GoKawiilLarge language models are amazing and extraordinary reasoning machines. However, companies need a new breed of systems that can act, observe consequences, and learn from reality as it happens. I have been writing for several months about what I see more and more as the central problem in enterprise AI. I’m seeing it not just from an academic perspective: Of course, I’m a university professor with more than 30 years of experience, but I’m also the director of innovation of an artificial intelligence startup, and that’s teaching me a whole lot of new skills. Traveling from theory to code and back, day in and day out, is proving to be an amazingly enriching journey.
A Baconian approach to the mostly Aristotelian corporate AI. And what that means for your business
Why This Matters
This article highlights the critical need for enterprise AI systems that can act, observe, and learn from real-world outcomes, moving beyond traditional large language models. For the tech industry and consumers, this shift promises more adaptive, intelligent, and effective AI solutions that better serve business needs and improve user experiences. Embracing this Baconian approach could lead to more responsible and practical AI deployments in the future.
Key Takeaways
- Enterprise AI must evolve to include action and learning from real-world consequences.
- Current large language models are insufficient for dynamic, real-time decision-making.
- A Baconian approach emphasizes observation and adaptation, shaping more effective AI systems.
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large language models
enterprise ai
baconian approach
artificial intelligence startup
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