What are Generative AI models? 

As an IBM Employee this video makes me proud ❤

Kate, this was awesome. It is so refreshing to find presenters who can take complicated material and explain it, in just a few minutes, in a fashion that makes it so reachable.

The explanation i`m assuming is great for a technical person which knows already a lot about generative ai models but for the larger public you need to explain it way simpler and not using technical terms. Analogies help a lot.

So far, by far the best video on Generative AI I’ve seen.

Over the last week, I have been trying to find out the differences between Generative AI and Foundation Models, but could not find the relevant and exact content and this one video has cleared all of that, too good!

Great explanation at a very basic level, very easy to follow the whole video. Thank you very much.

Intern Jr. GenAI Engineer here. You make me understand the larger scope of what Im doing.

The most concise summary / explanation of what is Generative AI 👍💯

These videos are really amazing and deserve waaaay more attention and credit. IBM Technology, you are doing a great Job!

I love that this is on RU-vid for free, thank you very much for a great basic understanding! :)

Very very... very good video. How to use 9 minutes to understand the concepts of Generative AI, Foundation Models, Large Langage Models, etc. Awesome !

These videos are simply amazing, thank you IBM.

Great presentation. Make such a complex topic seems affordable, means that there a lot work behind! Thanks

Excellent presentation. My only gripe is the masses will still think generative AI is simply predicting the next word, one of Jeffery Hinton's concerns. There's a lot more to it than that. When you ask a question, it needs to identify related material in the dataset and then construct specific parameters of the neural net in a way that addresses the structure and meaning of your input that makes sense. Kind of like what we do when we piece together sentences based on our experience. That requires great intelligence. This is why GenAI can already outperform humans in many academic and operational benchmarks, and it's beating us humans in more and more of these by the month. Once you go fully multimodal in these endeavours, we'll very quickly reach AGI.

I'm a '70s AI guy. I have done many parsers and translators. I have done old-style theorem proving and structure learning. I keep asking myself, in this multi-layer perceptron formulation, how is the parse tree represented? How are logic statements represented? How is knowledge manipulated? All I seem to get is generalities about training and "the next word". Where should I be looking?

When I was a kid in the 60s our local TV weatherman (Ralph Ramos) actually wrote like that for real. He stood behind a clear panel with a map outline and used a grease pencil to write temperatures on it backwards.

This video offers concise and informative insights into the AI journey, perfect for those who are new to the topic and seeking a clear understanding

Thanks Kate for this awesome video. Interesting to see the vast use cases of generating AI other than chatbots.

I’m more impressed that they mirrored the video so that her handwriting was flipped around for us.

Very clear and systematic introduction! Thanks

She just example the whole AI bubble , so awesomely, Kate great job, the best video I have watched so far on the internet. 👏👏👏

Outstanding presentation, thank you IBM

Great insights into the concepts of Generative AI. Thanks

Outstanding explanation. Thank you!!! Please continue such great work.

Your writing in reverse is surprisingly skillful; great coverage of models. Thank you

Thank you so much for the introduction to a very important topic.

Thanks Kate to simplifying the AI model explanation to general layman, interesting time to be in to see how AI is evolving like what science fiction movies have predicted all these year to become a reality.

Kate, this is really highly informative and one of the best videos I came across for gen ai.

Fantastic presentation and a great way to promote IBM offerings.

Excellent presentation Kate ! Thank you !

Interesting. Generative A.I: "predict the last word of the sentence based off the words it saw before". My very first A.I. program in college (ages ago) was a game "guess what I'm thinking". For each wrong guess the program was given a clue, thus building its knowledge-base. Prompt: What are you thinking of? (input: animal) (program: shark) (no. hint: mammal) (program: dog)(no. hint: has a trunk)...(no. hint: large ears)....(no. hint: grey) (program needs the answer: elephant). The program now has the definition of an elephant. Without knowing much about Generative A.I. it seems similar except "on steroids", lol "on the internet of data". Will have to follow the links above to learn more. Kate Soule, great explanation. Thanks.

Because the video is flipped horizontally, the text she writes may appear readable. This is a clever solution. Additionally, wearing black or dark clothes could further improve the text's readability.

Very nice insights into the topic. Well done Kate

Good Job Kate. Also, good use of the whiteboard and colour annotations. It helped that you also used simple language, didn't over-crowd the whiteboard and effectively used spacing between the concepts, as well as with the groupings for the workflow components ie: (FM /Prompting on the right of the board) , from the (LLMs)concepts on the left hand-side of the board. Thanks for sharing your gift of teaching. your contribution is appreciated. If you have a course or workshop that you teach on GenAI, I would be interested in learning more. (hint, hint) Cheers

They have uploaded the mirror of the complete video lecture ☺️

Very very clear presentation! thanks!

Thanks IBM Research Team, this videos are amazing as a learning resource

Can we take a second just to appreciate the skill necessary to write proper readable handwritten text in reverse?

Very precise and accurate video explains things clearly whats gonna up in future ! - thanks Mam.

Thanks. Easy to follow for non-tech. Great!

As impressive as AI is, Kate writing inverted is just as impressive.

Great explanation about the Generative AI Models I think your last proposition will make a great impact to the human kind with the help of Generative AI

Awesome Introduction! Looks like I'm hooked on this topic.

Great explanation. Thank you.

I loved it! Thanks for this amazing video :)

Great video!!! IBM used to be the undisputed leader of computer technology. Would be great to see Big Blue back in the game and become a leader again!!!! (Apple's market cap 2 trillion dollars, Nvidia's market cap 1 trillion dollars, IBM (the former world leader of computer technology) 120 billion dollars). Hope to see a publicly-available LLM from IBM soon!!!!

Excellent explanation, top of top

Excellent explanation and breakdown by Kate, brilliant woman !

good work and fluid presentation, many thanks

Amazing explaination!!

Was just amazing, thanks.

Book Recommendation: "A Primer to the 42 Most commonly used Machine Learning Algorithms (With Code Samples)."

Great content explained with simplicity!

using this to study for university, thank you!

Excellent explanation.

Thank you! I also commend your ability to write backwards so legibly

Like your podcasts(?) guys! Awesome!

Really well explained

Are the 2 latest foundation models you mentioned, molformer and Earth Science for climat change, available as demos?

Good Explanation Kate.

very well explanation for beginner like me

Very well explained

Thank you that was very helpful

Informative video. Thanks

Kate, great video!

This "blackboard" is so good :)

69th comment! As a researcher, it feels nice to see IBM will be researching with me this great new innovations! Best of luck IBM, you are going to need it!

I think I can make a slight correction here. Let me know if I'm wrong. The idea of "generative" in Generative AI isn't the ability to "generate" the next word, but in that the model is able to generate new observations (or data points) based on the distributions in the data. As she describes, LLMs are part of the idea of foundation models, and LLMs are the NLP derivative of FMs that are able to sample from those distribution of words (or tokens).

Very Well Explained !

Thank you so much for such an amazing video! It is informative, helpful and engaging. 😀

I am impressed how she is writing all of this backwards so we can see it correctly :)

Outstanding explanation, I'm even more impressed for your hability to invert your writing effortlessly... mindblowing! congratulations!

Excellent. Thanks

I know someone else who wrote inverted..he also painted well. impressive

Truly nice way of explaining. .

So basically what is happening in a large language model is that anyone can take a or the best teaching method of let's say the English language level one, two, three and advanced, uploaded it in a machine as a software, then have it be used by anyone that already knows and has probably learned level one, two, three, and advanced of the English language. Plus it seems that complex combinations of spreadsheet cells and whole spreadsheets are being passed as neural networks...! I do not understand on the other hand if that is pure genius or simply not. What it most certainly seems to be, is artificial intelligence, as it is obviously stated. If that is the case, here is some advice for anyone wanting to learn a language such as English on the complete cheap. Simply use the Google bar, or any other descent search engine. Begin with an I, an if, a the, a word and just fallow the suggestions in completing a sentence. Once happy with that begin in the same way and at a point along the sentence, simply use the alphabet in learning all sorts of sentences and words. Just as being in a course or school, without actually being there, have some extra windows for entertainment and anyone will be proficient at a language in no time.

I think I have a different viewpoint on the fact that Foundational model are a part of Generative AI ( 2:35), foundational models can drive predictive AI as well as Generative AI depending on the type of Neural Nets we use, as the name suggests it acts as a foundation for both alongwith customisation & automation.

I've been studying and getting certifications in Prompt Engineering, Mathematics, Coding, Data Science, Open Artificial Intelligence, Machine Learning, Deep Learning, and Neural Networks for a few years now. I can't find a job anywhere. When I'm in a interview and talk about the cost saving benefits and increase in productivity using Artificial Intelligence and automation, they usually end the interview right away and send a Dear John letter that they went with another candidate.

I just love this videos

Thank you

thanks kate this is useful to me.

Thank you.

Thank You

Awesome. Thank you

thanks to ch*tgpt i've finally been able to build my own RNN in c/c++ and dozens of other things. simple series prediction (audio spectral extension) took a few hours to train a FFNN to satisfactory results, but training a RNN on 96 input "hot one" text chars has taken over a month to train on my little asus L210 and i'm maybe a third of the way based on loss. there's one thing i've observed in decades of procedural media programming, procedure tends to express outside of human discretion and can expand or distend our experience and concept of expression. plus, per burroughs' cut-up method, you do tend to get a bit of EVP and transduction in procedure. aheheh.

Its highly impresive

Great explanation of course! I'm also equally impressive with her (mirror ?) writing skill.

Good explanation

Is there an assembly LLM kit sold in Amazon that I could assemble and understand?

@1:55 Isn't the training supervised rather than unsupervised? It is predicting the next word that follows the given text: the next word is known.

Very informative; thank you!

Great Video. Very Clear. Thanks.

I just listened to the first half of it, so far. Why is it that you're trying to predict the next word rather than give understanding to the computer program?

Yes, really informative. Thank you.

There is something really special here.

In future, large language models can generate high quality training data for a small language model and replicate their capabilities in a small model. Many children grow up and become smarter, wiser, richer/wealthier than their parents by growing up and learning in a relatively newer and better world. Similarly, LLMs will reproduce advanced capabilities into smaller models which will grow and eventually reproduce, leading to significant break-throughs.

Great info Kate. What kind of White Board are you writing on? It's so cool!

amazing and great presentation

Thank you, for the nice presentation

It can not only used to perform tasks. The real revolution: It can be used to check the abuse of power. Formulate a prompt beginning with "Evaluate the following scenario: xxx" and replace xxx with a description of an abusive situation in a family, at school or at work. The AI will pretty clearly point that fact out to the one being abused. So buckle up if you are someone who bullies and gaslights others: AI will make your abuses transparent within seconds once someone takes out the phone and describes what just happend. THAT is one of the revolutionary aspects of AI.