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In the post about GPT-4V, I mentioned the paper “Building Machines That Learn and Think Like People” (https://arxiv.org/abs/1604.00289) by Brenden Lake, Tomer Ullman, Joshua Tenenbaum, and Samuel Gershman. In that seminal paper the authors suggested that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it.
Plus: Google DeepMind's watermark.
When Taylor Webb played around with GPT-3 in early 2022, he was blown away by what OpenAI’s large language model appeared to be able to do. Here was a neural network trained only to predict the next word in a block of text—a jumped-up autocomplete. And yet it gave correct answers to many of the…
Large language models mimic human chatter, but scientists disagree on their ability to reason. Large language models mimic human chatter, but scientists disagree on their ability to reason.
Pictures conjured by the mind’s eye lack detail, despite how vividly you picture them