Latent Space Podcast 5/5/23 [Summary] - No Moat: Closed AI gets its Open Source wakeup call — ft. Simon Willison

Original Link: No Moat: Closed AI gets its Open Source wakeup call — ft. Simon Willison

Summary

Open Source AI Models Challenge Tech Giants

The Google Memo and Open Source Rise Simon Willison highlighted a leaked memo titled "We Have No Moat and neither does OpenAI" suggesting that while Google and OpenAI are building bigger language models, open source projects are quickly catching up. He mentioned Facebook's Lama as a milestone and Stanford's Alpaca as a substantial improvement. The memo argues against the belief that larger models mean more power, suggesting instead that smaller, flexible models could be the future. The high quality and richness of the analysis within the memo was stressed.

Open Source vs. Closed Systems swyx commented on how traditionally, the narrative was that open source would lag behind closed systems because closed systems could adopt from open. However, this memo suggests the opposite is now occurring, with open source outpacing closed models. Travis Fischer echoed this sentiment, noting that the rapid pace of AI advancements could only be matched by open source. He questioned the real competitive advantage when models become commoditized and how companies like Google can engage with open source without compromising their position in the market.

Infrastructure and Practicality Alessio Fanelli argued that while building the best model is essential, it's equally crucial to consider the infrastructure around running these models. Although many open-source tools are freely available, the cost and expertise required to run them might be prohibitive for many.

Running Models On Device Simon discussed the capabilities of current models, particularly how they can be run on personal devices. He cited Vicuna 13B, a model derived from Facebook's Lama, as an example of a model running directly in the browser. He also mentioned the emerging techniques to compress these models, making them even more accessible to everyday devices.

The Google Moat and Future Directions swyx raised a question about Google's apparent panic, considering their vast resources and existing user base with tools like Google Calendar, Docs, and more. He wondered why, given these advantages, there's a sense of urgency within Google concerning the advances of open-source AI.

Throughout the discussion, the underlying theme was the shifting dynamics between closed systems and the rapidly advancing open-source world in AI. The rapid strides open source has made, the possibility of 'installable' abilities on models, and Google's position in this changing landscape were all debated.

AI's Competitive Landscape: Talent Drain, Data Loops, and the Promise of LoRA

Travis Fischer touched on a significant challenge faced by companies like Google: retaining top talent. When the perception is that they are lagging in advancements, their best researchers may consider opportunities elsewhere, such as OpenAI or academic institutions. However, Fischer argued that Google, with its vast resources, shouldn't be underestimated and has the potential to regain momentum.

Alessio Fanelli highlighted the importance of high-quality data. While Google possesses vast amounts of data from their platforms, startups face the daunting task of accumulating enough high-quality data to compete. The idea is not to have the most data but to have the right kind of data. This is where first-party data loops become crucial.

Simon Willison and swyx discussed the emergence of the Open Source Models that are trained on publicly available data, reducing dependency on large-scale, proprietary datasets. They dove into the concept of LoRA (Lower Rank Adaptation), a technique where a portion of the model is frozen, and only a smaller segment is trained, saving computational costs and time. However, these LoRAs are tied to specific model versions, raising the question of compatibility and the relevance of constantly retraining base models.

In conclusion, the AI landscape is rapidly evolving, with the focus shifting from the sheer size of data and models to the quality and adaptability of models. It remains to be seen which strategies will prove most successful in the long run.

The Power of Optimized AI Models and Language Supersets

• Optimized Models: Simon Willison and Travis Fischer discussed the need for specialized AI models that can run directly on devices without requiring external API calls, especially in critical contexts where precision and speed are vital, like in fighter jets. Milliseconds can make a difference in such scenarios, and using an external API is not feasible.

• Mojo - The Next Big Programming Language: Simon introduced "Mojo", a newly announced programming language that acts as a superset of Python, which means any Python code will work in Mojo. However, Mojo introduces features that allow for highly optimized code. What sets Mojo apart is its ability to significantly improve the performance of existing Python code, evidenced by a demo that increased matrix multiplication performance by 2000 times. The language was designed by Chris Lattner, who had a hand in creating significant computing products like LLVM and Swift.

• Potential of Language Supersets: A topic raised by swyx revolves around the concept of language supersets and their transformative potential. He expressed intrigue on why the idea of Mojo wasn't conceived earlier, given the existing concept of language supersets. Simon explained that Mojo built upon a platform called MLIR, another Lattner project optimized for multiple cores and GPU access. This gave Mojo a robust foundation to build on. The ability to enhance performance while staying in a familiar ecosystem (like Python) was touted as its standout feature.

• Google and AI Strategy: The conversation shifted to strategic moves in the AI space. There were hints that Facebook might officially release the Lama weights, a significant development that could influence the AI landscape.

The discussion overall touched on the importance of AI optimization, the introduction and potential of the Mojo programming language, and strategic moves by tech giants in the AI domain.

Google and the AI Landscape

Google Leak Confirmed: Simon Willison revealed that insiders have confirmed the legitimacy of a leaked Google document.

Google's Ad Model at Risk: Both Simon and swyx discussed how ChatGPT, and other chatbots that don't serve ads, could pose a threat to Google's ad-driven revenue model. They also touched on the future of ad-supported chat models like a prototype of Bing with ads.

AI Safety Concerns:

• Simon distinguished between "science fiction" concerns of AI, like it turning against humans, and immediate threats, such as the misuse of AI for scams. He particularly highlighted the risk of AI-enabled romance scams.

• Discussions hinted at an undercurrent of unease among key industry figures about AI safety.

• swyx suggested Simon could document real, non-science fiction threats posed by AI today.

Prompt Injection: Simon expressed concern about the current risk from AI, where systems could be easily manipulated by attackers. swyx humorously suggested that individuals might start incorporating "prompt injections" into their bios to detect AI scraping.

Google vs. OpenAI: The document indicated that there might be internal concerns within Google about AI competition. swyx felt the document provided a glimpse into Google's internal sentiments. The debate also touched on the assertion that OpenAI, despite its innovations, lacked a sustainable competitive advantage or "moat".

The discussion concluded with Travis Fischer's perspective on the eventual commoditization of large language models, agreeing with the document's sentiment about OpenAI's uncertain edge in the long run.

AI Innovations and Concerns: A Dialogue with Simon Willison and Travis Fischer

Simon Willison discussed the challenge posed to his open-source project "datasets" by Chat GPT's capabilities, which could perform many of the tasks he had planned for the project's future. Nonetheless, he emphasized the growing convergence of data and AI and promoted his blog, simonwillis.net, and his newsletter which updates on AI advancements.

Travis Fischer announced the founding of his new company, aiming to develop a framework for creating reliable AI agents for specific use cases. He likened the broader AI projects to the complexities of self-driving vehicles and mentioned his desire to build from foundational, reliable primitives. Additionally, he referenced the success of his Chat PT Twitter bot which has amassed over 125,000 followers and has been sponsored by OpenAI, after a brief branding alteration.

The conversation concluded with swyx highlighting the shifting boundaries in AI usage and copyright, and expressing gratitude to the contributors and audience for their involvement.