Deciding on AI fashions is as a lot of a technical determination and it’s a strategic one. However selecting open, closed or hybrid fashions all have trade-offs.
Whereas talking at this yr’s VB Remodel, mannequin structure consultants from Basic Motors, Zoom and IBM mentioned how their corporations and clients contemplate AI mannequin choice.
Barak Turovsky, who in March grew to become GM’s first chief AI officer, mentioned there’s plenty of noise with each new mannequin launch and each time the leaderboard modifications. Lengthy earlier than leaderboards have been a mainstream debate, Turovsky helped launch the primary giant language mannequin (LLM) and recalled the methods open-sourcing AI mannequin weights and coaching knowledge led to main breakthroughs.
“That was frankly probably one of the biggest breakthroughs that helped OpenAI and others to start launching,” Turovsky mentioned. “So it’s actually a funny anecdote: Open-source actually helped create something that went closed and now maybe is back to being open.”
Components for choices differ and embody value, efficiency, belief and security. Turovsky mentioned enterprises typically desire a blended technique — utilizing an open mannequin for inside use and a closed mannequin for manufacturing and buyer going through or vice versa.
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IBM’s AI technique
Armand Ruiz, IBM’s VP of AI platform, mentioned IBM initially began its platform with its personal LLMs, however then realized that wouldn’t be sufficient — particularly as extra highly effective fashions arrived in the marketplace. The corporate then expanded to supply integrations with platforms like Hugging Face so clients may decide any open-source mannequin. (The corporate lately debuted a brand new mannequin gateway that provides enterprises an API for switching between LLMs.)
Extra enterprises are selecting to purchase extra fashions from a number of distributors. When Andreessen Horowitz surveyed 100 CIOs, 37% of respondents mentioned they have been utilizing 5 or extra fashions. Final yr, solely 29% have been utilizing the identical quantity.
Selection is essential, however typically an excessive amount of alternative creates confusion, mentioned Ruiz. To assist clients with their strategy, IBM doesn’t fear an excessive amount of about which LLM they’re utilizing in the course of the proof of idea or pilot section; the principle objective is feasibility. Solely later they start to take a look at whether or not to distill a mannequin or customise one primarily based on a buyer’s wants.
“First we try to simplify all that analysis paralysis with all those options and focus on the use case,” Ruiz mentioned. “Then we figure out what is the best path for production.”
How Zoom approaches AI
Zoom’s clients can select between two configurations for its AI Companion, mentioned Zoom CTO Xuedong Huang. One entails federating the corporate’s personal LLM with different bigger basis fashions. One other configuration permits clients involved about utilizing too many fashions to make use of simply Zoom’s mannequin. (The corporate additionally lately partnered with Google Cloud to undertake an agent-to-agent protocol for AI Companion for enterprise workflows.)
The corporate made its personal small language mannequin (SLM) with out utilizing buyer knowledge, Huang mentioned. At 2 billion parameters, the LLM is definitely very small, however it may possibly nonetheless outperform different industry-specific fashions. The SLM works greatest on advanced duties when working alongside a bigger mannequin.
“This is really the power of a hybrid approach,” Huang mentioned. “Our philosophy is very straightforward. Our company is leading the way very much like Mickey Mouse and the elephant dancing together. The small model will perform a very specific task. We are not saying a small model will be good enough…The Mickey Mouse and elephant will be working together as one team.”
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