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Disillusion in AI

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Summary

This article discusses the disillusionment of AI startups and the debate between speed and moat. It explains that although some impressive startups have impressive demos, it is not enough to evaluate their capabilities without using them first. It suggests that instead of attempting to build a new foundational model or a fork of existing ones, startups should iterate on their ideas on top of existing technologies. The article also mentions the rapid progression of computing power and how advancements in computing power has made some technologies obsolete. It further highlights the black box of AI and what it means for startups, as well as the marriage of AI and big tech and the resources that AI startups need to succeed.

Q&As

How have advances in computing power made some AI innovations obsolete?
Advances in computing power have made basic systems like search and learning much more powerful, making innovations designed to overcome the constraints of this technology obsolete.

How have costs and time to train AI models decreased since 2018?
The cost to train image classification systems and training times have decreased by 63.6% and 94.4%, respectively, since 2018, with similar trends being observed for tasks like recommendation and language processing.

What is the concept of chain-of-thought prompting and how does it impact LLMs?
Chain-of-thought prompting is the action of prompting the model in a way that gets it to list out its thought process when arriving at an answer to a question. This minor change can significantly boost the capability of the LLM without fine-tuning.

What is the "Cambrian explosion of AI" and how can startups benefit from it?
The Cambrian explosion of AI refers to the rapid influx of new AI papers being released every day. Startups can benefit from this by staying up to date on the latest research and quickly implementing new techniques.

What type of resources do AI startups typically covet and what can this tell us about potential partnerships?
AI startups typically covet resources such as distribution, devices, and developer mindshare. This can tell us that potential partnerships may involve optimizing hardware to effectively run AI models, offering models on cloud providers, or gaining access to a larger user base.

AI Comments

👍 This article is an insightful look into the current state of AI and the importance of staying current with the rapidly evolving technology. It's interesting to see the partnerships that big tech companies are making with AI startups in order to stay competitive.

👎 This article is quite technical and the discussion of the technical aspects of AI may be difficult to comprehend for readers without a technical background. Additionally, the article is quite long, making it difficult to parse through the details.

AI Discussion

Me: It's about the disillusionment of AI startups with the Speed vs. Moat debate, the obsolescence of certain advancements in computing power, and the marriage of AI and Big Tech.

Friend: That's interesting. It sounds like these startups are feeling the pressure from OpenAI and other big tech companies. What implications does this have?

Me: Well, the article argues that it's not worth it for AI startups to try to create their own proprietary models to compete with OpenAI. The cost and lead time to train OpenAI's models is too high, and they continue to release new features that make similar features released by standalone applications obsolete. Instead, the article suggests that startups should focus on building better products on top of existing models and iterating quickly. They should also stay up to date on the latest research and understand the black box nature of AI, so they can rapidly implement new techniques. Finally, they should be aware of what resources AI startups are looking for so they can predict the next big partnership announcement.

Action items

Research the latest AI research and techniques to stay up to date and quickly implement them.
Analyze the resources AI startups need to predict the next big partnership announcement.
Explore the concept of phase transitions and chain-of-thought prompting to better understand how LLMs work and predict their performance.

Technical terms

Speed vs. Moat: This refers to the debate between investing in speed (i.e. developing faster AI models) or investing in a moat (i.e. creating proprietary models to protect against competition).
Obsolescence from Advancements: This refers to the rapid progression of computing power, which can make innovations designed to overcome the constraints of this technology obsolete.
GPT-3: This is a 2-year-old technology developed by OpenAI that requires millions of dollars in training cost.
AWS: This stands for Amazon Web Services, which is a cloud computing platform that provides users access to their flagship models at a fraction of the cost for them to train one from scratch.
LLMs: This stands for Large Language Models, which are AI models that are often difficult to predict without a trial-and-error type of approach.
Chain-of-Thought Prompting: This is the action of prompting the model in a way that gets it to list out its thought process when arriving at an answer to a question.
Phase Transitions: This is when there is a sharp improvement in a model's performance after a certain threshold of training is done.