Part 01 - Will progress in Artificial Intelligence provide humanity with a boost of unprecedented strength to realize a better future, .

Part 02 - Will progress in Artificial Intelligence provide humanity with a boost of unprecedented strength to realize a better future, .

The ASU Origins Project is a transdisciplinary initiative that nurtures research, energizes teaching, and builds partnerships, offering new possibilities for .

This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future.
Who is this class for: This class is for people who would like to learn more about machine learning techniques, but don’t currently have the fundamental mathematics in place to go into much detail. This course will include some exercises that require you to work with code. If you've not had much experience with code before DON'T PANIC, we will give you lots of guidance as you go.
Topic Covered:
Fuctions
Definition of a derivative
Differentiation examples & special cases
differentiate some functions
Time saving rules
Product rule
Chain rule
Variables, constants & context
Differentiate with respect to anything
Jacobians - vectors of derivatives
Jacobian applied
The Sandpit
The Sandpit -2
The Hessian
Multivariate chain rule
Neural Networks
Simple neural networks
Power series
Visualising Taylor Series
Power series derivation
Power series details
Multivariable Taylor Series
Linearisation
Multivariate Taylor
Gradient Descent
Gradient descent in a sandpit
Lagrange multipliers
Constrained optimisation
Simple linear regression
Non-linear regression
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This course is created by Imperial College London
If you like this video and course explanation feel free to take the
complete course and get certificate from: https://www.coursera.org/specializations/mathematics-machine-learning

This video is provided here for research and educational purposes in the field of Mathematics. No copyright infringement intended. If you are content owner would like to remove this video from YouTube, Please contact me through email: ict_hanif@yahoo.com
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This is the third video to my image recognition basics series. Image recognition can be used for all sorts of things like facial recognition, identifying what is in pictures, character recognition, and more.

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This is the second video to my image recognition basics series. Image recognition can be used for all sorts of things like facial recognition, identifying what is in pictures, character recognition, and more.

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Twitter.com/sentdex

Part 02 - Will progress in Artificial Intelligence provide humanity with a boost of unprecedented strength to realize a better future, .

Part 01 - Will progress in Artificial Intelligence provide humanity with a boost of unprecedented strength to realize a better future, .

Elon Musk and other panellists talk AI and answer the question: “If we succeed in building human-level Artificial general intelligence (AGI), then what are the .

Sample code for this series: http://pythonprogramming.net/image-recognition-python/

There are many applications for image recognition. One of the largest that people are most familiar with would be facial recognition, which is the art of matching faces in pictures to identities. Image recognition goes much further, however. It can allow computers to translate written text on paper into digital text, it can help the field of machine vision, where robots and other devices can recognize people and objects.

Here, our goal is to begin to use machine learning, in the form of pattern recognition, to teach our program what text looks like. In this case, we'll use numbers, but this could translate to all letters of the alphabet, words, faces, really anything at all. The more complex the image, the more complex the code will need to become. When it comes to letters and characters, it is relatively simplistic, however.

How is it done? Just like any problem, especially in programming, we need to just break it down into steps, and the problem will become easily solved. Let's break it down!

First, we know we want to show the program an image, and have it compare it to patterns that it knows to make an educated guess on what the current image is. This means we're going to need some "memory" of sorts, filled with examples. In the case of this tutorial, we'd like to do image recognition for the numbers zero through nine. So we'd like to be able to show it any random 2, and have it know the image to be a 2 based on the previous examples of 2's that it has seen and memorized.

Next, we need to consider how we'll do this. A computer doesn't read text like we read text. We naturally put things together into a pattern, but a machine just reads the data. In the case of a picture, it reads in the image data, and displays, pixel by pixel, what it is told to display. Past that, a machine makes no attempt to decide whether it is showing a couch or a bird. So, our database of what examples are will actually be pixel information. To keep things simple, we should probably "threshold" the images. This means we store everything as black or white. In RGB code, that's a 255, 255, 255, or 0, 0, 0. That is per pixel. Sometimes there is alpha too! What we can then do is take any image, and, if the pixel coloring is say greater than 125, we could say, this is more of a "white" and convert it to 255 (the entire pixel). If it is less than 125 or equal to it, we could say this is more of a "black" and convert it to black. This might be problematic in some circumstances where we have a dark color on a darker color, usually a type of image meant to fool machines. We could have something in place instead to find the "middle" color on average for the current image, and threshold anything lighter to white and anything darker to black. This works very well for two-dimensional images of things like characters, but less well for things with shading that are meant to accompany the image, say of something like a ball.

Once we've done this, all we need to do is save the string of pixel definitions for a bunch of "example" texts. We can start with a bunch of fonts, plus some hand drawn examples. There are data dumps of a bunch of examples. This is an example of "training" our data.

If we have a decently sized database, then we are ready to try to compare some numbers. A good idea would be to hand-draw an example for your program to compare to. To compare, we'd just simply do the same thing to the question-image. We'd threshold the image into black or white pixels, then we take that pixel list, and compare it to all of our examples. In the end, we will have so many possible "hits." Whichever character has the most "hits" is likely to be the correct one. Done, we've recognized that image.

If you think about it, this is actually very similar to how we humans recognize things. Naturally, many children do not immediately distinguish between couches and love seats. What is the difference many of them ask. There is a bit of a grey area between them, and they have many similarities. Generally, a lot of learning comes by example. After seeing hundreds of couches, thousands of chairs, and hundreds of love-seats, a person soon begins to easily distinguish between them, because they have quite a bit of sample data to compare to. This is even how we read text. A number 5 really does mean nothing to a baby. They only begin to learn what a number 5 is as they are shown it over and over, being told it is "5." Eventually, they understand that to be a 5, and they can see 5 in multiple font types and still recognize it to be a 5.

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Keynote "Intelligent continuous improvement, when BPM meets AI" by Miguel Valdés (Bonitasoft CEO and co-founder) at the 15 International Conference on Business Process Management (BPM 2017)

Abstract: Artificial Intelligence (AI) technologies are evolving faster than ever thanks to the maturity of cloud computing, BigData and the accessibility of predictive and machine learning algorithms and frameworks. But, is BPM software ready to embrace AI?
Through continued modernisation BPM platforms goes beyond traditional process automation and optimisation use cases to play a key role in digital transformation in organisations of all sizes. Modern BPM applications requirements include advanced end user interfaces (UIs), access to big volumes of business data and real time updates of those processes, UIs and data. AI will be the next major wave of innovation in BPM.
In this session we will discuss the challenges and opportunities involved in the shift towards the use of AI technologies in BPM. We will particularly cover uses cases in which AI enables intelligent continuous improvement of business processes and BPM applications. We will also discuss about pros and cons of different AI technologies when it relates to BPM.

Bio: Miguel leads Bonitasoft mission: to democratize Business Process Management (BPM), bringing powerful and affordable BPM to organizations and projects of all sizes. Bonitasoft builds a BPM-based application platform to create customized business applications without the cost and rigidity of long, unpredictable custom development cycles. As the world’s fastest-growing BPM provider, Bonitasoft has thousands of customers and an open source community of more than 120,000 members. Prior to Bonitasoft, Miguel led R&D, pre-sales and support for the BPM division of Bull Information Systems, a major European systems provider. Miguel is a recognized thought-leader in business process management and passionate about open source community building.

'Welcome to BPM 2017' by Josep Carmona (Universitat Politècnica de Catalunya) and Keynote "Intelligent continuous improvement, when BPM meets AI" by Miguel Valdés (Bonitasoft CEO and co-founder) at the 15 International Conference on Business Process Management (BPM 2017)

Abstract: rtificial Intelligence (AI) technologies are evolving faster than ever thanks to the maturity of cloud computing, BigData and the accessibility of predictive and machine learning algorithms and frameworks. But, is BPM software ready to embrace AI?
Through continued modernisation BPM platforms goes beyond traditional process automation and optimisation use cases to play a key role in digital transformation in organisations of all sizes. Modern BPM applications requirements include advanced end user interfaces (UIs), access to big volumes of business data and real time updates of those processes, UIs and data. AI will be the next major wave of innovation in BPM.
In this session we will discuss the challenges and opportunities involved in the shift towards the use of AI technologies in BPM. We will particularly cover uses cases in which AI enables intelligent continuous improvement of business processes and BPM applications. We will also discuss about pros and cons of different AI technologies when it relates to BPM.

Bio: Miguel leads Bonitasoft mission: to democratize Business Process Management (BPM), bringing powerful and affordable BPM to organizations and projects of all sizes. Bonitasoft builds a BPM-based application platform to create customized business applications without the cost and rigidity of long, unpredictable custom development cycles. As the world’s fastest-growing BPM provider, Bonitasoft has thousands of customers and an open source community of more than 120,000 members. Prior to Bonitasoft, Miguel led R&D, pre-sales and support for the BPM division of Bull Information Systems, a major European systems provider. Miguel is a recognized thought-leader in business process management and passionate about open source community building.

Filmed at the NAV People's Annual User Day on Tuesday 14th November 2017, we present the first part of the keynote session on artificial intelligence.

The first of four sessions, designed for individuals with no background in Artificial Intelligence whatsoever- very novice-friendly!

Jagvinder Thind explains Types of Networks according to Network Design in Hindi.Networking Videos in Hindi explains Difference between peer to peer and Client Server Networks

Part 01 - https://youtu.be/rZe-A2aDOgA

Will progress in Artificial Intelligence provide humanity with a boost of unprecedented strength to realize a better future, or could it present a threat to the very basis of human civilization? The future of artificial intelligence is up for debate, and the Origins Project is bringing together a distinguished panel of experts, intellectuals and public figures to discuss who’s in control. Eric Horvitz, Jaan Tallinn, Kathleen Fisher and Subbarao Kambhampati join Lawrence Krauss.

Recorded Saturday, February 25th, 2017

Eric Horvitz is managing director of Microsoft Research’s main Redmond Lab, an American computer scientist, and technical fellow at Microsoft. Horvitz received his PhD and MD degrees at Stanford University, and has continued his research and work in areas that span theoretical and practical challenges of machine learning and inference, human-computer interaction, artificial intelligence, and more. He is a fellow of numerous associations and academies, has received numerous awards, given both technical lectures and presentations for diverse audiences, and been featured in the New York Times and Technology Review.

Jaan Tallinn is co-founder of Skype, Estonian programmer, investor and physicist. He is partner and co-founder of the development company Bluemoon, Board of Sponsors member of the Bulletin of the Atomic Scientists, and one of the founders of the Centre for the Study of Existential Risk and the Future of Life Institute. He strongly promotes the study of existential risk and artificial intelligence, and the long-term planning and mitigation of potential challenges.

Kathleen Fisher is a professor in and the chair of the Computer Science Department at Tufts University. Previously, she was a program manager at DARPA where she started and managed the HACMS and PPAML programs, a consulting faculty member in the Computer Science Department at Stanford University, and a principal member of the Technical Staff at AT&T Labs Research. Kathleen's research focuses on advancing the theory and practice of programming languages and on applying ideas from the programming language community to the problem of ad hoc data management.

Subbarao Kambhampati is a professor of Computer Science at ASU, and is the current president of the Association for the Advancement of AI (AAAI). His research focuses on automated planning and decision making, especially in the context of human-aware AI systems. He is an award-winning teacher and spends significant time pondering the public perceptions and societal impacts of AI. He was an NSF young investigator, and is a fellow of AAAI. He received his bachelor’s degree from Indian Institute of Technology, Madras, and his PhD from University of Maryland, College Park.

Lawrence Krauss is an author, professor, physicist, and public intellectual.

Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.

Video by Black Chalk Productions.

Part 02 - https://youtu.be/eXc5cWEkb4Y

Will progress in Artificial Intelligence provide humanity with a boost of unprecedented strength to realize a better future, or could it present a threat to the very basis of human civilization? The future of artificial intelligence is up for debate, and the Origins Project is bringing together a distinguished panel of experts, intellectuals and public figures to discuss who’s in control. Eric Horvitz, Jaan Tallinn, Kathleen Fisher and Subbarao Kambhampati join Lawrence Krauss.

Recorded Saturday, February 25th, 2017

Eric Horvitz is managing director of Microsoft Research’s main Redmond Lab, an American computer scientist, and technical fellow at Microsoft. Horvitz received his PhD and MD degrees at Stanford University, and has continued his research and work in areas that span theoretical and practical challenges of machine learning and inference, human-computer interaction, artificial intelligence, and more. He is a fellow of numerous associations and academies, has received numerous awards, given both technical lectures and presentations for diverse audiences, and been featured in the New York Times and Technology Review.

Jaan Tallinn is co-founder of Skype, Estonian programmer, investor and physicist. He is partner and co-founder of the development company Bluemoon, Board of Sponsors member of the Bulletin of the Atomic Scientists, and one of the founders of the Centre for the Study of Existential Risk and the Future of Life Institute. He strongly promotes the study of existential risk and artificial intelligence, and the long-term planning and mitigation of potential challenges.

Kathleen Fisher is a professor in and the chair of the Computer Science Department at Tufts University. Previously, she was a program manager at DARPA where she started and managed the HACMS and PPAML programs, a consulting faculty member in the Computer Science Department at Stanford University, and a principal member of the Technical Staff at AT&T Labs Research. Kathleen's research focuses on advancing the theory and practice of programming languages and on applying ideas from the programming language community to the problem of ad hoc data management.

Subbarao Kambhampati is a professor of Computer Science at ASU, and is the current president of the Association for the Advancement of AI (AAAI). His research focuses on automated planning and decision making, especially in the context of human-aware AI systems. He is an award-winning teacher and spends significant time pondering the public perceptions and societal impacts of AI. He was an NSF young investigator, and is a fellow of AAAI. He received his bachelor’s degree from Indian Institute of Technology, Madras, and his PhD from University of Maryland, College Park.

Lawrence Krauss is an author, professor, physicist, and public intellectual.

Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.

Video by Black Chalk Productions.

APPLIED ARTIFICIAL INTELLIGENCE CONFERENCE 2018 #AAI18
HOW APPLIED ARTIFICIAL INTELLIGENCE IS CHANGING SOCIETY, ENTERPRISE, AND PEOPLE
BootstrapLabs is pleased to announce that its annual Applied Artificial Intelligence Conference will return for the third year on April 12, 2018 in San Francisco.

#AAI18 is a must-attend event for those involved or interested in the most current progress of AI technologies and products.

The one day conference brings together the brightest and most experienced professionals in the field of AI for an immersive day of learning, discussion, and connection. This year’s agenda will focus on the latest and future impact of AI applications and commercialization across a breadth of sectors, including Transportation, Healthcare, Finance, Future of Work and Cybersecurity.

Inviting world class perspectives from research, entrepreneurship, investing, and business transformation, the event aims to capture the deepest insights available in the AI landscape today. You’ll have access to practical wisdom and methodologies on how to take advantage of AI’s powerful potential. Plus, speakers and panelists will engage in a thought-provoking discourse about how AI is reshaping business, society, and life as we know it.

BootstrapLabs is a leading venture capital firm focused on Applied Artificial Intelligence in Silicon Valley. Leveraging an extensive community of professionals with deep domain expertise, BootstrapLabs stays at the forefront of the latest technology development, venture investment trends, and startup ecosystem evolution.

Contact us at info@bootstraplabs.com for more information.

9:00 am – 9:10 am | Welcome Message
Ben Levy, Co-founder, BootstrapLabs

9:10 am – 9:35 am | Opening Keynote: Human + Machine: Reimagining Work in the Age of AI
Paul Daugherty, Chief Technology & Innovation Officer, Accenture

9:35 am – 10:05 am | Panel: Enterprise AI Applications and the Future of Work
Moderator: Andrew Salzman, Partner, Chasm Group

Panelists:

Igor Jablokov, Founder and CEO, Pryon Inc
Matt Swanson, CEO, Augment
Nisha Talagala, Chief Technology Officer at ParallelM
Sabrina Atienza, Founder, Qurious.io
10:05 am – 10:25 am | Keynote: AutoML: The Future of Machine Learning
Alex Holub, Founder, Vidora

10:25 am – 10:50 am | Keynote + Fireside Chat: Death of Moore’s Law
Speakers:

Tom Campbell, Founder & President, FutureGrasp, LLC
Madhav Thattai, Chief Operating Officer, Rigetti Computing
Sateesh Kumar, Founder, Pathtronic
10:50 am – 11:15 am | Coffee Break
11:15 am – 11:45 am | Panel: Industrial & Manufacturing AI Applications
Moderator: Macario Namie, Head of IoT Strategy at Cisco

Panelists:

Karen Kerr, Executive Managing Director, GE Ventures
George Mathew, CEO, Kespry
Matt Man, Founder, Indus.ai
Ramya Ravichandar, Director Product Management at FogHorn Systems, Inc
11:45 am – 12:10 pm | Keynote + Fireside Chat: How AI shapes the future of our energy system
Speakers:

Thomas Birr, SVP Innovation & Business Transformation, innogy
Stephen Comello, Director, Sustainable Energy Initiative, Stanford
Nicolai Wadstrom, Founder and CEO, BootstrapLabs
12:10 pm – 12:35 pm | Panel: Applied AI and Cybersecurity – Making the enterprise more secure
Moderator: Debra J. Farber, Data Privacy & Security Advisor

Panelists:

Will Summerlin, Founder, Pinn Technologies
Chris Merz, VP Security & Decision Products, Mastercard
Sean Kanuck, Director of Cyber, Space and Future Conflict, IIS