Lifeboat Foundation

Traditional semiconductor chip design typically begins with a lengthy and often arduous process of specification definition, RTL model creation and documentation, before engineering teams can set out on designing actual circuits. However, what if this IP creation and team-based design review phase could be cut down to a few weeks, rather than months, with literally hundreds of hours of design team meetings saved in the process?

This is the goal of a new “ChipGPT” type tool, that Cadence Design Systems is bringing to market, which employs Large Language Models and generative AI to do much of the heavy lifting in this early semiconductor definition and design verification phase.

As a new… More.

Continue reading “Renesas Taps Cadence For ChipGPT-Like AI-Powered Semiconductor Design” »

Sometimes when you dig into the technology underneath your favorite devices and applications, you almost wish you hadn’t.

Still, it’s good to get an idea of what hackers are doing, how teams are responding, and what’s going on with the mobile devices that we all rely on more and more with each new year. Some of that has an intersection with AI/ML, in ways that might surprise you.

Check out Adam Chlipala’s talk on modern methods: applying this sort of data science to the practice of computer programming is going to be pretty heavy for anyone who isn’t a coder.

Morgan Stanley released a report Monday, predicting a semiconductor-driven hopeful outlook for Musk’s company.

Tesla’s shares were up 9.5 percent yesterday. But what drove them up?

The investment banking firm issued a research note that upgraded the Elon Musk-owned automotive company’s rating from ‘equalweight’ to ‘overweight’ with a price target of $400 from a prior price target of $250. An ‘overweight’ rating means that the analysts, in this case Morgan Stanley (MS), expects Tesla’s stock to outperform its industry in the market.

Continue reading “Tesla’s Dojo supercomputer could fuel a $500 billion jump” »

Musk, Zuckerberg, Altman, Gates, and Huang were in attendance.

US lawmakers met with the who’s who of the tech industry on Wednesday to discuss regulations for artificial intelligence and potentially work towards a law that protects US citizens from the dangers of the technology.

In attendance were Tesla CEO Elon Musk, Meta CEO Mark Zuckerberg, Alphabet CEO Sundar Pichai, NVIDIA CEO Jensen Huang, Microsoft CEO Satya Nadella, IBM CEO Arvind Krishna, former Microsoft CEO Bill Gates, and AFL-CIO labor federation President Liz Shuler, reported Reuters.

Summary: Researchers have unearthed how human brains inherently perform calculations akin to high-powered computers through Bayesian inference, enabling precise, swift environmental interpretation. This statistical method melds prior knowledge and new evidence, permitting us to quickly and accurately discern our surroundings.

This study showcases how our brain’s visual system structure is innately designed to execute Bayesian inference on the sensory data it gathers. Such revelations promise breakthroughs in areas spanning from AI’s machine learning to novel therapeutic strategies in clinical neurology.

Large language model (LLM) AI chatbots may be able to outperform the average human at a creative thinking task where the participant devises alternative uses for everyday objects (an example of divergent thinking), suggests a study published in Scientific Reports. However, the human participants with the highest scores still outperformed the best chatbot responses.

Divergent thinking is a type of thought process commonly associated with creativity that involves generating many different ideas or solutions for a given task. It is commonly assessed with the Alternate Uses Task (AUT), in which participants are asked to come up with as many alternative uses for an everyday object as possible within a short time period. The responses are scored for four different categories: fluency, flexibility, originality, and elaboration.

Mika Koivisto and Simone Grassini compared 256 human participants’ responses with those of three AI chatbots’ (ChatGPT3, ChatGPT4, and Copy. Ai) to AUTs for four objects—a rope, a box, a pencil, and a candle. The authors assessed the originality of the responses by rating them on semantic distance (how closely related the response was to the object’s original use) and creativity.

UCF

Mighty morphin’ hypersonic engine.

Quantum computing has the potential to revolutionize the world, allowing massive health and science computation problems to be solved exponentially faster than by classic computing. But quantum computers have a big drawback—they can only operate in subzero temperatures.

“In order to make quantum computers work, we cannot use them at room temperature,” said Ahmed El-Gendy, Ph.D., an associate professor of physics at The University of Texas at El Paso. “That means we will need to cool the computers and cool all the materials, which is very expensive.”

Now, physicists at The University of Texas at El Paso believe they have made a quantum leap in that regard. Led by El-Gendy, the team has developed a highly magnetic quantum computing material—100 times more magnetic than pure iron—that functions at regular temperature. The material is described in a summer issue of the journal Applied Physics Letters.

For a history-making two months, a pig’s kidney worked normally inside a brain-dead man. And while the dramatic experiment ended this week, it’s raising hope for eventually testing pig kidneys in living patients.