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Last Update | 18th August 2024
I had to close the video because the background music was so annoying.
Interesting. Now, follow that up with the H neuron tests. Both the before model and after cutting out the less used material. In fact, if possible, they should look at how much of the H neuron was cut out when they cut out the 1/3rd part. It would be interesting to see some of these researches start to cross their efforts and maybe find some overlap.
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Hey do you think that there amAI models going to give me covid like you know how China invented covid😂
Why wasn't it compared against Grok?
…which I have found is falling to pieces this last few weeks.
damn, even expert AIs are losing their jobs to AI
Why am I not surprised the totalitarian Commies implemented Expert Euthanasia.
Great breakdown! 🚀 It’s wild to see Yuan 3.0 Ultra taking the lead on Docmatix (67.4%) and SummEval (62.8%), especially against the Feb 2026 updates from the US giants.
But for anyone actually putting this into production, the "hallucination gap" is the real story. While Yuan is a speed demon for RAG and summarizing massive PDFs, its AA-Omniscience hallucination rate is still significantly higher than Gemini 3.1 Pro and GPT-5.3 Codex.
The Logic Gap: Gemini 3.1 Pro DeepThink just hit a record 77.1% on ARC-AGI-2, which measures actual reasoning, not just pattern matching. Yuan 3.0 is great at finding info, but it tends to "fever dream" more when it hits a logic wall.
The Grounding Gap: OpenAI’s 5.3 Codex has much tighter factual guardrails for technical workflows. If you ask it a question it doesn't know, it’s far more likely to admit it, whereas Yuan’s LAEP pruning—while making it efficient—seems to have clipped some of its internal "fact-checking" experts.
The Verdict: Use Yuan if you need to summarize 1,000 pages in seconds without breaking the bank. But if you're doing high-stakes medical, legal, or complex coding where a single hallucination is a dealbreaker, Gemini 3.1 and GPT-5.3 are still the "Truth Kings" of 2026. 🧠⚖
Are the rewards a system gets is efficiency of power?
So they train it to use less power which is the reward and that is also the efficiency in trying to solve problems with less search or power usage?
A very interesting video on innovation in the AI space. Thanks for calling this research to our attention.
RAM😢
Hello 👋🤗👋… thanks 🙏👍🙏 for sharing your content.
Every time a massive model drops, people debate benchmarks and parameters. But the real takeaway is acceleration. The infrastructure race is moving faster than most realize. 🚀 That’s why reading AI Millionaire FastScale by Mark Voss hit differently for me. The book frames AI as a system for building scalable income machines, not just tech news. Once you understand how to plug these models into automated workflows, the opportunity layer becomes obvious. Few people are seeing that yet.
A trillion parameter model isn’t just a tech flex… it’s a signal that AI capability curves are going vertical. The tools are getting absurdly powerful. 🤖 That perspective really clicked after I read AI Millionaire FastScale by Mark Voss. It lays out the blueprint for turning AI infrastructure into automated income systems. When you start thinking in scalable AI leverage instead of tools, global model races become fuel for new businesses. Most people are still watching from the sidelines.
Very interesting
I like your content
Very useful
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What about scale experts parameters size proportionally instead of drop them. A lately less training expert is not synonym of weak, he might just have a more stable understanding or(depending on how the training is done)the training data might involve him with less probability because of the nature of the training data.
99% of tokens that model uses is used for simple tasks, in which you will not seee a difference between a 10b or 1t sys. Its the hard tokens that matter (like core coding tokens or math solutions). Just because their not doing much, doesnt mean the work is unimportant