Amazon Web Services is building a supercomputer in collaboration with Anthropic, the AI startup in which the e-commerce giant has an $8 billion minority stake. Hundreds of thousands of AWS’s flagship Trainium chips will be amassed in an “Ultracluster” that when it is completed in 2025 will be one of the largest supercomputers in the world for model training, Amazon says. The company announced the general availability of AWS Trainium2-powered Amazon Elastic Compute Cloud (EC2) virtual servers as well as Trn2 UltraServers designed to train and deploy AI models and teased next-generation Trainium3 chips. Continue reading AWS Building Trainium-Powered Supercomputer with Anthropic

Apple’s iOS 18 public beta 2 has arrived, with new wallpapers for CarPlay, a newly designed Hidden Apps folder in the Apps Library and the ability to use dark mode widgets in broad daylight, among other updates. Public beta 2 includes iPadOS 18, but does not include Apple Intelligence, which is expected this fall. However, a separate Apple Intelligence preview was introduced this week. In addition, a new Apple research paper leads some to believe its Apple Intelligence AI models were pre-trained in the cloud using Google Tensor Processing Units, leading to speculation that Big Tech be considering alternatives to Nvidia. But Apple has always been an AI outlier. Continue reading Apple Intelligence Preview and Updated iOS 18 Beta Released

Masayoshi Son, CEO of Japan’s SoftBank, wants to transform the tech conglomerate’s Arm subsidiary into an AI powerhouse, and he is investing $64 billion (10 trillion yen) to implement the plan, which includes turning the UK-based unit into an AI chip supplier. Son announced that by spring 2025 Arm is expected to have its first prototype, followed by mass production by contract suppliers and commercial sales in the fall. Arm designs but does not manufacture circuitry, supplying what it calls “chip architecture” to customers including Nvidia and Qualcomm. Continue reading SoftBank’s Arm Plans to Supply AI Chips, Open Data Centers

Alphabet reported revenue of $80.5 billion for Q1, a 15 percent increase fueled largely by online advertising from Google Search and YouTube. The figure topped analyst estimates of $78.8 billion. Profit soared, rising 57 percent to more than $23.6 billion, wildly overperforming the forecast of $18.9 billion. The strong performance resulted in Alphabet announcing its first ever shareholder dividend, at 20 cents per share, which pays out on June 17. Alphabet’s board approved a $70 billion stock repurchase program, and the news-filled earnings event drove Alphabet shares up 13 percent in after-hours trading. Continue reading Alphabet Profit Up 57 Percent, Prompting First-Ever Dividend

Google is closing the year by heralding 2024 as the “Gemini era,” with the introduction of its “most capable and general AI model yet,” Gemini 1.0. This new foundation model is optimized for three different use-case sizes: Ultra, Pro and Nano. As a result, Google is releasing a new, Gemini-powered version of its Bard chatbot, available to English speakers in the U.S. and 170 global regions. Google touts Gemini as built from the ground up for multimodality, reasoning across text, images, video, audio and code. However, Bard will not as yet incorporate Gemini’s ability to analyze sound and images. Continue reading Google Announces the Launch of Gemini, Its Largest AI Model

Stability AI, developer of Stable Diffusion (one of the leading visual content generators, alongside Midjourney and DALL-E), has introduced SDXL Turbo — a new AI model that demonstrates more of the latent possibilities of the common diffusion generation approach: images that update in real time as the user’s prompt updates. This feature was always a possibility even with previous diffusion models given text and images are comprehended differently across linear time, but increased efficiency of generation algorithms and the steady accretion of GPUs and TPUs in a developer’s data center makes the experience more magical. Continue reading Stability AI Intros Real-Time Text-to-Image Generation Model

Cerebras Systems has unveiled the Condor Galaxy 1, powered by nine networked supercomputers designed for a total of 4 exaflops of AI compute via 54 million cores. Cerebras says the CG-1 greatly accelerates AI model training, completing its first run on a large language AI trained for Abu Dhabi-based G42 in only 10 days. Cerebras and G42 have partnered to offer the Santa Clara, California-based CG-1 as a cloud service, positioning it as an alternative to Nvidia’s DGX GH200 cloud supercomputer. The companies plan to release CG-2 and CG-3 in early 2024. Continue reading Cerebras, G42 Partner on a Supercomputer for Generative AI

Meta Platforms has shared additional details on its next generation of AI infrastructure. The company has designed two custom silicon chips, including one for training and running AI models and eventually powering metaverse functions like virtual reality and augmented reality. Another chip is tailored to optimize video processing. Meta publicly discussed its internal chip development last week ahead of a Thursday virtual event on AI infrastructure. The company also showcased an AI-optimized data center design and talked about phase two of deployment of its 16,000 GPU supercomputer for AI research. Continue reading Meta In-House Chip Designs Include Processing for AI, Video

Facebook has used Intel CPUs for many of its artificial intelligence services, but the company is changing course to adapt to the pressing need to better filter live video content. At the Viva Technology industry conference in Paris, Facebook chief AI scientist Yann LeCun stated that the company plans to make its own chips for filtering video content, because more conventional methods suck up too much energy and compute power. Last month, Bloomberg reported that the company is building its own semiconductors. Continue reading Facebook to Develop Live Video Filtering Chips for Faster AI

Google, which created tensor processing units (TPUs) for its artificial intelligence systems some years ago, will now make those computer chips available to other companies via its cloud computing service. Google is currently focusing on computer vision technology, which allows computers to recognize objects; Lyft used these chips for its driverless car project. Amazon is also building its own AI chips for use with the Alexa-powered Echo devices to shave seconds off its response time and potentially increase sales. Continue reading Google Offers Its AI Chips to All Comers via Cloud Computing

In 2012, Microsoft chief executive Steve Ballmer and computer chip researcher Doug Burger believed they had found the future of computing: chips that could be programmed for specific tasks, dubbed field programmable gate arrays (FPGAs). Project Catapult, as it was called, was intended to shift the underlying technology of all Microsoft servers in that direction. FPGAs now form the basis of Bing. Soon, the specialized chips will be capable of artificial intelligence at a tremendous speed — 23 milliseconds versus four seconds. Continue reading Microsoft Speeds Up AI with New Programmable FPGA Chips

Google has just built its own chip as part of its efforts to speed up artificial intelligence developments. The company revealed that this is just the first of many chips it plans to develop and build. At the same time, an increasing number of businesses are migrating to the cloud, lessening the need for servers that rely on chips to function. That’s led some to believe that Google and other Internet titans that follow its lead will impact the future of the chip industry, particularly such stalwarts as Intel and Nvidia. Continue reading Google Develops its Own Chip to Speed Up Machine Learning