Oracle and Nvidia announced Tuesday a new partnership that promises to drive accelerated computing and Artificial Intelligence (AI) functionality for Oracle Cloud Infrastructure (OCI). Oracle is bringing more Nvidia hardware to OCI and Nvidia's AI Enterprise platform will be available on OCI instances, the companies said, adding that the development is part of a new multi-year partnership signed between the two businesses. "Combined with OCI's AI cloud infrastructure of bare metal, cluster networking, and storage, this provides enterprises a broad, easily accessible portfolio of options for AI training and deep learning inference at scale," Oracle and Nvidia said. Oracle CEO Safra Catz said in a statement that the expanded alliance with Nvidia will help Oracle deliver new solutions in industries ranging from healthcare to manufacturing, telecommunications to financial services. Jensen Huang, Nvidia CEO and founder, underscored his now-familiar mantra that AI and accelerated computing are the future of business.

Last month, the Chinese ecommerce giant Alibaba revealed a powerful new cloud computing system designed for artificial intelligence projects. It is used by Alibaba's cloud customers to train algorithms for tasks like chatbot dialogue and video analysis, and was built using hundreds of chips from US companies Intel and Nvidia. Last week, the US announced new export restrictions that will make future projects like that unlikely. The Biden administration's rules forbid companies from exporting advanced chips needed to train or run the most powerful AI algorithms to China. The sweeping new controls are designed to keep the country's AI industry stuck in the dark ages while the US and other Western countries advance.

THERE'S AN APOCRYPHAL story about how NVIDIA pivoted from games and graphics hardware to dominate AI chips – and it involves cats. Back in 2010, Bill Dally, now chief scientist at NVIDIA, was having breakfast with a former colleague from Stanford University, the computer scientist Andrew Ng, who was working on a project with Google. "He was trying to find cats on the internet – he didn't put it that way, but that's what he was doing," Dally says. Ng was working at the Google X lab on a project to build a neural network that could learn on its own. The neural network was shown ten million YouTube videos and learned how to pick out human faces, bodies and cats – but to do so accurately, the system required thousands of CPUs (central processing units), the workhorse processors that power computers.

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.

The machines are taking over. Artificial intelligence -- better known as AI -- is undergoing a renaissance right now. After years of under-the-radar research, AI is making its way to an application near you. The possibilities are nearly endless; from self-driving cars to credit card fraud flagging to playing Jeopardy, AI is being deployed in countless new ways. While we don't need to worry about Skynet just yet, some of those machine learning applications are less noble.