Stephen Levy on the “Transformers” paper: “8 Google employees invented Modern AI. Here’s the inside story. They met by chance, got hooked on an idea, and wrote the “Transformers” paper—the most consequential tech breakthrough in recent history…All eight authors have since left Google. Like millions of others, they are now working in some way with systems powered by what they created in 2017. I talked to the Transformer Eight to piece together the anatomy of a breakthrough, a gathering of human minds to create a machine that might well save the last word for itself.”
NYTimes: “The title of [Adam Phillips’] his new book, “On Giving Up,” covers the vast territory between hope and despair. We can give up smoking, sugar or a bad habit; but we can also give up on ourselves. “We give things up when we believe we can change; we give up when we believe we can’t.” It’s this extreme and despairing definition of “giving up” that we tend to fixate on, to the neglect of what Phillips calls “the other, minor forms of giving up.” When we do think of giving up in this “minor” sense of cessation or withdrawal, it’s something that needs to be justified, because we valorize completion and commitment.”
Douglas Irwin: “International trade contributes to productivity growth in at least two ways: It serves as a conduit for the transfer of foreign technologies that enhance productivity, and it increases competition in a way that stimulates industries to become more efficient and improve their productivity, often forcing less productive firms out of business and allowing more productive firms to expand.” [via CafeHayek]
Economist: “In order to reap the fruits of this “accelerated-computing”, Nvidia wants to vastly expand its customer base. Currently the big users of its GPUs are the cloud-computing giants, such as Alphabet, Amazon and Microsoft, as well as builders of gen-AI models, such as OpenAI, maker of ChatGPT. But Nvidia sees great opportunity in demand from firms across all industries: health care, retail, manufacturing, you name it. It believes that many businesses will soon move on from toying with ChatGPT to deploying their own gen-AIs. For that, Nvidia will provide self-contained software packages that can either be acquired off the shelf or tailored to a company’s needs. It calls them NIMs, or Nvidia Inference Microservices. Crucially, they will rely on (mostly rented) Nvidia GPUs, further tying customers into the firm’s hardware-software ecosystem.” FT: “Nvidia’s “full-stack” approach begins with the fact that most of what it sells are not individual GPUs but complete systems built around its chips, which are designed to optimise the performance of its components. As important as the processors are the interconnects between its chips, shuttling data around faster, and the central processing units needed to manage the process. Huang’s technology “stack” also extends to software, in the form of the programming models and libraries of code to make it easier for developers to tap into the power of its chips. Known as Cuda, this has long been recognised as an important competitive moat for Nvidia — though competition is finally emerging in the form of rival software ecosystems. For most developers, already steeped in Nvidia’s technology, the costs of switching don’t yet match the benefits. And the more tools that Nvidia and its partners create to take its technology deeper into individual industries — something currently happening at lightning speed — the more invested they will be in its underlying chip architecture.”
