Import AI 214: NVIDIA's $40bn ARM deal; a new 57-subject NLP test; AI for plant disease detection

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Should you buy NVIDIA's new GPU? Read this and find out:
...Short answer: yes, though be prepared to cry a little upon opening your wallet…
Every year, NVIDIA announces some GPUs, and some machine learning researchers stare tearfully at the thousands of dollars of hardware they need to buy to stay at the frontier, then crack open their wallets and buy a card. But how, exactly, are NVIDIA's new GPUs useful? Tim Dettmers has written a ludicrously detailed blog post which can help people understand what GPU to buy for Deep Learning and what the inherent tradeoffs are.

Is the Ampere architecture worth it? NVIDIA's new 'Ampere' architecture cards come with a bunch of substantial performance improvements over their predecessors that makes it worth buying. Some particular highlights include: "sparse network training and inference. Other features, such as the new data types should be seen more as an ease-of-use-feature as they provide the same performance boost as Turing does but without any extra programming required," writes Dettmers.
    Read more: Which GPU(s) to Get for Deep Learning: My Experience and Advice for Using GPUs in Deep Learning (Tim Dettmers, blog).

Plus: NVIDIA to acquire ARM for $40 billion:
…Acquisition may reshape the chip industry, though let's check back in a year…
Late on Sunday, news broke that NVIDIA is going to acquire ARM from Softbank. ARM invents and licenses out chip designs to all of the world's top phone makers and Internet-of-Things companies (and, increasingly, a broad range of PCs, and burgeoning server and networking chips). The acquisition gives NVIDIA control of one of the planet's most strategically important semiconductor designers, though how well ARM's design-license business model works alongside NVIDIA's product business remains to be seen.
  "Arm will continue to operate its open-licensing model while maintaining the global customer neutrality that has been foundational to its success," NVIDIA said in a press release.

What does this have to do with AI? For the next few years, we can expect the majority of AI systems to be trained on GPUS and specialized hardware (e.g, TPUs, Graphcore). ARM's RISC-architecture chips don't lend themselves as well to the sort of massively parallelized computing operations required to train AI systems efficiently. But NVIDIA has plans to change this, as it plans to "build a world-class [ARM] AI research facility, supporting developments in healthcare, life sciences, robotics, self-driving cars and other fields".
  An ARM supercomputer? The company also said it "will build a state-of-the-art AI supercomputer, powered by Arm CPUs". (My bet is we'll see Arm CPUs as the co-processor linked to NVIDIA GPUs, and if NVIDIA executes well I'd hope to see them build a ton of software to make these two somewhat dissimilar architectures play nice with eachother).

Does this matter? Large technology acquisitions are difficult to get right, and it'll be at least a year till we'll have a sense of how much this deal matters for the broader field of AI and semiconductors. But NVIDIA has executed phenomenally well in recent years and the ever-growing strategic importance nations assign to computation means that, with ARM, it has become one of the world's most influential companies with regard to the future of computation. Let's hope they do ok!
  Read more: NVIDIA to Acquire Arm for $40 Billion, Creating World's Premier Computing Company for the Age of AI (NVIDIA press release).
Thanks for reading. If you have suggestions, comments or other thoughts you can reach me at jack@jack-clark.net or tweet at me@jackclarksf