Simon Willison’s Weblog

One consideration is that such a deep ML system could well be developed outside of Google-- at Microsoft, Baidu, Yandex, Amazon, Apple, or even a startup. My impression is that the Translate team experienced this. Deep ML reset the translation game; past advantages were sort of wiped out. Fortunately, Google’s huge investment in deep ML largely paid off, and we excelled in this new game. Nevertheless, our new ML-based translator was still beaten on benchmarks by a small startup. The risk that Google could similarly be beaten in relevance by another company is highlighted by a startling conclusion from BERT: huge amounts of user feedback can be largely replaced by unsupervised learning from raw text. That could have heavy implications for Google.

— Eric Lehman, internal Google email in 2018 # 11th February 2024, 10:59 pm

You likely have a TinyML system in your pocket right now: every cellphone has a low power DSP chip running a deep learning model for keyword spotting, so you can say “Hey Google” or “Hey Siri” and have it wake up on-demand without draining your battery. It’s an increasingly pervasive technology. [...]

It’s astonishing what is possible today: real time computer vision on microcontrollers, on-device speech transcription, denoising and upscaling of digital signals. Generative AI is happening, too, assuming you can find a way to squeeze your models down to size. We are an unsexy field compared to our hype-fueled neighbors, but the entire world is already filling up with this stuff and it’s only the very beginning. Edge AI is being rapidly deployed in a ton of fields: medical sensing, wearables, manufacturing, supply chain, health and safety, wildlife conservation, sports, energy, built environment—we see new applications every day.

— Daniel Situnayake # 16th January 2024, 6:49 pm

As an NLP researcher I’m kind of worried about this field after 10-20 years. Feels like these oversized LLMs are going to eat up this field and I’m sitting in my chair thinking, “What’s the point of my research when GPT-4 can do it better?”

— Jeonghwan Kim # 16th March 2023, 5:39 am

You will not use the Software for any act that may undermine China’s national security and national unity, harm the public interest of society, or infringe upon the rights and interests of human beings.

— The GLM-130B License # 10th January 2023, 10:45 pm

These kinds of biases aren’t so much a technical problem as a sociotechnical one; ML models try to approximate biases in their underlying datasets and, for some groups of people, some of these biases are offensive or harmful. That means in the coming years there will be endless political battles about what the ‘correct’ biases are for different models to display (or not display), and we can ultimately expect there to be as many approaches as there are distinct ideologies on the planet. I expect to move into a fractal ecosystem of models, and I expect model providers will ‘shapeshift’ a single model to display different biases depending on the market it is being deployed into. This will be extraordinarily messy.

— Jack Clark # 16th November 2022, 11:04 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

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

Without deep understanding of the basic tools needed to build and train new algorithms, he says, researchers creating AIs resort to hearsay, like medieval alchemists. “People gravitate around cargo-cult practices,” relying on “folklore and magic spells,” adds François Chollet, a computer scientist at Google in Mountain View, California.

— Matthew Hutson # 26th December 2018, 4:53 pm