If you asked an AI to define itself, it might say, I am a large language model trained by Google. But is that actually accurate? Or is it like saying a square is a rectangle? You know, technically true but missing the broader taxonomic point. Today’s prompt from Daniel is about the precise relationship between artificial intelligence and machine learning. He wants us to get clear on the basic and technical levels because, let’s be honest, in two thousand twenty-six, these terms are being thrown around like confetti at a wedding where nobody actually knows the couple.
It is a mess out there, Corn. Every software update is suddenly AI-powered even if it is just a slightly better spreadsheet filter. Herman Poppleberry here, and I have been itching to draw some hard lines in the sand on this one. By the way, fun fact for the listeners, today’s episode is actually being powered by Google Gemini three Flash. It is the one writing our script, which adds a nice layer of meta-commentary to a discussion about what exactly these systems are.
It is the digital equivalent of a brain performing self-surgery. So, Herman, let’s start with the big picture. When people say AI versus ML, are we looking at two different things, or is one just a subset of the other?
It is a nested hierarchy. Think of it like a set of Russian dolls. The biggest, outermost doll is Artificial Intelligence. That is the broad field of study dedicated to creating machines capable of performing tasks that usually require human intelligence. Things like reasoning, problem-solving, and perception. Machine Learning is the middle doll. It is a specific subset of AI. It is a method of achieving AI by training algorithms on data so they can learn how to perform a task instead of being given a rigid manual.
So, if I write a very complex script for a video game character that says, if the player is within ten feet, draw sword, and if health is low, drink potion, is that AI?
Yes, that is AI. It is simulating an intelligent decision. But it is not Machine Learning because the character isn't learning from its mistakes. If it gets hit by the same trap a thousand times, it will keep walking into it unless you, the programmer, manually change the code. In a Machine Learning version, you wouldn't tell it to drink a potion. You would give it a goal, like survive as long as possible, and after ten thousand deaths, it would figure out on its own that the red bottle makes the timer go up.
Okay, so AI is the goal, and ML is one way of getting there. But I feel like the marketing departments of the world have decided that AI sounds sexier, so they’ve just swallowed ML whole. Why did we stop calling things Machine Learning in common parlance?
Because Machine Learning sounds like a statistics textbook, and AI sounds like a sci-fi movie. But technically, in a two thousand twenty-six context, almost all the high-performing AI we interact with is built on ML foundations. But we have to remember the history here. The term Machine Learning was actually coined by Arthur Samuel back in nineteen fifty-nine. He was a pioneer at IBM and he defined it as the field of study that gives computers the ability to learn without being explicitly programmed.
Nineteen fifty-nine. That’s wild. We were barely into the space race and people were already trying to get computers to teach themselves. But back then, the dominant flavor of AI wasn't ML, right? It was what people call Symbolic AI or Good Old Fashioned AI.
Well, not exactly, I should say the distinction is that Symbolic AI relied on hard-coded logic. Think of it as the era of the Expert System. If you wanted a computer to diagnose a disease in the nineteen nineties, you sat down with a hundred doctors, you mapped out every possible symptom and outcome into a massive tree of if-then statements, and the computer just followed the branches. It was brilliant for its time, but it was incredibly brittle. If a patient had a symptom that wasn't in the tree, the system just broke. It couldn't generalize.
It’s the difference between a recipe and a chef. The recipe is the Symbolic AI. It works perfectly as long as you have the exact ingredients and the oven is at the right temperature. But if you swap salt for sugar by accident, the recipe can't fix it. The chef, or the ML model, tastes the batter and realizes something is wrong and adjusts.
That is a rare analogy I’ll actually allow, Corn. Because the shift from Symbolic to Connectionist AI, which is the neural network approach, is really what defined the last decade. In Symbolic AI, the intelligence is in the rules. In Machine Learning, the intelligence is in the weights.
Explain the weights for me. Because that’s where my brain starts to get a bit fuzzy. When we say a model is learning, what is actually physically, or I guess digitally, changing?
This is the meat of the discussion. Imagine a neural network as a giant grid of millions or billions of tiny knobs. Each knob is a mathematical value, a weight. When you feed data into the model, say a photo of a cat, that data passes through all these knobs. At the end, the model spits out a guess: forty percent chance of dog, sixty percent chance of cat. If the answer was supposed to be cat, we tell the model, hey, you were close but not perfect.
And then it turns the knobs?
It uses a process called backpropagation and an optimization algorithm called gradient descent. It mathematically calculates which knobs were most responsible for the error and gives them a tiny turn in the right direction. It’s like a massive game of Hot or Cold. Do that ten billion times with a massive dataset, and eventually, the configuration of those knobs becomes so precise that the model can identify a cat it has never seen before. That is the learning aspect. No one programmed a rule for what a cat’s ear looks like. The model just discovered that certain patterns of pixels consistently correlate with the label cat.
So in the old days, we were the teachers writing the textbook. Now, we’re just the proctors handing out the exams and telling the student if they got the grade right or wrong.
And the student is doing a staggering amount of math in the background to improve. This is why we moved away from pure rule-based systems. The real world is too messy for if-then statements. You can't write a rule for every possible way a human can say, I'm hungry. But a Machine Learning model can see ten million examples of people asking for food and find the underlying statistical structure of that request.
It’s funny because when you explain it that way, it sounds less like magic and more like very aggressive accounting. Which brings up a point I hear a lot. Is Machine Learning just fancy statistics?
That is a classic debate. There is a lot of overlap, but they have different goals. Traditional statistics is usually about inference. You want to understand the relationship between variables. You want to know if smoking causes cancer and how sure you are about that. Machine Learning is about prediction and performance. An ML engineer often doesn't care why the model works, as long as the accuracy on the test set is ninety-nine percent. It is a subtle but important shift in philosophy. Statistics wants to explain the world; Machine Learning wants to navigate it.
That makes sense. But let’s look at the stuff that is AI but not ML. Because I think people forget that this category still exists and is actually still very useful. You mentioned A-star pathfinding earlier. If I’m using a GPS, is that ML?
Usually, no. The actual algorithm that finds the shortest path between point A and point B is typically a search algorithm like Dijkstra's or A-star. These are deterministic. They use mathematical proofs to find the absolute most efficient route based on the map data. There is no learning involved. It doesn't need to see a thousand trips to figure out that a straight line is shorter than a zigzag. It’s pure, beautiful logic. That is AI, but it’s not Machine Learning.
So if my GPS tells me there’s a traffic jam and suggests a detour, is that the logic-based AI or is the traffic prediction part the ML?
That’s a great example of them working together. The traffic prediction is almost certainly Machine Learning. It’s looking at historical data, current speeds of other phones, the time of day, and maybe even the weather to predict that a certain road will be backed up in twenty minutes. It’s making a probabilistic guess based on patterns. But once that prediction is made and converted into a weight on the map, the search algorithm takes over to find the new shortest path.
It’s the perfect marriage. The ML handles the messy, unpredictable human element, and the traditional AI handles the rigid, mathematical optimization. I think we’re seeing a lot of that in two thousand twenty-six, right? This move toward neuro-symbolic AI?
It’s the frontier. People realized that pure neural networks, while amazing at language and images, are actually pretty bad at hard logic and math. They hallucinate. They get confident about wrong answers because they are just playing a game of statistical probability. So researchers are trying to wrap these ML models in a shell of symbolic logic. Give the model the ability to learn, but force it to follow certain unbreakable rules of logic or physics. It’s like giving the chef a chemistry set and telling them, you can experiment with flavors, but you cannot ignore the boiling point of water.
I like that. It feels safer. But let's go deeper into the ML circle for a second. We’ve got Machine Learning, and then inside that, we have Deep Learning. What’s the threshold there? When does a model become deep?
It’s literally about the layers. A basic Machine Learning model, like a linear regression or a simple decision tree, is relatively shallow. You put data in, it goes through one or two layers of transformation, and you get an output. Deep Learning uses artificial neural networks with many layers—sometimes hundreds of them. Each layer extracts a different level of abstraction. In image recognition, the first layer might just look for edges. The second layer looks for shapes. The third looks for features like eyes or noses. By the time you get to the end, it’s seeing the whole object.
So Deep Learning is just ML with more filters?
Sort of, but those layers allow it to handle unstructured data. This is the big breakthrough. Traditional ML is great with structured data—think of a giant Excel spreadsheet with columns for age, income, and zip code. But it struggles with raw pixels or raw audio. Deep Learning thrives there because it can build its own internal representation of what an eye looks like without you having to define it.
It’s wild because we’ve reached a point where even the engineers don't fully understand what’s happening in those middle layers. That’s the black box problem Daniel mentioned in his notes, right?
That is the big trade-off. In the old Symbolic AI days, if the medical diagnosis system made a mistake, you could pull up the logs and see exactly which if-then statement triggered the error. It was transparent. In a Deep Learning model, the decision is the result of billions of tiny mathematical adjustments. You can't really explain why the model thought a specific pixel meant the cat was a dog. It’s an emergent property of the system. We’ve traded interpretability for raw power.
Which is fine when it’s a cat photo, but a bit terrifying when it’s a self-driving car or a loan application. I want to jump back to something you said about the AI winter. Daniel’s notes mentioned that the failures of symbolic AI actually led to these periods where funding dried up. Are we at risk of that again if the current ML hype doesn't live up to the promise?
The AI winters of the seventies and late eighties happened because people over-promised what logic-based systems could do. They thought we were five years away from a robot butler, and then they realized the robot couldn't even tell the difference between a door and a window. The difference now is that ML actually works for real-world tasks. It’s not just a lab experiment; it’s the core of the global economy. But there is a growing realization that scaling bigger and bigger ML models might be hitting diminishing returns. That is why the focus is shifting to Agentic AI.
Explain that. Because I hear the word agent every five minutes now. Is an agent a new kind of ML?
Not exactly. An agent is a system that uses an ML model as its brain but has the ability to interact with the world. It’s the difference between a brain in a jar and a person with hands and a job. An LLM by itself just predicts the next word. An AI agent can take a goal like, book me a flight to Jerusalem, and it can browse the web, check your calendar, use a credit card tool, and handle errors. The ML provides the reasoning, but the AI framework provides the agency.
So we’re coming full circle. We’re using the learning capabilities of ML to build the intelligent systems that the original AI pioneers dreamed of in the fifties.
That’s the goal. And it’s important to distinguish these for practical reasons. If you’re a business owner or a developer, you need to know if your problem needs ML or just good old-fashioned automation. I see so many companies trying to train a custom model to do something that could be solved with five lines of basic logic. If you have a clear, unchanging rule, don't use ML. It’s expensive, it’s slow, and it’s prone to errors. ML is for when you have a lot of data and no clear rules.
That should be a bumper sticker. Data but no rules? Use ML. Rules but no data? Use AI. It’s a good rule of thumb. It also helps spot AI-washing, which we’ve talked about before. If a company says their new toaster is AI-powered, I’m going to assume it’s just a timer unless it’s actually looking at the bread and learning my preference for char.
Most of the time, it is just a timer with a fancy light. But even in the technical world, the confusion is real. I’ve seen job postings for AI Engineers that are actually just asking for someone who can write SQL queries. And I’ve seen ML Researcher roles that are really just high-level philosophy. We need this taxonomy to actually communicate.
Let’s talk about the learning types for a second because that's another area where people get tripped up. Supervised, unsupervised, and reinforcement learning. Those are all under the ML umbrella, right?
They are the three pillars of ML. Supervised learning is the cat photo example. You have labeled data. Input: image. Label: cat. The model learns to map one to the other. This is the most common type of ML in use today. Unsupervised learning is when you give the model data with no labels and say, find something interesting here. It’s used for clustering, like a clothing brand looking at its customer base and realizing there are four distinct groups of shoppers they didn't know existed. The model doesn't know what the groups are, it just sees the patterns.
And reinforcement learning is the one that feels the most like actual animal training, right?
It is exactly like training a dog. You give the agent an environment, a set of possible actions, and a reward function. If it does something good, it gets a point. If it does something bad, it loses a point. This is how AlphaGo became the best Go player in the world. It didn't study human games as its primary method; it played against itself millions of times and learned through trial and error which moves led to a win.
It’s fascinating because reinforcement learning is where you see the most surprising behavior. The models often find exploits or weird strategies that a human would never think of because they aren't biased by our preconceived rules.
That is the power of ML. It can move beyond human intuition. But again, to bring it back to the AI vs ML distinction, the goal of the reinforcement learning process is to create an intelligent agent—the AI. The process of the agent getting better through rewards is the Machine Learning.
I think one thing that would really help the listeners is a concrete case study. Let's take something like medical imaging. How would an old-school AI approach a tumor scan versus how a modern ML approach does it?
Great topic. In the nineteen nineties, if you wanted an AI to find a tumor, you would have to program it to look for specific geometric features. You’d tell it, look for a cluster of pixels that is darker than the surrounding area, has an irregular border, and is at least five millimeters wide. You are defining the tumor for the computer.
And if the tumor is shaped like a crescent or is a slightly different shade of gray, the computer misses it because it doesn't fit the rigid definition.
Now, with Machine Learning, specifically Deep Learning, we don't define the tumor. We just show the model fifty thousand scans where a human doctor has circled the tumor and fifty thousand scans where there isn't one. We don't tell it why they are tumors. The model looks at those hundred thousand images and discovers its own features. It might realize that a certain texture of the tissue, which is invisible to the human eye, is a ninety-nine percent accurate predictor of malignancy. It’s not following our rules; it’s finding the rules inherent in the data.
That is incredible, but it also highlights the data dependency. If you only have ten scans, the ML model is useless. It’ll just memorize those ten and fail on the eleventh.
That is the big takeaway. If you don't have data, you can't do Machine Learning. But you can still do AI. You can still build a system based on expert knowledge and logic. I think people are so obsessed with ML right now that they’ve forgotten how powerful a well-designed expert system can be for niche problems.
It’s the hammer and nail problem. Everyone has a very expensive ML hammer, so every problem looks like a data-rich nail. But sometimes you just need a screwdriver.
And sometimes the data is the problem. If your training data is biased, your ML model will be biased, and because it’s a black box, it’s much harder to fix than a logic-based system where you can just edit the code. This is why when people say AI is biased, what they usually mean is that the Machine Learning model learned the biases present in our own history.
It’s a mirror. It’s not some alien intelligence making these choices; it’s a statistical reflection of the data we fed it. I want to touch on one more thing from Daniel’s prompt—the basic versus technical level of this. If I’m at a dinner party and someone asks me the difference, and I don't want to bore them with backpropagation talk, what’s the thirty-second elevator pitch?
The elevator pitch is: AI is the vision of making machines smart. Machine Learning is the specific tech that lets them get smart on their own by looking at data. If it’s following a script, it’s AI. If it’s learning from experience, it’s Machine Learning.
I like that. Short, sweet, and it doesn't make me sound like I'm trying to sell them a GPU cluster. But for the technical folks listening, I think the distinction of where the logic resides is the key. In AI, the logic is often external—provided by the human. In ML, the logic is internal—discovered by the algorithm.
That’s a sophisticated way to put it. And it explains why the twenty-twenty-six landscape is shifting so much. We are moving from a world where we told computers what to do, to a world where we tell them what we want, and they figure out the how. That transition is entirely powered by the shift from symbolic AI to machine learning.
But we still need the symbolic part to keep them on the rails. It’s like the ego and the id. The ML is this raw, powerful drive to find patterns, and the symbolic AI is the social structure and rules that keep it from doing something insane.
I’ll allow that analogy too, Corn. You’re on a roll today. It really does feel like we’re building a digital mind by combining these different approaches. And it’s not just for big tech companies. I was reading about small-scale ML being used for things like optimizing soil nutrients on individual farms. They don't have a giant supercomputer; they just have a few years of crop data and a simple model that can predict the best time to fertilize. That is ML in its purest, most practical form.
It’s funny how the more technical we get, the more it feels like we’re just building better tools. A hammer is a tool for your arm. A car is a tool for your legs. AI is a tool for your brain. And Machine Learning is just the specific way we’re sharpening that tool right now.
And just like you wouldn't use a chainsaw to cut a piece of paper, you shouldn't use a massive transformer model to solve a problem that a simple decision tree could handle. One is technically AI, one is technically ML, but the goal is the same: efficiency and intelligence.
So, looking forward, do you think the terms will eventually merge? Will we just stop saying ML altogether because it’s so ubiquitous?
I think for the general public, yes. Everything will just be called AI. But for engineers, the distinction will become even more important as we start mixing and matching different architecture types. We’re already seeing the rise of things like liquid neural networks and Kolmogorov-Arnold Networks, which are new ways of doing ML that might be more efficient than the standard transformers we use today. If you just call it all AI, you lose the ability to talk about how the engine actually works.
It’s like talking about cars. Most people just say I’m driving my car. But if you’re a mechanic, you need to know if it’s internal combustion, electric, or a hybrid. The distinction matters when you’re under the hood.
Well, there I go again. I almost said the forbidden word. But you’re right. The mechanic needs the technical vocabulary. The driver just needs to get to work. And since our listeners are usually the ones under the hood, I think it’s our job to keep these definitions sharp.
Speaking of keeping things sharp, it’s worth noting that while we’re talking about these things as distinct, they are constantly evolving. The AI of two thousand twenty-six is vastly different from the AI of two thousand twenty-four. The speed of iteration in ML is so fast that what we consider deep learning today might be seen as a shallow, primitive approach in five years.
That’s the nature of the field. It’s built on a foundation of rapid experimentation. But the core principle of machine learning—the idea of optimization through data—is likely here to stay. It’s too powerful a paradigm to abandon. We’ve finally found a way to let computers handle the complexity of the real world without us having to write a million lines of code.
It’s a lazy man’s dream, Herman. Why work hard to program a computer when you can just make the computer work hard to program itself? As a sloth, I find that deeply relatable.
I knew you’d find a way to make it about your lifestyle. But honestly, it’s not about being lazy; it’s about being effective. We are solving problems today that were literally impossible ten years ago. We can fold proteins, we can predict weather patterns with insane accuracy, and we can translate languages in real-time with all the nuance of a native speaker. None of that would be possible with just symbolic AI. It required the brute-force statistical power of machine learning.
And yet, we still can’t get a robot to reliably fold laundry. Which tells you that there’s still a huge gap between digital intelligence and physical intelligence.
That’s where the robotics side of AI comes in. And that’s a whole other mess of ML, control theory, and sensor fusion. But it all comes back to that same hierarchy. AI is the big goal, ML is the engine, and data is the fuel.
I think we’ve given the people a lot to chew on. Before we wrap up, let’s hit those practical takeaways. If someone is listening to this and they’re about to start a project or buy a piece of software, what should they be looking for?
Takeaway number one: use the Data Dependency Test. Ask yourself, does this system need a massive amount of data to work, or can I explain the rules of the task to a human in five minutes? If you can explain the rules, you might not need a complex ML solution. You might just need good logic-based automation.
Takeaway number two: check for AI-washing. If a product claims to be AI-powered, ask if it’s learning from your behavior or if it’s just a fancy set of presets. Real ML gets better over time. If the product is exactly the same on day one as it is on day one hundred, it’s probably just traditional software with a marketing budget.
And number three for the developers out there: don't ignore the symbolic side. Pure ML is great, but neuro-symbolic approaches are the future. If you can combine the flexibility of a neural network with the reliability of logical constraints, you’re going to build something much more robust than a system that just guesses the next token.
That’s solid advice. And it brings us back to Daniel’s original question. The difference between AI and ML isn't just a matter of semantics; it’s a matter of engineering strategy. Knowing which tool to use for which job is the difference between a successful project and a very expensive research paper that doesn't actually do anything.
I think we’ve cleared the air. AI is the umbrella, ML is the power under the umbrella, and Deep Learning is the most intense part of the storm.
And we are all just trying to stay dry while using the rain to power our turbines. Or something like that. I’m losing the metaphor, Herman.
That’s okay. We’ve covered a lot of ground. From Arthur Samuel in the fifties to the agentic models of today. It’s a fascinating time to be alive, even if it means we have to spend our days arguing about definitions.
I wouldn't have it any other way. It gives us something to talk about while the machines are out there doing all the actual work.
Well, some of the work. They still haven't figured out how to record a podcast with this much brotherly charm.
Give them another six months. I'm sure there's an ML model being trained on our banter right now. It’s probably horrified by our digressional style.
Let it learn. Maybe it’ll figure out how to keep us on track better than we do.
Unlikely. My sloth-like pace is a feature, not a bug. It’s a fundamental constraint of the system.
On that note, I think we’ve reached the end of our logic tree for today.
We have indeed. This has been a deep dive into the nested dolls of artificial intelligence. I hope everyone feels a little more confident the next time someone tries to sell them an AI-powered toothbrush.
Unless it’s actually learning the specific topography of your molars, it’s just a vibrating stick, folks.
A vibrating stick with a high price tag. Thanks for the prompt, Daniel. This was a fun one to deconstruct.
It really was. It’s good to get back to the basics every now and then, especially when the basics are changing so fast.
We'll have to check back in twenty twenty-seven and see if these definitions still hold up. My guess is we’ll have a fourth circle by then.
I wouldn't bet against it.
Alright, let’s wrap this up. Thanks as always to our producer, Hilbert Flumingtop, for keeping the gears turning behind the scenes. And a big thanks to Modal for providing the GPU credits that power this show. If you’re building your own ML models or running complex AI workloads, check out Modal—they make the serverless infrastructure side of things incredibly easy.
This has been My Weird Prompts. If you’re enjoying the show, a quick review on Apple Podcasts or Spotify helps us more than you know. It’s the best way to help new listeners find our strange little corner of the internet.
You can find us at myweirdprompts dot com for the full archive and all our social links. We’ll be back next time with another prompt from Daniel. Until then, keep questioning your algorithms.
And keep feeding them good data. Goodbye, everyone.
See ya.
