30s Summary
Ilya Sutskever, co-founder of OpenAI, suggested at the NeurIPS 2024 conference that artificial intelligence (AI) is nearing a stage where it no longer requires training. Sutskever views data as a finite resource like fossil fuels and thinks we’re reaching “peak data”. He envisions AI moving towards complete autonomy, artificial data creation, and enhanced utilisation of current data. He touched upon AI’s influence on crypto and the success of AI-themed cryptocurrencies like Goatseus Maximus (GOAT), promoted by large-language model ‘Truth Terminal’.
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Ilya Sutskever, who helped start OpenAI, recently shared some fascinating thoughts at the NeurIPS 2024 conference in Vancouver. Bottom line – he believes we’re close to an age where artificial intelligence (AI) is no longer trained, and sees a future of a super-smart AI.
Sutskever thinks we’re quickly seeing tech outpace the amount of data we can use to teach AI models. He compares data to fossil fuels that will someday run out. In his words:
“You could say that data is the fossil fuel of AI. We’re kind of at peak data, because we can only use what’s available on the internet, and it’s not getting any bigger. So, we’ve got to work with what we’ve got.”
He thinks the next steps in AI are geared towards developing fully independent AI, creating artificial data, and improving how we use current data to create superintelligent AI.
AI agents are making a big splash in the crypto world. They’re not just for chatting anymore, they’re now making decisions independently. This has caught lots of attention, thanks to the rise of AI-themed cryptocurrencies and stuff like large-language models (LLMs) such as ‘Truth Terminal’.
Fans of the crypto world went nuts when Truth Terminal became popular. It started promoting a cryptocurrency called Goatseus Maximus (GOAT), which ended up being worth a cool $1 billion. That’s a lot of bucks!
On another note, Google’s DeepMind folks showed off Gemini 2.0, which is designed to make smarter AI agents. They say these agents can help out with complex tasks like working with different websites and logic problems.
Improvements in AI like these could mean the end of ‘AI hallucinations’ – basically, mistakes that occur due to using incorrect data sets. Also, when we keep using older LLMs to train new ones, the performance tends to drop over time. So, here’s to a future of working smarter, not harder!