Choice Hotels CIO Brian Kirkland joins Caroline Hyde ahead of Memorial Day weekend to discuss how AI has been transforming the travel industry.
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The term “Generative AI” has appeared as if out of thin air over the past few months. Looking at Google trends, we can see an aggressive growth in interest even just over the past 12 months. This interest can be attributed to the release of Generative models like DALL-E 2, Imagen, and ChatGPT. But what does “Generative AI” actually mean?

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📖 Prefer to read it? Here is our article covering Generative AI: https://www.assemblyai.com/blo....g/introduction-gener

What is Generative AI?
In short, Generative AI refers to any Artificial Intelligence model that generates novel data, information, or documents.

Generative AI vs. Discriminative AI
Generative AI is most easily described by contrasting it with Discriminative AI. As we have seen, Generative AI is useful when we want to generate data, information, or documents. On the other hand, Discriminative AI is useful when we want to make some sort of decision.

00:00 What is Generative AI?
00:43 Real-life use cases
01:38 What kind of models make Generative AI?
02:36 Mathematical comparison to Discriminative models
04:19 The main promise of Generative AI
06:09 Try out LeMUR
06:25 What do you think about Generative AI?

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At Computex 2023, Nvidia CEO Jensen Huang reveals what communications like video conferencing will look like when generative AI is applied.

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We are witnessing a fundamental shift in AI driven by self-supervision and by the ability to create foundation models that power generative AI. Join Darío Gil, SVP and Director of IBM Research as he demystifies the technology and shares a set of principles to guide your generative AI business strategy. Experience IBM’s new data and AI platform for generative AI and learn about the breakthroughs that IBM Research is bringing to this platform and to the world of computing.

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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.

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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.

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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.

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#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

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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

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

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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.

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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.

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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