Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.
This week: Sources say it’s an open source future for OpenAI, and improving AI perf with Mojo.
1. OpenAI FOSS LLM Rumor
First up this week: An anonymous source told The Information’s Jon Victor that OpenAI will release an open source version of its large language model (LLM). But does that make any sense?
Analysis: Elon gets his way
Iron Man trotted out his old story to CNBC—about how he bankrolled OpenAI in the early days (although the sum seems to have halved from $100M to just $50M). He stressed, though, that the whole point was that OpenAI was supposed to be … well, Open. Specifically, an open source nonprofit.
Ananya Mariam Rajesh: OpenAI readies new open-source AI model … reports
OpenAI is preparing to release a new open-source language model to the public [said] a person with knowledge of the plan. … OpenAI is unlikely to release a model that is competitive with GPT, the report said.
Usama Jawad: Following ChatGPT’s success, OpenAI may be releasing an open-source AI model
A new report claims that ChatGPT’s creator, OpenAI, is looking to release a new, open-source AI model soon. … This would be a significant development considering that GPT-4, the large language model (LLM) powering ChatGPT, is closed-source.
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If the latest rumor is to be believed, OpenAI is changing course and looking to release an alternative to GPT-4, one that is accessible to the public. … Currently, OpenAI charges customers $20/month [for] ChatGPT Plus, which is powered by GPT-4.
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Other players have been making waves in this space already. Meta publicly released its open-source Large Language Model Meta AI (LLaMA) a few months ago. Even the smallest variant of the model is trained on over a trillion tokens, giving researchers and enthusiasts a large playing field to explore the underlying technology and even improve it. It’s likely that OpenAI is thinking along the same lines.
Who will put this in context? Will Will Douglas Heaven?
Last week a leaked memo reported to have been written by … a senior engineer at Google, said out loud what many in Silicon Valley must have been whispering for weeks: An open-source free-for-all is threatening Big Tech’s grip on AI.
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Open-source large language models … are dropping like candy from a piñata: … HuggingChat … StableLM … Alpaca … Dolly … Cerebras-GPT.
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The future of how AI is made and used is at a crossroads. … In many ways, that’s a good thing. Greater access to these models has helped drive innovation—it can also help catch their flaws. AI won’t thrive if just a few mega-rich companies get to gatekeep this technology or decide how it is used.
It’s all Elon’s fault. Mister Musk talks to David Faber:
I fully admit to being a huge idiot. … I am the reason OpenAI exists. … Man, fate loves irony—next level. … It wouldn’t exist without me.
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I came up with the name. The name OpenAI refers to open source. The intent was: ‘What’s the opposite of Google?’ It would be an open source nonprofit.
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Is it … legal to start a company as a nonprofit and then take the IP and transfer it to a for-profit that then makes tons of money? … That’s the exact opposite of what I gave you the money for. … I do worry that Microsoft actually may be more in control than … the leadership team at OpenAI realizes.
That resonates with nr2x:
These tools are the result of academic work, funded by the government for decades—and by user content, also generated for decades. … Corporate AI is just 90% selling back public goods at a markup. They should be legally forced to open source.
Meanwhile, u/wencc points out that the clue is in the name:
So you mean OpenAI is going to open their AI?
2. Mojo Risin’
Mojo, a compiled superset of Python, is making a bid to be the language for AI. Most models are coded in Python today, but it’s horribly slow.
Analysis: If anyone can, Lattner’s team can
Chris Lattner, the creator of Swift, MLIR and LLVM, co-founded Modular, the company behind Mojo. So I think we can assume it’s not a fly-by-night operation.
Jeremy Howard: Mojo may be the biggest programming language advance in decades
Nearly all AI models today are developed in Python, thanks to the flexible and elegant programming language, fantastic tools and ecosystem, and high performance compiled libraries. … But Python is many thousands of times slower than languages like C++.
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Mojo is Python. … Maybe it’s better to say Mojo is Python++. … A key trick in Mojo is that you can opt in at any time to a faster “mode” as a developer, by using “fn” instead of “def” to create your function. In this mode, you have to declare exactly what the type of every variable is, and as a result Mojo can create optimised machine code to implement [it]. If you use “struct” instead of “class”, your attributes will be tightly packed into memory, such that they can even be used in data structures without chasing pointers around. These are the kinds of features that allow languages like C to be so fast, and now they’re accessible to Python programmers.
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At its core is MLIR, which has already been developed for many years, initially kicked off by Chris Lattner at Google. He had recognised what the core foundations for an “AI era programming language” would need, and focused on building them. [There’s] reason to be optimistic about the future of Mojo. Although it’s still early days for this project, my guess … is that it’s going to develop faster and further than most of us expect.
He’s lit. He’s Jeff Delaney:
Python is a wonderful language for productive programming, but it has one big problem—it’s too slow. And going slow means you’ll get made fun of by the Rust and C++ Chads of the world. … Mojo [is] a superset of Python that’s … up to 35,000 times faster than your grandpa’s Python. … Here are five things that you need to know:
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1. This is not some random guy’s side project on GitHub, but rather it comes from a company founded by … the guy who created the Swift programming language and the LLVM compiler tool chain. if anybody could fix Python’s problems it’s him.
2. It’s a language designed for programming on AI Hardware like GPUs running Cuda and other accelerators. It achieves this by leveraging multi-level intermediate representation (MLIR) to scale … without a ton of complexity. And it even has built-in auto tuning to optimize your code. …
3. It’s designed as a superset of Python—in the same way TypeScript is a superset of JavaScript. So you don’t need to learn another language [and] you can still use your favorite libraries, like numpy, pandas, etc.
4. It adds strong type checking … and error checking for memory management. It has an ownership system and borrow checkers similar to Rust and also supports manual memory management with pointers like C++. It’s a pragmatic language that gives you safety but also the flexibility to be unsafe when needed. …
5. Currently it’s not available to the public. It’s still in very early development. It will be open sourced in the future but currently there is a waitlist to try it out. I was lucky enough to get Early Access so let’s fire it up:
Wait. Pause. 35,000x perf? Get off my lawn, urges monkeyxpress:
Not this again. … You just need to have someone who has a bit of an idea of how the hardware is working. Trying to ‘solve’ this problem of ignorance with fancy solutions is unlikely to work that well. … I just don’t understand why it’s so hard for someone to learn the basics of CPU/GPU architecture. If you’re doing serious AI research this should not be beyond you.
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I see the same ignorance in a lot of JavaScript. It is possible to absolutely destroy a modern CPU doing something very simple if you just ignore the underlying hardware, while on the other hand you can write code that runs quite well by being a little respectful. Unfortunately … ignorance is bliss for most developers.
The Moral of the Story:
If they say I never loved you, you know they are a liar
—Jim Morrison
You have been reading The Long View by Richi Jennings. You can contact him at @RiCHi or tlv@richi.uk.
Image: Dollar Gill (via Unsplash; leveled and cropped)
