Difference between revisions of "Current State"

From
Jump to: navigation, search
m
m
Line 157: Line 157:
 
||
 
||
 
<youtube>0qVOUD76JOg</youtube>
 
<youtube>0qVOUD76JOg</youtube>
−
<b>HH4
+
<b>The art of neural networks | Mike Tyka | TEDxTUM
−
</b><br>BB4
+
</b><br>Did you know that art and technology can produce fascinating results when combined? Mike Tyka, who is both artist and computer scientist, talks about the power of neural networks. These algorithms are capable to transform computers into artists that can generate breathtaking paintings, music and even poetry.  Dr. Mike Tyka studied Biochemistry and Biotechnology at the University of Bristol. He obtained his Ph.D. in Biophysics in 2007 and went on to work as a research fellow at the University of Washington, studying the structure and dynamics of protein molecules. In particular, he has been interested in protein folding and has been writing computer simulation software to better understand this fascinating process. In 2009, Mike and a team of artists created Groovik’s Cube, a 35 feet tall, functional, multi-player Rubik’s cube. Since then, he co-founded ATLSpace, an artist studio in Seattle and has been creating metal and glass sculptures of protein molecules. In 2013 Mike went to [[Google]] to study neural networks, both artificial and natural. This work naturally spilled over to his artistic interests, exploring the possibilities of artificial neural networks for creating art.
 
|}
 
|}
 
|}<!-- B -->
 
|}<!-- B -->
Line 166: Line 166:
 
||
 
||
 
<youtube>wJqf17bZQsY</youtube>
 
<youtube>wJqf17bZQsY</youtube>
−
<b>HH5
+
<b>Stanford Seminar - Artificial Intelligence: Current and Future Paradigms and Implications
−
</b><br>BB5
+
</b><br>EE380: Computer Systems Colloquium Seminar Artificial Intelligence: Current and Future Paradigms and Implications Speaker: Scott Phoenix, Vicarious Artificial intelligence has advanced rapidly in the last five years. This talk intends to provide high-level answers to questions like: What can the evolution of intelligence in the animal kingdom teach us about the evolution of AI?
 +
How should people who are not AI researchers view the societal transformation that is now underway? What are some of the social, economic, and political implications of this technology as it exists now? What will future AI systems likely be capable of, and what are the largest expected impacts of these systems? The talk will be understandable for non-computer scientists. About the Speaker:
 +
Scott Phoenix is CEO and a founder of Vicarious, an AI research company building general intelligence for robots. Vicarious has received over $110 million in funding from pioneers like Mark Zuckerberg, Elon Musk, and Jeff Bezos. Prior to co-founding Vicarious, Mr. Phoenix was an entrepreneur-in-residence at Founders Fund. He earned his BAS in Computer Science and Entrepreneurship from the University of Pennsylvania.
 
|}
 
|}
 
|<!-- M -->
 
|<!-- M -->
Line 174: Line 176:
 
||
 
||
 
<youtube>9iODaocJjxI</youtube>
 
<youtube>9iODaocJjxI</youtube>
−
<b>HH6
+
<b>[[NVIDIA]]: Deep Learning - Extracting Maximum Knowledge from Big Data Using Big Compute
−
</b><br>BB6
+
</b><br>Deep learning (DL) and AI are fundamentally changing the way data is used in computation. They are enabling computing capabilities that will transform almost every industry, scientific domain, and public usage of data and compute. The recent success of DL algorithms can be seen as the culmination of decades of progress in three areas: research in DL algorithms, broad availability of big data infrastructure, and the massive growth of computation power produced by Moore’s law and the advent of parallel compute architectures. A key advantage of deep learning is that you can use the same techniques for many applications, as compared to algorithms which are typically specific to a single area. In practice, deep learning has been employed successfully in such diverse areas as healthcare, transportation, industrial IoT, finance, entertainment, and retail, in addition to high-performance computing. Examples will illustrate how the approach works and how it complements high-performance data analytics and traditional business intelligence.  Recorded: August 9th, 2017
 
|}
 
|}
 
|}<!-- B -->
 
|}<!-- B -->
Line 183: Line 185:
 
||
 
||
 
<youtube>s3MuSOl1Rog</youtube>
 
<youtube>s3MuSOl1Rog</youtube>
−
<b>HH7
+
<b>MIT Sloan: Intro to Machine Learning (in 360/VR)
−
</b><br>BB7
+
</b><br>This is a guest talk for course 15.S14: Global Business of Artificial Intelligence and Robotics (GBAIR) taught in Spring 2017.
 
|}
 
|}
 
|<!-- M -->
 
|<!-- M -->
Line 191: Line 193:
 
||
 
||
 
<youtube>bDZ2q6OktQI</youtube>
 
<youtube>bDZ2q6OktQI</youtube>
−
<b>HH8
+
<b>[[TensorFlow]] Dev Summit 2019 Livestream
−
</b><br>BB8
+
</b><br>#TFDevSummit brings together a diverse mix of machine learning users from around the world for two days of highly technical talks, demos, and conversation with the TensorFlow team and community.
 
|}
 
|}
 
|}<!-- B -->
 
|}<!-- B -->
Line 200: Line 202:
 
||
 
||
 
<youtube>eN9Lb3vXsAw</youtube>
 
<youtube>eN9Lb3vXsAw</youtube>
−
<b>HH9
+
<b>AI in 2020
−
</b><br>BB9
+
</b><br>Almost exactly 4 years ago I decided to dedicate my life to helping educate the world on Artificial Intelligence. There were hardly any resources designed for absolute beginners and the field was dominated by PhDs. In 2020, thanks to the extraordinary contributions of everyone in this community, all that has changed. It’s easier than ever before to enter into this field, even without an IT background. We’ve seen brave entrepreneurs figure out how to deploy this technology to save lives (medical imaging, automated diagnosis) and accelerate Science (AlphaFold). We’ve seen algorithmic advances (deepfakes) and ethical controversies (automated surveillance) that shocked the world. The AI field is now a global, cross-cultural movement that's not limited to academics alone. And that’s something all of us should be proud of, we’re all apart of this. I’ve packed a lot into this episode! I’ll give my annual lists of the best ML language and libraries to learn this year, how to learn ML in 2020, as well as 8 predictions about where this field is headed. I had a lot of fun making this, so I hope you enjoy it!
 
|}
 
|}
 
|<!-- M -->
 
|<!-- M -->
Line 208: Line 210:
 
||
 
||
 
<youtube>mvwBgAqrheo</youtube>
 
<youtube>mvwBgAqrheo</youtube>
−
<b>HH10
+
<b>AI in 2040
−
</b><br>BB10
+
</b><br>What does the field of Artificial Intelligence look like in 2040? It's a really hard question to answer since there are still so many unanswered questions about the nature of reality and computing. In this episode, I'll make my best predictions about AI hardware, AI software, and the societal impact of AI in 2040. We'll cover quantum mechanics, neuromorphic computing, DNA storage, decentralized computing, basic income, and mind-body machines. Enjoy!
 
|}
 
|}
 
|}<!-- B -->
 
|}<!-- B -->

Revision as of 15:15, 31 August 2020

YouTube search... ...Google search

Andrew Ng: Deep Learning, Education, and Real-World AI | Lex Fridman Podcast #73
Andrew Ng is one of the most impactful educators, researchers, innovators, and leaders in artificial intelligence and technology space in general. He co-founded Coursera and Google Brain, launched deeplearning.ai, Landing.ai, and the AI fund, and was the Chief Scientist at Baidu. As a Stanford professor, and with Coursera and deeplearning.ai, he has helped educate and inspire millions of students including me.

Andrew Ng - The State of Artificial Intelligence
Dec 15, 2017 Professor Andrew Ng is the former chief scientist at Baidu, where he led the company's Artificial Intelligence Group. He is an adjunct professor at Stanford University. In 2011 he led the development of Stanford University’s main MOOC (Massive Open Online Courses) platform and also taught an online Machine Learning class that was offered to over 100,000 students, leading to the founding of Coursera.

The Next Leap: How A.I. will change the 3D industry - Andrew Price
Blender Conference 2018 - Thursday 25 October at the Theater. Support Blender by joining the Development Fund https://fund.blender.org/

Artificial Intelligence in 2020
This video recaps developments in AI from 2019! Happy New Year!

Machine Learning: Living in the Age of AI | A WIRED Film
“Machine Learning: Living in the Age of AI,” examines the extraordinary ways in which people are interacting with AI today. Hobbyists and teenagers are now developing tech powered by machine learning and WIRED shows the impacts of AI on schoolchildren and farmers and senior citizens, as well as looking at the implications that rapidly accelerating technology can have. The film was directed by filmmaker Chris Cannucciari, produced by WIRED, and supported by McCann Worldgroup.

Jeff Dean’s Lecture for YC AI
Jeff Dean is a Google Senior Fellow in the Research Group, where he leads the Google Brain project.

Top 5 Uses of Neural Networks! (A.I.)
Hi, welcome to ColdFusion. Experience the cutting edge of the world around us in a fun relaxed atmosphere.

Use Cases - Ep. 12 (Deep Learning SIMPLIFIED)
Despite its popularity, machine vision is not the only Deep Learning application. Deep nets have started to take over text processing as well, beating every traditional method in terms of accuracy. They also are used extensively for cancer detection and medical imaging. When a data set has highly complex patterns, deep nets tend to be the optimal choice of model.

Demo URLs Clarifai - http://www.clarifai.com Metamind - https://www.metamind.io/language/twitter

As we have previously discussed, Deep Learning is used in many areas of machine vision. Facebook uses deep nets to detect faces from different angles, and the startup Clarifai uses these nets for object recognition. Other applications include scene parsing and vehicular vision for driverless cars.

Advances in machine learning and TensorFlow (Google I/O '18)
Artificial intelligence affects more than just computer science. Join this session to hear a collection of short presentations from top machine learning researchers: the TensorFlow engineers working on robotics, and the Magenta team exploring the border between machine learning and art.

AWS Summit Singapore - Machine Learning in Practice
With the launch of several new Machine Learning (ML) services on AWS, now is your chance to learn how to quickly apply ML to solve real-world business problems, no prior ML experience necessary. During this session, you will learn about vision services to analyze your images and video for facial comparison, object detection and detecting text (Amazon Rekognition and Amazon Rekognition Video), building conversational interfaces for chatbots (Amazon Lex), and core language services for converting audio to text (Amazon Transcribe), converting text to speech (Amazon Polly), identifying topics and themes in text (Amazon Comprehend) and translating between two languages (Amazon Translate). Speaker Steve Shirkey, Solutions Architect, ASEAN, AmazonAWS

A.I. is Progressing Faster Than You Think!
Sergey Brin ColdFusion

Prof. [Creatives#Yoshua Bengio|Yoshua Bengio]] - Deep learning & Backprop in the Brain
[Creatives#Yoshua Bengio|Yoshua Bengio]] is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning. [Creatives#Yoshua Bengio|Bengio]] received his Bachelor of Science, Master of Engineering and PhD from McGill University. Recorded: September 8, 2017

Using Deep Learning to Extract Feature Data from Imagery
Vector data collection is the most tedious task in a GIS workflow. Digitizing features from imagery or scanned maps is a manual process that is costly, requiring significant human resources to accomplish. Building footprint extraction from imagery provides an even more complex challenge due to shadows, tree overhang, and the complexity of roofs. Often times, this feature extraction work is performed by GIS analysts whose time would be better spent performing analysis and producing actionable reports for decision makers, rather than collecting data. Object detection is a particularly challenging task in computer vision. Today’s advanced deep neural networks (DNN) use algorithms, big data, and the computational power of the GPU to change this dynamic.

The art of neural networks | Mike Tyka | TEDxTUM
Did you know that art and technology can produce fascinating results when combined? Mike Tyka, who is both artist and computer scientist, talks about the power of neural networks. These algorithms are capable to transform computers into artists that can generate breathtaking paintings, music and even poetry. Dr. Mike Tyka studied Biochemistry and Biotechnology at the University of Bristol. He obtained his Ph.D. in Biophysics in 2007 and went on to work as a research fellow at the University of Washington, studying the structure and dynamics of protein molecules. In particular, he has been interested in protein folding and has been writing computer simulation software to better understand this fascinating process. In 2009, Mike and a team of artists created Groovik’s Cube, a 35 feet tall, functional, multi-player Rubik’s cube. Since then, he co-founded ATLSpace, an artist studio in Seattle and has been creating metal and glass sculptures of protein molecules. In 2013 Mike went to Google to study neural networks, both artificial and natural. This work naturally spilled over to his artistic interests, exploring the possibilities of artificial neural networks for creating art.

Stanford Seminar - Artificial Intelligence: Current and Future Paradigms and Implications
EE380: Computer Systems Colloquium Seminar Artificial Intelligence: Current and Future Paradigms and Implications Speaker: Scott Phoenix, Vicarious Artificial intelligence has advanced rapidly in the last five years. This talk intends to provide high-level answers to questions like: What can the evolution of intelligence in the animal kingdom teach us about the evolution of AI? How should people who are not AI researchers view the societal transformation that is now underway? What are some of the social, economic, and political implications of this technology as it exists now? What will future AI systems likely be capable of, and what are the largest expected impacts of these systems? The talk will be understandable for non-computer scientists. About the Speaker: Scott Phoenix is CEO and a founder of Vicarious, an AI research company building general intelligence for robots. Vicarious has received over $110 million in funding from pioneers like Mark Zuckerberg, Elon Musk, and Jeff Bezos. Prior to co-founding Vicarious, Mr. Phoenix was an entrepreneur-in-residence at Founders Fund. He earned his BAS in Computer Science and Entrepreneurship from the University of Pennsylvania.

NVIDIA: Deep Learning - Extracting Maximum Knowledge from Big Data Using Big Compute
Deep learning (DL) and AI are fundamentally changing the way data is used in computation. They are enabling computing capabilities that will transform almost every industry, scientific domain, and public usage of data and compute. The recent success of DL algorithms can be seen as the culmination of decades of progress in three areas: research in DL algorithms, broad availability of big data infrastructure, and the massive growth of computation power produced by Moore’s law and the advent of parallel compute architectures. A key advantage of deep learning is that you can use the same techniques for many applications, as compared to algorithms which are typically specific to a single area. In practice, deep learning has been employed successfully in such diverse areas as healthcare, transportation, industrial IoT, finance, entertainment, and retail, in addition to high-performance computing. Examples will illustrate how the approach works and how it complements high-performance data analytics and traditional business intelligence. Recorded: August 9th, 2017

MIT Sloan: Intro to Machine Learning (in 360/VR)
This is a guest talk for course 15.S14: Global Business of Artificial Intelligence and Robotics (GBAIR) taught in Spring 2017.

TensorFlow Dev Summit 2019 Livestream
#TFDevSummit brings together a diverse mix of machine learning users from around the world for two days of highly technical talks, demos, and conversation with the TensorFlow team and community.

AI in 2020
Almost exactly 4 years ago I decided to dedicate my life to helping educate the world on Artificial Intelligence. There were hardly any resources designed for absolute beginners and the field was dominated by PhDs. In 2020, thanks to the extraordinary contributions of everyone in this community, all that has changed. It’s easier than ever before to enter into this field, even without an IT background. We’ve seen brave entrepreneurs figure out how to deploy this technology to save lives (medical imaging, automated diagnosis) and accelerate Science (AlphaFold). We’ve seen algorithmic advances (deepfakes) and ethical controversies (automated surveillance) that shocked the world. The AI field is now a global, cross-cultural movement that's not limited to academics alone. And that’s something all of us should be proud of, we’re all apart of this. I’ve packed a lot into this episode! I’ll give my annual lists of the best ML language and libraries to learn this year, how to learn ML in 2020, as well as 8 predictions about where this field is headed. I had a lot of fun making this, so I hope you enjoy it!

AI in 2040
What does the field of Artificial Intelligence look like in 2040? It's a really hard question to answer since there are still so many unanswered questions about the nature of reality and computing. In this episode, I'll make my best predictions about AI hardware, AI software, and the societal impact of AI in 2040. We'll cover quantum mechanics, neuromorphic computing, DNA storage, decentralized computing, basic income, and mind-body machines. Enjoy!


Mass Communication

Humor

Retrieved from "https://primo.ai/index.php?title=Current_State&oldid=15737"