Machine Learning and Catastrophic Forgetting

really talking more about my oversight than anything else. Maybe I did hear about it, but then I forgot about it, you know, catastrophically. So the thing they talked about was catastrophic forgetting in artificial intelligence, specifically in machine learning systems built on artificial neural networks. Now, before we talk about catastrophic forgetting, which as I mentioned, is related to neural networks and machine learning, we really need to do a

quick reminder, not a quick reminder. We need to do a full reminder on how all this works. And that's going to require us to do a whole lot of remembering. Not a catastrophic amount, but a lot. So the history of artificial intelligence as a discipline is one of intense and important debates in fields like computer science. Now, I have often talked about how AI can be seen as the convergence of several other disciplines into its own field, and there's more than one way to approach the challenge

of artificial intelligence. And in the history of AI, we actually saw that play out, and some would argue the way it played out means that we're actually just now playing catch up. So different schools of thought pushed these different approaches forward as this should be the prevailing methodology we use to devel artificial intelligence. This is important because the development of AI does not exist in a vacuum

right It exists in our real world. Research requires funding, and when you've got different sides arguing that their approach to artificial intelligence is superior and that the alternatives are not just inferior, but potentially limited to the point of being useless, well you've got a metaphorical wrestling match going on. The winner takes home the big prize of getting funding for their research, and the loser has to scrabble for whatever they can find, and often they will see their

work languish as a result. By the way, this is why I often bring stuff up in this podcast that is outside the realm of tech. I've received a lot of messages over the years from folks saying that I should leave out stuff like money or politics. Politics is the big one. But to me, that doesn't make sense cause tech exists within our world, a world that is

largely shaped by money and politics. I don't think we can separate the tech from all of that because I believe that if you were to somehow magically remove those influences, If somehow money and politics never played a part in the development of technology, our tech would look very different from what it does today. Not necessarily better or worse, but different. I mean, think about Thomas Edison. He was very much driven by financial success, like his work in

tech was really mostly about making lots of money. And without the making lots of money part, you don't really have his drive to really bring together the brightest minds of his generation and set them to work on creating incredible technology. So I think we have to take all these things into consideration. Anyway, that's a total rabbit trail,

and I apologize. Let's get back to our story. It really begins around nineteen forty three when a pair of researchers at the University of Chicago first proposed the concept of the basic unit of a neural network. Those researchers were Warren McCullough and Walter Pets, And in fact, they demonstrate their idea by showing a simple electrical circuit the

very basis for what would become a neural network. So their proposal was a system that would use those simple circuits to mimic the neurons that we have in our noggins. So our brain consists of a bunch of these neurons, and you might wonder how much is a bunch, Well, we're talking about on average, around one hundred billion neurons in the human brain. These neurons interconnect with each other.

It's not just a one to one, right, You've got these interconnections between all these different neurons, not with every neuron connected to every other neuron, but lots of interconnections. And if we're looking at just the connection, you would count more than one hundred trillion of them in the typical human brain. And these connections in our brains make

up neural circuits. Those circuits light up, and that represents us doing lots of different stuff from experiencing the world around us so perception to thinking about a past memory. You know that typically is like recreating the same pathway over and over, and sometimes we don't recreate it exactly correctly, and our memory ends up not being a perfect representation of the thing that we actually experienced. This is why things like eyewitness testimony is not always very reliable, because

our memories aren't infallible. They can trick us and we can have all those pathways light up when we learn a new skill and we start forming new pathways, and then as we practice this skill, we start to reinforce those pathways. So McCulla and Pitts propose that we create machines capable of doing essentially a similar thing that our brains do, so kind of a neuromimicry, not exactly one to one the way our brains work, but inspired by

the way our brains work. Now, we would be limited by what the technology of the day would be able to do, because there's no feasible way we could create a massive electrical system with one hundred billion individual simple circuits with more than one hundred trillion connections between them. That would be beyond our capability it would be beyond our resources. We could, however, create systems that used interconnected circuits to process information and to teach such a system

to do specific tasks. Now, in nineteen forty nine, Donald Hebb wrote a book about biological neurons, and he titled this book the Organization of Behavior and suggested neural pathways get stronger with additional use, kind of like you know, if you exercise your muscles, you build strength over time. Will so is the same with neural pathways. And if you don't use those muscles well, then your muscles get weaker. Well,

same with neural pathways. If you end up learning a skill, but then over a great amount of time you no longer practice that skill, you're gonna lose some of your ability. Maybe not all of it, but at least some of it. And you have to you know, like I think about wrestlers who come back from from retirement. Professional wrestlers, they call it ring rust. You got to knock off the ring rust and get back into step and kind of get back into your groove. And it takes a little time.

Typically sometimes you know, you can get back into the game faster than others, but you get the idea, and also heb ended up proposing the concept of cells that fire together wire together, meaning that neurons that fire at the same time end up strengthening faster than other neurons do. So when you get into that system, you can actually reinforce those pathways. And for AI this would be really important.

And it wasn't very long after Donald have had published this work that researchers in the field of AI tried to apply that concept that philosophy to computer science. By the mid nineteen fifties, the burgeoning computer science Lab and AI Lab at MIT was building out neural networks based on Hebb's ideas. Meanwhile, another computer scientist named Frank Rosenblatt was looking at primitive neural systems and he started with flies,

like house flies. He wanted to explore systems that were involved when a fly would quickly move away after detecting a possible threat, like instantly, or at least appearing to us to instantly react to something. So, for example, a fly swater coming at it like you might be moving the fly swater very quickly, and yet the fly is able to move super fast with no perceivable delay. Right, we know that we have a delay from when we

perceive something to when we can act on something. Like if you've ever been in a fender bender in a car accident, you know that that there's a delay between when you see the issue when you can hit the brake, and that can lead to accidents. Well, with flies, that delay seems to be super super small. So Rosenblatt was really interested in exploring the neurological reasons for that. How

can that happen? It has to be really simple, right, There has to be a simple and more or less direct pathway that exists to allow a fly to react to detecting a potential threat like that. And if you could replicate that with electronics, you could have a very simple but potentially powerful artificial intelligence system. So he came up with this system that would be based off that very simple direct pathway that you would see in something

like a fly, and he called it the perceptron. So he went back to the simple circuit design that was proposed by Pitts and McCullough and he built out the Mark one perceptron or perceptron I guess I should say, So let's talk about a perceptron like not big p but a little p perceptron. This is probably what we would call a neural node in a modern neural network. So the purpose of the perceptron was to accept inputs

and produce an output based on some threshold. Like if the inputs meet a certain threshold, one output would be produced. If they failed to do so, a different output would be produced. The inputs, in turn would be assigned weights, which would factor into the output the perceptron would generate. So when we're talking weights, I mean you eights as in like how heavy something is, or in this case,

how much impact that thing has. So we're talking about how much impact one input has relative to other inputs. Let me use a really mundane human example to kind of explain what this means. Let's say that your friend asks you to go see a movie with them, and it's going to be playing tonight at nine pm. But you've had a really busy day and you might not be able to even eat dinner until around nine pm.

And if you go see this movie, it might mean having to skip dinner or to try and eat something really fast and unhealthy before you go to the movie. What's more, you got a really big day tomorrow and you feel like you really need to be well rested for it. However, at the same time, you haven't seen this friend in ages, and you really like this person and you've wanted to hang with them for a really long time. Plus the movie they're suggesting is one you've

hell he wanted to see and you haven't gone yet. Well, you would likely assign at least unconsciously weights to each of these factors before you make your decision. You know, if getting some dinner without having to rush and also to be really well rested for tomorrow are really important

to you, you'll probably reluctantly decline the offer. But if you really crave some time with your friend and you really want to see that movie before all the spoilers come out on Facebook or whatever, maybe you'll say yes. Your decision depends upon the weights you assign those factors, those inputs, even if you don't consciously think about it that way. Well. The Perceptron system worked in a similar way, produced outputs by taking the inputs into consideration, including each

input's weight. Moreover, the more you submitted inputs, the more the system would quote unquote learn how to weight each of those inputs, all with the goal of bringing the actual output that the process or you know, generates closer to the one you want it to generate. Okay, I just said a lot there. We've got some more to get through. But before we get to that, let's take

a quick break. All right. Before the break, we were talking about inputs and weights and the idea of getting an output that is close to what you want the system to do. That's not a guarantee, right, The system could generate an output that's quote unquote wrong, you know, depending on whatever task you've set this machine learning system to learn. And that gets a bit conceptual. So let's talk about a simple example that I love to use.

If you've been listening to tech sta for a while, you've heard this before, and that's talking about pictures of cats. Because cats ruled the internet. I don't know if they still do. They won't talk to me, so they just knock things off shelves. Anyway, if your goal is to teach a computer system to differentiate photos that include a cat from photos that do not include a cat, well, you would need to train the system, and part of that includes feeding the system a whole bunch of photographs.

Some of those would have cats in them, some would not, and chances are the system would misidentify photos, maybe a significant number of those photos. You would probably have false positives where the system thinks there's a cat there and there's not, and false negatives where it doesn't think there's a cat there but there is. At that point, your goal is to try and teach the system to close the gap between the actual results it produces and what

you want it to produce. In some systems that means you might have to go in manually to adjust the input weights to increase the weight of one input versus another in an effort to cut down on mistakes. So the perceptron was interesting, but it was very limit in complexity. It was essentially a single layer where you'd feed a bunch of inputs in and you would get an output, So it was suitable for a subset of computational challenges, but anything beyond that was well beyond its own reach

as a single layer network. By the late nineteen fifties, other researchers had created new neural networks that were multi layered, so a node or neuron didn't just accept inputs, it would generate outputs that then would become inputs for another layer down. So instead of just having one layer of nodes,

you would have multiple layers of nodes. Typically you would have one at the quote unquote top of the network, and you would have outputs at the bottom, and the ones in between would be often referred to as hidden layers, and who knows how many there would be. So anyway, you would feed data to the system. The initial nodes would generate information as outputs that would become inputs for the next layer down, which would then continue the process and so on and so forth until you get to

the output. So now you had artificial neural networks that could tackle more complex challenges, and you would have multiple steps in the process. Didn't necessarily mean they were automatically better than the perceptron, was just that they were able to tackle more complicated tasks. What followed is something that will probably sound really familiar to you if you ever follow technology or fads, the hype around machine learning and artificial intelligence, And keep in mind this is like the

nineteen sixties. It grew beyond the technology's actual capabilities. At that time, people started to project what this technology would be able to do, and they did so thinking it was going to be in a very short turnaround, like we're right on the very precipice of a monstrous breakthrough that will bring the science fiction future into the press. So when it was realized that we weren't at that, like,

that's not how progress typically works. It's usually much more gradual and humble than that, Well, then enthusiasm around AI began to take a hit. And as I mentioned already, a big part of AI research really comes down to funding, and it gets really challenging to secure funding when public opinion dims on a technology. We've seen this happen lots of times, right, Like three D television was a fad

that was pushed. Now, granted, that one, you could argue was more of an example of manufacturing companies that make televisions trying to push a technology on consumers and the consumers just weren't interested. You could argue that was the case there. But virtual reality in the nineteen nineties definitely followed this pathway. There was this excitement around virtual reality. Then that excitement faded to almost nothing when people realized that the actual state of the art of the technology

was far below where they expected it to be. And suddenly people who are working in VR couldn't get funding for their work, and they kind of had to scrounge around in order to keep the development going at all, and then eventually we would see that come back around again. You could argue that NFTs recently went through this too, where the hype went well beyond what NFTs could actually do.

I've been really down on NFTs in general. I do think that there are potential legitimate uses for NFTs, but I think the early examples were frivolous and almost solely centered around speculation, as in like financial speculation, and as a result, there was nothing for it to do other than to create a bubble that would ultimately burst, which

is what happened. And maybe NFTs will recover from that and become something that's more fundamentally useful in the Internet in the future or in digital commerce in the future, but it's going to have to get over the catastrophe that happened when the rug was pulled out from underneath n FTS, and that was all, you know, predictable and preventable. But like I've said before, like I've lifted the joke from Peter Cook, we've learned from our mistakes. We can

repeat them almost exactly. Anyway, This same sort of hype cycle activity happened with neural networks and machine learning in the nineteen sixties. Then enter Marvin Minsky and Seymour Pappart of MIT's AI lab. They were leading that lab at the time. In nineteen sixty nine, they co authored a book titled Perceptrons. They were actually critical of that artificial

neural network approach to AI and machine learning. They were concerned that the limitations of the technology meant that you and you need an unrealistically huge system of artificial neurons, perhaps then using that system to compute an infinite number of variations of the same process or task if you wanted to train the weights so that they were of

the optimal value. So, in other words, they thought, it's too impractical, and it's going to take too much compute time, and you're never going to achieve the result you want. You're never going to get to that most perfect system. And they believed it just had fundamental inescapable flaws. They had different systems in mind. Now Minski and Separate tried to push their systems forward, and I could do a full episode about them too, and their ideas were not bad.

They were different. It was a different approach. But this also meant that researchers who had been pushing the development of our artificial neural networks felt forced to move on to different projects because financial support for anything connected to the concept of neural networks effectively disappeared, right like funding

just dropped for that. Because here you had these experts in computer science saying, yeah, this approach, while interesting, has already hit an insurmountable obstacle and it's not going to go any further. It's gone as far as it can go.

And so a lot of computer scientists blamed Minsky and Separate for essentially demolishing funding for neural networks for more than a decade, and in fact, this would become an era that retrospectively, computer scientists would reference as the AI Winter got all Game of Thrones up in here Now. In nineteen eighty two, there was a hint of spring thawing out that AI Winter researchers in Japan were starting

to resurrect work on neural network projects. And meanwhile, a scientist named John Hopfield submitted a research paper to the Neattional Academy of Sciences that brought neural networks back into discussion here in the United States, and because Japan was actively investing in developing that technology, institutions in the United States began to open up the purse strings a bit because there was a concern that if there were something to this artificial neural network concept, if in fact those

obstacles weren't insurmountable, as min skin Separate had suggested, the US could potentially fall behind another country because it would fail to fund its development. So, in a desire not to have Japan take the ball and run with it, the United States began to invest again in artificial neural network research and development. In the mid nineteen eighties, computer scientists essentially rediscovered the usefulness of a process called back propagation.

And I've already talked about nodes and weights and stuff, but this is going to require a little bit more explanation to understand what by propagation is all about. So let's kind of try to visualize a neural network. So you've got your input nodes. Just think of a bunch of circles. If you were drawing it from top to bottom, this would be your top layer. This is like the funnels where you're going to feed data into the system.

Now you've got a whole bunch of these at the top, and they can accept the data that you're feeding in. They process that data and then based upon you some operation, they will then send an output to a node one layer down. So there's lots of other nodes in the layers below, or maybe not as many as you have initial layers. You might actually have fewer, and the layers above will send to you know, data to a specific node depending upon what the outcome is. Whatever the output is,

So these nodes accept the input. These inputs have a bias and a weight to them, and this is one the hidden layers. They will then create an output and send that on to nodes another layer down. So this goes on until you get to your output layer where you get your final result, and then you can determine whether or not the final result matches what you were hoping for. So did your system properly identify which photos

do and don't have cats in them? Now, as I mentioned earlier, you typically get results that aren't perfect, but we want to train the system to improve with every test. Back propagation is one way to do this. So with that propagation, you actually start with the final output. You've already done a test run, right, and you've got your output, and maybe your test has five possible final outcomes, but only one of those is the outcome you actually want. Okay,

we'll say it's outcome number one. We're saying I want this system to more often than not come to the conclusion that it's outcome number one one. But you run your test. It's got one thousand little tasks in it, and you run your test, you find out that it only arrives at outcome number one five percent of the time, which is actually worse than random chance. Right, it should be twenty percent for random chance, But it's only getting

there five percent of the time. Something is going really wrong with your system for it to mistakenly go to one of the other options and very rarely go to the correct one. So let's say you also noticed the outcome number three. It goes to that one forty percent of the time. So it's making this mistake forty percent of the time and only getting it right five percent of the time. So things are seriously out of whack.

You need to find which connections which would involve the biases and the weights that are within your system that are leading it to mistakenly arrive at the wrong outcome. So frequently you want to reduce those factors, and simultaneously you need to boost the ones that lead the system to arrive at outcome number one, because that's the answer you actually want the system to get to. All Right, I've been droning on for a bit, Let's take another

quick break. When we come back, I'll finish up explaining this and then we'll move on to catastrophic forgetting. Okay, so we were talking about how you are looking at a system that is coming to the wrong conclusion ninety five percent of the time. It is a broken system. You have to then figure out what factors are causing this to happen, and they are numerous, right, They extend all the way up to the very top of your neural network, the other end where the input comes in.

But you can't just change everything all at once. You've got to figure this out systematically, and that's what backpropagation is really all about. It detects which links one layer up from the output have the greatest impact on the outcome. Right, changing everything would be tedious, It would be impractical. You might even make things worse. Some of these neural networks are confoundingly complicated, so it's not really a feasible solution.

So instead you look at the connections that are having the biggest impact on your outcome. So you want things where if you make a small change in either the bias or the weight, or maybe both, you'll see a larger end effect on the outcome. All the connections are arguably important, but some are more important than others. Backpropagation works backwards from the result toward the other end of

the network to tweak those connections. It boosts ones that lead to the correct or desired response, and it reduces the values of those that lead to incorrect or undesired responses.

If we were to think of this like the classic example and chaos theory, this could potentially involve us studying a hurricane as it hits land and tracing its history back as it moved through the ocean, and we would eventually arrive at the point where it was a tropical storm, and then we would go further back and see the factors that led to the creation of that storm, and maybe if we tracked it all the way back we would even find that one of a billion factors that

made the storm was in fact, a butterfly was flapping its wings on the other side of the world, and that contributed to it. Maybe we find out that butterfly flap of its wings had an impact, but it was negligible, and that if the butterfly hadn't flapped its wings, the hurricane still would have happened. That would be an example of, well, we don't bother adjusting the weight of the impact of that butterfly flapping its wings because it doesn't matter for

the end result. But what if we were to discover that that butterfly flap of its wings is the only reason the hurricane happened that, or at least was the primary reason that all the other factors pale in comparison, Well, then we'd want to make sure we boost the weight of that input, because clearly that butterfly is fundamental for hurricanes. I think hurricanes are really dangerous, and I would ask

butterflies to kind of chill, all right. I mean, I don't want butterflies to go away, just you know, maybe stop flapping so much. Anyway, the formula for back propagation gets into some calculus that is well beyond my knowledge and skill. So rather than attempt to stumble my way through an explanation that I don't actually understand, I think it's best to leave the concept at the high level that I have described right now. So just know that it gets way more granular than what I've talked about.

But essentially, you're looking at those factors that led to the ultimate decision and saying which ones of these had the greatest impact, and how can I tweak them so that I can shape the outcome to one I wanted. If we were thinking about that example I gave about whether or not you go to the movies, maybe in present day you starts thinking about past experiences where you made a decision to go out when you had a big day, then the following day, and how that impacted you,

perhaps negatively. Maybe you're like, man, I should have gotten a promotion by now, and then you think, well, I do go to the movies an awful lot. You might say, I need to adjust some of the factors that affect my decision making process and perhaps prioritize my career. Or if you've decided that late stage capitalism is terrible, evil, and that you're going to try and live a hedonistic

lifestyle of a wandering soul. Maybe you say, I'm going to go and see my movie with my friend, and yeah, that's just how it is, because that's the most important thing to me. You only go around this crazy world once. After all, I'm not telling you which way to go. I'm still finding my own way. But yeah, backpropagation would be how you would go back and say, all right, well, because I don't like the outcome that happened, and I need to change the way. These factors weigh in on

the decision making process that goes through the whole system. Now, the advancements in the science of neural networks proved that the technology no longer operated under the constraints that concern Minsky and support in the late sixties, So once again funding found its way to neural network research and development projects. Now let's finally talk about forgetting and what makes it catastrophic.

So you could, in theory, develop an artificial neural network and have a library of training data, and the only thing you ever do with this network is you feed that same set of training data to that same neural network over and over. In an effort to get performance

as close to perfect as you possibly can. Just you know, it's kind of like if you have a car and you're constantly tweaking it so it will perform better, and maybe you change one thing and it boosts performance in one area, but it kind of negatively impacts performance in another area, so then you got to tweak something else.

You could be doing that with an artificial neural network forever and just be using the same set of training data, and all you're trying to do is make a system that could handle that training data better than any other system in the world. And that would be interesting, but

it would be useless from a practical standpoint. You could say, like, hey, you want to see my machine that can sort through only this collection of photographs and pick out the ones that have cats in them and the ones that don't

pretty pretty darn effectively, but not perfectly. It's not really an interesting value proposition, right, So more likely you are eventually going to start feeding lots of different kinds of data to this neural network, And yeah, you train the network on certain data sets, but your goal is to feed new sets of data data the system has never encountered before. And rely on the system's ability to process

this information correctly to get the result you want. And we might even be talking about stuff the human beings can't easily do. Right. But see, the training data is going to mean that the network will start to create and reinforce certain pathways, and those pathways will over time get stronger and stronger, just as we said at the beginning of this episode. But new data is going to necessitate new pathways. Sometimes, when the system begins to form

these new pathways, it forgets the old pathways. So it's possible for a neural network to actually get worse at the task it had previously been trained to do with the actual training material. In fact, in a true catastrophe, the system might forget the objective and doesn't recognize what the desired outcome is meant to be, so the results can appear random and meaningless. It's as if the system

has developed some form of amnesia. So this is prevalent, most prevalent anyway, in systems that rely on unguided learning. With guided learning, you have engineers who are carefully selecting the data that gets fed into a system. An unguided system would collect raw data from wherever and attempt to deliver desired results, and that those are the kinds of

neural networks that are more prone to catastrophic forgetting. But as I said, machine learning systems tackle new data, maybe even new tasks, and then you get the risk of the system forgetting stuff. So I jokingly say, it's kind of like when I learned something new, it has to push out something old, like you know, my friend's phone number or something. Suddenly I can no longer remember it because I learned some new interesting fact, as if I have met my capacity for being able to know things.

So learning anything new necessitates having to forget something I used to know, like gat Ye, because now gat Ye is just somebody that I used to know. But wait, there's more. Just as a system can experience catastrophic forgetting, it can also experience catastrophic remembering. This is when a system mistakenly believes it is doing one process a task it had previously been trained to do, rather than the

one it's actually trying to do. So let's say we've got an artificial neural network, and originally we taught it to recognize the photos that have cats in them versus the ones that don't. But now we have retrained the same artificial neural network to try and recognize handwritten text, except when we feed handwritten text to the system, suddenly the system believes it's trying to determine where the cats are. This is something that can happen with machine learning systems too,

and you still get bad results out of it. So this is a real problem. Now, these are not insurmountable problems. There are some solutions that are actually intuitive. For example, any game out their nose that it's best to save your game just before you head into a big boss battle, just in case things don't go the way you planned. Well. With artificial neural networks, it's maybe not a bad idea to make a copy of a network before you retrain it to do something new. Then you still have the

backup if things do go pair shape. There are other approaches to decreasing the risk of catastrophic forgetting or catastrophic remembering. An article in applied Mathematics titled Overcoming catastrophic forgetting in neural networks describes a system in which the researchers purposefully slowed down the network's ability to change the weights involved in important tasks. From previous training cycles. So this makes teaching the system to do new tasks a little more

challenging because it's protecting these weights. It's preventing the system's ability to be completely plastic, which means the system has to work around these constraints and still learn how to do the new task, but in the process it means it doesn't forget how to do the previous tasks. This article is interesting because the tasks the researchers actually used the purposes of training, Like, what were they teaching the artificial neural network to do well. They were teaching it

how to play Atari twenty six hundred games. So they would start with one game and train the system on how to play the game. Then they would give the system a new game with different game mechanics, and the system would have to learn how to play this new game, but they wanted to see if it could still remember how to play the original game. That was kind of

the system they were working on. They were tweaking things so that the machine learning artificial neural network as a whole could learn how to play multiple Atari twenty six hundred games without forgetting how to do the previous ones. This is a non trivial task. I mean, it takes a lot of work to see exactly how to preserve things so that you're not slowing down the learning process too much, but you're also not inviting the possibility of

catastrophic forgetting. Now that's just one example of how researchers are looking to mitigate the problem of catastrophic forgetting in catastrophic remembering, there are other methods as well, and maybe I'll do another episode where I'll go into more detail on some of those. They do get pretty complicated, and in fact, eventually Rerilli and I even eventually pretty early on, I hit my limit for as far as I can

understand the actual mechanics of the system. So rather than try and punch above my weight, I think it's best to kind of be a little more general, but just to have that understanding to kind of get a better appreciation of some of the challenges relating to artificial intelligence

in general and machine learning in particular. And again, like this machine learning issue, it's a bigger problem with more sophisticated systems that are meant to do unsupervised and unguided learning, right, those are the ones that are going to be more prone to these issues. If we're talking about supervised and guided learning, where engineers are being very careful with the data being fed to a system, it's less likely to happen.

But the whole promise, or at least the you know, not the promise of the technology itself, but the promise of the people who are funding it, is that this technology is going to reach a point where it's able to learn on its own and be able to do things better than people can do, to free us up to doing, you know, stuff we want to do instead of stuff we have to do. That's like the science fiction dream version of AI. As we all know, getting

there is much more painful. It's not like a simple process of Hey, we've made everything easy to do now, and you don't have to worry called day. You can enjoy your life and pursue your dreams and develop your hobbies and your interests, and you can have fulfillment and somehow money isn't important anymore. Like that seems to be the Star Trek version of the future that people want it to go in. But as we have seen, the

process of getting there is way more painful. As you know, people face a reality of potentially being out of work because of AI, or maybe being paid way less to do work because the AI is doing most of it. These are not That's not Star Trek future. That's getting like into Blade Runner future, So we don't want that one. By the way, the tears in the Rain speech is fantastic, but you do not want to live in the Blade

Runner world. Trust me, you might not want to live in the Star Trek world either, because those outfits don't look that comfortable. Anyway. That's my little discussion about AI, machine learning and castrophic forgetting in cast trophic. Remembering again, this is just one of the challenges associated with AI

and machine learning. I don't mean to suggest it's the one and only, or even that it's the most important one, but it is one that I had not really heard of until I listened to that Skeptics Guide to the Universe episode over the weekend, and it was really interesting to dive into the material and read up about it and to get a better understanding of what it means and how it works. And as I said, we'll probably revisit this topic in the future, especially since AI is

such a big deal these days. Okay, but that's it for this episode. Of tech Stuff. I hope you are all well, and I will talk to you again really soon. Tech Stuff is an iHeartRadio production. For more podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts, or wherever you listen to your favorite shows.