Announcing agentic AI for healthcare patient engagement in Amazon Connect (Preview) - Amazon Web Services (AWS)
<a href="https://news.google.com/rss/articles/CBMivwFBVV95cUxObWNNSDljc0k0Z3V3ZnVQdTlUVG9oTnRqT2wtR0I0Z3RZcXZfaUxEQTlVQlloMlBwc1hSTm1GUkRXWlMxcDVxWmZVNlNjMGd0SXFhLTVMdWdXc3hWLTQ0UDRpRzA2bDNodWt1NFhlNDdnM3FWRmM2aVJCYnJBV2hudWtKa1YzZnFWRjNFREZKVWRvWVRfRGtEN0JyZkE2cktGek56a01vYUM5VUZWcElOY0VMUGpLLXVjQTVBdXNOdw?oc=5" target="_blank">Announcing agentic AI for healthcare patient engagement in Amazon Connect (Preview)</a> <font color="#6f6f6f">Amazon Web Services (AWS)</font>
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[P] GPU friendly lossless 12-bit BF16 format with 0.03% escape rate and 1 integer ADD decode works for AMD & NVIDIA
Hi everyone, I am from Australia : ) I just released a new research prototype It’s a lossless BF16 compression format that stores weights in 12 bits by replacing the 8-bit exponent with a 4-bit group code . For 99.97% of weights , decoding is just one integer ADD . Byte-aligned split storage: true 12-bit per weight, no 16-bit padding waste, and zero HBM read amplification. Yes 12 bit not 11 bit !! The main idea was not just “compress weights more”, but to make the format GPU-friendly enough to use directly during inference : sign + mantissa: exactly 1 byte per element group: two nibbles packed into exactly 1 byte too https://preview.redd.it/qbx94xeeo2tg1.png?width=1536 format=png auto=webp s=831da49f6b1729bd0a0e2d1f075786274e5a7398 1.33x smaller than BF16 Fixed-rate 12-bit per weight , no
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The cognitive impact of coding agents
A fun thing about recording a podcast with a professional like Lenny Rachitsky is that his team know how to slice the resulting video up into TikTok-sized short form vertical videos. Here's one he shared on Twitter today which ended up attracting over 1.1m views! That was 48 seconds. Our full conversation lasted 1 hour 40 minutes. Tags: ai-ethics , coding-agents , agentic-engineering , generative-ai , podcast-appearances , ai , llms , cognitive-debt
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