Mind Readings: Intentional Inefficiency in AI

Mind Readings: Intentional Inefficiency in AI

In today’s episode, we explore the surprising concept of “intentional inefficiency” in the world of AI. Discover why slowing down and introducing human oversight can actually improve your AI systems, especially when using generative AI tools. You’ll learn how to strike the right balance between efficiency and control, ensuring your AI operates ethically and effectively. Get ready to rethink your approach to AI and unlock its full potential!


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Christopher Penn: In today’s episode, let’s talk about speed bumps and intentional inefficiency. Yesterday, I was doing some work for clients using generative AI to build synthetic customers and then extract data about those customers to power some market research initiatives. It’s a fun exercise; they get a chance to create essentially virtual people and just talk to them, say, “Hey, about this, how would you react to this stuff?” It’s like a virtual focus group.

Today’s biggest, most powerful generative AI models—like Claude 2, Bard, ChatGPT with GPT-4 turbo, or Gemini 1.5—all these models can do this particular technique really, really well and generate some pretty incredible results. Along the way in this process, one part where the most efficient thing to do would have been to write a Python script that took the output from one generative AI system and looped it back into another one, which would have made the process seamless—one button, and the output from one part of the conversation goes to another part of the conversation, and boom, you get the results immediately.

Sounds like a good idea, right? The reason I didn’t do that was because, well, you need a human. I manually copied and pasted between the two systems, which seems like a bad idea. It’s very inefficient. I’m not a particularly good data scientist. But if I had done that, there would have been no human in the loop, no human review, no human oversight along the way. And the machines would have just taken content from one thing, pushed it into the next thing, pushed it into the next thing. And that means there would be no checks on it, no validation, no, “Hey, that’s not appropriate,” or “That’s wrong,” or “That’s stereotyped,” or “That’s outright false.” All those things can happen if you just let machines run and do their thing without any inspection.

So, it is okay to have that inefficiency. Just because data is flowing doesn’t mean the data is any good. Fluid flows real well through pipes, but you don’t want that in your fresh water piping. AI systems are similar; they take things like prompts and encapsulate them in code that then runs. But sometimes, sometimes you want an inefficiency. You want to have speed bumps, you want to slow things down so that you can check your work, so that you can validate that a system is doing what it’s supposed to be doing. If you just smooth everything out and automate it all the way through, you run the risk of not allowing that to happen.

With end-to-end automation with no oversight, you run a very high risk of systems doing things that you don’t want them to do, things that are very high risk, giving advice that’s questionable or outright wrong. So, when building systems that are using generative AI in particular, ask yourself, “How are we ensuring human oversight in that system?” Are you going to intentionally introduce inefficiency to permit human review? And yes, it will be a bottleneck. Yes, it will be slower than letting the system just run fully autonomously. But it will give you the ability to inspect it, and it will let you cover your butt. So that if the system does misbehave, you can at least say, “Yeah, well, we had safeguards in there.” It may not have adhered to them, but your chances are, if you have human review, you’ll catch it earlier as well.

So, think about those speed bumps. And think about where in your generative AI processes you will intentionally have inefficiency so that you can keep systems well-behaved.

Thanks for tuning in. Talk to you next time.

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