Letters
One year since the launch of ChatGPT on November 30, 2022, it’s amazing how many large language models are available. A year ago, ChatGPT was pretty much the only game in town for consumers (using a web user interface) who wanted to use a large language model (LLM)...
Letters
I’ve always believed in democratizing access to the latest advances in artificial intelligence. As a step in this direction, we just launched “Generative AI for Everyone” on Coursera.
Letters
I wrote earlier about how my team at AI Fund saw that GPT-3 set a new direction for building language applications, two years before ChatGPT was released. I’ll go out on a limb to make another prediction
Letters
Andrej Karpathy, one of the Heroes of Deep Learning who currently works at OpenAI, quipped, “The hottest programming language is English.” While I appreciate the sentiment...
Letters
Improvements in chatbots have opened a market for bots integrated with dating apps. I’m concerned that AI romantic partners create fake relationships that displace, rather than strengthen, meaningful human relationships.
Letters
While AI is a general-purpose technology that’s useful for many things, it isn’t good for every task under the sun. How can we decide which concrete use cases to build? If you’re helping a business figure out where to apply AI, I’ve found the following recipe useful as a brainstorming aid…
Letters
I’d like to share a part of the origin story of large language models that isn’t widely known. A lot of early work in natural language processing (NLP) was funded by U.S. military intelligence agencies that needed machine translation and speech recognition capabilities.
Letters
Machine learning development is an empirical process. It’s hard to know in advance the result of a hyperparameter choice, dataset, or prompt to a large language model (LLM).
Letters
An increasing variety of large language models (LLMs) are open source, or close to it. The proliferation of models with relatively permissive licenses gives developers more options for building applications.
Letters
Do large language models understand the world? As a scientist and engineer, I’ve avoided asking whether an AI system “understands” anything. There’s no widely agreed-upon, scientific test for whether a system really understands — as opposed to appearing to understand —
Letters
Internalizing this mental framework has made me a more efficient machine learning engineer: Most of the work of building a machine learning system is debugging rather than development.
Letters
Prompt-based development is making the machine learning development cycle much faster: Projects that used to take months now may take days. I wrote in an earlier letter that this rapid development is causing developers to do away with test sets.