When ChatGPT was released in November 2023, it could only be accessed through the cloud because the model behind it was downright enormous.
Today I am running a similarly capable AI program on a Macbook Air, and it isn’t even warm. The shrinkage shows how rapidly researchers are refining AI models to make them leaner and more efficient. It also shows how going to ever larger scales isn’t the only way to make machines significantly smarter.
The model now infusing my laptop with ChatGPT-like wit and wisdom is called Phi-3-mini. It’s part of a family of smaller AI models recently released by researchers at Microsoft. Although it’s compact enough to run on a smartphone, I tested it by running it on a laptop and accessing it from an iPhone through an app called Enchanted that provides a chat interface similar to the official ChatGPT app.
In a paper describing the Phi-3 family of models, Microsoft’s researchers say the model I used measures up favorably to GPT-3.5, the OpenAI model behind the first release of ChatGPT. That claim is based on measuring its performance on several standard AI benchmarks designed to measure common sense and reasoning. In my own testing, it certainly seems just as capable.
Microsoft announced a new “multimodal” Phi-3 model capable of handling audio, video, and text at its annual developer conference, Build, this week. That came just days after OpenAI and Google both touted radical new AI assistants built on top of multimodal models accessed via the cloud.
Microsoft’s Lilliputian family of AI models suggest it’s becoming possible to build all kinds of handy AI apps that don’t depend on the cloud. That could open up new use cases, by allowing them to be more responsive or private. (Offline algorithms are a key piece of the Recall feature Microsoft announced that uses AI to make everything you ever did on your PC searchable.)
But the Phi family also reveals something about the nature of modern AI, and perhaps how it can be improved. Sébastien Bubeck, a researcher at Microsoft involved with the project, tells me the models were built to test whether being more selective about what an AI system is trained on could provide a way to fine-tune its abilities.
The large language models like OpenAI’s GPT-4 or Google’s Gemini that power chatbots and other services are typically spoon-fed huge gobs of text siphoned from books, websites, and just about any other accessible source. Although it’s raised legal questions, OpenAI and others have found that increasing the amount of text fed to these models, and the amount of computer power used to train them, can unlock new capabilities.
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