Evaluating feature steering: A case study in mitigating social biases (17 minute read) This study explores using feature steering in AI models to interpretably modify outputs. It reveals a "steering sweet spot", where changes do not degrade capabilities. The study results show steering can alter social bias in targeted domains but also brings unexpected off-target effects. Further research is required to refine feature steering for safer, more reliable outcomes in AI models. | | ThunderKittens 2 (17 minute read) Thunder Kittens is a framework for writing extremely performant GPU Kernels. It is built on the idea that GPUs actually want to operate on small 16x16 tiles of data. In turn, the useability is quite high, and 40% faster kernels only take a few hundred lines of code. | | Fine-tuning LLMs to 1.58bit: extreme quantization made easy (24 minute read) BitNet, developed by Microsoft Research, introduces a transformer architecture that reduces LLM computational and memory requirements by using ternary precision (-1, 0, 1) equating to 1.58 bits per parameter. Models are required to be trained from scratch. BitNet can also fine-tune existing models to this low-precision format, maintaining strong performance on downstream tasks. This approach significantly reduces energy consumption and improves inference speed using specialized kernels for efficient matrix multiplication. | 25% of Smartphone Owners Don't Want AI as Apple Intelligence Debuts (6 minute read) A CNET survey revealed that only 18% of smartphone users are motivated by AI features to upgrade their devices, with privacy and cost being significant concerns. Major manufacturers like Apple, Google, and Samsung are integrating more AI capabilities in their phones, yet many users prioritize battery life and storage over AI functions. AI subscriptions are set to become common, but nearly half of users are unwilling to pay for these features. | | Love TLDR? Tell your friends and get rewards! | Share your referral link below with friends to get free TLDR swag! | | Track your referrals here. | Want to advertise in TLDR? 📰 | If your company is interested in reaching an audience of AI professionals and decision makers, you may want to advertise with us. If you have any comments or feedback, just respond to this email! Thanks for reading, Andrew Tan & Andrew Carr | | | |
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