AI video - Contenido educativo
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Everybody's talking about artificial intelligence these days, AI.
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Machine learning is another hot topic.
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Are they the same thing or are they different?
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And if so, what are those differences?
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And deep learning is another one that comes into play.
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I actually did a video on these three, artificial intelligence, machine learning, and deep learning,
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and talked about where they fit.
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And there were a lot of comments on that, and I read those comments,
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and I'd like to address some of the most frequently asked questions
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so that we can clear up some of the myths and misconceptions around this.
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In addition, something else has happened since that video was recorded,
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and that is the absolute explosion of this area of generative AI.
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Things like large language models and chatbots seem to be taking over the world.
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We see them everywhere.
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Really interesting technology.
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And then also things like deepfakes.
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These are all within the realm of AI, but how do they fit within each other?
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How are they related to each other?
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We're going to take a look at that in this video and try to explain how all these technologies relate and how we can use them.
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First off, a little bit of a disclaimer.
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I'm going to have to simplify some of these concepts in order to not make this video last for a week.
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So those of you that are really deep experts in the field, apologies in advance.
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But we're going to try to make this simple, and that will involve some generalizations.
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First of all, let's start with AI.
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Artificial intelligence is basically trying to simulate with a computer something that would match or exceed human intelligence.
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What is intelligence?
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Well, it could be a lot of different things, but generally we tend to think of it as the ability to learn, to infer, and to reason, things like that.
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So that's what we're trying to do in the broad field of AI, of artificial intelligence.
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And if we look at a timeline of AI, it really kind of started back around this time frame.
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And in those days, it was very premature.
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Most people had not even heard of it.
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And it basically was a research project.
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But I can tell you, as an undergrad, which for me was back during these times, we were doing AI work.
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In fact, we would use programming languages like Lisp or Prolog.
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And these kinds of things were kind of the predecessors to what became later expert systems.
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And this was a technology, again, some of these things existed previous, but that's when it really hit kind of a critical mass and became more popularized.
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So expert systems of the 1980s, maybe in the 90s.
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And again, we use technologies like this.
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all of this was something that we did before we ever touched in to the next topic I'm going to
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talk about. And that's the area of machine learning. Machine learning is, as its name implies,
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the machine is learning. I don't have to program it. I give it lots of information and it observes
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things. So for instance, if I start doing this, if I give you this and then ask you to predict
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what's the next thing that's going to be there, well, you might get it, you might not. You have
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very limited training data to base this on. But if I gave you one of those and then ask you what
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to predict would happen next, well, you're probably going to say this. And then you're
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going to say it's this. And then you think you got it all figured out. And then you see one of
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these. And then all of a sudden, I give you one of those and throw you a curveball. So this,
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in fact, and then maybe it goes on like this. So a machine learning algorithm is really good
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at looking at patterns and discovering patterns within data. The more training data you can give
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the more confident it can be in predicting. So predictions are one of the things that machine
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learning is particularly good at. Another thing is spotting outliers like this and saying,
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oh, that doesn't belong in, it looks different than all the other stuff because the sequence
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was broken. So that's particularly useful in cybersecurity, the area that I work in,
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because we're looking for outliers. We're looking for users who are using the system in ways that
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they shouldn't be or ways that they don't typically do. So this technology, machine learning,
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is particularly useful for us. And machine learning really came along and became more
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popularized in this time frame, in the 2010s. And again, back when I was an undergrad writing my
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class, we were doing this kind of stuff. We never once talked about machine learning. It might have
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existed, but it really hadn't hit the popular mindset yet. But this technology has matured
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greatly over the last few decades, and now it becomes the basis of a lot we do going forward.
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The next layer of our Venn diagram involves deep learning. Well, it's deep learning in the sense
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that with deep learning, we use these things called neural networks. Neural networks are ways
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that in a computer we simulate and mimic the way the human brain works, at least to the extent that
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we understand how the brain works. And it's called deep because we have multiple layers of those
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neural networks. And the interesting thing about these is they will simulate the way a brain
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operates. But I don't know if you've noticed, but human brains can be a little bit unpredictable.
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You put certain things in, you don't always get the very same thing out. And deep learning is the
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same way. In some cases, we're not actually able to fully understand why we get the results we do
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because there are so many layers to the neural network, it's a little bit hard to decompose
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and figure out exactly what's in there. But this has become a very important part and a very
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important advancement that also reached some popularity during the 2010s and as something
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that we use still today as the basis for our next area of AI. The most recent advancements
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in the field of artificial intelligence, all really are in this space, the area of generative
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AI. Now, I'm going to introduce a term that you may not be familiar with. It's the idea of
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foundation models. Foundation models is where we get some of these kinds of things. For instance,
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an example of a foundation model would be a large language model, which is where we take language
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language and we model it and we make predictions in this technology where if I see certain
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types of words, then I can sort of predict what the next set of words will be.
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I'm going to oversimplify here for the sake of simplicity, but think about this as a little
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bit like the autocomplete.
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When you start typing something in and then it predicts what your next word will be, except
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in this case with large language models, they're not predicting the next word, they're predicting
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the next sentence, the next paragraph, the next entire document.
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So there's really an amazing exponential leap in what these things are able to do.
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And we call all of these technologies generative because they are generating new content.
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Some people have actually made the argument that the generative AI isn't really generative,
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that these technologies are really just regurgitating existing information and putting it in different format.
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Well, let me give you an analogy.
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If you take music, for instance, then every note has already been invented.
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So, in a sense, every song is just a recombination, some other permutation of all the notes that already exist already and just putting them in a different order.
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Well, we don't say new music doesn't exist.
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People are still composing and creating new songs from the existing information.
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I'm going to say Gen AI is similar.
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It's an analogy, so there'll be some imperfections in it, but you get the general idea.
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Actually, new content can be generated out of these, and there are a lot of different forms that this can take.
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Other types of models are audio models, video models, and things like that.
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Well, in fact, these we can use to create deepfakes.
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And deep fakes are examples where we're able to take, for instance, a person's voice and recreate that
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and then have it seem like the person said things they never said.
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Well, it's really useful in entertainment situations, in parodies and things like that,
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or if someone's losing their voice, then you could capture their voice,
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and then they'd be able to type and you'd be able to hear it in their voice.
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But there's also a lot of cases where this stuff could be abused.
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The chatbots, again, come from this space.
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The deepfakes come from this space.
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But they're all part of generative AI and all part of these foundation models.
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And this, again, is the area that has really caused all of us to really pay attention to AI.
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The possibilities of generating new content or, in some cases, summarizing existing content
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and giving us something that is bite-sized and manageable.
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This is what has gotten all of the attention.
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This is where the chatbots and all of these things come in.
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In the early days, AI's adoption started off pretty slowly.
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Most people didn't even know it existed, and if they did, it was something that always seemed like it was about 5 to 10 years away.
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But then machine learning, deep learning, and things like that came along, and we started seeing some uptick.
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Then foundation models, Gen AI, and the light came along, and this stuff went straight to the moon.
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These foundation models are what have changed the adoption curve and now you see AI being adopted everywhere.
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And the thing for us to understand is where this is, where it fits in, and make sure that we can reap the benefits from all of this technology.
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If you like this video and want to see more like it, please like and subscribe.
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If you have any questions or want to share your thoughts about this topic, please leave a comment below.
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- Materias:
- Ciencias de la computación 2
- Niveles educativos:
- ▼ Mostrar / ocultar niveles
- Educación Secundaria Obligatoria
- Ordinaria
- Primer Ciclo
- Primer Curso
- Segundo Curso
- Segundo Ciclo
- Tercer Curso
- Cuarto Curso
- Diversificacion Curricular 1
- Diversificacion Curricular 2
- Primer Ciclo
- Ordinaria
- Subido por:
- Elena Del Mar S.
- Licencia:
- Todos los derechos reservados
- Visualizaciones:
- 1
- Fecha:
- 25 de octubre de 2025 - 12:20
- Visibilidad:
- Clave
- Centro:
- IES JANE GOODALL
- Duración:
- 10′
- Relación de aspecto:
- 1.78:1
- Resolución:
- 1920x1080 píxeles
- Tamaño:
- 584.65 MBytes