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.


 

 


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.

 


 


IBM and ABB Collaborate to Boost Industry 4.0

Swiss-based engineering giant Asea Brown Boveri (ABB) and IBM have just announced a strategic collaboration that brings together ABB’s industry leading digital sensor and control offering, ABB Ability, with IBM Watson AI-based IoT system to provide a more comprehensive intelligent solution fir control and defect monitoring issues in utilities, industry and transport & infrastructure.

IBM Watson has been moving gradually into a range of new territories as applications for its cognitive capabilities are being explored. This move brings both companies closer to “Industry 4.0” where competition includes not only software companies, but also manufacturing firms such as GE.

The IBM/ABB solution is aimed at varying goals such as improving quality control, reducing downtime and increasing speed and yield of industrial processes by enabling current sensor and data gathering systems to become “cognitive” by using collected data to understand and take actions. ABB brings a deep domain knowledge and extensive portfolio of digital solutions to the mix, which is combined with IBM’s AI capabilities and vertical industry applications. The first two joint industry solutions will bring real-time cognitive insights to finding defects on the factory floor, and optimize maintenance of smart grids.

According to ABB CEO Ulrich Spiesshofe:

“This powerful combination marks truly the next level of industrial technology, moving beyond current connected systems that simply gather data, to industrial operations and machines that use data to sense, analyze, optimize and take actions that drive greater uptime, speed and yield for industrial customers. With an installed base of 70 million connected devices, 70,000 digital control systems and 6,000 enterprise software solutions, ABB is a trusted leader in the industrial space, and has a four decade long history of creating digital solutions for customers. IBM is a leader in artificial intelligence and cognitive computing. Together, IBM and ABB will create powerful solutions for customers to benefit from the Fourth Industrial Revolution.”

Part of ABBs business strategy is collaboration with other vendors, which includes partnering with IBM, Microsoft, and Wipro, among others in delivery of its digital solutions. Prior to the IBM deal, ABB has been using predictive and prescriptive analytics, plus customized models based extensive industry expertise, to identify and prioritize emerging maintenance needs based on probability of failure and asset criticality.

For IBM, this the collaboration aids in bringing Watson deeper into the crucial Industry 4.0 space, a key area for technological progress in IoT and AI. According to IBM CEO, Ginni Rometty:

“This important collaboration with ABB will take Watson even deeper into industrial applications — from manufacturing, to utilities, to transportation and more. The data generated from industrial companies’ products, facilities and systems holds the promise of exponential advances in innovation, efficiency and safety. Only with Watson’s broad cognitive capabilities and our platform’s unique support for industries can this vast new resource be turned into value, with trust. We are eager to work in partnership with ABB on this new industrial era.”

We can expect an onslaught of collaborations, mergers and acquisitions, and talent wars across this valuable sector as major industrial and IT forces join in the fray.


 


Two for the Road: GE Acquires ServiceMax and Bit Stew on the Path to Industry 4.0

ServiceMax and Bit Stew represent two important pathways in the development of Industry 4.0, or the Industrial Internet of Things. ServiceMax, the larger acquisition, points to the need to manage, monitor, and control industrial devices, including handing the complex requirements of service and repair. This functionality, aided by AI, can act as a kind of lymphatic system to keep systems running and provide new service opportunities through availability of status data. Bit Stew, on the other hand, is a pure AI/machine learning play, adding a level of immediate processing to system data to permit its ingestion. It is a smart ETL solution adapted to the IoT and increasing data requirements of AI and advanced analytics.

Service First

GE is buying cloud-based field service management software provider ServiceMax for $915 million as it moves rapidly forward in pursuit of Industry 4.0. ServiceMax is a part of that strategy. As a component of the industrial Internet, ServiceMax is a fully digital solution operating in the cloud to aid service technicians in repairing large machines. It operates on a Salesforce platform and has been used by GE for the past several years. What is new it is that it is now being incorporated within the company and therefore becomes integral to GE’s growing service business.

ServiceMax automates workforce optimization, provides scheduling and dispatch, insures parts logistics and inventory are correct and takes care of routine details of service contracts. The software has been used by more than 300 customers including Kodak, Coca-Cola, at others in fields such as oil and gas and medical devices. One of its chief attributes is capability to instrument equipment, automate repair and service provisions, and provide an increasing level of intelligence in the control of equipment in the field. Earlier this year, ServiceMax introduced its Connected Field Service solution that connects machines directly to technicians; it also added Service Performance Metrics, a measurement framework for field services to optimize business activity.

By incorporating ServiceMax, GE is aided in servicing heavy-duty machinery and can bolster its services revenue. It also gives GE equipment an advantage in its close alignment with the newly acquired firm. Of greater significance, perhaps, it is the fact that ServiceMax will become part of GE Digital, which was established last year and maintains a separate operating system called Predix, which is used to optimize equipment operation. Predix is a core product that makes it possible for GE to expand its digital operations.

The addition of ServiceMax to GE’s Predix platform provides more data which may be leveraged to create new opportunities in the Industrial Internet of Things. The possibility of providing service information along with other instrumentation from large machines and submitting this to analysis makes it possible to create new services based around a growing fabric of connected devices.

A Different Stew

GE is also buying Bit Stew systems for $153 million. Vancouver-based Bit Stew is an AI and IoT company whose base product, MIx Core, uses machine learning to automate the process of acquiring data from machines and readying it for analysis by conventional and advanced analytics. MIx Core automates data modeling, mapping and ingestion in a manner similar to ETL by providing a more sophisticated application of intelligence at the coalface. Its technology greatly reduces the time needed to understand the data and make it accessible to near real time analysis.

The application of machine learning to data input creates another layer of AI. As we had considered earlier in The Composite Nature of Embedded Cognition, the addition of AI capabilities to processes results in a deeper and less predictable system. The capabilities of immediately analyzing data and potentially adding prediction before submitting it to further processing presents some similarities to brain function, which combines different processes at every stage of cognition.

For GE, this provides another tool in extending the reach of its Predix industrial operating system. As Predix grows to increase its capability through addition of more complex data and a greater variety of sources, it becomes possible to integrate a wide range of activities within the industrial and manufacturing sector. As with the ServiceMax, acquisition of Bit Stew brings us closer to an integrated digital manufacturing environment which could be operated remotely, autonomously, or as a service.

All Go for the Industrial IoT

As GE continues to position itself as a digital industrial company we can expect further moves aimed at expanding digital services and automation to create a universal platform of interconnected devices. Other manufacturing companies such as Samsung are moving in the same direction. Mergers and acquisitions in this space tend to be the research tool of choice.

As the digital revolution continues, we can expect equipment to act more and more like software; it will require security updates; continuous change in function; frequent release of updates to physical and digital mechanisms; and a continual reevaluation of processes. These initiatives will frame the digital connected world of the future and create new opportunities for application of artificial intelligence to the optimization not only of digital, but also of physical assets.


Guest Blog: Machines Are Taking All The Jobs? What Decision-Makers Say And Do

Original blog link:

Machines Are Taking All The Jobs? What Decision-Makers Say And Do

by Gil Press

A new PwC survey provides fresh and illuminating data on the burning questions of the day: Are machines going to take over our jobs? And how much do we rely (or over-rely) today on machines, automation, and algorithms?

Experts are confident that machines are going to replace many workers. A much-quoted report from Oxford University has estimated that “about 47% of total US employment is at risk” for being fully automated. The machine threat to employment is even greater in developing economies—a recent report from Oxford estimates that 77% of jobs in China and 69% of jobs in India are “at high risk of automation.”

But maybe estimating the type of jobs that the machines are going to replace is the wrong approach. Tom Davenport, who just published a book on strategies for coping with automation, Only Humans Need Apply: Winners and Losers in the Age of Smart Machines (co-authored with Julia Kirby), told the Wall Street Journal recently: “Computers don’t tend to replace whole jobs; they replace specific tasks.”

The McKinsey Global Institute (MGI) agrees: “…a focus on occupations is misleading. Very few occupations will be automated in their entirety in the near or medium term. Rather, certain activities are more likely to be automated, requiring entire business processes to be transformed, and jobs performed by people to be redefined.”

MGI estimates that 45% of work-related tasks can be automated. This finding does not bode well for knowledge workers who were sure their cognitive skills could not be automated and that they will always outrun the machines. Even CEOs, according to MGI, spend over 20% of their time on activities that can be automated with current technology.

What has been missing in this discussion is data on how much we rely (or don’t) on machines today, rather than estimates based on experts’ assessments of how automation-prone are various occupations and activities. Specifically, has the era of big data and increasingly sophisticated algorithms changed the nature of business decision-making? What is the extent by which business executives rely on machines today when they make strategic decisions?

A new PwC survey of more than 2,100 business decision-makers across more than 10 countries and 15 industries sheds new light on these questions. It frames the discussion as follows: “Executives who once relied firmly on their intuition and experience are now face-to-face with machines that can learn from massive amounts of data and inform decisions like never before.”

59% of the decision-makers PwC surveyed say that the analysis they require relies primarily on human judgment rather than machine algorithms. That means that 41% already tend to rely more on algorithms than their own experience, judgment, and intuition. “We are not talking about pricing a seat on an airline,” says (via email) Dan DiFilippo, Global & US Data and Analytics Leader at PwC. “We are talking about big, strategic decisions that almost certainly involve some combination of human and machine, but clearly we see a significant involvement of the machine.”

The most interesting findings are about the type of decisions that tend to be assisted by machine algorithms and the ones that rely more on human judgement. In the chart above, “respondents who answered closest to zero are nearest to the survey’s overall average reliance on analysis from machine algorithms and human judgment. The farther away from the center point, the greater reliance on either mind or machine,” says PwC.

“Shrinking existing business” was deemed by survey respondents as the type of decision that relies most on human judgement and “Investment in IT” as the one relying most on algorithms. “Investment in IT,” says DiFilippo, “can cover many areas including shop floor automation, CRM systems, HR systems, risk management systems, etc., all of which have varying degrees of machine algorithms and can be assessed by machine algorithms.”

The breakdown of results by country offers a striking juxtaposition of China and Japan with the former as the country/region relying more than others on machine algorithms and the latter as the country/region second only to Central and Eastern Europe in its reliance on human judgement. One would think that China and Japan will have similar attitudes toward and use of algorithms in decision-making but this is apparently not the case. It’s possible, however, that the results are due to different interpretations of the survey questions. Says DiFlippo: “We don’t have a precise answer or explanation for this—we are still working to gather more on this front.”

Finally, the breakdown of results by industry shows that different economic sectors differ in the degree by which decision makers rely on their own judgement vs. relying on machine algorithms. Conclude DiFilippo: “Involving the machine can help reduce/eliminate bias (at the individual, department or organization level), add more accuracy and/or more computing power to crank through a high volume of scenarios that human can’t do (or can’t do in a timely manner), and importantly—and the data supports this—there is a sense that the machine can help de-risk the strategic decision… we see that those who had a high degree of machine algorithms felt a high degree of managed and known risks.”

So should we search for the right mix of minds and machines in the context of a specific decision or should we succumb to a universal McAfee’s Law and agree that “as the amount of data goes up, the importance of human judgment should go down”? What’s your experience with trusting machine algorithms rather than your own judgment?

Gil Press

Gil Press is Managing Partner at gPress, a marketing, publishing, research and education consultancy. Previously, he held senior marketing and research management positions at NORC, DEC and EMC. Hewas Senior Director, Thought Leadership Marketing at EMC, where he launched the Big Data conversation with the “How Much Information?” study (2000 with UC Berkeley) and the Digital Universe study (2007 with IDC). He blogs at http://whatsthebigdata.com/ and http://infostory.com/ Twitter: @GilPress


Guest Blog: Only Humans Need Apply Is A Must-Read On AI For Facebook Executives

Original blog link:

Only Humans Need Apply Is A Must-Read On AI For Facebook Executives

by Gil Press

Under pressure to remove alleged human bias from its “Trending Topics” section, in August. Facebook fired the editors who were selecting and writing headlines for the stories, explaining that this “will make the product more automated.” The results of trusting algorithms more than humans have continued to make headlines ever since with the Trending “product” promoting a fake news story about Fox News’ Meghan Kelly, a conspiracy article claiming the 9/11 twin towers collapsed because of “controlled demolition,” and Apple AAPL +0.78%’s Tim Cook announcing that Siri will physically come out of the phone and do all the household chores (a story from an Indian satirical website, Faking News, that was Trending’s top story on the day of the iPhone 7 launch event), to mention just a few of the more embarrassing machine failures.

Silicon Valley has never displayed much love for fallible humans, but has shown a lot of confidence in the continuous improvement and now, self-improvement, of machines. Do humans still have an important role to play in our automated lives which are increasingly controlled by sophisticated algorithms and seemingly smarter machines?

In Only Humans Need Apply: Winners and Losers in the Age of Smart Machines, knowledge work and analytics expert Tom Davenport and Julia Kirby, a contributing editor for the Harvard Business Review, offer optimistic, upbeat and practical answers to this much-debated question. “The upside potential of the advancing technology is the promise of augmentation—in which humans and computers combine their strengths to achieve more favorable outcomes than either could do alone,” they write.

There is not much difference, contend Davenport and Kirby, between technologies of automation and technologies of augmentation. The difference lies in the goals and attitudes behind the application of these technologies. Automation is unidirectional and focuses “primarily or exclusively on cost reduction” via the elimination of human labor. In contrast, “augmentation approaches tend to be more likely to achieve value and innovation” and they are bidirectional, making “humans more capable of what they are good at” and “machines even better at what they do.”

It is a shortsighted (and short-term) strategy for companies to favor automation over augmentation: “If the goal is to provide truly exceptional or differentiated products and services at scale, only an augmentation arrangement can accomplish that,” write Davenport and Kirby. They advocate a “workplace that combine sophisticated machines and humans in partnerships of mutual augmentation” and mutual benefit.

Gil Press

Gil Press is Managing Partner at gPress, a marketing, publishing, research and education consultancy. Previously, he held senior marketing and research management positions at NORC, DEC and EMC. Hewas Senior Director, Thought Leadership Marketing at EMC, where he launched the Big Data conversation with the “How Much Information?” study (2000 with UC Berkeley) and the Digital Universe study (2007 with IDC). He blogs at http://whatsthebigdata.com/ and http://infostory.com/ Twitter: @GilPress


Human-Centered Robotics–The Video

Human-Centered Robotics is a somewhat broadly defined area looking at interactions between AI-driven robots and humans. Robots and humans must coexist in society, in the home, and in the workplace. An issue of particular urgency is how process robots, particularly in manufacturing, will operate efficiently without harming people, and without requiring complex programming skills to function in a changing environment.

Robotics companies and academic institutions are working to ease the way for an interactive robotics future. Here are a few videos of the current state of the human-robot interaction art:

Automatica 2016

University of Texas at Austin

DIAG Robotics Lab, Sapienza University of Rome

Nao Robot Task Learning