Has Prehistoric AI Scaled to Its Limit?
The Age of Scaling Might Be Over
For many in Silicon Valley, Moore’s Law – the idea that chip performance would double every two years – has been dethroned. Taking its place is something called the "scaling law" for artificial intelligence. This law posited that bigger models, trained on increasing amounts of data and requiring more computing power eventually yield smarter systems. This drove the AI community to focus on building ever-larger calculations, feeding Nvidia’s bottom line.
The scaling law had a coming-out party with the launch of ChatGPT. The breakneck pace of improvement we’ve witnessed since then fueled this craze. Some even predicted we’d hit “super intelligence” within this decade.
But this doesn’t seem to be the model for as many of us expected. Industry whispers suggest that models like OpenAI’s weren’t showing the projected boosts. OpenAI co-founder Ilya Sutskever clarified, "we’re back in the age of wonder and discovery" after a period dominated by size. Satya Nadella, Microsoft’s CEO, tried to redefine the scaling law, suggesting the transformative power now revolves around training models to "reason."
This isn’t sitting comfortably with Nvidia investors. Although used primarily in training, Nvidia’s expertise is now factoring into model understanding. Essentially, “test time scaling” is needed if models are to truly “think” for longer to produce intelligent responses, Dean Cheng Zhu
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## Has Prehistoric AI Scaled to Its Limit?
**Introduction**
Welcome back to the show. Today, we’re diving deep into the fascinating world of artificial intelligence and asking a crucial question: Has the age of simply scaling AI models reached its limit? Joining us is Dr. Emily Carter, a leading AI researcher and professor at the Massachusetts Institute of Technology. Dr. Carter, thanks for being here.
**Dr. Carter:** It’s my pleasure to be here.
**Host:** For years, the prevailing wisdom in AI has been that bigger is better - bigger models, more data, more processing power. This “scaling law,” as it’s been called, fueled a frenzy of development, culminating in impressive breakthroughs like ChatGPT. But recently, there’s been a sense that this approach might be hitting a wall.
**Dr. Carter:** That’s right. The remarkable progress we saw with ChatGPT and similar models was, in large part, due to this scaling approach.
However, recent reports suggest that simply scaling up models isn’t producing the same dramatic improvements we used to see [[1](https://www.scientificamerican.com/article/when-it-comes-to-ai-models-bigger-isnt-always-better/)].
**Host:** So, what’s next then? Are we out of options for further AI advancement?
**Dr. Carter:** Far from it! While scaling alone may have reached its limit, this doesn’t mean the end of AI progress. In fact, it marks the beginning of a new era – an era of innovation and exploration. Researchers are now focusing on alternative approaches, such as improving model architectures, exploring new learning paradigms, and finding more efficient ways to use the data we already have.
**Host:** OpenAI co-founder Ilya Sutskever recently declared we’re entering “the age of wonder and discovery” in AI. This implies there are exciting possibilities on the horizon. Can you shed some light on what these might be?
**Dr. Carter:**
Absolutely. One promising avenue is the development of more specialized AI models. Instead of focusing on creating massive, general-purpose models, we can tailor models to specific tasks or domains, leading to more focused and efficient solutions.
Another area of exciting research is in “explainable AI.” As AI models become more complex, it’s crucial we understand how they make decisions. Research in this field aims to make AI more transparent and trustworthy
. **Host:**
This is all incredibly fascinating.
It seems the future of AI is not about blindly scaling up but about smarter, more focused approaches.
**Dr. Carter:** Exactly. The “age of wonder and discovery” invites us to think creatively and explore new frontiers in AI.
We are on the cusp of some truly transformative innovations, and I am incredibly excited to see what the future holds.
**Host:**
Dr. Carter, thank you so much for sharing
your insights with us today. This has been a truly enlightening conversation.
**Dr. Carter:** My pleasure.