Lifeboat Foundation: Safeguarding Humanity
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Blog – Page 2474

The only AI Hardware startup to realize revenue exceeding $100M has finished the first phase of Condor Galaxy 1 AI Supercomputer with partner G42 of the UAE. Other Cerebras customers are sharing their CS-2 results at Supercomputing ‘23, building momentum for the inventor of wafer-scale computing. This company is on a tear.

Four short months ago, Cerebras announced the most significant deal any AI startup has been able to assemble with partner G42 (Group42), an artificial intelligence and cloud computing company. The eventual 256 CS-2 wafer-scale nodes with 36 Exaflops of AI performance will be one of the world’s largest AI supercomputers, if not the largest.

Cerebras has now finished the first data center implementation and started on the second. These two companies are moving fast to capitalize on the $70B (2028) gold rush to stand up Large Language Model services to researchers and enterprises, especially while the supply of NVIDIA H100 remains difficult to obtain, creating an opportunity for Cerebras. In addition, Cerebras has recently announced it has released the largest Arabic Language Model, the Jais30B with Core42 using the CS-2, a platform designed to make the development of massive AI models accessible by eliminating the need to decompose and distribute the problem.

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A massive burst of gamma rays produced by the explosion of a star almost two billion light-years away was so powerful that it changed Earth’s atmosphere, according to scientists.

The brightest gamma-ray burst ever seen and detected impacted Earth’s atmosphere. It came from a supernova and may reveal why Earth has had mass extinctions in its past.

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The AI tool can indicate the risk of heart attacks, as well as information on narrowings of the arteries and other clinical risk factors.

Stevanovicigor/ iStock.

In a development that could help the people at risk, an artificial intelligence technology has been created that can gaze into the future and predict the 10-year risk of deadly heart attacks.

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The “Science for Diplomacy” initiative is another way the Hong Kong Laureate Forum is advancing social impact.

Baba Tamim/Interesting Engineering.

HKLF brings the world’s greatest scientists and the next generation of scientific leaders together to share ideas, cooperate, and inspire social impact; read a press release handed out to Interesting Engineering (IE) on Monday, November 13, 2023.

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Colon cancer remains a leading cause of cancer-related deaths, and there has been a rise in the incidence of early-onset colon cancer or colon cancer diagnosed before the age of 50 years old. Early-onset colon cancer has several differences in clinical presentation, as well as histopathology, genetic alteration, and molecular profiling. Early-onset colon cancer can be differentiated into familial type that includes hereditary familial syndrome and sporadic type. Demographic variance also exists in both developing and developed countries. Due to the rising incidence of colon cancer diagnosed in younger age, it is imperative to examine the available evidence regarding the mortality rate of early-onset colon cancer. Colon cancer is affected by numerous modifiable and non-modifiable risk factors.

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Throughout history, sonar’s distinctive “ping” has been used to map oceans, spot enemy submarines and find sunken ships. Today, a variation of that technology – in miniature form, developed by Cornell researchers – is proving a game-changer in wearable body-sensing technology.

PoseSonic is the latest sonar-equipped wearable from Cornell’s Smart Computer Interfaces for Future Interactions (SciFi) lab. It consists of off-the-shelf eyeglasses outfitted with micro sonar that can track the wearer’s upper body movements in 3D through a combination of inaudible soundwaves and artificial intelligence (AI).

With further development, PoseSonic could enhance augmented reality and virtual reality, and track detailed physical and behavioral data for personal health, the researchers said.

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An experimental computing system physically modeled after the biological brain has “learned” to identify handwritten numbers with an overall accuracy of 93.4%. The key innovation in the experiment was a new training algorithm that gave the system continuous information about its success at the task in real time while it learned. The study was published in Nature Communications.

The algorithm outperformed a conventional machine-learning approach in which training was performed after a batch of data had been processed, producing 91.4% accuracy. The researchers also showed that memory of past inputs stored in the system itself enhanced learning. In contrast, other computing approaches store memory within software or hardware separate from a device’s processor.

For 15 years, researchers at the California NanoSystems Institute at UCLA, or CNSI, have been developing a new platform technology for computation. The technology is a brain-inspired system composed of a tangled-up network of wires containing silver, laid on a bed of electrodes. The system receives input and produces output via pulses of electricity. The individual wires are so small that their diameter is measured on the nanoscale, in billionths of a meter.

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