The Nobel Prize in Physics has been awarded to two scientists, Geoffrey Hinton and John Hopfield, for their work on machine learning.
British-Canadian Professor Hinton is sometimes referred to as the “Godfather of AI” and said he was flabbergasted.
He resigned from Google in 2023, and has warned about the dangers of machines that could outsmart humans.
Two of San Francisco’s leading players in artificial intelligence have challenged the public to come up with questions capable of testing the capabilities of large language models (LLMs) like Google Gemini and OpenAI’s o1. Scale AI, which specializes in preparing the vast tracts of data on which the LLMs are trained, teamed up with the Center for AI Safety (CAIS) to launch the initiative, Humanity’s Last Exam.
Featuring prizes of US$5,000 (£3,800) for those who come up with the top 50 questions selected for the test, Scale and CAIS say the goal is to test how close we are to achieving “expert-level AI systems” using the “largest, broadest coalition of experts in history.”
Why do this? The leading LLMs are already acing many established tests in intelligence, mathematics and law, but it’s hard to be sure how meaningful this is. In many cases, they may have pre-learned the answers due to the gargantuan quantities of data on which they are trained, including a significant percentage of everything on the internet.
STOCKHOLM — John Hopfield and Geoffrey Hinton were awarded the Nobel Prize in physics Tuesday for discoveries and inventions that formed the building blocks of machine learning.
“This year’s two Nobel Laureates in physics have used tools from physics to develop methods that are the foundation of today’s powerful machine learning,” the Nobel committee said in a press release.
Hopfield’s research is carried out at Princeton University and Hinton works at the University of Toronto.
Researchers from Tohoku University and the Massachusetts Institute of Technology (MIT) have unveiled a new AI tool for high-quality optical spectra with the same accuracy as quantum simulations, but working a million times faster, potentially accelerating the development of photovoltaic and quantum materials.
Organizations are losing between $94 — $186 billion annually to vulnerable or insecure APIs (Application Programming Interfaces) and automated abuse by bots. That’s according to The Economic Impact of API and Bot Attacks report from Imperva, a Thales company. The report highlights that these security threats account for up to 11.8% of global cyber events and losses, emphasizing the escalating risks they pose to businesses worldwide.
Drawing on a comprehensive study conducted by the Marsh McLennan Cyber Risk Intelligence Center, the report analyzes over 161,000 unique cybersecurity incidents. The findings demonstrate a concerning trend: the threats posed by vulnerable or insecure APIs and automated abuse by bots are increasingly interconnected and prevalent. Imperva warns that failing to address security risks associated with these threats could lead to substantial financial and reputational damage.
Researchers have developed an algorithm that could dramatically reduce the energy consumption of artificial intelligence systems.
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Scientists at BitEnergy AI created a method called “Linear-complexity multiplication” (L-Mul) that replaces complex floating-point multiplications in AI models with simpler integer additions.
