I suspect GPT-4's "secret sauce" in terms of edging out competitors is that OpenAI is better about managing data contractors than the other folks. Of course it's a haze of NDAs to learn specifics, and clearly the contractors are severely underpaid compared to OpenAI employees/executives. But a lone genius with a platinum credit card can't create a new world-class LLM without help from others.
… built on the back of a disposable workforce…
There is something grim and dystopian, thinking about the countless small hands feeding the machine.
Dystopian indeed, this is pretty much how Manhattan Project and CERN were done, with many independent contractors doing different parts, and only a few has the overview. A page out of corporate management book, it very much allows concentration of power in the hands of a few.
https://twitter.com/emollick/status/1786213463456448900
$22 in 2008 -> $33 today https://data.bls.gov/cgi-bin/cpicalc.pl?cost1=22&year1=20080...
Comparing LLMs to the Manhattan Project based on budget alone is stupid and arrogant. The comparison only "makes itself" because Ethan Mollick is a childish and unscientific person.
Just want to clarify. The comparison to Manhattan Project or CERN is referencing "the countless small hands feeding the machine." In projects such as these, roles and jobs are divided into small parts, that people who are working on it don't really see the forest from the tree, and that only a few that has the picture of the whole project.
I am wondering if "CERN was pushed on the masses by the few" is an oblique reference to public fears that the LHC would destroy the world.
https://arstechnica.com/information-technology/2024/03/once-...
Yes, that's my opinion too. GAOs (Grassroots AI Organisations) are constrained by access to data and the hardware needed to process the data and train the model on it. I look forward to a future where GAOs will crowdsource their computations in the same way many science labs borrow computing power from people around the world.
But only for the same reasons. Linux runs on very nearly every piece of hardware ever made. The APIs you have to implement in order to run "Linux programs" are large and full of old complexity that exists for compatibility. Chromium is full of code to try to make pages render even though they were designed for Internet Explorer 6.
Conversely, some university programs have students create a basic operating system from scratch. It's definitely something a small team can do as long as you don't care about broad hardware support or compatibility with existing applications. In principle a basic web browser is even simpler.
It actually teaches you how to build llama iteratively, test, debug and interpret the training loss rather than just desribing the code.
I have implemented inference of Whisper https://github.com/Const-me/Whisper and Mistral https://github.com/Const-me/Cgml/tree/master/Mistral/Mistral... models on all GPUs which support Direct3D 11.0 API. The performance is IMO very reasonable.
A year might be required when the only input is the research articles. In practice, we also have reference Python implementations of these models. Possible to test different functions or compute shaders against the corresponding pieces from the reference implementations, by comparing saved output tensors between the reference and the newly built implementation. Due to that simple trick, I think I have spent less than 1 month part-time for each of these two projects.
Great overview. One gap I've been working on (daily) since October is the math working towards MA's Mathematics for Machine Learning course (https://mathacademy.com/courses/mathematics-for-machine-lear...).
I wrote about my progress (http://gmays.com/math) if anyone else is interested in a similar path. I recently crossed 200 days of doing math daily (at least a lesson a day). It's definitely taking longer than I want, but I also have limited time (young kids + startup + investing).
The 'year of self study' definitely depends on where you're starting from and how much time you have, but it's very doable if you can dedicate an hour or two a day.
This is an indication that we’re at the infancy of this field.
"the code for AGI will be simple" - John Deremetrius Carmack
Of course, this is not all there is to a modern LLM, it would probably take another thousand lines or two to implement training, and many more than that to make it fast on all the major CPU and GPU architectures. If you want a flexible framework that lets a developer define any model you want and still goes as fast as it can, the complexity spirals.
Most programmers have an intuition that duplicating a large software project from scratch, like Linux or Chromium for example, would require incredible amounts of expertise, manpower and time. It's not something that a small team can achieve in a few months. You're limited by talent, not hardware.
LLMs are very different. THe code isn't that complicated, you could probably implement training and inference for a single model architecture, from scratch, on a single kind of GPU, with reasonable performance, as an individual with a background in programming and who still remembers their calculus and linear algebra, with a year or so of self study. What makes LLMs difficult is getting access to all the hardware to train them, getting the data, and being able to preprocess that data.