Why AI Needs to Get Real About Perception and Action

The buzz around AI is usually about data crunching and neural networks, but let's talk about something that's often overlooked: the role of perception and action. Most AI systems treat cognition as a sterile, internal process, ignoring how real-world engagement shapes thinking. It's time to rethink that.

what's Enactive AI?

Enactive approaches to AI flip the script. Instead of seeing perception as passive data processing, this approach emphasizes action and interaction. Imagine a world where AI doesn't just sit back and process sensory input but actively shapes experiences through engagement. This isn't just theory, it's about making AI systems that understand the world like a living being would.

Think about it. We're not just brains in jars. Our understanding of the world is deeply rooted in how we interact with it. Enactive AI aims to bring that same level of dynamic, embodied, and interactive character to machines.

Reinforcement Learning: A Step in the Right Direction?

Reinforcement Learning (RL) gets close to these ideas. It's all about feedback, action, and interaction between agents and environments. Sounds enactive, right? Well, yes and no. RL captures some of this dynamic, but it still misses key elements like true autonomy and embodiment. It's like cooking half a recipe and expecting gourmet results.

Why should you care? Because the gap between AI theory and practice is vast. The press release said AI transformation. The employee survey said otherwise. If AI is going to help, say, in workforce planning or change management, it needs to engage dynamically with its environment. Otherwise, it's just window dressing.

The Road Ahead for Mainstream AI

To truly make a leap, mainstream AI needs to embrace these enactive principles beyond just theory. We've seen management buy licenses for AI tools, but what happens when nobody tells the team how to use them effectively? A broader incorporation of enactive ideas can make AI systems more adaptive and context-aware, which is what we need to close the gap between the keynote and the cubicle.

Imagine AI systems that don't just solve problems but also understand them like a well-trained colleague. This isn't science fiction, it's a necessary evolution. So, why aren't more AI developers jumping on this bandwagon?

The real story here isn't about inventing new AI models. It's about making existing ones smarter in the way that really counts. Because, AI that's detached from real-world dynamics is like a car with no steering wheel, going nowhere fast.