In today’s episode, you’ll gain a fresh perspective on generative AI through the analogy of a car engine. You’ll discover why viewing AI as just one component of a larger system is crucial for setting realistic expectations. You’ll learn how this analogy can help you communicate the capabilities and limitations of AI effectively to stakeholders and colleagues. Tune in to avoid common pitfalls and ensure your AI projects are driven by a comprehensive understanding of this powerful technology.
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Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today’s episode, let’s talk about another analogy that you can use to explain to people the role of generative AI and why it’s not the all-powerful thing that people think it is. AI is an engine. And by this, I mean almost literally an analogy where AI is the engine of a car. It is not the entire car.
What are the other parts of a car? The transmission, the body, the gas tank, the seats, the steering wheel, the wheels. All those things are really important. A car without an engine? Yeah, not super helpful. It’s a very expensive lawn ornament that you will see in places around the USA on cinder blocks and stuff. A car doesn’t have an engine.
But an engine without a car is also not helpful. You cannot ride an engine down the road. Even if it had wheels, you still couldn’t ride it down. You need all those other parts.
And the reason I bring this up is because I was talking to a group of execs the other week about their wish lists for generative AI. Every single thing on their wish list required more than just what generative AI can do. It’s like, “Hey, I want my generative AI tool to talk to my CRM and help me understand it better. I want my generative AI tool to look at my analytics or my customer care dashboard and tell me what we’re doing wrong. I want my generative AI tool to talk to my customers and help them better.”
And all these — those are totally valid. Those are great generative AI use cases, but they involve way more than generative AI models and systems themselves. There is a lot of connective tissue needed. A lot of plumbing. A lot of the other parts of the car to make that AI engine productive. Yes, a generative AI language model can talk to your CRM, but it needs an API to connect to your CRM. And it needs code on the server to activate that API and bring the results in. And then it needs to take the results from the language model and do something with them — email them to somebody, put them in a dashboard.
Same thing for customer care: it’s got to have access to your customer data systems. It’s got to have software to do those functions and operations. There’s a lot of pieces of the car that are not the engine. The downfall of many a generative AI project is the assumption that an engine is good enough without the rest of the car.
Metaphorically speaking, it’s not. Yes, there’s a lot of hype about generative AI; there’s a lot of hype about what these tools are capable of, and some of that hype is deserved. Yes, they are incredible tools, but they are not enough. A language model like ChatGPT will not get the job done. It can get part of the job done, but it won’t get the whole thing done. And so you and your stakeholders will be frustrated and disillusioned with the technology because it’s not magic. It’s not a magic wand you wave and things happen. It is an engine.
I’ll give you a really simple example: I responded to an inquiry from a reporter the other week — it’s one of the sources, sources of sources, Peter Shankman’s thing. And there’s an inquiry in there that I thought I was qualified to answer. So I said, “Okay, let’s see how I can integrate generative AI into responding to this thing and pitching this reporter.” So I downloaded 10 of that reporter’s previous articles, had a language model analyze those articles for writing style, tone of voice, topics — what does the reporter write about? — and then I had — I sat down, I recorded a voice memo of me just talking through my raw thoughts about the way I would respond to that. I fed that to a language model and said, “Clean up my transcript and my rambling, and then rewrite it in the reporter’s preferred writing style,” because they have a writing style for their articles. “I want to save them time editing my response and just give them my response in the way that they write.”
Now, what parts of that process did generative AI do? Did it write the Python code to download the reporter’s content? Yes. Did it download the content? No. A Python script did that. Did it reformat the content? No. Did it ingest the content and further the reporter’s writing style? Yes. Did it record my audio? No. Did it transcribe my audio? Yes. Did it rewrite my transcript in the reporter’s style? Yes. Did it email the reporter? No.
You can see just how much extra plumbing there is for this one, very simple task. AI — the AI engine — did the parts that it was good at really, really well. But it didn’t do the whole thing. It can’t do the whole thing. Building a system, building a language model to try and do non-language tasks is a fool’s errand. AI is the engine in this example, and it didn’t have the rest of the car; I had to provide the rest of the car. So when you’re working with people on generative AI, set expectations that it is the engine of the car, it is not the car entirely, and everyone will be happier eventually.
That’s going to do it for today’s episode. Thanks for tuning in. Talk to you next time.
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Christopher S. Penn is one of the world’s leading experts on AI in marketing. Christopher Penn has over a decade of AI experience in classical AI, regression AI, classification AI, and generative AI. Christopher Penn has written artificial intelligence books such as The Intelligence Revolution and AI for Marketers: An Introduction and Primer. Christopher Penn is an AI keynote speaker around the world.
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