiences.

By Christof Koch

A future where the thinking capabilities of computers approach our own is quickly coming into view. We feel ever more powerful machine-learning (ML) algorithms breathing down our necks. Rapid progress in coming decades will bring about machines with human-level intelligence capable of speech and reasoning, with a myriad of contributions to economics, politics and, inevitably, warcraft. The birth of true artificial intelligence will profoundly affect humankind’s future, including whether it has one.

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12K likes, — kanekallaway on September 12, 2024: “OpenAI just launched something massive. The first model of its a kind…” o1” designed for deep reasoning. General AI reasoning has always been the white whale of the space. Whoever figured out how to build advanced models that could reason through multi-step problems on their own, would lay the rails for the path to AGI. It’s still way too early to say if this model will do it, but based on the demos and early feedback, there is something super advanced here. o1 is different from all previous versions of GPT because it thinks before it answers, like a human would. Then, the model lays out its complex logic path to get to an answer.

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We present the first open-source benchmark to evaluate LLMs in their ability to operate as agents in simulated clinical environments. Diagnosing and managing a patient is a complex, sequential decision making process that requires physicians to obtain information—such as which tests to perform—and to act upon it. Recent advances in artificial intelligence (AI) and large language models (LLMs) promise to profoundly impact clinical care. However, current evaluation schemes overrely on static medical question-answering benchmarks, falling short on interactive decision-making that is required in real-life clinical work. Here, we present AgentClinic: a multimodal benchmark to evaluate LLMs in their ability to operate as agents in simulated clinical environments. In our benchmark, the doctor agent must uncover the patient’s diagnosis through dialogue and active data collection. We present two open benchmarks: a multimodal image and dialogue environment, AgentClinic-NEJM, and a dialogue-only environment, AgentClinic-MedQA. Agents in AgentClinic-MedQA are grounded in cases from the US Medical Licensing Exam~(USMLE) and AgentClinic-NEJM are grounded in multimodal New England Journal of Medicine (NEJM) case challenges. We embed cognitive and implicit biases both in patient and doctor agents to emulate realistic interactions between biased agents. We find that introducing bias leads to large reductions in diagnostic accuracy of the doctor agents, as well as reduced compliance, confidence, and follow-up consultation willingness in patient agents. Evaluating a suite of state-of-the-art LLMs, we find that several models that excel in benchmarks like MedQA are performing poorly in AgentClinic-MedQA. We find that the LLM used in the patient agent is an important factor for performance in the AgentClinic benchmark. We show that both having limited interactions as well as too many interaction reduces diagnostic accuracy in doctor agents.

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