Lifeboat Foundation

In context: The U.S. is eager to establish domestic foundry capabilities to produce AI chips, and Intel is working hard to position itself to be the provider. It was only natural, then, for CEO Pat Gelsinger to ask U.S. Commerce Department Secretary Gina Raimondo to put in a good word on Intel’s behalf with America’s chip designers including the likes of Apple, Nvidia, Amazon, and Google.

Ever since Intel announced plans to open up its manufacturing capabilities beyond in-house CPUs, the company has targeted TSMC as its top competitor in the global foundry market. Nonetheless, it’s been a source of frustration that fellow U.S. companies rely so heavily on TSMC for most of their semiconductor manufacturing needs.

Intel CEO Pat Gelsinger has turned to US Commerce Department Secretary Gina Raimondo for help, and according to sources that spoke to CNBC, she is trying to deliver.

Outperforms dermatologists in detecting melanoma, offering better diagnosis for challenging cases and improving patient care. 🩺🖥️

Heinlein, Maron, Hekler et al. evaluate an AI algorithm for detecting melanoma and compare its performance to that of dermatologist on a prospectively collected, external, heterogeneous dataset. The AI exhibits a significant performance advantage, especially in diagnosing challenging cases.

We break down everything we know about 1X Technologies’ new humanoid robot designed for homes, the Neo Beta.

Read the CNET Article:
Humanoid Robots Head Home: Meet NEO Beta https://cnet.us/8o7

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Honesty is the best policy… most of the time. Social norms help humans understand when we need to tell the truth and when we shouldn’t, to spare someone’s feelings or avoid harm. But how do these norms apply to robots, which are increasingly working with humans? To understand whether humans can accept robots telling lies, scientists asked almost 500 participants to rate and justify different types of robot deception.

“I wanted to explore an understudied facet of robot ethics, to contribute to our understanding of mistrust towards emerging technologies and their developers,” said Andres Rosero, Ph.D. candidate at George Mason University and lead author of the study in Frontiers in Robotics and AI. “With the advent of generative AI, I felt it was important to begin examining possible cases in which anthropomorphic design and behavior sets could be utilized to manipulate users.”

The widespread use of artificial intelligence (AI) tools designed to process large amounts of data has increased the need for better performing memory devices. The data storage solutions that could help to meet the computational demands of AI include so-called high-bandwidth memories, technologies that can increase the memory bandwidth of computer processors, speeding up the transfer of data and reducing power consumption.

Currently, flash memories are the most prominent memory solutions capable of storing information when a device is turned off (i.e., non-volatile memories). Despite their widespread use, the speed of most existing flash memories is limited and does not best support the operation of AI.

In recent years, some engineers have thus been trying to develop ultrafast flash memories that could transfer data faster and more efficiently. Two-dimensional (2D) materials have shown promise for fabricating these better performing memory devices.

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 accurate predictions. Using these tools to analyze data collected in previous studies in neuroscience and psychology could help to shed light on the environmental and genetic factors influencing well-being.

Shut up and take my money.

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.

Marfan syndrome is a genetic disorder that leads to a greater risk of aneurysms developing in a patient’s aorta. Early detection is key to survival. Researchers at Yale School of Medicine studied the use of artificial intelligence in the diagnostic process. The results could eventually lead to an easier and more accessible test.