What is a large language model? How can it be used to enhance your business? In this conversation, Ali Rowghani, Managing Director of YC Continuity, talks with Raza Habib, CEO of Humanloop, about the cutting-edge AI powering innovations today—and what the future may hold.
They discuss how large language models like Open AI's GPT-3 work, why fine-tuning is important for customizing models to specific use cases, and the challenges involved with building apps using these models. If you're curious about the ethical implications of AI, Raza shares his predictions about the impact of this quickly developing technology on the industry and the world at large.

Thanks to Raza and Humanloop for joining: https://humanloop.com/

Apply to Y Combinator: https://www.ycombinator.com/apply/
Work at a Startup: https://www.ycombinator.com/jobs

Chapters (Powered by https://bit.ly/chapterme-yc) -
00:00 - Intro
01:30 - Large Language Models (LLM)
04:32 - What is fine-tuning a model?
07:38 - Build Apps using LLM
09:46 - Future of the Developer Job
11:32 - Breakthroughs
15:17 - OpenAI Mission
17:30 - LLM for Startups
18:51 - Hiring at HumanLoop

Generative AI refers to artificial intelligence algorithms that enable using existing content like text, audio files, or images to create new plausible content. In other words, it allows computers to abstract the underlying pattern related to the input, and then use that to generate similar content. The video explains how Generative AI can be used for distinct purposes.

For More Information Visit: https://www.analyticsinsight.net/

#AnalyticsInsight

Learn more about Watsonx : https://ibm.biz/BdPuQs
Learn more about foundation models → https://ibm.biz/BdPpq6
Explore foundation models for earth science → https://ibm.biz/BdPpqU
Explore foundation models for chemistry → https://ibm.biz/BdPpqN

Generative AI has stunned the world with its ability to create realistic images, code, and dialogue. Here, IBM expert Kate Soule explains how a popular form of generative AI, large language models, works and what it can do for enterprise.

Get started for free on IBM Cloud → https://ibm.biz/exploreonibmcloud

Subscribe to see more videos like this in the future → http://ibm.biz/subscribe-now

#LLMs #GenerativeAI #FoundationModels #EnterpriseAI #Watsonx

Bryan Catanzaro, Vice President, Applied Deep Learning Research, NVIDIA, shares how #generativeAI (GA) enables businesses to develop better products and services and deliver original content tailored to the unique needs of customers and audiences.

Watch this full #GTC23 session at https://bit.ly/45aQTSW.

Whether you like it or not, generative AI like ChatGPT and Stable Diffusion are about to change not only how you work, but how the content you consume is produced. Forbes spoke with a number of leading voices in the AI space to determine both the benefits and the dangers of this next wave of technological innovation, and find out why both tech giants as well as cutting edge startups are racing to grab their share of the market.

0:00 Introduction
1:17 What is generative AI?
2:02 Why Forbes decided to cover this story
2:18 The rise of Open AI
3:11 AI's recent hype
4:30 Stability AI and Stable Diffusion
6:39 Bill Gates thoughts on generative AI
7:53 Where AI can help in workflows
9:56 The issues with AI that need to be resolved
12:04 How do we set safeguards for AI to protect society?
15:14 How will we further incorporate AI in the future?
19:14 The idea of "platform democracy"
20:56 How we used AI for this video

Read the full story on Forbes: https://www.forbes.com/sites/a....lexkonrad/2023/02/02

Subscribe to FORBES: https://www.youtube.com/user/F....orbes?sub_confirmati

Fuel your success with Forbes. Gain unlimited access to premium journalism, including breaking news, groundbreaking in-depth reported stories, daily digests and more. Plus, members get a front-row seat at members-only events with leading thinkers and doers, access to premium video that can help you get ahead, an ad-light experience, early access to select products including NFT drops and more:

https://account.forbes.com/membership/?utm_source=youtube&utm_medium=display&utm_campaign=growth_non-sub_paid_subscribe_ytdescript

Stay Connected
Forbes newsletters: https://newsletters.editorial.forbes.com
Forbes on Facebook: http://fb.com/forbes
Forbes Video on Twitter: http://www.twitter.com/forbes
Forbes Video on Instagram: http://instagram.com/forbes
More From Forbes: http://forbes.com

Forbes covers the intersection of entrepreneurship, wealth, technology, business and lifestyle with a focus on people and success.

Generative AI is the technology behind the wave of new online tools used by millions around the world. As the technology is ever more widely deployed, what are its current strengths and its weaknesses?

00:00 - What is generative AI?
00:46 - Breakthroughs and take-up of the technology
02:03 - Strengths
03:32 - Weaknesses

Sign up to The Economist’s daily newsletter: https://econ.st/3QAawvI

How to invest in AI: https://econ.st/3IF8EA7

Find more of our latest coverage on AI: https://econ.st/3OS1cFZ

Why large AI models will transform how we live and work: https://econ.st/3MtADUz

Watch: Chatbots will change how we use the internet: https://econ.st/41HELXb

Big tech and the pursuit of AI dominance: https://econ.st/43J3UCl

It doesn’t take much to make machine-learning algorithms go awry: https://econ.st/3A6O8Ue

Don’t fear an AI-induced jobs apocalypse just yet: https://econ.st/3ULbubz

The race of the AI labs heats up: https://econ.st/3MUh1e7

The five best books to understand AI: https://econ.st/3olOX9e

1843: The inventor who fell in love with his AI: https://econ.st/3MUiVeS

Enroll in this course on Google Cloud Skills Boost → https://goo.gle/3MdGgHs

What is Generative AI and how does it work? What are common applications for Generative AI? Watch this video to learn all about Generative AI, including common applications, model types, and the fundamentals for how to use it.

Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech

Stanford CS224N NLP with Deep Learning Winter 2019 Lecture 1 – Introduction and Word Vectors

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Associate Professor Percy Liang
Associate Professor of Computer Science and Statistics (courtesy)
https://profiles.stanford.edu/percy-liang

Assistant Professor Dorsa Sadigh
Assistant Professor in the Computer Science Department & Electrical Engineering Department
https://profiles.stanford.edu/dorsa-sadigh

To follow along with the course schedule and syllabus, visit:
https://stanford-cs221.github.....io/autumn2021/#sched

0:00 Introduction
0:06 Machine learning backpropagation
0:18 Motivation: regression with four-layer neural networks
2:09 Computation graphs
3:26 Functions as boxes
6:11 Basic building blocks
8:40 Function composition
10:02 Linear classification with hinge loss
13:45 Two-layer neural networks
24:17 A note on optimization
25:55 How to train neural networks
29:21 Summary

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his discussion on factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA).

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Complete Playlist for the Course:
http://www.youtube.com/view_pl....ay_list?p=A89DCFA6AD

CS 229 Course Website:
http://www.stanford.edu/class/cs229/

Stanford University:
http://www.stanford.edu/

Stanford University Channel on YouTube:
http://www.youtube.com/stanford

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

Professor Emma Brunskill, Stanford University
http://onlinehub.stanford.edu/

Professor Emma Brunskill
Assistant Professor, Computer Science
Stanford AI for Human Impact Lab
Stanford Artificial Intelligence Lab
Statistical Machine Learning Group

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs234/index.html

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Depe55

Professor Christopher Manning, Stanford University
http://onlinehub.stanford.edu/

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/....cs224n/index.html#sc

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Associate Professor Percy Liang
Associate Professor of Computer Science and Statistics (courtesy)
https://profiles.stanford.edu/percy-liang

Assistant Professor Dorsa Sadigh
Assistant Professor in the Computer Science Department & Electrical Engineering Department
https://profiles.stanford.edu/dorsa-sadigh

To follow along with the course schedule and syllabus, visit:
https://stanford-cs221.github.....io/autumn2021/#sched

0:00 Introduction
0:06 Machine learning: differentiable programming
0:47 Deep learning models
1:24 Feedforward neural networks
4:23 Representing images
5:18 Convolutional neural networks
10:29 Representing natural language
11:51 Embedding tokens
13:01 Representing sequences
14:17 Recurrent neural networks
17:38 Collapsing to a single vector
19:33 Long-range dependencies
19:59 Attention mechanism
26:17 Layer normalization and residual connections
28:38 Transformer
31:30 Generating tokens
32:36 Generating sequences
33:46 Sequence-to-sequence models
35:22 Summary FeedForward Conv MaxPool

#artificialintelligence #machinelearning

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Dev1Yj

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/....cs224n/index.html#sc

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on expectation-maximization in the context of the mixture of Gaussian and naive Bayes models, as well as factor analysis and digression.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Complete Playlist for the Course:
http://www.youtube.com/view_pl....ay_list?p=A89DCFA6AD

CS 229 Course Website:
http://www.stanford.edu/class/cs229/

Stanford University:
http://www.stanford.edu/

Stanford University Channel on YouTube:
http://www.youtube.com/stanford