Technology

cross-posted from: https://lemmy.dbzer0.com/post/72570609

https://www.scmp.com/tech/article/3361119/alibaba-says-newest-qwen-ai-model-second-only-anthropics-claude-fable-5

(if the video player doesn't work)

One last example :

Of course, any author of science-fiction warned about robots rebelling against their creators, right now LLMs have zero memory but it seems impossible to prevent a random engineer in h·er·is basement to create a rogue a.i., good luck to us

OnePlus arrived on the scene in 2014 with brash marketing and a compelling pitch: What if your phone was cheaper and faster? More than a decade later, the market is much different, and so is OnePlus. Confirming months of rumors and speculation, OnePlus has confirmed it’s ending phone releases in North America and Europe.

Key takeaways from President Xi's speech in his first ever appearance at the World AI Conference in Shanghai:

• Started the speech by referring to his signature maxim, "great changes unseen in a century are unfolding across the world"

• Said that the world has "entered an unprecedented period of active innovation on AI technology", which means "great opportunities as well as challenges for governance”

• reaffirmed commitment to open source to promote AI "openness and win-win"

• warns against "over stretching" the concept of national security as applied to AI where one country's national security is prioritised over others

• China opposes emergence of “new historical injustices” in AI (one of the most strongly worded parts of the speech)

• China in next 5 years will provide 5000 opportunities to developing countries in "AI training and seminar programmes" and "cooperation centres" - names ASEAN, League of Arab States, African Union, CELAC, SCO and BRICS

The researchers trained a trillion-parameter model using zero reinforcement learning to see how reasoning capabilities emerge at a massive scale without relying on human-annotated data. They found that pushing the parameter count to a trillion drastically improves both sample efficiency and the overall performance ceiling when compared to a smaller 104-billion parameter baseline strongly validating the concept that raw scale and computation eventually outpace hand-crafted human heuristics.

They also discovered that the training process reliably unfolds in two distinct sequential stages. The model starts with a discovery phase where it actively expands its reasoning boundaries by unlocking dormant pathways, and then it moves into a sharpening phase where it refines its policy within those established limits. Notably, the model spontaneously developed advanced cognitive strategies entirely on its own.

It began using structured formatting, parallel reasoning, self-verification, context anxiety, and even anthropomorphic expressions of frustration during complex tasks without any explicit human prompting. To keep the training stable at such a massive scale, the team relied on simple optimization techniques like clipped importance sampling and mixed-precision control. They also created a new evaluation framework to judge the actual quality of the reasoning steps based on comprehensibility, reproducibility, and token efficiency instead of just looking at the final answer.