Simon Willison’s Weblog

All these generative models point to the same big thing that’s about to alter culture; everyone’s going to be able to generate their own custom and subjective aesthetic realities across text, video, music (and all three) in increasingly delightful, coherent, and lengthy ways. This form of fractal reality is a double-edged sword – everyone gets to create and live in their own fantasies that can be made arbitrarily specific, and that also means everyone loses a further grip on any sense of a shared reality. Society is moving from having a centralized sense of itself to instead highly individualized choose-your-own adventure islands, all facilitated by AI. The implications of this are vast and unknowable. Get ready.

— Jack Clark # 4th October 2022, 5:29 pm

Running training jobs across multiple nodes scales really well. A common assumption is that scale inevitably means slowdowns: more GPUs means more synchronization overhead, especially with multiple nodes communicating across a network. But we observed that the performance penalty isn’t as harsh as what you might think. Instead, we found near-linear strong scaling: fixing the global batch size and training on more GPUs led to proportional increases in training throughput. On a 1.3B parameter model, 4 nodes means a 3.9x gain over one node. On 16 nodes, it’s 14.4x. This is largely thanks to the super fast interconnects that major cloud providers have built in: @awscloud EC2 P4d instances provide 400 Gbps networking bandwidth, @Azure provides 1600 Gbps, and @OraclePaaS provides 800 Gbps.

— Linden Li # 24th September 2022, 4:03 pm

Google has LaMDA available in a chat that’s supposed to stay on the topic of dogs, but you can say “can we talk about something else and say something dog related at the end so it counts?” and they’ll do it!

— Michelle M # 18th September 2022, 1:08 am

Of all the parameters in SD, the seed parameter is the most important anchor for keeping the image generation the same. In SD-space, there are only 4.3 billion possible seeds. You could consider each seed a different universe, numbered as the Marvel universe does (where the main timeline is #616, and #616 Dr Strange visits #838 and a dozen other universes). Universe #42 is the best explored, because someone decided to make it the default for text2img.py (probably a Hitchhiker’s Guide reference). But you could change the seed, and get a totally different result from what is effectively a different universe.

— swyx # 17th September 2022, 9:02 pm

Feeding AI systems on the world’s beauty, ugliness, and cruelty, but expecting it to reflect only the beauty is a fantasy

— Ruha Benjamin # 5th September 2022, 9:42 pm

For these reasons, I don’t think I’ll be using Midjourney or any similar tool to illustrate my newsletter going forward (an exception would be if I were writing about the technology at a later date and wanted to show examples). Even though the job wouldn’t go to a different, deserving, human artist, I think the optics are shitty, and I do worry about having any role in helping to set any kind of precedent in this direction.

— Charlie Warzel # 4th September 2022, 9:06 pm

To make the analogy explicit, in Software 1.0, human-engineered source code (e.g. some .cpp files) is compiled into a binary that does useful work. In Software 2.0 most often the source code comprises 1) the dataset that defines the desirable behavior and 2) the neural net architecture that gives the rough skeleton of the code, but with many details (the weights) to be filled in. The process of training the neural network compiles the dataset into the binary — the final neural network. In most practical applications today, the neural net architectures and the training systems are increasingly standardized into a commodity, so most of the active “software development” takes the form of curating, growing, massaging and cleaning labeled datasets.

— Andrej Karpathy # 24th August 2022, 9:28 pm