Quantum AI: The Video

The frequently touted advantages of quantum computing are becoming more possible as early systems begin to appear and researchers begin to explore software. Since this developing technology focuses upon solutions for very large data sets and complex calculations, AI seems a natural application. In reality, the situation is more nuanced. There are, indeed, possibilities—particularly in machine learning—but they may demand a new approach and new types of models.

Quantum AI is now being actively pursued. It could have tremendous implications for solving complex and intractable problems, and could even bring researchers closer to a general AI. At the very least, it will provide competitive advantages to first movers who are able to harness its possibilities. It is still too early to determine how much can be gained over conventional and specialized neuromorphic processors, but recent developments are making it possible to explore this area, and interest is beginning to grow.

In the following videos, we explore quantum AI from a variety of viewpoints, from business to technical detail. The videos are a mixture of presentations and discussions available under standard YouTube license, with landing page descriptions added. Minor edits have been made where necessary.

The Race to Quantum AI (Space And Intelligence)

Published on Mar 11, 2017

The initial enthusing over AI has faded and the sci-fi scenarios are mostly over. Even with the emergence of new machine-learning techniques the ultimate goal of the field—some form of General AI—remains a distant vision. Still, powerful machine learning is spreading into new industries and areas of daily life and will heighten attention on the unintended consequences that may result.

Quantum AI The New Frontier in Artificial Intelligence (Welcome To The Future)

Published on Aug 17, 2017

A talk by Dr. Colin P. Williams, Director of Strategy & Business Development, D-Wave Systems.

Universal Deep Quantum Learning (QuICS)

Published on Oct 6, 2015

Universal Deep Quantum Learning QuICS Workshop on the Frontiers of Quantum Information and Computer Science given by Seth Lloyd (MIT). Quantum systems can generate non-classical correlations that can’t be generated by classical systems. This talk investigates whether quantum devices can recognize patterns that can’t be recognized classically. Deep quantum learning architectures are compared with deep classical learning architectures, and conditions are identified under which universal deep quantum learners can recognize patterns that can’t be recognized by classical learners.

Google’s Quantum AI Lab (The Artificial Intelligence Channel)

Published on Sep 4, 2017

Hartmut Neven talks about possible roles of quantum effects and subjective experience in Artificial Intelligence.


 

 

 

 


AI in Finance: The Video

Financial Technology (FinTech) is traditionally conservative, but has been using machine learning for some time. The industry is now on the verge of a technology explosion, as AI and blockchain create unique challenges across this sector. AI can provide advantages in risk analysis, fraud detection and in marketing, as well as in predicting market behaviour. Every area of finance has specific interests and risks in moving forward with these projects, from ML-driven hedge funds with limited transparency to credit profiling with potential regulatory issues.

As with many other sectors, marketing and customer relations is the first area in which AI will make its mark. In finance, however, there are numerous areas in which AI will have important repercussions. Following are several videos that look at aspects of FinTech AI.

Finance is one of several vertical industries that we will look at in this series, as we explore the ongoing issues when AI technologies are incorporated into businesses, studies, and lives.

The videos are a mixture of presentations and discussions avaialable under standard YouTube license, with landing page descriptions added. Minor edits have been made where necessary.

Why AI is the Future of FinTech (BootstrapLabs)

Published on Jul 24, 2017

The latest innovation and the positive impact of Artificial Intelligence technologies from the Applied AI Conference, an event for people who are working, researching, building, and investing in Applied Artificial Intelligence technologies and products.

Panel Moderator: Jean-Baptiste Su, Principal Analyst. Atherton Research & FORBES Technology Columnist

Speakers:
Parth Vasa, Head of Data Science, Bloomberg Engineering, Bloomberg LP
Massimo Mascaro, Director, Data Engineering and Data Science, Intuit
Sangeeta Chakraborty, Chief Customer Officer, Ayasdi
Mark Nelsen, Senior Vice President of Risk and Authentication Products, Visa

AI and the Future of Finance (IIF)

Published on Oct 15, 2017

Perspective from  The Institute of International Finance.

IBM Watson on Cognitive Computing & Artificial Intelligence Are Transforming Financial Services (LendIt Conference)

Published on Mar 7, 2017

IBM Watson Group’s Brian Walter shows how ‘Cognitive Computing & Artificial Intelligence Are Transforming Financial Services’ at LendIt USA 2017 in New York City.

LendIt USA is the world’s biggest show in lending and fintech.

The Future of Corporate Finance: Automation powered by SAP Leonardo Machine Learning (SAP)

Published on Jul 20, 2017

Leverage next generation automation technologies to significantly increase the level of automation in your Shared Services Organization and drastically increase the efficiency of your Financial Shared Services staff. Smart automation with machine learning is self-learning and continuously improving, thus eliminating maintenance efforts. Staff can move away from daily routines and focus on strategic tasks such as growth & planning. SAP Leonardo Machine Learning can be a key enabler.


 

 

 

 

 

 

 


AI in Medicine: The Video

Applications for AI and machine learning have blossomed recently in the medical and healthcare sectors, providing new opportunities and possibilities across everything from medical image recognition to rearch and diagnostics. While covering this vast territory in brief is impossible, a small sample of current developments and thinking in this area is helpful in understanding the current state of AI.

Healthcare is one of several vertical industries that we will look at in this series, as we explore the ongoing issues when AI technologies are incorporated into businesses, studies, and lives.

The videos are a mixture of presentations, seminars and discussions avaialable under standard YouTube license, with landing page descriptions added. Minor edits have been made where necessary.

Artificial Intelligence in Medical Research and Applications (IJCAI Video Competition)

Published on Aug 21, 2017

In various medical fields and healthcare, we are facing an astonishingly serious problem—that is, we are drowning in heterogeneous patient data while starving for expert knowledge for interpretations. To assist medical practitioners for detecting, diagnosing, and treating various medical conditions, groups of computer science researchers combine domain experts’ intelligence with artificial intelligence by building computational models for the Big Medical Data available. This video demonstrates the research and applications of artificial intelligence by showcasing three applications domains, including dermatology, cardiology, and psychology.

By Xuan Guo , Akshay Arun , Prashnna Gyawali , Sandesh Ghimire , Erin Coppola, Omar Gharbia , Jwala Dhamala. Rochester Institute of Technology.

Man, Machine, and Medicine: Mass General Researchers Using AI (NVIDIA)

 Published on Dec 7, 2016
Researchers at Mass General Hospital are using artificial intelligence and deep learning to advance patient care.

Keynote Speech. Artificial Intelligence in Medicine (CoMST 學術分享頻道)

Published on Oct 24, 2017

Speaker: Leo Anthony Celi MD MS MPH
MIT Institute for Medical Engineering and Science
Beth Israel Deaconess Medical Center, Harvard Medical School.

Precision Medicine and Drug Discovery (AIMed)

Published on Jan 13, 2017


 

 


Smart Farming with AI and Robotics: The Video

Following up on the previous post about AI and robotics in agriculture (Agricultural Robots and AI in Farming; Promises and Concerns), it seemed appropriate to provide some video on this fascinating and highly significant area. Agricultural robots face substantial challenges in handling an enormous variety of tasks; they also need to take special care in handling plants, produce, and animals. Agriculture is a critical area of  development that often goes unnoticed in the Industry 4.0 story. But these solutions are exploding and are likely to have enormous effects upon employment, finance, and society.

The videos are a mixture of talks, presentations, teaching material and product demonstrations available under standard YouTube license, with landing page descriptions added. Minor edits have been made where necessary.

The Future of Farming with AI: Truly Organic at Scale (Machine Learning Society)

Published on May 17, 2017

A talk by Ryan Hooks, CEO & Founder, Huxley. Weblink with slides at http://www.mlsociety.com/events/the-f…

As climate change and global demographics begin to put excessive strain on the traditional farming model, the need for an agriculturally intelligent solution is vital. By 2050, the world population will increase by over 2 billion people. Current crop yields and freshwater resources will not be sufficient to sustain a population over 9 billion people.

On May 15th 2017, the Machine Learning Society hosted this event to showcase high tech farming techniques used in vertical and urban farming. Our keynote speaker is Ryan Hooks of Huxley. Huxley uses computer vision, augmented reality (AR), and A.I. to greatly improve yield, while driving the down cost and resources requirements. Huxley is creating an “operating system for plants” to grow with 95% less water, 2x the speed, 2/3 less carbon output, and half the nutrients needed.

Automation, Robotics & Machine Learning in Agriculture (Blue River Technology)

Published on May 13, 2016

Keynote presentation by Ben Chostner, VP Business Development of Blue River Technology, at the May 2016 Agri Investing Conference in Toronto.

Farmers: These ARE the Droids You’re Looking For (New Scientist)

Published on May 18, 2016

Autonomous robots created at the University of Sydney can count fruit on trees, spray weeds, and even herd cows.  All pictures courtesy of Professor Salah Sukkarieh, University of Sydney, Australia.

Robots Take Over the Dairy Farm (mooviechannel)

Published on Jan 8, 2015

Gareth Tape of Hardisworthy Farm in Devon calls the technology ‘life-changing’ – both for him and his cows. Watch the video to find out why.

Robots and Drones Agriculture’s Next Technological Revolution NHK Documentary (Japan Tokyo)

Published on Jul 9, 2017

While still a student, Shunji Sugaya started an IT company focused on artificial intelligence and robots for use on the farms of the future. Agriculture in Japan faces serious challenges like an aging population and shrinking workforce. Sugaya imagines robots and drones that reduce labor demands and farms that are run using big data. Today we look at Sugaya and the young engineers at his company in their efforts to shape the future of agriculture and fishing with cutting-edge technology.


 

 


Machine Learning Nuts and Bolts, Wheels and Gears: The Video

Getting down to the nitty-gritty, it’s time to take a practical view of what is involved in setting up Machine Learning (ML) projects. There is a lot of material available, but it can be difficult to find accessible information that can guide you through the development and implementation maze. Here we look at how to access ML APIs, how to use TensorFlow, and common algorithms that you might wish to use for various purposes.

A more practical understanding gives you an edge in discussing possbilities, as also heading you toward the right track if you wish to add these skills to your programming arsenal.

The videos are a mixture of talks, presentations and teaching material available under standard YouTube license, with landing page descriptions added. Minor edits have been made where necessary.

Basic Machine Learning Algorithms Overview – Data Science Crash Course Mini-series (Hortonworks)

Published on Aug 1, 2017

A high-level overview of common, basic Machine Learning algorithms by Robert Hryniewicz

Hello World – Machine Learning Recipes (Google Developers)

Published on Mar 30, 2016

Six lines of Python is all it takes to write your first machine learning program! In this episode, we’ll briefly introduce what machine learning is and why it’s important. Then, we’ll follow a recipe for supervised learning (a technique to create a classifier from examples) and code it up.

Deep Learning, Self-Taught Learning and Unsupervised Feature Learning (Stanford Graduate School of Business)

Published on May 13, 2013

Talk by Andrew Ng.

TensorFlow and Deep Learning without a PhD, Part 1 (Google Cloud)

Published on Mar 8, 2017

With TensorFlow, deep machine learning transitions from an area of research to mainstream software engineering. In this video, Martin Gorner demonstrates how to construct and train a neural network that recognises handwritten digits. Along the way, he’ll describe some “tricks of the trade” used in neural network design, and finally, he’ll bring the recognition accuracy of his model above 99%.

Content applies to software developers of all levels. Experienced machine learning enthusiasts, this video will introduce you to TensorFlow through well known models such as dense and convolutional networks. This is an intense technical video designed to help beginners in machine learning ramp up quickly.

TensorFlow and Deep Learning without a PhD, Part 2 

(Google Cloud)

Published on Mar 10, 2017

Deep learning has already revolutionized machine learning research, but it hasn’t been broadly accessible to many developers. In this video, Martin Gorner explores the possibilities of recurrent neural networks by building a language model in TensorFlow. What this model can do will impress you.

Developers with no prior machine learning experience are welcome. We do recommend that you watch the previous video unless you already know about dense and convolutional networks and are only interested in recurrent networks.

This is an intense technical video designed to help beginners in machine learning ramp up quickly.


 

 


Legacy Modernization: The Video

Digitization is becoming increasingly imporatant as companies attempt to capitalize on new technologies, greater efficiency, and the benefits of cloud, mobile computing, analytics and artificial intelligence. Yet getting to the point where digitization is possible demands modernization of legacy IT. Nowhere is this more apparent than among the huge and expensively maintained government systems. Many large companies also suffer from software modernization issues.

With increasing attention turned to modernization due to Senate passage of the Modernizing Government Technoloyg (MGT) Act, now is a good time to re-examine this issue and consider why modernization is crtical to progress in new technology.  Here are a few videos pointing to modernization issues and possibilities as they are viewed today.

The videos are a mixture of seminars and discussions avaialable under standard YouTube license, with landing page descriptions added. Minor edits have been made where necessary.

New Economics of IT: Modernization (Avanade Inc)

Published on Dec 5, 2016

Critical to achieving a New Economics of IT is modernization. Avanade’s approach to helping organizations modernize is holistic, spanning the entire IT environment and catering for on premises, cloud and hybrid deployment models. Our methodology is underpinned by three key modernization approaches: Application Modernization, Infrastructure Modernization and Workplace Modernization.

Round Tripping & Refactoring (The Software Revolution, Inc. (TSRI))

Published on Aug 20, 2015
 Technology in Transition: IT Modernization (Accenture)

Published on Aug 25, 2017

As government systems age, security risk goes up and cost efficiency goes down.

Chris Howard — IT Modernization Summit (FedScoop)

Published on Apr 20, 2017


 


Coming Soon to Nagoya: RoboCup 2017: The Video

It’s almost time for this year’s  RoboCup competition in Nagoya, Japan, July 27-30. This event has expanded to include more discussions and competitions in a diverse range of robotics activities. The robots demonstrate incremental developments in autonomous goal-directed behavior, improving a little bit each year.  RoboCup is a springboard for discussion of critical areas of robotic development and also provides a showcase for university robotic programs and recruitment.

RoboCup has been held annually for 21 years. More than 3,500 dedicated scientists and developers from more than 40 countries are expected. The event features a number of activities:

RoboCup Soccer includes autonomous mobile robots separated into leagues: Humanoid, Standard Platform, Middle Size, Small Size and Simulation.

RoboCup Industrial, includes RoboCup Logistics and RoboCup@Work. It is a competition between industrial mobile robots focusing on logistics and warehousing systems in anticipation of Industry 4.0.

RoboCup Rescue includes Rescue Robot League and Rescue Simulation League. It employs technologies developed from RoboCup Soccer, to promote simulations that will contribute toward development of autonomous robots for use in rescue efforts at disaster sites.

RoboCup @Home  applies these technologies to people’s everyday lives, evaluated according to how the robots cooperate with human beings to complete various tasks.

RoboCupJunior includes Soccer, Rescue and Onstage Competition to stimulate children’s curiosity and encourage them to participate in robot design and production.

Official RoboCup 2017 Video

Best Goals of RoboCup 2016 (CNET )

Published on Jul 14, 2016

Can Robots Beat Elite Soccer Players? (TEDxYouth)

Published on Apr 23, 2013

As Professor of Computer Science at UT Austin, Dr. Peter Stone’s interests run from competing in the international RoboCup soccer tournament to developing novel computing protocols for self-driven vehicles. His long-term research involves creating complete, robust, autonomous agents that can learn to interact with other intelligent agents in a wide range of complex and dynamic environments.

What Soccer-Playing Robots Have to do with Healthcare (TEDx Talks)

Published on Sep 29, 2012

Steve McGill is a second year PhD student at the University of Pennsylvania studying humanoid robotics and human robot interaction under Professor Dan Lee. As part of Team DARwIn, he captured the first place medal at the international RoboCup humanoid soccer competition in Istanbul, Turkey. Steve is interested in applying this robotic technology for deployment in the field for human intercommunication.

 


 


RPA as the Software Salient of AI: The Video

Robotic Process Automation (RPA) is bringing together elements of Robotics and AI to fuel a new vision of automation across numerous industries. RPA is not about industrial robots,  but it is about knowledge-based processes and services handled through intelligent software. Some of its most intriguing uses are within the financial services industry.

RPA is growing quickly, but the concept is somewhat flexible and still being refined. AI is a great enabler, but software robots have been performing many of these tasks for some time. Adoption figures are likely to be misleading, since any organization can tick off the “RPA box” with a few simple software agents. RPA represents the beginning of  AI /process integration. It is generally understood to be a part of the transition to a more complex AI-centered back office.

In these videos, we review discussions of the current state of RPA, what it entails, and where it is heading. The videos are provided under YouTube license, with discussions provided from their landing pages mildly edited.

Robotic Process Automation (RPA). The Next Productivity Revolution (PwC Australia)

Published on Mar 7, 2016

Being relatively simple to implement, RPA can deliver benefits quickly and return on investment of over 300%.

RPA Future (EdgeVerve)

Published on Mar 16, 2017

Robin George, AVP & Head of Business Development, EdgeVerve systems (an Infosys company) interviews Craig Le Clair, VP & Principal Analyst, Forrester.

 

Applying Robotic Process Automation in Finance and Risk (Accenture )

Published on Oct 12, 2016

Accenture Finance & Risk Practice is helping our financial services clients better manage the onslaught of data and regulations with the use of robotic process automation (RPA). Robots can drive cost, time and accuracy efficiency, and work 24/7 around key tasks such as anti-money laundering and order-to-cash. Ultimately, this frees up valuable employees to focus on higher value work that only humans can do.

HfS Webinar: Beyond RPA (HfS Research)

Published on Apr 24, 2017

With RPA already on its path to being adopted by mainstream enterprise organizations, the question that keeps coming up is where the industry is heading and what is next for those already on this path.

The emergence of AI and machine learning powered robots is deeply entwined with the way organizations are looking to structure their front and back office operations in this emerging Digital OneOffice environment.

Phil Fersht, CEO and Chief Analyst of HfS Research, Guy Kirkwood, COO of UiPath, Andrew Rayner, CTO of Genfour, and David Poole, CEO of Symphony Ventures will discuss the cultural shift brought by the advancements in cognitive RPA solutions.


 

 


SoftBank Buys Boston Dynamics, Promises More Robots

Japanese technology company SoftBank is acquiring acclaimed robot maker Boston Dynamics from Alphabet (Google), along with Japanese bipedal robotics company Schaft, both of which were acquired by Alpahabet in 2013. This is part of SoftBank’s move into the robotics space, exemplified by the SoftBank Robotics “Pepper” humanoid robot whose roles have been increasing beyond the consumer space recently. SoftBank’s robot lineup now includes Pepper, Boston Dynamics’ BigDog and Handle, Schaft’s S-One, and related projects, and Fetch robotics’ warehouse fulfillment robots, making it a versatile player in this space with multi-mission capability.

According to the press release, Masayoshi Son, Chairman and CEO of SoftBank Group, said:

“Today, there are many issues we still cannot solve by ourselves with human capabilities. Smart robotics are going to be a key driver of the next stage of the Information Revolution, and Marc and his team at Boston Dynamics are the clear technology leaders in advanced dynamic robots. I am thrilled to welcome them to the SoftBank family and look forward to supporting them as they continue to advance the field of robotics and explore applications that can help make life easier, safer and more fulfilling.”

Boston Dynamics is known for its DARPA military-oriented robots, including BigDog, Handle, and the humanoid robot Alpha. It has been struggling to find a market for its products, in this stage of  development, and Alphabet has been trying to sell the operation since last year.

SoftBank has a wide range of related interests and commitments within this area, including advanced telecommunications, internet services, AI, smart robotics, IoT, clean energy technology providers, and ARM processors. It entered the robotics market through acquisition of Aldebaran Robotics in 2012. Aldebaran, creator of the Nao and Romeo robots, was renamed Softbank Robotics and created a social robot called Pepper, which is being tested in a growing range of consumer and business settings.

Softbank’s robotics ventures are centered in its Tokyo-based subsidiary SoftBank Robotics Holding Corp, established in 2014, with offices in Japan, France, U.S. and China. SoftBank Robotics has more than 500 employees working in Paris, Tokyo, San Francisco, Boston and Shanghai. Its robots are used in more than 70 countries for research, education, retail, healthcare, tourism, hospitality and entertainment.

The CEO of Boston Dynamics Explains Each Robot in the Fleet (jurvetson)

SoftBank Robotics Meet Pepper (SoftBank Robotics America)


 

 

 


Microservices, Platforms, and the Infrastructure of AI: The Video

AI and Machine Learning are often at their best as composite services  performing as part of a coordinated platform. In this brief set of videos, we look at AI APIs, microservices, platforms, and how they can be brought together to achieve larger goals.  These are recent discussions in a rapidly evolving territory where exciting visions continue to emerge.

The videos are a mixture of seminars and discussions avaialable under standard YouTube license, with landing page descriptions added. Minor edits have been made where necessary.

Machine Learning APIs by Example (Google Cloud)

Published on Mar 9, 2017

Think your business could make use of Google’s machine learning expertise when it comes to powering and improving your business applications, but do you get stuck on building and training your own custom model? Google Cloud Platform (GCP) offers five APIs: Google Cloud Vision API, Cloud Speech API, Cloud Natural Language API, Cloud Translation API and Cloud Video API. These APIs access pre-trained machine learning models with a single API call. In this video, Sara Robinson shares an overview of each API. Then she dives into code with a live demo.

Fireside Chat: Crafting the Future of Technology – Microservices & Platforms (Zinnov Management Consulting)

Published on Mar 30, 2017

Kevin Prendeville, Managing Director – PE & LS, Accenture
Pankaj Chawla, MD & CTO – Products and Platforms Engineering, Accenture
Peter Schmutzer, Director Purchasing, Intel
Siddhartha Agarwal, VP, Product Management & Strategy, Oracle

We are in the midst of a revolution in artificial intelligence: the art and engineering of getting computers to perform tasks that, until recently, required natural intelligence — in other words, people. We can speak to our phones, software can identify faces, and algorithms can teach themselves to play Atari video games. Top-of-the-line sedans have the ability to drive autonomously on the open road. Progress has been driven by machine learning techniques, such as deep convolution networks, dating to the 1980s, by Moore’s law, and by continual improvements in machine architecture. Progress is so dizzying that a few futurists, technologists, and philosophers have publicly mused about where the swift ascension of machines is taking us. In this session, we will have a sneak-peek into the future of AI and what it holds for us.

APIs and Artificial Intelligence (Google Cloud)

Published on Mar 9, 2017

A fundamental goal for every business is to keep users attention and focus on their products or services. Combining APIs with AI leads to better stickiness.

Having Fun with Robots Using Microservices on Docker and Kubernetes (Devoxx)

Published on Nov 10, 2016

Controlling and building a single robot is already a challenge, but how would that work if you want to have a swarm of robots interact with each other? How do we control and interact with them whilst all robots are slightly different?