Google’s launch of Gemini can be seen as the latest advancement in generative AI, highlighting a shift toward multimodality.
At launch, ChatGPT (GPT3.5) revolutionized content production, and subsequent large multimodal models (LMMs) like GPT4 and Gemini have the potential to revolutionize sectors such as manufacturing, e-commerce, and agriculture.
These new LMMs are trained on images and code, rather than on text alone. Gemini adds audio and video, allowing the AI to directly perceive the physical world.
The race is on among tech companies and open source communities to add new modalities that enhance LMMs’ industrial applications.
Such multimodal capability will be transformational for industry, says Leonid Zhukov, director of the BCG Global AI Institute.
Traditional AI is constrained by preset rules—users decide what they want the AI to do and train it for that task. While GenAI models break free from this constraint, LMMs go even further. They can take in so many forms of data that they could respond to seemingly unlimited situations in the physical world, including those that users can’t predict, Zhukov explains.
Companies’ current 10-20% efficiency gains from GenAI bots could expand into new domains with LMMs, he says.
And this is just the beginning. “Today’s LMMs can see and hear the world. Tomorrow they could also be trained on digital signals from equipment, IoT sensors, or customer transaction data—to create a complete picture of your enterprise’s health on its own, without explicit instruction,” Zhukov says.
Here are just a few potential industrial applications:
Firms need to prepare to integrate multimodal models. According to Zhukov, leaders should:
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