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Nvidia falls 14% in premarket trading as China's DeepSeek triggers global tech sell-off

cross-posted from: https://lemm.ee/post/53805638

319 comments
  • Okay, cool...

    So, how much longer before Nvidia stops slapping a "$500-600 RTX XX70" label on a $300 RTX XX60 product with each new generation?

    The thinly-veiled 75-100% price increases aren't fun for those of us not constantly-touching-themselves over AI.

  • Was watching bbc news interview some American guy about this and wow they were really pushing that it's no big deal and deepseek is way behind and a bit of a joke. Made claims they weren't under cyber attack they just couldn't handle having traffic etc.

    Kinda making me root for China honestly.

  • Some things to learn in here ? :
    https://github.com/deepseek-ai
    Large-scale reinforcement learning (RL) ?

    ::: spoiler chat (requires login via email or Google...) Chat with DeepSeek-R1 on DeepSeek's official website: chat.deepseek.com, and switch on the button "DeepThink"
    :::

    ::: spoiler aha moments (in white paper) from page 8 of 22 in :
    https://raw.githubusercontent.com/deepseek-ai/DeepSeek-R1/refs/heads/main/DeepSeek_R1.pdf

    One of the most remarkable aspects of this self-evolution is the emergence of sophisticated behaviors as the test-time computation increases. Behaviors such as reflection—where the model revisits and reevaluates its previous steps—and the exploration of alternative approaches to problem-solving arise spontaneously. These behaviors are not explicitly programmed but instead emerge as a result of the model’s interaction with the reinforcement learning environment. This spontaneous development significantly enhances DeepSeek-R1-Zero’s reasoning capabilities, enabling it to tackle more challenging tasks with greater efficiency and accuracy.

    Aha Moment of DeepSeek-R1-Zero
    A particularly intriguing phenomenon observed during the training of DeepSeek-R1-Zero is the occurrence of an “aha moment”. This moment, as illustrated in Table 3, occurs in an intermediate version of the model. During this phase, DeepSeek-R1-Zero learns to allocate more thinking time to a problem by reevaluating its initial approach. This behavior is not only a testament to the model’s growing reasoning abilities but also a captivating example of how reinforcement learning can lead to unexpected and sophisticated outcomes.

    This moment is not only an “aha moment” for the model but also for the researchers observing its behavior. It underscores the power and beauty of reinforcement learning: rather than explicitly teaching the model on how to solve a problem, we simply provide it with the right incentives, and it autonomously develops advanced problem-solving strategies. The “aha moment” serves as a powerful reminder of the potential of RL to unlock new levels of intelligence in artificial systems, paving the way for more autonomous and adaptive models in the future.
    :::

    https://github.com/huggingface/open-r1
    Fully open reproduction of DeepSeek-R1

    https://en.m.wikipedia.org/wiki/DeepSeek
    DeepSeek_R1 was released 2025-01-20

319 comments