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Everybody's talking about artificial intelligence these days, AI.
IBM explains AI, machine learning, deep learning, and generative AI, clarifying their differences and relationships with real-world examples.
Key Takeaways
- AI is a broad field focused on simulating human intelligence through learning and reasoning.
- Machine learning enables computers to learn patterns from data and make predictions without explicit programming.
- Deep learning uses neural networks with multiple layers to model complex data but can be difficult to interpret.
- Generative AI represents a significant leap by creating new content using foundation models like large language models.
- Understanding the distinctions and relationships between these technologies helps clarify their applications and potentials.
Summary
- Artificial intelligence (AI) aims to simulate or exceed human intelligence, including learning, reasoning, and inference.
- Machine learning involves algorithms that learn from data patterns without explicit programming, useful for predictions and anomaly detection.
- Deep learning uses multi-layered neural networks to mimic brain function, often producing complex, sometimes unpredictable results.
- Generative AI, the latest advancement, includes foundation models like large language models that generate new content such as text, audio, and video.
- The video addresses common myths and misconceptions about AI, machine learning, deep learning, and generative AI.
- Expert systems in the 1980s and 1990s were early AI applications using languages like Lisp and Prolog.
- Machine learning gained popularity in the 2010s and is foundational for many current AI applications.
- Deep learning also rose to prominence in the 2010s and underpins many modern AI breakthroughs.
- Generative AI models predict and create content beyond simple autocomplete, generating sentences, paragraphs, or entire documents.
- The video uses analogies, such as music composition, to explain how generative AI creates new content by recombining existing information.
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Machine learning is another hot topic.
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Are they the same thing or are they different, 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 and talked about where they fit.
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And there were a lot of comments on that, and I read those comments and I'd like to address some of the most frequently asked questions 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, and that is this the absolute explosion of this area of generative AI.
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Things like large language models and chatbots have seemed to be taking over the world, we see them everywhere.
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Really interesting technology, and then also things like deep fakes.
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These are all within the realm of AI.
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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.
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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 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.
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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.
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Things like that.
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So that's what we're trying to do in the broad field of AI.
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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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Uh, and in those days, it was very premature, most people had not even heard of it.
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Uh, and uh, it it basically was a research project.
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But I can tell you, uh, as an undergrad, which for me was back during these times.
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Uh, we were doing AI work.
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In fact, we would use programming languages like Lisp.
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Uh, or Prolog.
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Uh, and these kinds of things, uh, 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.
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But that's when it really, uh, hit the kind of a critical mass.
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And became more popularized.
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So expert systems of the 1980s, maybe in the 90s.
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And and again, we used technologies like this.
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All of this, uh, was was something that we did.
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Before we ever touched into the next topic I'm going to talk about.
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And that's the area of machine learning.
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Machine learning is as its name implies.
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The machine is learning.
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I don't have to program it.
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I give it lots of information and it it observes things.
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So for instance, if I start doing this.
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If I give you this and then ask you to predict what's the next thing that's going to be there?
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Well, you might get it, you might not.
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You have very limited training data to base this on.
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But if I gave you one of those and then ask you what to predict would happen next.
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Well, you're probably going to say this.
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And then you're going to say it's this.
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And then you think you got it all figured out.
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And then you see one of these and then all of a sudden.
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I give you one of those and throw you a curve ball.
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So this, in fact.
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And then maybe it it goes on like this.
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So a machine learning algorithm is really good at looking at patterns.
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And discovering patterns within data.
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The more training data you can give it, the more confident it can be in predicting.
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So predictions are one of the things that machine learning is is particularly good at.
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Another thing is spotting outliers.
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Like this and saying, oh, that doesn't belong.
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In the, it looks different than all the other stuff.
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Because the sequence was broken.
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So that's particularly useful in cybersecurity, the area that I work in.
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Because we're looking for outliers.
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We're looking for users who are using the system in ways that they shouldn't be.
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Or ways that they don't typically do.
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So this technology, machine learning is particularly useful for us.
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And machine learning really came along, uh, and became more popularized.
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Uh, in this time frame.
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Uh, in the the 2010s.
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Uh, and again, uh, back when I was an undergrad.
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Writing my dinosaur class.
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We were doing this kind of stuff.
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We never once talked about machine learning.
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It might have existed, but it really wasn't, hadn't hit the popular, uh, mindset yet.
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Uh, but this technology has matured greatly over the last few decades.
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And now it becomes the basis of a lot we do going forward.
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The next layer of our Venn diagram.
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Involves deep learning.
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Well, it's deep learning in the sense that with deep learning.
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We use these things called neural networks.
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Neural networks are ways that in a computer, we simulate and mimic the way the human brain works.
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At least to the extent that we understand how the brain works.
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And it's called deep because we have multiple layers of those neural networks.
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And the interesting thing about these is they will simulate the way a brain operates.
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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.
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And deep learning is the same way.
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In some cases, we're not actually able to fully understand why we get the results we do, uh, because there are so many layers to the neural network.
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It's a little bit hard to to decompose and figure out exactly what's in there.
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But this has become a very important part and a very important advancement.
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That also reached some popularity during the 2010s.
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And as something that we use still today as the basis for our next area of AI.
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The most recent advancements in the field of artificial intelligence.
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All really are in this space.
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The area of generative AI.
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Now, I'm going to introduce a term that you may not be familiar with.
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It's the idea of foundation models.
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Foundation models is where we get some of these kinds of things.
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For instance, an example of a foundation model.
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Would be a large language model.
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Which is where we take language.
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And we model it.
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And we make predictions in this technology.
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Where if I see certain types of 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.
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But think about this as a little bit like the autocomplete when you start typing something in.
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And then it predicts what your next word will be.
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Except in this case, with large language models, they're not predicting the next word.
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They're predicting the next sentence.
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The next paragraph.
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The next entire document.
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So there's a 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.
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Because they are generating new content.
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Uh, some people have actually made the argument that that generative AI isn't really generative.
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That that these technologies are really just regurgitating existing information.
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And putting it in different format.
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Well, let me give you an analogy.
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Uh, if you take music, for instance.
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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.
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And just putting them in a different order.
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Well, we don't say new new music doesn't exist.
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People are still composing and creating new songs.
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From the existing information.
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I'm going to say Gen AI is similar.
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It's a it's an analogy, so there'll be some imperfections in it.
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But you get the general idea.
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Actually new content can be generated out of these.
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And there are a lot of different forms that this can take.
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With other types of models are, uh, audio models.
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Uh, video models.
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And things like that.
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Well, in fact, these we can use to create deep fakes.
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And deep fakes are examples where we're able to take, for instance, a person's voice.
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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.
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Uh, in parodies and things like that.
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Uh, 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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Uh, the chatbots again come from this space.
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The deep fakes 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 five to 10 years away.
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But then machine learning, deep learning and things like that came along.
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And we started seeing some uptick.
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Then foundation models, Gen AI and the light came along.
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And this stuff went straight to the moon.
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These foundation models are what have changed the adoption curve.
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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.
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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.
Topics:Artificial IntelligenceAIMachine LearningDeep LearningGenerative AILarge Language ModelsNeural NetworksFoundation ModelsExpert SystemsIBM Technology
