What are Generative AI models?

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.

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.

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.

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

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

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

Excellent presentation Kate ! Thank you !

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

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

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

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

Outstanding presentation, thank you IBM

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?

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

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

Great insights into the concepts of Generative AI. Thanks

These videos are simply amazing, thank you IBM.

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. 👏👏👏

Fantastic presentation and a great way to promote IBM offerings.

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.

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

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.

As an IBM Employee this video makes me proud ❤

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

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

Excellent explanation, top of top

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.

Very very clear presentation! thanks!

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

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

Great explanation. Thank you.

Very well explained

using this to study for university, thank you!

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

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.

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

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

Amazing explaination!!

Excellent explanation and breakdown by Kate, brilliant woman !

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

I loved it! Thanks for this amazing video :)

Excellent explanation.

Nice .. are you writing over mirrors or this is some digital tool

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

Great content explained with simplicity!

Was just amazing, thanks.

Like your podcasts(?) guys! Awesome!

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).

Thank you that was very helpful

Good Explanation Kate.

This "blackboard" is so good :)

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

Really well explained

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

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

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!!!!

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

Kate, great video!

Informative video. Thanks

very well explanation for beginner like me

Very Well Explained !

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

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

@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.

Excellent. Thanks

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

LLM can complete sentences based on data/stats/tuning/prompting/etc, but that doesn't say how the program computes what to say in the first place.
If I ask GPT "what is chemistry", how does it know what to say, it can't be all based on sentence completion.

as for the Trust issue, is there some kind of "bill of meterials" for what has been processed which could be reviewed? i think this would be crucial for transparency

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!

Truly nice way of explaining.
.

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

Good explanation

Yes, really informative. Thank you.

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.

how are you writing this on screen? what tech is this? can you please share

amazing and great presentation

Great Video. Very Clear. Thanks.

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.

Very informative; thank you!

thanks kate this is useful to me.

Creating a Large Neural Network (LNN) for AGI is the next logical step; including modular rotational-to-translative cognitive functions aka COGFUNCT in next gen quantum computing code; to better precess & realistically simulate a smooth AI dialectic process for generated AI dialog.
COGREG & COGFUNCT are composed of embedded QUREGs & QUFUNCTs via quantum wave functions in quantum magic states available to a quantum computer.

Also, how do you do this graphics presentation? :) It is like you are able to write in reverse. It looks like the camera is simply filming your reflection, but that doesn't seem like it would work.

Thank you! How did do that? Are you writing backwards the whole time? If so, bravo!

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.

Thank you, for the nice presentation

I just need the ai to build sql query from several unmaintained and varying sources. that'd be helpful

I just love this videos

Quality vid IBM.

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

She writes inverted, works on time machines, teaches AI to be awesome, has robots at home that take care of her cats but hasn't found the doodle apps that make her illustrations quicker.... Impressive. Jokes aside, very cool topic, really is a fascinating time to be.

This way of writing in class is really cool. What method did they use? Is there a glass between the camcorder and the presenter?

thanks a lot for this very good explanation. But I have a question, why do we name them "Generative AI" since it seems to be probability instead of real intelligence ?

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

nice video!!

Thank you.

Thank you