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All AI models might be the same

Project CETI is a large-scale effort to decode whale speech. If AI models do learn a universal language, we might be able to use it to talk to whales. Growing up, I sometimes played a game with my friends called “Mussolini or Bread.” It’s a guessing game, kind of like Twenty Questions. The funny name comes from the idea that, in the space of everything, ‘Mussolini’ and ‘bread’ are about as far away from each other as you can get. One round might go like this: Is it closer to Mussolini or bre

All AI Models Might be The Same

Project CETI is a large-scale effort to decode whale speech. If AI models do learn a universal language, we might be able to use it to talk to whales. Growing up, I sometimes played a game with my friends called “Mussolini or Bread.” It’s a guessing game, kind of like Twenty Questions. The funny name comes from the idea that, in the space of everything, ‘Mussolini’ and ‘bread’ are about as far away from each other as you can get. One round might go like this: Is it closer to Mussolini or bre

LGND wants to make ChatGPT for the Earth

The Earth is awash in data about itself. Every day, satellites capture around 100 terabytes of imagery. But making sense of it isn’t always easy. Seemingly simple questions can be fiendishly complex to answer. Take this question that is of vital economic importance to California: How many fire breaks does the state have that might stop a wildfire in its tracks, and how have they changed since the last fire season? “Originally, you’d have a person look at pictures. And that only scales so far,”

Muvera: Making multi-vector retrieval as fast as single-vector search

Neural embedding models have become a cornerstone of modern information retrieval (IR). Given a query from a user (e.g., “How tall is Mt Everest?”), the goal of IR is to find information relevant to the query from a very large collection of data (e.g., the billions of documents, images, or videos on the Web). Embedding models transform each datapoint into a single-vector “embedding”, such that semantically similar datapoints are transformed into mathematically similar vectors. The embeddings are