Google fans' wait is over at last as four new AI-powered smartphones are revealed – about a month ahead of Apple's new offerings. Today, Google has unveiled its next generation of Pixel smartphones with the release of the 799 Pixel 9, 999 Pixel 9 Pro, and 1,099 Pixel 9 Pro XL. These will be joined by the 1,749 Pixel 9 Pro Fold, a long-awaited successor to the Google Pixel Fold. All of Google's latest offerings are boosted with the latest AI features thanks to an on-phone AI assistant and even more powerful chips. Google's AI-powered phones are now be capable of everything from creating recipes based on what's in the fridge to adding people into selfies.

However, a new, more powerful version of the OpenAI software is due to be released next year, with chief executive Sam Altman saying the firm's new products would make its current ones look like "a quaint relative".

Google has shown off a new artificial intelligence system that can create images based on text input. Its Imagen diffusion model, created by the Brain Team at Google Research, offers "an unprecedented degree of photorealism and a deep level of language understanding." This isn't the first time we've seen AI models like this. OpenAI's DALL·E (and its successor) performed similar witchcraft, turning text into visuals. Google's version, however, tries to create more realistic images.

Google has shown off an artificial intelligence system that can create images based on text input. The idea is that users can enter any descriptive text and the AI will turn that into an image. The company says the Imagen diffusion model, created by the Brain Team at Google Research, offers "an unprecedented degree of photorealism and a deep level of language understanding." This isn't the first time we've seen AI models like this. OpenAI's DALL-E (and its successor) generated headlines as well as images because of how adeptly it can turn text into visuals.

Google claims that it has developed artificial intelligence software that can design computer chips faster than humans can. The tech giant said in a paper in the journal Nature on Wednesday that a chip that would take humans months to design can be dreamed up by its new AI in less than six hours. The AI has already been used to develop the next iteration of Google's tensor processing unit chips, which are used to run AI-related tasks, Google said. "Our method has been used in production to design the next generation of Google TPU," wrote the authors of the paper, led by Google's co-heads of machine learning for systems, Azalia Mirhoseini and Anna Goldie. To put it another way, Google is using AI to design chips that can be used to create even more sophisticated AI systems.

Google claims that it has developed artificial intelligence software that can design computer chips faster than humans can. The tech giant said in a paper in the journal Nature on Wednesday that a chip that would take humans months to design can be dreamed up by its new AI in less than six hours. The AI has already been used to develop the latest iteration of Google's tensor processing unit chips, which are used to run AI-related tasks, Google said. "Our method has been used in production to design the next generation of Google TPU," wrote the authors of the paper, led by Google's head of machine learning for systems, Azalia Mirhoseini. To put it another way, Google is using AI to design chips that can be used to create even more sophisticated AI systems.

It's official: there's a brand new Nest Hub in town--and it's available for pre-order now with a release date slated for March 30. The Nest Hub (second-gen) is the first new smart display from Google's smart home brand since the 2019 release of the Nest Hub Max. Pre-order the Nest Hub (second-gen) at Best Buy for $99.99 Google claims the second-gen Nest Hub offers 50% more bass over the first-gen smart screen, and is built with a machine learning chip (1.2 TeraOPS of processing power) that helps the built-in Google Assistant learn your most common commands and respond to them faster than ever before. It also uses Google's proprietary Soli Sensing technology that allows you to use gestures to play and pause video--a huge help when you're cooking. Like the Nest Hub and Nest Hub Max, the second-gen hub supports popular streaming services like Netflix, YouTube TV, and Disney so you can keep up with all of your favorite shows.

Google's fourth-generation tensor processing units (TPUs), the existence of which weren't publicly revealed until today, can complete AI and machine learning training workloads in close-to-record wall clock time. That's according to the latest set of metrics released by MLPerf, the consortium of over 70 companies and academic institutions behind the MLPerf suite for AI performance benchmarking. It shows clusters of fourth-gen TPUs surpassing the capabilities of third-generation TPUs -- and even those of Nvidia's recently released A100 -- on object detection, image classification, natural language processing, machine translation, and recommendation benchmarks. Google says its fourth-generation TPU offers more than double the matrix multiplication TFLOPs of a third-generation TPU, where a single TFLOP is equivalent to 1 trillion floating-point operations per second. It also offers a "significant" boost in memory bandwidth while benefiting from unspecified advances in interconnect technology.

In a study published today in the journal JAMA Oncology, Google researchers claim to have developed an AI system that accurately identifies signs of prostate cancer in biopsies. Building on an algorithm that grades large, surgically removed cancerous segments of prostates, they say their system -- which was developed with support from the Naval Medical Center in San Diego and Verily, Alphabet's life sciences division -- works on the smaller samples extracted during the initial part of cancer care to get diagnoses and prognoses. Prostate cancer biopsies are commonly taken to better evaluate tumors' aggressiveness. The Gleason score, a grading system that classifies cancer cells based on how closely they resemble normal prostate gland tissue, is used to detect problematic masses. But determining which of three Gleason patterns a tumor falls into and assigning a grade based on the relative amounts of pattern in the whole sample is a challenging task -- one that relies on subjective visual inspection and experience.

In a preprint paper coauthored by Google AI lead Jeff Dean, scientists at Google Research and the Google chip implementation and infrastructure team describe a learning-based approach to chip design that can learn from past experience and improve over time, becoming better at generating architectures for unseen components. They claim it completes designs in under six hours on average, which is significantly faster than the weeks it takes human experts in the loop. While the work isn't entirely novel -- it builds upon a technique proposed by Google engineers in a paper published in March -- it advances the state of the art in that it implies the placement of on-chip transistors can be largely automated. If made publicly available, the Google researchers' technique could enable cash-strapped startups to develop their own chips for AI and other specialized purposes. Moreover, it could help to shorten the chip design cycle to allow hardware to better adapt to rapidly evolving research.