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Join Jeff Crume as he dives into the distinctions between Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Foundation Models and how these technologies have evolved over time. He also explores the latest advancements in Generative AI, including large language models, chatbots, and deepfakes – and clarifies common misconceptions, simplifies complex concepts, and discusses the impact these technologies have on various fields.
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Very pleasant to listen & watch with the diagram.
Thanks so much.
Great summary… and subscribed
You explained it really well! It was a simple and concise touch to all facets you mentioned, appreciated!
I just transitioned into Tech, no prior knowledge in Tech and I enrolled for a certificate backend course online 3 months ago. I need you to pls advice me if I should proceed with it or switch to a course in AI
They should change the name to augmented intelligence because they are actually not learning. I learn it open up my brain up to a whole different level. Developing a technique to put what learn into motion is the real battle during the learning process.
7:21 Here is a common critical mistake. It may be categorized as an oversimplification, but for many people they misunderstand this limits of this analogy, to the point of being fundamentally wrong.
A better one may be memes. Learning all the musical notes is like learning all the meme formats. New meme format are being created all the time. If a model generated new memes based on it’s understanding of memes, you might call the things it generates memes. But they aren’t. The way memes are recombined into a new meme is a process with meaning. Yes today referencing is oft simply done for the sake of pointing to a known quantity, and that's all. But it can be done to use that specific meaning/symbol and creating new meaning in another context where it has novelty.
You could expect a modern model to not have the ability to understand that deeply, yet people will pooh-pooh criticism along these lines using analogies like that of musical notes.
A model could be trained to the point of a though understand of all the theory of western music, for example. It would no doubt be trail on a lot of real music. So it will have the ability to make references. Do you believe any modern model would be capable of understanding the meaning that a musician may specifically want to incorporate by using specific references? In complimentary ways? In deliberately contrasting ways?
This may sound harsh, but I frequently hear clear evidence of major blind spots. Again, I can’t say what this person really thinks when they are keeping things simple, but many people do have these blind spots that lead to other people, typically coming from the humanities, being dismissive, when the claim is made that these models have attained certain equivalencies.
I just banged this out and have no schooling (well grade school I do have), so I’m hoping this may be received by anyone as remotely coherent xD
So well explained. Please post more videos like this. Love the instruction. It was easy to understand.
Im new subscriber here.