In today’s episode, you’ll discover a mind-blowing revelation about the future of programming from OpenAI co-founder Andrej Karpathy. You’ll learn how the lines between human language and computer code are blurring, making AI more accessible than ever. Discover how to unlock the true power of AI by “programming” in plain English, using simple yet effective techniques. Get ready to revolutionize your understanding of AI and unlock its potential in ways you never thought possible!
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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.
Christopher Penn: In today’s episode, let’s talk about a quote I read last year that I loved from Andrej Karpathy, co-founder of OpenAI. He said in January 2023, “The hottest programming language in 2023 is plain English.”
This is specific to generative AI, large language models to be precise. Karpathy, co-founder of OpenAI, worked at Tesla and built some of their vision software. He’s truly one of the pioneers in the space. If you watch his YouTube channel, you can see him build a language model from scratch. It’s like watching someone build an automobile from scratch. They go out in the backyard with their forge and start forging pieces of metal, and you think, “This dude knows how these things work.”
This quote has stuck with me because of what it implicitly says, what it teaches us about using generative AI and large language models, what capabilities it unlocks. Here’s why:
We think—correctly most of the time—of large language models (the ones that power ChatGPT and other tools) as conversational models. We talk to them like people. We ask questions, get specific, show examples—all sorts of things we naturally do as humans. I say in a lot of my keynotes, “Treat this like the world’s smartest intern.” This intern has 255 PhDs—a really smart intern—but they don’t know your company, so you still have to explain things, show examples, and have a conversation.
That conversational style of working with language models works. Models understand that, get that, and work with that because it’s what they’re trained on—vast amounts of human language. They’re pretty good at it; that’s why so many of us use these language models.
But they’re also trained on vast datasets of real programming languages: C, Python, Java, PHP, R, COBOL, even old mainframe code. They speak these computer languages exceptionally well because computer languages are unambiguous.
Humans speak and write with a great deal of ambiguity; machines don’t. If you get creative and use the language in your code, it just doesn’t work. You get errors. There are only so many ways you can word things and stay within the syntax of computer languages.
That’s why language models are so good at them. They can produce results that are highly predictable. You know there’s not going to be a character after the left arrow in an R variable; it just can’t be. The statement will not work; the code will break, so no one does that. There are no examples of that, so it’s not learned, and things work fine.
Humans have so much ambiguity in our language, especially in tonal languages, but even in non-tonal languages. English is largely regarded as a non-tonal language, meaning that the way you say something influences the meaning. English is technically non-tonal, but you can come up with different meanings in a sentence based on how you emphasize things.
You can say, “That’s what she said.” Or, “That’s what she said?” Or, “That’s what she said?” Saying that with four different tones creates four different sentences. When a machine looks at that text, it’s like, “I don’t know what that means.”
Again, programming languages don’t have that. “That’s what she said about this problem.” Programming languages do not have that level of ambiguity; they’re much less forgiving of mistakes and bad grammar. They’re more precise.
So where am I going with all this? Let’s go back to Karpathy’s quote: “The hottest programming language in 2023 is plain English.” What if English was a programming language?
Well, in a generative model, it is.
You can do things like control structures—if/then statements, while statements, for loops (familiar friends from computer programming languages) in English instead of computer code. You know, “If the user says this, do that. If the user doesn’t include this, do this. Until the user says stop, do this.” Just write out those instructions in plain language, maybe format and indent things.
Do you see how powerful that is? You can make your prompts (your use of large language models) incredibly powerful by adopting programming structures, the logic of programming, into regular language. You don’t have to write in computer code, but you’re thinking in computer code. You’re thinking, “How do I set up this set of statements in plain English, in a prompt?”
Because large language models understand both human language and computer language, they can operate on human language formatted like computer programming languages to the same good effect. You can program in human language. You can program in Danish, Japanese, or Ukrainian by adopting these structures, the logic of computer programming, into your language.
When I learned this, it was mind-blowing. It’s mind-opening! You can program today’s AI with the same programming skills used in computer languages, only with the language you speak and write every single day. You will be amazed at how much more you can accomplish with AI because you’re now bringing that structure and logic to generative AI to prompting in language that everyone can understand.
Give that some thought. That’s going to do it for this episode. Thanks for tuning in, and I’ll talk to you on the next one. If you enjoyed this video, please hit the like button, subscribe to my channel if you haven’t already, and if you want to know when new videos are available, hit the bell button to be notified as soon as new content is live.
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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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