AI a job killer? In California it’s complicated Los Angeles Times

Google has developed a new quantum chip called Willow, which significantly reduces errors as it scales up, a major breakthrough in quantum error correction. Willow also performed a computation in under five minutes that would take a supercomputer 10 septillion years, demonstrating its potential for solving complex problems beyond the reach of classical computers. This achievement marks a significant step towards building commercially relevant quantum computers that can revolutionize fields like medicine, energy, and AI. My colleagues sometimes ask me why I left the burgeoning field of AI to focus on quantum computing. My answer is that both will prove to be the most transformational technologies of our time, but advanced AI will significantly benefit from access to quantum computing. Quantum algorithms have fundamental scaling laws on their side, as we’re seeing with RCS.

The deep neural network models that power today’s most demanding machine-learning applications have grown so large and complex that they are pushing the limits of traditional electronic computing hardware. This new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time. In a study published Sept. 13 in the journal Diamond and Related Materials, scientists found a way to reduce the heat needed to grow diamonds enough so they can now be incorporated into the standard silicon manufacturing process. The breakthrough means faster and more energy-efficient diamond-based computer chips are a much more realistic proposition. Our assessment of how Willow outpaces one of the world’s most powerful classical supercomputers, Frontier, was based on conservative assumptions.

Tuesday, when deputies at several sheriff’s stations were unable to log in to the mobile computers in their patrol cars, the department said in a statement. Many of the layoffs have come in the Bay Area, putting a pause to two decades of growth at California’s computer-systems design firms and related services. Most of the cuts probably reflect a pullback after excessive pandemic-driven hiring, due in part to ecommerce and remote work. Shakil Kamran attended a Salesforce conference in 2017, set on trying to break into tech and transition out of his retail management career, which regularly involved 60-hour weeks and left little time to spend with his son.

The share of Los Angeles-area companies wasn’t significantly higher than the national average, but about 8% of firms in the Southland said they expected to adopt AI in the next six months. In Silicon Beach, which has emerged as a home for companies focusing on AI and augmented reality, firms are hiring for hundreds of AI-related jobs, including content writers and software developers who will train the technology. Achieving such low latency enabled them to efficiently train a deep neural network on the chip, a process known as in situ training that typically consumes a huge amount of energy in digital hardware. Deep neural I built a NAS networks are composed of many interconnected layers of nodes, or neurons, that operate on input data to produce an output. One key operation in a deep neural network involves the use of linear algebra to perform matrix multiplication, which transforms data as it is passed from layer to layer. In the long run, the photonic processor could lead to faster and more energy-efficient deep learning for computationally demanding applications like lidar, scientific research in astronomy and particle physics, or high-speed telecommunications.

Treasury Department breached through BeyondTrust service

Theirs is a different approach to making a quantum computer that’s capable of working at room temperature – whereas Google’s chip has to be stored at ultra low temperatures to be effective. On Friday, researchers from Oxford University and Osaka University in Japan published a paper, external showcasing the very low error rate in a trapped-ion qubit. Google has unveiled a new chip which it claims takes five minutes to solve a problem that would currently take the world’s fastest super computers ten septillion – or 10,000,000,000,000,000,000,000,000 years – to complete. Researchers propose a simple fix to an existing technique that could help artists, designers, and engineers create better 3D models. Research from the MIT Center for Constructive Communication finds this effect occurs even when reward models are trained on factual data. Researchers at MIT, NYU, and UCLA develop an approach to help evaluate whether large language models like GPT-4 are equitable enough to be clinically viable for mental health support.

Protecting the quantum diamond

That’s because their specific crystal lattice structure lets them withstand high electrical voltages, while they can also dissipate heat incredibly well because they are not electrically conductive. But to be made in the lab, diamonds also require extremely high temperatures — well beyond the heat computer chips can withstand as they are being manufactured — so they cannot easily be integrated into chipmaking processes. The photonic system achieved more than 96 percent accuracy during training tests and more than 92 percent accuracy during inference, which is comparable to traditional hardware. In addition, the chip performs key computations in less than half a nanosecond. “This is especially useful for systems where you are doing in-domain processing of optical signals, like navigation or telecommunications, but also in systems that you want to learn in real time,” he says.

We’ve consistently used this benchmark to assess progress from one generation of chip to the next — we reported Sycamore results in October 2019 and again recently in October 2024. The researchers built an optical deep neural network on a photonic chip using three layers of devices that perform linear and nonlinear operations. Building on a decade of research, scientists from MIT and elsewhere have developed a new photonic chip that overcomes these roadblocks. They demonstrated a fully integrated photonic processor that can perform all the key computations of a deep neural network optically on the chip. Photonic hardware, which can perform machine-learning computations with light, offers a faster and more energy-efficient alternative. However, there are some types of neural network computations that a photonic device can’t perform, requiring the use of off-chip electronics or other techniques that hamper speed and efficiency.

Intel’s latest microcode update fails to fix Arrow Lake performance issues

With our round-up of the latest news, learn how AI PCs are poised to shape the future of computing. In the San Bernardino Mountains, the dry and slightly warmer weather has meant no fresh, natural powder — though not particularly unusual for this time of year. The first snowpack survey of the season in the Sierra Nevada shows that L.A.’s water supply looks good right now. But there are warning signs that we are in for dry seasons in the coming months.

/ A weekly newsletter about the best and Verge-iest stuff you should know about. Pricing starts at $6,999 for the Pro Edition and goes up to $12,999 for the Limited Edition which includes several upgrades. A recent Chromium build suggests Chrome could get support for linking directly to highlighted text in PDFs just like you can on a normal webpage, writes code sleuth Leopeva64 in a post that Bleeping Computer spotted. For all the hype and hoopla around generative AI technology, there just aren’t enough staffers.

Among the hundreds of AI startups looking for talent is Quest Labs, a Bay Area firm co-founded by Debparna Pratiher. The 27-year-old previously worked as a product manager at Nvidia, the highly publicized Silicon Valley supplier of chips used in gaming and other high-performance computing, particularly AI. Neither method was perfect, but both were much better than the conventional method in avoiding damage to the nitrogen-vacancy centers, the researchers said in the study. They added that their next steps are exploring new methods of creating high-quality hydrogenated diamond surfaces with ideal nitrogen-vacancy centers. The scientists aimed to create a single layer of hydrogen on the surface of the quantum diamond, evenly distributed, without changing anything beneath the surface. In the July study, they explored techniques for adding that single layer onto the surface of the diamond in a more reliable way, without causing any damage.