Machine Learning vs. Deep Learning vs. Foundation Models
Learn how watsonx helps you utilize AI → ibm.biz/BdMpXV
The recent interest in AI as meant a lot of people have been encountering new vocabulary. Martin Keen is to help you sort it out. This video runs through key terms like machine learning, deep learning, foundation models, and large language models and how they're related to each other.
Пікірлер: 28
One of "THE" best explanations of all types of AI models
I love this guy's energy, very informative
Beautifully explained. Thank you.
Learnt a new term claro. Like that and this. Great explanation!
Great way to start the day 💪🤖
Thank you for bringing this video
Your videos are amazing, thanks
Superb explanation
Excellent!
Very informative
Awesome
What is there under AI, other than Machine Learning?
Hello, what about data science
Where does NLP fit in?
How do you write so well backwards on the glass?
@IBMTechnology
8 ай бұрын
See ibm.biz/write-backwards
How valuable is data, authentication for the training of these tools, refined thoughts, at rapid speed. Would a new supply chain movement towards generating a new standardize benchmark system, be useful? Potential sufficient to correct the potential errors, of miscommunication via scholarly debate. Perhaps chaos, but perhaps the cure. 😅 all in the amount of effort
@xaviermagnus8310
8 ай бұрын
Chaos. Pretty much every field breaks down to assumptions somewhere. A lot of words but no explicit gain in this. Any piece of data almost worthless. The mass has the value. You're assuming not only that there is a definite right/wrong... but that we know it well enough to be sure.
Multiple regenerated training data how is this used to reinforce data trends of the final output. I call the issue training Emphasis.
eXcellent. Thank you.
where does hugging face and cohere fall?
@eprabhat
25 күн бұрын
Hugging Face & Cohere can be seen as community platforms to support AI universe
Where is NLP located?
@wtpollard
24 күн бұрын
NLP is a topic under AI. Nowadays, NLP is pursued using deep-learning models, but that's a relatively new development. Google Translate, for example, has been around since ~2006, but it only started using neural networks (deep learning) in 2016.
Not sure I agree that RL belongs under ML
Enormity isn't size, it's more like being horrorified.
There's a huge circle that encapsulates all the boxes and it's called tooling. Not sarcastic.