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DeepSeek-V3 Breaks New Ground: The World's Largest Open-Source AI Model! (opentools.ai)

submitted 8 months ago by yogthos@lemmy.ml to c/technology@hexbear.net

23 comments fedilink hide all child comments

cross-posted from: https://lemmy.ml/post/24102825

DeepSeek V3 is a big deal for a number of reasons.

At only $5.5 million to train, it's a fraction of the cost of models from OpenAI, Google, or Anthropic which are often in the hundreds of millions.

It breaks the whole AI as a service business model that OpenAI and Google have been pursuing making state-of-the-art language models accessible to smaller companies, research institutions, and even individuals.

The code is publicly available, allowing anyone to use, study, modify, and build upon it. Companies can integrate it into their products without paying for usage, making it financially attractive. The open-source nature fosters collaboration and rapid innovation.

The model goes head-to-head with and often outperforms models like GPT-4o and Claude-3.5-Sonnet in various benchmarks. It excels in areas that are traditionally challenging for AI, like advanced mathematics and code generation. Its 128K token context window means it can process and understand very long documents. Meanwhile it processes text at 60 tokens per second, twice as fast as GPT-4o.

The Mixture-of-Experts (MoE) approach used by the model is key to its performance. While the model has a massive 671 billion parameters, it only uses 37 billion at a time, making it incredibly efficient. Compared to Meta's Llama3.1 (405 billion parameters used all at once), DeepSeek V3 is over 10 times more efficient yet performs better.

DeepSeek V3 can be seen as a significant technological achievement by China in the face of US attempts to limit its AI progress. China once again demonstrates that resourcefulness can overcome limitations.

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[–] yogthos@lemmy.ml 3 points 8 months ago

For sure, and I think it's a really important thing to keep in mind that our own logic is far from being infallible. Humans easily fall for all kinds of logical fallacies, and we find formal reasoning to be very difficult. It takes scientists years of training to develop this mindset, and they are still unable to eliminate the problem of biases and other fallacies. This is why we rely on concepts like peer review to mitigate these problems.

An artificial reasoning system should be held to a similar standard as our own reasoning instead of some ideal of rational thought. I think that the key aspects that need to be focused on is consistency, ability to explain the steps, and being able to integrate feedback to correct mistakes. If we can get that going, then we'd have systems that can improve themselves over time and that can be taught the way we teach humans.