“The era of artificial intelligence is coming.” But how?
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Two recent dazzling news stories from the world of technology and finance have caught our attention. One is NVIDIA’s market cap of a whopping $2 trillion, and the other is Dell’s ambitious prediction that “all PCs will eventually become AI PCs.” These two events clearly show the dynamic changes in the technology industry and the remarkable development of AI technology, but the interpretation of them varies from person to person.
First of all, Nvidia's market cap of over $2 trillion reflects the enormous expectations among investors for AI technology. This means that demand for AI technology, such as the data center chips that Nvidia provides, is increasing. In particular, the surge in orders for AI inference and training chips, such as Nvidia's H100 Tensor Core GPU, is evidence of how strong Nvidia is in the market.
However, not everyone is positive. Torsten Slök, chief economist at Apollo Global Management, warns that the AI market is overheated, and he sees the current situation as a 'bubble' worse than the dot-com bubble in the 1990s. He argues that the current valuation of the top 10 companies in the S&P 500 is overvalued compared to the tech bubble era, suggesting that investors could be exposed to potential risks. ($APO is a private equity firm with about 800 trillion won in annual assets under management. - Wikipedia )
On the other hand, the CEO of JP Morgan recently said in a CNBC interview that the recent AI boom is not a bubble, but is intertwined with the real economy, and that everyone will soon experience this change. He mentioned that it will be a singularity that will allow us to cure cancer or reach unknown areas that humans have not yet reached, but he also made a loophole to say that he is an optimist at the end of the interview. (lol)
Meanwhile, Dell’s announcement offers another interesting vision for the future of AI technology. Dell envisions that in the long term, all PCs will have AI capabilities, reflecting technological advancements and changing user demands. As AI hardware such as neural processing units (NPUs) increasingly integrate with CPUs, we can expect AI technology to become deeply embedded in everyday computing tasks.
I was asked to draw an illustration based on this article...
Recently, I've been meeting many people in person and introducing or setting up AI models, and as I talk, I really feel that both individuals and companies have a clear need for AI, and ultimately want to have 'my own AI'. The biggest issue here is security. Companies want to use AI while preventing the leak of internal or confidential data, and individuals are concerned about privacy issues, so they think it's creepy to have AI learn everything about them, and ultimately want on-device/local AI. Currently, the solution seems to be mostly fine-tuning SLM.
Domestic and foreign startups have also recently started selling models tailored to specific companies. In effect, they create a foundation model, fine-tune each company's data, upload important or essential data to VectorDB, and then feed it RAG and minimize top_k, etc. to reduce hallucination. Although this method requires a lot of work, it seems to be clearly profitable. In some cases, models used as foundations are open-sourced models that are fine-tuned and modified to call them their own models... As long as the model performs well, that's fine-tuning, right? It's not illegal.
Personally, I think it will be an area of who makes SLM easy to train and use in terms of UI/UX. I'm interested in how to make this possible to do locally these days. In fact, how to run it on a 4070~4090 or Apple silicon chip at home. If you think about using it in a closed network, a company, or a device, it seems more fun to focus on this. If anyone wants to talk about this, please feel free to DM or email me anytime. (I heard that a DM function was created on the slash page.)
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