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Comment by sghiassy | Hacker News Reader

Is it just me or does Meta seem to be doing better than most companies right now open sourcing their AI research?

they've been on this wave for years.

thanks to meta i have been creating instrumentals of some of my favorite songs with vocals https://github.com/facebookresearch/demucs

Yes, I'd also like to know why Meta has chosen this path, whereas many of the other big players haven't. Usually they all settle upon the same viewpoint.

It started by accident, with the original llama weights being leaked by two separate employees. They've since embraced opening the weights, which I'm all for.

As for why? I have a theory: Meta is not in a position to capitalize upon the model itself. Yes, they can use it internally, and maybe their competitors can copy it to - but there are no real competitors to Facebook or Instagram that can benefit from it enough to make it a differentiating facet.

Thus, releasing stuff for open source does two things:

1) Make them more attractive to research talent (Apple famously recently started to publish research because their traditional secrecy was causing issues with hiring top talent) and...

2) Continues to undermine the ability to make $$$ off of model alone, driving it towards being a commodity rather than the long term profit engine for other companies.

Wrong. FAIR has been open sourcing ML models and source code for the last 10+ years. It did not start with llama. Also, llama was not leaked by employees, but people in the broader community, who the weights were shared with.

For example:

Faster R-CNN - state of the art image segmentation, released in 2017.

FastText - text embedding models, 2016.

FAISS - vector DB, 2018.

https://github.com/orgs/facebookresearch/repositories has over 1,000 repos.

I was speaking about LLM weights specifically and llama, not all models and work at FAIR.

Per your links, it's clear that FAIR does have a good history of open source work.

This is very true. But we should also mention that Facebook sadly have been and are on a negative trajectory of openness. As someone working closely with them, there was a culture of nearly complete openness in the early years of their existence. Research was promptly shared in its entirety and licensing was compliant with open science. However, as the "AI boom" has grown, there is an increasing internal culture of holding parts of research back (my understanding is that this pressure comes from the C-suite). Licensing that previously was open source compliant, has had non-commercial clauses added more and more frequently and even non-standard complex agreements as we have seen for LLaMA 2 and 3. This is sad and the culture of openness is ultimately at risk as they become more and more like OpenAI, Google DeepMind, etc.

As I frequently point out, Facebook are free to decide on their own culture as they see fit and I am not entitled to their work. But it saddens me that they believe that compromising on their initial ideals is the way forward, rather than sticking to them through thick and thin. This, ultimately, makes it more and more difficult for me as an academic that believe in these ideals to work with them.

> It started by accident, with the original llama weights being leaked by two separate employees.

This is not true. Meta Fair has been built on openness from day 1. We published many papers and open-source d many repositories to reproduce the work

In short: "Commoditize the complement."

> weights being leaked

You can hardly call that "leak" when they basically were sending the weights to thousands of people who applied for access. It is not that they kept them secret.

Facebook/Meta has been doing open research in ML and NLP for far longer than the current LLM era: Convolutional Neural Networks for Sentence Classification (2014), FastText (2016), PyTorch (2016), fairseq (2017), LASER (2018), RoBERTa (2019), XLM-R (2019), and BART (2019),

They were always present at ACL with decent open research as far as I have been studying/working in NLP (2014).

It's part of a strategy to attract top talent in the field. If you want top researchers you have to let them publish, which in turn hones a reputation of solid research, attracting more talent.

> It's part of a strategy to attract top talent in the field. If you want top researchers you have to let them publish, which in turn hones a reputation of solid research, attracting more talent.

This. Although, it turns out, if you pay them well enough (like OpenAI), they'll forego publishing. If you can make enough to retire by 40, why work at places that pay less?

ofc, not all researchers think that way, and there are those who are in it for the science, not just money.

They’ve chosen the path of commoditizing their complement. To ensure that ML capabilities are not a differentiating factor in the market, make ML capabilities a commodity available to everyone at the marginal cost.

There isn't conflict in business model. And if people can help to improve their models, they can extract better value by themselves.

Also, maybe they need to improve their brand. Hoarding data for over a decade, maybe bringing something back now.

1/ Build a community (think Linux for OS, Android for mobile, React for frontend) and figure out monetization later. What is clear to most people is that something fundamental is changing in how we build and consume applications.

2/ Prevent OpenAI from cornering the future $$$ market. Unfortunately, Google search is hit as well, but it is more due to the generational shift.

3/ Attract the best AI researchers. A product is a good as its core set of people (often just a few).

Same reason they open sourced reactjs or encourage jestjs usage. If they give it away they can benefit by being embedded in the stack. Then all the scale issues they can beat implementers on because they have the money to run it. To keep ahead of people implementing their tech they have their own data trove to train on. You only get a small piece of it. It’s all to position themselves as a sensible solution.

All their users are writing "content" for free and communicate with each other. AI generated "content" does not really fit into this.

They do not want their users to go to ClosedAI or similar and communicate with an Artificial Stupidity instead talking to each other on Facebook.

So it is in their interest to undermine the market for Artificial Stupidities by releasing the models for free.

Apt user name and salient points.

Just wait until they have a fully intelligent automated pipeline for lifestyle ingestion (e.g. pervasive analysis of all communication and AV) into AI management (Timeline / Memories) backposted into web 2.0 feeds like Facebook.

It is called "tipping the market" and it is a well-known strategy in the business of platforms. [1]

Google did the same thing when they released android for free.

[1] https://www.harperacademic.com/book/9780062896322/the-busine...

Zuckerberg clearly lays out his position in an interview with Dwarkish, from about 2-3 months ago. Worth a watch if you’re curious.

My guess is that when Llama leaked on 4chan and it blew up in the community it somewhat forced their hand to go with an open strategy. But they also have a history with pytorch of reaping the benefits of an open strategy. The benefits are well explained in Google's leaked "We Have No Moat" document.

How so? Maybe in the past, but nothing announced today is open.

They are unable to compete with the top players on quality, so they instead compete by winning the largest user base with open models.

Note that due to the licenses no company will touch any of these with a 10 ft pole. Great for individuals looking to experiment though

The current LLaMA license prevents hyperscalers(>100M MAU?) to use it, but it allows anyone else to test it and then later apply for a commercial license.

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