Microsoft to invest $10 bn for Japan AI data centers

Microsoft said Friday it will invest $10 billion in Japan over the next four years to build artificial intelligence data centers and related infrastructure.Microsoft said Friday it will invest $10 billion in Japan over the next four years to build artificial intelligence data centers and related infrastructure.Machine learning & AI[#item_full_content]

Involving people without AI expertise in the development and evaluation of artificial intelligence applications could help create better, fairer, and more trustworthy automated decision-making systems, new research suggests. After enlisting members of the public to evaluate the potential impacts of two real-world applications, researchers from UK universities will present a paper at a major international computing conference which suggests how “participatory AI auditing” could improve AI decision-making in the future.Involving people without AI expertise in the development and evaluation of artificial intelligence applications could help create better, fairer, and more trustworthy automated decision-making systems, new research suggests. After enlisting members of the public to evaluate the potential impacts of two real-world applications, researchers from UK universities will present a paper at a major international computing conference which suggests how “participatory AI auditing” could improve AI decision-making in the future.Machine learning & AI[#item_full_content]

Anthropic CEO Dario Amodei has said that AI could surpass “almost all humans at almost everything” shortly after 2027. While AI’s capabilities are certainly improving, such rapid progress might seem at odds with findings that show AI is still failing at 95%+ of remote freelance projects, and continues to struggle with hallucination, long term planning, and forms of abstract reasoning that humans find easy. But recent work from METR has found evidence that LLMs can gain capabilities in rapid surges—jumping from succeeding almost never to almost always in just a few years. If this is true across the economy, it could mean that workers could be blindsided by AI advances.Anthropic CEO Dario Amodei has said that AI could surpass “almost all humans at almost everything” shortly after 2027. While AI’s capabilities are certainly improving, such rapid progress might seem at odds with findings that show AI is still failing at 95%+ of remote freelance projects, and continues to struggle with hallucination, long term planning, and forms of abstract reasoning that humans find easy. But recent work from METR has found evidence that LLMs can gain capabilities in rapid surges—jumping from succeeding almost never to almost always in just a few years. If this is true across the economy, it could mean that workers could be blindsided by AI advances.Business[#item_full_content]

A team from the Universitat Politècnica de València, part of the Valencian University Research Institute for Artificial Intelligence (VRAIN) and ValgrAI, has participated in the development of ADeLe, a new methodology that offers precise explanations and predictions regarding whether large language models (LLMs) will succeed or fail at specific new tasks they have not yet performed. Furthermore, this methodology identifies exactly the limits of any given model’s reasoning capacity.A team from the Universitat Politècnica de València, part of the Valencian University Research Institute for Artificial Intelligence (VRAIN) and ValgrAI, has participated in the development of ADeLe, a new methodology that offers precise explanations and predictions regarding whether large language models (LLMs) will succeed or fail at specific new tasks they have not yet performed. Furthermore, this methodology identifies exactly the limits of any given model’s reasoning capacity.Machine learning & AI[#item_full_content]

Artificial intelligence is increasingly being used to help optimize decision-making in high-stakes settings. For instance, an autonomous system can identify a power distribution strategy that minimizes costs while keeping voltages stable.Artificial intelligence is increasingly being used to help optimize decision-making in high-stakes settings. For instance, an autonomous system can identify a power distribution strategy that minimizes costs while keeping voltages stable.Computer Sciences[#item_full_content]

Fair decisions, clear reasons: Creating fuzzy AI with fairness built in from the start

Although AI is not intentionally biased, it can inherit biases from the data fed into it, learning and repeating them until the system becomes inherently unfair. This is complicated by the problem of identifying where the AI system introduced the bias, as most AI systems display their final decision without showing the steps that made it. Unfair patterns may go unnoticed simply because they are hard to identify.Although AI is not intentionally biased, it can inherit biases from the data fed into it, learning and repeating them until the system becomes inherently unfair. This is complicated by the problem of identifying where the AI system introduced the bias, as most AI systems display their final decision without showing the steps that made it. Unfair patterns may go unnoticed simply because they are hard to identify.Computer Sciences[#item_full_content]

‘Moltbook’ risks: The dangers of AI-to-AI interactions in health care

A new report examines the emerging risks of autonomous AI systems interacting within clinical environments. The article, “Emerging Risks of AI-to-AI Interactions in Health Care: Lessons From Moltbook,” appears in the Journal of Medical Internet Research. The work explores a critical new frontier: as high-risk AI agents begin to communicate directly with one another to manage triage and scheduling, they create a “digital ecosystem” that can operate beyond active human oversight.A new report examines the emerging risks of autonomous AI systems interacting within clinical environments. The article, “Emerging Risks of AI-to-AI Interactions in Health Care: Lessons From Moltbook,” appears in the Journal of Medical Internet Research. The work explores a critical new frontier: as high-risk AI agents begin to communicate directly with one another to manage triage and scheduling, they create a “digital ecosystem” that can operate beyond active human oversight.Machine learning & AI[#item_full_content]

New app designed to improve conference experience

A new app developed by Yun Huang, associate professor in the School of Information Sciences at the University of Illinois Urbana-Champaign, aims to make navigating conferences less work and more fun, so that attendees can meet others, discover fresh ideas, and “experience academic life as an exciting adventure.”A new app developed by Yun Huang, associate professor in the School of Information Sciences at the University of Illinois Urbana-Champaign, aims to make navigating conferences less work and more fun, so that attendees can meet others, discover fresh ideas, and “experience academic life as an exciting adventure.”Consumer & Gadgets[#item_full_content]

Brain-inspired chip could make some AI tasks up to 2,000 times more energy efficient

A new type of computer chip that uses the physics of materials to process information could make some artificial intelligence (AI) systems far more energy efficient, researchers have found. Loughborough University physicists have developed a device that can process data that changes over time directly in hardware, rather than relying on software running on conventional computers.A new type of computer chip that uses the physics of materials to process information could make some artificial intelligence (AI) systems far more energy efficient, researchers have found. Loughborough University physicists have developed a device that can process data that changes over time directly in hardware, rather than relying on software running on conventional computers.Hardware[#item_full_content]

From smartphone facial recognition to autonomous vehicles, artificial intelligence (AI) has long been protected as a black box. However, a joint research team from KAIST and international institutions has uncovered a new security threat capable of peeking at AI blueprints from behind walls. The team also presented corresponding defense technologies. This discovery is expected to be utilized in strengthening AI security across various sectors, including autonomous driving, health care, and finance.From smartphone facial recognition to autonomous vehicles, artificial intelligence (AI) has long been protected as a black box. However, a joint research team from KAIST and international institutions has uncovered a new security threat capable of peeking at AI blueprints from behind walls. The team also presented corresponding defense technologies. This discovery is expected to be utilized in strengthening AI security across various sectors, including autonomous driving, health care, and finance.Security[#item_full_content]

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