Large-scale generative language and vision-language models (LLMs and VLMs) excel in few-shot in-context learning for decision making and instruction following. However, they require high-quality exemplar demonstrations to be included in their context window. In this work, we ask: Can LLMs and VLMs generate their own examples from generic, sub-optimal demonstrations? We propose In-Context Abstraction Learning (ICAL), a method that builds a memory of multimodal experience from sub-optimal demonstrations and human feedback. Given a task demonstration that may contain inefficiencies or mistakes, a VLM abstracts the trajectory into a generalized program by correcting inefficient actions and annotating cognitive abstractions: causal relationships, object state changes, temporal subgoals, and task-relevant visual elements.

While grading essays for his world religions course last month, Antony Aumann, a professor of philosophy at Northern Michigan University, read what he said was easily "the best paper in the class." It explored the morality of burqa bans with clean paragraphs, fitting examples and rigorous arguments. A red flag instantly went up. Mr. Aumann confronted his student over whether he had written the essay himself. The student confessed to using ChatGPT, a chatbot that delivers information, explains concepts and generates ideas in simple sentences -- and, in this case, had written the paper.

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To understand the potential of these new systems, it helps to know how current machine translation works. The current de facto standard is Google Translate, a system that covers 103 languages from Afrikaans to Zulu, including the top 10 languages in the world–in order, Mandarin, Spanish, English, Hindi, Bengali, Portuguese, Russian, Japanese, German, and Javanese. Google's system uses human-supervised neural networks that compare parallel texts–books and articles that have been previously translated by humans. By comparing extremely large amounts of these parallel texts, Google Translate learns the equivalences between any two given languages, thus acquiring the ability to quickly translate between them. Sometimes the translations are funny or don't really capture the original meaning but, in general, they are functional and, overtime, they're getting better and better.

I have a confession to make. I feel like a fraud. Every few days, I receive an email from either a friend, a friend of a friend, or a random company that asks me for my insights in Artificial Intelligence. These include entrepreneurs who have just sold their startups, Stanford MBA graduates who reject half a million dollar offers, venture capitalists, even major bank executives. A couple of years earlier, I wouldn't even have the courage to approach those people, let alone dreaming about them wanting to talk to me.

A linguistics company is using AI to shorten the time it takes to learn a new language. It takes about 200 hours, using traditional methods, to gain basic proficiency in a new language. This AI-powered platform claims it can teach from beginner to fluency in just a few months – through once-daily 20 minute lessons. Learning a new language is hard. Some people seem to pick up new dialects with ease, but for the rest of us it's a trudge through rote memorization.