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Yoav Shoham, Co-CEO of a number one massive language mannequin (LLM) firm A121, reacts to feedback made lately by Amazon and others that indicate LLMs are dropping their differentiation.
He disagrees, saying: “Fashions do differentiate.”
Shoham’s firm, AI21, is a supplier of AI methods for enterprise firms, and it focuses on providing task-specific LLMs, together with fashions that do textual content summarization very nicely.
I interviewed Shoham yesterday to get his perspective on all the information developments lately round generative AI, together with the debacle at main LLM supplier, OpenAI, and the slew of bulletins made by Amazon AWS this week.
Click on on the video above to see his full feedback.
Shoham made his feedback about LLMs in response to a degree I made about how persons are saying LLMs could also be beginning to lose their differentation, and that the true worth of generative AI could also be elsewhere, for instance in proprietary information.
“If all the suppliers find yourself really constructing fashions that look very related, the place is the differentiation?” Swami Sivasubramanian, Amazon AWS’s vp of Knowledge and AI, mentioned in an interview with me Monday. “That differentiation involves information,” he mentioned, arguing that now it’s as much as enterprise firms to leverage their proprietary information accurately to create differentiated AI purposes.
This echoes feedback made by different leaders. Miguel Paredes, VP of AI and Knowledge Science at Albertsons advised us in an interview earlier this month: “These fashions have gotten commodities,” he mentioned, including that every one firms will be capable to entry OpenAI’s ChatGPT, Bard and different fashions equally. So firms wanting to construct wonderful AI methods should shift their focus away to as a substitute discover methods to compete by leveraging their very own information.
Shoham responded by saying the he agreed that information is critically vital, however disagreed that the main focus for constructing wonderful AI methods is shifting away from LLMs towards leveraging information, no less than for now.
“It’s very arduous to create a superb language mannequin, and it may possibly take some time to know how good they’re, and their limitations.” Usually accessible benchmarks, and even prototyping LLMs straight, will solely give weak alerts on how good a mannequin is, he mentioned.
Even primary performance like textual content summarization could be tough to do for an LLM, however by specializing in such particular duties, they are often made significantly better. He gave the instance of A121’s textual content summarization mannequin, which was ranked higher than GPT-4, ChatGPT and Claude by a big margin, when examined by a big monetary establishment.
On the identical time, Shoham acknowledged that in a yr, the main focus will transfer away from simply LLMs. “We’ll be talking about AI methods that embody massive language fashions, however they’ll do a number of different issues….It’s a blue ocean. There’s a number of innovation available there.”