The advancements in this space have moved so fast, it’s hard to extract a predictive model on where we’ll end up and how fast it’ll get there.

Meta releasing LLaMA produced a ton of innovation from open source that showed you could run models that were nearly the same level as ChatGPT with less parameters, on smaller and smaller hardware. At the same time, almost every large company you can think of has prioritized integrating generative AI as a high strategic priority with blank cheque budgets. Whole industries (also deeply funded) are popping up around solving the context window memory deficiencies, prompt stuffing for better steerability, better summarization and embedding of your personal or corporate data.

We’re going to see LLM tech everywhere in everything, even if it makes no sense and becomes annoying. After a few years, maybe it’ll seem normal to have a conversation with your shoes?

These are amazing. Dell, Lenovo and I think HP made these tiny things and they were so much easier to get than Pi’s during the shortage. Plus they’re incredibly fast in comparison.

I’ve got a background in deep learning and I still struggle to understand the attention mechanism. I know it’s a key/value store but I’m not sure what it’s doing to the tensor when it passes through different layers.

Subscribed. That last episode of AAA was heartbreaking.

Any data sets produced before 2022 will be very valuable compared to anything after. Maybe the only way we avoid this is to stick to training LLMs on older data and prompt inject anything newer, rather than training for it.

I hate these filthy neutrals…