Jeremiah Lowin - Explaining the New AI Paradigm

Takeaways

• Pretrained transformers are a new class of AI models that are capable of transforming one sequence of inputs into another sequence of inputs using complex rules and heuristics. These models are called transformers because they fundamentally transform one sequence of inputs into another sequence of inputs.
• These models are particularly useful for language tasks such as translation, because they can take into account the complexities of different languages, such as word order, syntax, grammar, and idioms.
• The transformer models are incredibly powerful and can take in a variety of inputs, including text, images, and time series data. These models can also generate latent representations of the input data, which are amorphous, difficult for humans to interpret, but can be transformed into other forms.
• The powerful latent representation generated by these models allows them to be highly general-purpose and capable of generating specific outcomes, such as programming Spiderman to smash watermelons, which is not something that would have been anticipated by the developers of the model.
• The core idea of AI and machine learning has remained consistent over the past 15-20 years, with the main focus being on taking inputs, transforming them through a latent representation, and then outputting the results.
• The improvement in AI models between iterations can be attributed to a variety of factors such as more data and more training, but the exact process is often not known.
• Reinforcement learning is a type of training where the model takes a series of actions and is then told how good its actions were. This can be very difficult to implement as it requires a way to judge the quality of the output.
• The current state-of-the-art models will be obsolete within the next 12 months due to new architectures, new applications and completely new things.
• The main constraint on building these models is not capital, but rather time, energy, intuition, and ingenuity. The cost of training these models is not as significant as the cost of staffing the team and coming up with new ideas and research.
• The number of different AI models in the world may not be as important as the solid base models that can be fine-tuned and customized for specific industries and applications. The uniqueness of the dataset will be more important.
• The first class of problems that will be subsumed by AI are those where generating something (e.g. text, images) is difficult, such as in marketing, copywriting, and theme park design.
• In these areas, the problem to be solved is likely to be idiosyncratic and unique to the user, requiring a customized response, and that the faster a solution can be provided, the better.
• The most defensible products or businesses may have a component of incorporating existing and ongoing customer data or feedback. This may be the "moat" that protects the business.
• The goal is to create a chatbot that can provide relevant information from a corpus of 1,000 transcripts. The first step is to point the chatbot to the relevant data, or the corpus of transcripts. The second step is to teach the chatbot how to answer a question by providing examples of high-quality responses. The chatbot should not just point the user to a specific podcast, but rather provide a synthesis of the relevant information.

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