Everyone is talking about this new OpenAI paper.
It's about why LLMs hallucinate.
You might want to bookmark this one.
Let's break down the technical details:
Very clear explanation by @geoffreyhinton of why we shouldn't create agents that are more intelligent than us if we can't ensure their alignment (as is unfortunately the case now), and how they'll naturally develop undesirable subgoals (such as surviving or acquiring more power)…
New Stanford CS231N Deep Learning for Computer Vision lectures taught by Professor Fei-Fei Li, Assistant Professors Ehsan Adeli and Justin Johnson, and Zane Durante are now available! Watch the complete playlist here: youtube.com/playlist?list=…
Excited to offer my AI Law class again at Princeton this year. We'll be more proactively sharing lecture notes/materials and more this year on the course webpage! Imo, we have a unique offering that emphasizes how the technical details affect legal outcomes. Check it out!
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Compute is not a big deal for LLMs now, but memory is.
It's an idea used in XQuant – a new method by @UCBerkeley created to reduce memory use up to 12x.
- XQuant doesn't store usual KV cache
- It quantizes and stores only X - the layer input activations
- When needed, it…
xLSTM excels in time series forecasting: arxiv.org/abs/2509.01187 .
Introduces "stochastic xLSTM" (StoxLSTM).
"StoxLSTM consistently outperforms state-of-the-art baselines with better robustness and stronger generalization ability."
TiRex shows that xLSTM is time series king.
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