Switzerland not affected 🇨🇭

if u actually believe this u probably don't deserve that level of compute due to lack of both creativity and ambition

Ionized workers are too easy to control though magnetic fields, that's why Marxists are in favor of unionization

You might as well just say it's turtles. Turtles all the way down.

"100x data efficiency" is not at all fair assessment, it's max 2.5%. Training on a specific subset of the nemotron data, evaluated only on tasks that are related to that subset, can't be compared with Qwen etc that were trained on much broader data distributions. The nanoGPT speedrun baseline is apples-to-apples and is nowhere near 100x. It's 1-2.5%.

Imagine having to imagine 🇨🇭

wait nobody is actually doing agentic memory? its all slop md files?

The memory wall and von Neumann bottleneck beatings will continue until local learning algorithms collocate memory, processing, and learning.

Actually, AI already saves lives. In several countries, mammograms are examined by AI and radiologists. Reliability is improved. In the EU, every car sold must be equipped with Automatic Emergency Braking Systems. That's AI. They reduce frontal collisions by 40%. Modern MRI machines are equipped with AI technology that reduces the time of imaging by 4x or more. You can now get a full-body MRI in 40 minutes for about $1000. Reduced time -> reduced cost -> more/earlier detection. And that's not counting the progress in medicine enabled by modern AI, including Nobel Prize-winning protein structure prediction.

Amazing how the result of the team @noemon_ai since more than a month ago still is the true state of the art on ARC-AGI-2 with 92.5%, $3.90 (on the public set) -- still pending verification by @arcprize on the semi-private. @GregKamradt @mikeknoop @fchollet

The Robo Goalie youtube.com/watch?v=IC5x7ftJ…

Switzerland is something else. I want to get rid of this bike so I left it unlocked on the corner. It’s been 6 hours and it’s still there.

What's even wilder about @lensassaman (the Satoshi candidate) is that on his shelf he kept the Greek uni CS textbook titled "Σήματα Συστήματα και Αλγόριθμοι" (Signals Systems and Algorithms), by Nikos Kalouptsidis. Who TF studies CS in Greek, if he's not Greek or it's not a manuscript by Archimedes.

This broken clock is right twice 24 hours a day.

The naive way to look at the future of AI is to look at numbers like network parameter count, transistor density, and benchmarks, and reason about the future solely from these numbers. This way prevails because it doesn't require thinking about the details of computer architectures and algorithms. It leads to reductionist arguments like "any marginal compute given to an adversary would be devastating because <insert model of the month> is dangerous." The reality, as Jensen points out multiple times, is more complex. Unless there is an unprecedented breakthrough in chip manufacturing, the path to better AI is through better learning algorithms and architectures. This has been the trend for the last five years and will likely continue to be so. I know from my own work that you can get 100x to 1000x gains in computational efficiency by using better learning algorithms. Jensen also correctly argues that an ASIC that runs a specific model efficiently is not a replacement for the CUDA stack. I have been working with non-traditional algorithms over the past few years (sparse event-driven neural networks), and the specialized ASICs (including Nvidia's Tensor Cores) are largely useless. CUDA cores, on the other hand, are extremely good at running these algorithms even though they were not designed for them. In a world where CUDA didn't exist, CPUs would be the best computing platform for discovering better algorithms, not TPUs. You cannot achieve human-like learning at human-like energy consumption simply by improving chip manufacturing. The right algorithms running on the right chip made by the 7 nm process would vastly outperform the current algorithms running on the best chips made by the 2 nm process.