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…

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.



Agricultural Robots and AI in Farming; Promises and Concerns

With constant attention given to Industry 4.0, autonomous vehicles and industrial robots, there is one significant area of robotics that is often under-reported—the growing use of autonomous agricultural robots and AI-driven smart systems in agriculture. Although automation has been practiced on the farm for many years, it is not been as widely visible as its cousins on the shop floor. But technology being deployed on farms today is likely to have far reaching consequences.

We are on the edge of an explosion in robotics that will change the face of agriculture around the world, affecting labor markets, society, and the wealth of nations. Moreover, developments today are global in extent, with solutions being created in the undeveloped world as well as in the developed world, stretching across every form of agriculture from massive row crop agribusiness and livestock management, down to precision farming and crop management in market gardens and enclosed spaces.

Agriculture Robots Today

Agriculture is vital to the health of the ever-expanding human population, and to the wide range of interrelated industries that make up the agricultural sector. Processes include everything from planting, seeding, weeding, milking, herding, and gathering, to transportation, processing, logistics, and ultimately to the market—be it the supermarket, or increasing retail online. The UN predicts that world population will rise from 7.3 billion to 9.7 billion in 2050. Robots and intelligent systems will be critical in improving food supplies. Analyst company Tractica estimates shipments of agricultural robots will increase from 32,000 units in 2016 to 594,000 units annually by 2024—when the market is expected to reach $74.1 billion per year.

While automation has been in place for some time and semi-autonomous tractors are increasingly common, farms pose particular problems for robots. Unlike highway travel, which is difficult enough for autonomous vehicles, agricultural robots need to be able to respond to unforeseen events, plus handle and manipulate objects within their environment. AI makes it possible to identify weeds and crops; discern crop health and status; and to be able to take action delicately enough to avoid damage in actions such as picking. At the same time, these robots must navigate irregular surfaces and pathways, find locations on a very fine scale,  and sense specific plant conditions across the terrain.

Agricultural robots using AI technologies are responding to economies in the agricultural sector as well as to rising labor costs and immigration restrictions. The first areas of general impact are in large businesses, since robots have high investment and maintenance costs and there is a lack of skilled operators. Conditions will change as robots become cheaper, more widely available, and capable of performing more diverse tasks. This will require evolution of AI technologies, expansion of collaboration abilities among robots; and man-machine combined operations. The ability of robots to work with humans could be particularly significant due to the wide range of discrimination tasks involved in food safety, quality control, and weed and pest removal. Robots will be guided by human supervisors with skills in agriculture and knowledge of robotic and agricultural systems.

Opportunities and Growth

According to the International Federation of Robotics, agricultural robots are likely to be the fastest growing robotics sector by 2020. Different sectors of agricultural markets will respond differently. Large businesses with row crops are early responders, since they have funds to invest and shrinking margins. For these companies, there are huge benefits in reducing labor costs and instituting more precise farming methods. As picking and weed killing and pest removal systems become more widely available, citrus orchards and difficult-to-pick crops are likely to be next. Robots capable of picking citrus, berries, and other delicate fruit in difficult locations are already starting appear. There are applications in virtually every part of the agricultural sector.

Other uses will appear as robots become more common and less expensive. Robots can make a difference not only in harvesting, but also in precision of water application and fertilizer. In areas where water is contentious, such as California and the Middle East, more efficient watering will make it possible to grow larger crops with greater efficiency and less water, avoiding creation of political and social crises.

In the developing world, opportunities are enormous but individual farmers have fewer resources. For this reason, smaller robots and robot clusters are likely to be more widely used, possibly with emerging Robot-as-a-Service (RaaS) operators providing  labor on a per-usage or rental basis. Robotics will be able to save enormously on chemical costs and water used for irrigation, which will have significant economic impacts, as well as environmental benefits.

Progress and Caution

As use of agricultural robots continues to expand, they will take on increasinly complex tasks, and replace a larger portion of agricultural labor–a critical component of global employment. In many countries, particularly in the developing world, this will create shifts in employment which will empower trends such as rural migration to cities, and reduce overall availability of labor–intensive jobs. More training will be needed by more people; this will impact education, socialization, and finance; particularly in countries with large populations.

There are many implications as AI and agricultural robots are deployed. New ideas are blossoming, startups are on the rise, and we can expect a wide range of consequences as  next generation agricultural robotics and AI continue to emerge.


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.



Challenges and Opportunities in the AI User Interface

While the general integration of artificial intelligence into business processes is likely to have a profound effect, one area common to all applications will be particularly impacted–the User Interface (UI). We have already seen the beginning of  AI in digital assistants such as Alexa (see Digital Assistants Coming of Age with Alexa) that interpret natural language commands and respond with speech or actions. While this type of Conversational Interface (CI) is likely to become ubiquitous, there are other significant UI impacts that may be at least as important.

Today, capabilities of applications have grown geometrically and complexity continues to increase. Meanwhile, mobile platforms have limited display space and fewer controls. This leads to a new need for simplification. The choices presented to the user in menu upon menu within office products, for example can be exhaustive and make it difficult to locate specific actions. Yet there is great demand for additional processing to meet the needs of new use cases.

AI to the Rescue 

Natural language processing provides a start for simplifying access to the deeper complexities of programs by understanding commands and responding without a keyboard. But the conversational interface is also evolving as AI progresses. We are still at a rudimentary stage, returning to the need to memorize commands like the old TTY interfaces. What is needed is to address the selection of actions based upon context. This is the beginning of a new form of interface in which AI is able to translate a human request for a desired result into a command sequence to one or more applications to perform tasks that will meet the user’s ultimate goal.

An AI User Interface

For example, someone might ask “Should I go to the office today?”. The system might then assess health information, determine if there is an illness; check the weather for extremes; check databases of current natural disasters that might be applicable; check holiday schedules; check company instructions and so forth, all in an instant. But to do this, the AI needs a broader range of knowledge and data than is commonly available for current AI applications, and a capacity to understand what such a request might entail. In fact, as with many such puzzles, there is the beginnings of a requirement for an artificial general intelligence which would think across a wide range of territory rather than within the simple parameters of a question. The AI would think about thinking.

Such an interface demands situational awareness and an understanding of the overall context in which a question is posed. The AI needs to define the specifics of where information would be found; it also needs to understand how to convey and/or act upon that intelligence, and make complex choices.

Implications of an AI UI

As software continues to grow in  complexity, it is certain that AI will provide a bridging interface between human and machine. This interface will become more flexible and more powerful, and it will evolve to perform more duties. Just as simplification of the UI makes it possible to perform complex tasks on the computer with only a basic understanding of operating principles, people will begin to interact with computers in a conversational way and evolve information requests to meet an increasingly sophisticated interaction. The growing sophistication of this interaction and will feed the development of further AI UI capabilities.

Development of a sophisticated AI-based UI is not solely about natural language processing, however. All computer interactions can likely be improved by the addition of an AI layer. While conversation is a priority, AI will be able to  to reduce the clutter and confusion of menu-based interactions by providing selection based upon context, and capability to interact based on desired results rather than tool selection. In effect, this is much like movements in  software development, such as the growing Agile and DevOps movements. Writing software to meet specific customer needs is much better than coding based around technology. This same rule must apply to the actions of programs themselves.

Into the Future

AI will also be applied to the processes of developing artificial intelligence. We have already seen programs that read a user interface screen image and convert it directly into code. In the next iteration, we can expect to see AI solutions which turn actions into instructions which may feed multiple AI systems to further a desired operation. While such a general intelligence creates its own issues, it will arrived in incremental steps. Increasing complexity of AI routines, increasing integration, and increasing componentization will open the way for AI to operate across a broader range of data and make decisions about thinking and about interaction that can generally be applied across all computer systems.


Skills for Data Science and Machine Learning: The Video

Here we look at the skills and employment opportunities that are surfacing as we move into an era of advanced analytics and ubiquitous AI.

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.

What Kaggle has Learned from Almost a Million Data Scientists (O’Reilly

Published on May 25, 2017

A talk by Anthony Goldbloom from Kaggle.  Kaggle is a community of almost a million data scientists, who have built more than two million machine-learning models while participating in Kaggle competitions. Data scientists come to Kaggle to learn, collaborate, and develop the state of the art in machine learning. Anthony Goldbloom shares lessons learned from top performers in the Kaggle community and explores the types of machine-learning techniques typically used, some of the tricks he’s seen, and pitfalls to avoid. Along the way, Anthony discusses work habits and skills that help data scientists succeed.

Andrew Ng: Artificial Intelligence is the New Electricity (Stanford Graduate School of Business)

Published on Feb 2, 2017

On Wednesday, January 25, 2017, Baidu chief scientist, Coursera co-founder, and Stanford adjunct professor Andrew Ng spoke at the Stanford MSx Future Forum. The Future Forum is a discussion series that explores the trends that are changing the future. During his talk, Professor Ng discussed how artificial intelligence (AI) is transforming industry after industry.

What Are the Top Skills Needed to Be a Data Scientist? (SAS Software)

Published on Oct 26, 2015

Dr. Goutam Chakraborty of Oklahoma State University outlines the top skills you need to be successful as a data scientist. From SAS Academy for Data Science

Top Data Scientist D J Patil’s Tips to Build a Career in Data (FactorDaily)

Published on Jul 20, 2017

D J Patil, who along with top data scientist Jeff Hammerbacher coined the term “data scientist” tells you what it takes to be a data scientist During the Obama administration, Patil became the first ever chief data scientist to be appointed by the US government.




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