Artificial intelligence might be running into a cul-de-sac thanks to data shortages and technological constraints, according to aIt’s almost three years since generative AI burst onto the scene in the form of chatbots, AI image generators, and music generators. Such a large leap forward caused many people to wonder what’s next.reports that Google, OpenAI, and Anthropic are all struggling to build more advanced AI.
One of the reasons cited is that “it’s become increasingly difficult to find new, untapped sources of high-quality, human-made training data that can be used to build more advanced AI systems.” This is truly fascinating. It’s well-known that AI companies ripped virtually all available data across the open web to build various models. It is safe to bet that almost all photos online have been taken for AI training purposes.can’t survive without fresh photography.says that with all of the open web scraped, tech companies are having a hard time plugging the gap. Some of them are turning to AI images but researchers have found that this method has limitations.
“The AGI bubble is bursting a little bit,” Margaret Mitchell, chief ethics scientist at AI startup Hugging Face, tells. Mitchells says that AI companies will need to take “different training approaches” for AGI to be achieved. “It is less about quantity and more about quality and diversity of data,” adds Lila Tretikov, head of AI strategy at New Enterprise Associates and former deputy chief technology officer at Microsoft. “We can generate quantity synthetically, yet we struggle to get unique, high-quality datasets without human guidance.”
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