Industrial Internet Consortium Creates Vision on AI for Industrial IoT

The Industrial Internet Consortium® (IIC™), which works to accelerate the adoption of the Industrial Internet of Things (IIoT), has announced it is leading the vision for industrial artificial intelligence (AI) with new bodies of work and will be sharing insights at the IoT Solutions World Congress (IOTSWC). Building on the successful Industrial IoT Analytics Framework Technical Report (IIAF), which […]

The Industrial Internet Consortium® (IIC™), which works to accelerate the adoption of the Industrial Internet of Things (IIoT), has announced it is leading the vision for industrial artificial intelligence (AI) with new bodies of work and will be sharing insights at the IoT Solutions World Congress (IOTSWC).

Building on the successful Industrial IoT Analytics Framework Technical Report (IIAF), which offered industrial analytics guidance in evolving fields such as big data, AI and machine learning, IIC is expanding its program of work to focus on AI in industrial IoT applications. The Industrial Analytics Task Group has been expanded and renamed to Industrial AI Task Group.

“No single technology has the potential to change how we do business in virtually every sector of the economy more than artificial intelligence,” said Wael William Diab, Industrial Artificial Intelligence Task Group Chair, Secretary of the IIC Steering Committee, IOTSWC 2018 AI Forum Chair and Senior Director for Huawei. “To achieve transformative business outcomes, IT and OT stakeholders will have to work closely together on the integration of AI into industrial applications.”

The Industrial AI Task Group recently hosted a workshop at the IIC quarterly meeting in Helsinki this past May to promote IIC’s vision of AI. In a panel session moderated by Diab, four IIC experts talked about the importance of AI in industrial applications. The panel was well received and attended by over 100 IIC members at the IIC quarterly meeting in Helsinki. Here are some of panelists’ remarks:

“AI is still in its infancy but with great potential in industrial applications,” said Shi-Wan Lin, Co-Chair of the IIC Technology Working Group and CEO & Co-Founder of Thingswise. “There’s still lots of work to be done but applying AI in the vast amount of industrial data will enable IIoT systems to create higher value in the years to come.”

“While AI toolsets will help solve customer’s problems, you also need good, clean consistent data and analytics to put it into action,” said Christopher Ganz, IIC Steering Committee Member and Group Vice President for Service Research & Development at ABB.

“Custom silicon is very important for AI,” said Liang Guang, IIC Member and Senior Standards Manager at Huawei Technologies. “While some of the big vendors have used custom hardware designs, heterogeneous accelerators can offer the performance needed for AI on the cloud.”

“There are many industries advancing AI,” said Christoph Fritsch, IIC Member and Senior Director of Industrial, Scientific and Medical Markets at Xilinx. “We’ll continue to see the number of applications that use AI grow in a wide variety of industries in the next few years.”

This October, IOTSWC will launch its inaugural AI & Cognitive Systems Forum. The forum will explore the various aspects of AI from a foundational technology perspective to transformational use cases that open up endless business possibilities in IIoT. In addition, it will showcase how AI-enabled IIoT solutions can provide enhanced insights, complex decision making, self-learning and self-healing in industrial environments.

A workshop, called Deciphering AI, will precede the forum serving as an “AI 101” with the goal of providing both background to the attendees on AI as well as the key concerns and challenges with deploying AI. The IOTSWC AI & Cognitive Systems Forum as well as the Deciphering AI Workshop will be chaired by Diab and IIC Member Edy Liongosari, Chief Research Scientist at Accenture Labs, with contributions from and sessions led by several IIC members. Diab and Liongosari are also members of the IOTSWC Program Committee.

For more information, visit the Industrial Internet Consortium.

Successful AI Companies Build Insurmountable Leads Using Data Strategy

The most recent issue of MIT Technology Review shows their annual list of 35 Innovators Under 35.  Of these, 15 are AI-based – 43%.  Another 3 are in Computational Synthetic Biology that depends on deep learning. Similarly the website Angel.co which tracks the formation and investment in startups shows about 6,800 companies specifically relating to AI.  That’s probably understated.  […]

The most recent issue of MIT Technology Review shows their annual list of 35 Innovators Under 35 Of these, 15 are AI-based – 43%.  Another 3 are in Computational Synthetic Biology that depends on deep learning.

Similarly the website Angel.co which tracks the formation and investment in startups shows about 6,800 companies specifically relating to AI.  That’s probably understated.  I’d round up to an even 10,000.

So it’s no surprise that AI is the siren song that launched 10,000 ships.  The real question is how many will survive for even the next three years?

We’re not talking about how existing companies should capitalize on AI to enhance their business.  We’re talking about how to become the next Google, Facebook, or Amazon with a lead so dominant that no one can catch up.

The Single Key Strategy that Defines AI Success: Data Dominance

Start to look at individual companies and you’ll see that they are focused on their technology, the user experience, and their product or platform.  This perspective will take them no further than being just another product or perhaps only a feature.  It will not take them to becoming a long term viable company that will return their investor’s capital, much less the desired multiple.

To create a successful AI company you must create such a wide moat that no one can catch up unless they pay your price.  That moat is not about technology.  There are essentially no monopolies on deep learning technologies, only leaders that can quickly be copied.

The secret to a wide moat in AI is to have a virtual monopoly on the data you are using to train.  In this case monopoly also means such a large lead in users and data volume that no one can reasonably catch up.

How to Create a Data Monopoly

All AI companies face the same barrier when starting out:  how to obtain enough data to train their product.

Everyone recognizes this virtuous feedback cycle, but without users you can’t generate sufficient data, and so it continues.

The question they should be asking, even before taking investment is how the data can be acquired in a way that is strategically defensible.  The answer to this question will simply eliminate many markets and applications where data is not defensible or competitors already have substantial leads.

For example, there’s no wide moat available in advertising.  Google dominates search-based advertising and Facebook dominates social media based advertising.  General e-commerce?  Can’t beat the lead that Amazon has in learning about our personal shopping desires.  These three industry giants clearly have defensible positions by virtue of their dominant data.

So How Then to Identify and Collect Defensible Data

A defensible data strategy is not something you can sprinkle on any AI startup.  It starts by carefully selecting the industry and the problem to be solved.  These are not easy to find, but here are some examples to get your thought processes started.

You’ll find here a unique blend of identifying markets and market needs where the addition of AI creates opportunity.  You’ll also see examples of creating new types of data in existing markets that competitors can’t duplicate.

Here are a few selected examples that exemplify good data strategies:

Blue River Technology:  This is a company that offers agricultural optimization by evaluating each plant individually at each stage of growth.  There are plenty of competitors that use drones or stationary sensors to divide a field into smaller segments to be optimized but no competitor that does this on a plant-by-plant basis.

Their technology platform looks like 30 foot wide arms on the front of a tractor that literally takes an image of each plant (think lettuce for example) as the arm passes over.  Based on their AI model the platform makes an AI-driven instantaneous decision to provide water, fertilizer, or to apply an herbicide.  No sense putting energy into a plant that’s not going to make it or if it’s a weed.  Blue River calls this ‘see and spray’.

The process of getting the training data wasn’t simple and involved a significant investment in running their prototype platform over farm fields to acquire images of individual plants which were then coded for health, sickness, and optimum use of fertilizer and water.  They now have the world’s largest database of plant images which continues to grow with each pass of their equipment over a field.  Their lead in plant level AI image training data in unassailable.

[Editor’s Note: Deere & Company acquired Blue River Technology in September 2017 for $305 million.]

Read the source article in Data Science Central.

How to Map Out a Career Path in This Early Stage of AI

By Stephanie Glass, Head of Product Marketing, AI Solutions, Aera Technology Artificial intelligence is already reshaping society as we know it in both business and consumer realms. Early use cases with Alexa, autonomous vehicles and AI-driven supply chains provide just a glimpse of the disruption that AI is poised to deliver in the near future and […]

By Stephanie Glass, Head of Product Marketing, AI Solutions, Aera Technology

Artificial intelligence is already reshaping society as we know it in both business and consumer realms. Early use cases with Alexa, autonomous vehicles and AI-driven supply chains provide just a glimpse of the disruption that AI is poised to deliver in the near future and for years to come.

Stephanie Glass

Yet despite all the AI hype and initial successes, it remains in its infancy. That makes now the ideal time for young people to build the knowledge, skill sets and connections they need to capitalize on the fast-growing market for AI jobs and build a strong AI career.

One reason is simply practical. Gartner predicts that AI may eliminate 1.8 million jobs by 2020, yet is on track to create 2.3 million new positions. It clearly makes sense to map out an AI career path as new roles emerge that focus on problem-solving, collaboration and strategic decision-making.

The second reason is that AI offers a truly exciting opportunity to make an impact on our world. AI is an ideal career path for undergraduates and recent graduates with a passion for technology and an entrepreneurial spirit who relish a challenge and a front-line role in game-changing innovation.

AI has been called “the next Industrial Revolution,” and I think that analogy is spot on. The convergence of machine learning, big data and internet-scale cloud compute power is opening opportunities to transform healthcare, grow the economy, reduce waste, save energy, improve education, strengthen security and decrease poverty.

There’s virtually no area of society that will be untouched by AI. Given the myriad possibilities, how does a young person chart his or her course in the AI field?

Practical steps to an AI career

One first step is to simply narrow down key interests and strengths. AI offers technological roles in data science, machine learning development and architecting an AI technology stack. And it offers plenty of positions as a user or analyst with AI software, or in sales and marketing, HR, customer support and more.

Company type is another important consideration, as B2B and B2C can be distinctly different work environments. Candidates should also target industries they gravitate toward, be it technology, manufacturing, retail, higher education, services or others. To narrow down interests, hands-on research and learning is available through avenues such as:

AI and data science courses. Many free and low-cost online learning opportunities in AI can be found at providers such as Udacity, Coursera, Codeacademy and fast.ai. These courses are a great way to build knowledge and enrich a resume.

Industry and company conferences. AI experts and thought leaders discuss trends at conferences hosted by NIPS, Re-Work, O’Reilly, Gartner, AI World and others, while AI vendors also sponsor conferences. Students and young professionals are often able to gain free or discounted admission.

Meetup.com. The website lists many regional in-person meetups among AI enthusiasts, as well as online groups and discussions on topical areas including machine learning, Python, R, data science and advanced statistics.

General immersion. Keeping up-to-date on fast-moving AI news and trends pays off. So does using AI products, sharing code on GitHub or entering a code contest on the global Kaggle community. In short, would-be AI professionals should learn as much as they can, as fast as they can.

With this knowledge, young people are better able to address two key questions that can guide AI careers:

  1. What’s my prospective employer’s technology stack?
  2. What are the employer’s leadership motivations and potential social impact?

As a Stanford graduate now working in artificial intelligence, those were critical considerations for me as I looked to move from traditional software companies into the AI space. I had decided to pursue an AI career because of the positive change that AI promises to unleash. It was a matter of finding an employer that aligned with my interests and skills.

Read the source article in Entrepreneur.

Human Challenges Face Today’s AI Business Strategies

The hype surrounding artificial intelligence (AI) is intense despite that fact that as yet, artificial intelligence (AI) for most enterprises is still at an early, or planning, stage. While a lot has been done, there is a lot more to do before it becomes commonplace. However, that hasn’t stopped speculation about the impact on employment […]

The hype surrounding artificial intelligence (AI) is intense despite that fact that as yet, artificial intelligence (AI) for most enterprises is still at an early, or planning, stage. While a lot has been done, there is a lot more to do before it becomes commonplace. However, that hasn’t stopped speculation about the impact on employment and what it might mean for workers, especially those whose jobs are repetitive and considered low skilled.

In October last year,  a survey carried out by analytics giant Cary, N.C-based analytics giant SAS showed that the vast majority of organizations have begun to talk about artificial intelligence, and a few have even begun to implement suitable projects. There is much optimism about the potential of AI, although fewer were confident that their organization was ready to exploit that potential.

AI Human Challenges 

The reason for this is not because there is a lack of technologies on the market. What the research uncovered was that the challenges come from a shortage of data science skills to maximize value from emerging AI technology, and deeper organizational and societal obstacles to AI adoption. Some of the figures contained in the report show that:

  • 55 percent of survey respondents felt that the biggest challenge related to AI was the changing scope of human jobs in light of AI’s automation and autonomy.
  • 41 percent of respondents raised questions about whether robots and AI systems should have to work “for the good of humanity” rather than simply for a single company, and how to look after those who lost jobs to AI systems.

It also showed that several organizations had a senior-level sponsor for AI and advanced analytics. In some cases, this was a member of the C-suite, and in a few, the CEO. In others, it was a more junior director, usually one with an interest in the area. One respondent mentioned that the organization planned to appoint a Chief Data Officer within the next six months, who would take on responsibility for this area.

And it’s not the only research that has raised the issue of the impact AI will have on jobs. Recently, we were able to identify seven jobs that might be overtaken by the growth in the use of AI in the enterprise. That said there are ways that enterprises – and individuals – can meet the challenge.

Building a Talent Pipeline

AI is generating a demand for new skill sets in the workplace. However, there is a widespread shortage of talent that possess the knowledge and capabilities to properly build, fuel, and maintain these technologies within their organizations, according to Mohit Joshi president and head of banking, financial services and insurance, as well as, healthcare and life sciences at Bengalaru, India-based Infosys. The simple answer is up-skilling. “The lack of well-trained professionals who can build and direct a company’s AI and digital transformation journeys noticeably hinders progress and continues to be a major hurdle for businesses. But there is also opportunity here too and a way to redeploy workers who face redundancy because of AI,” he says.

To mitigate this, businesses should look inward and create on-the-job training and  to build these skills internally. With the proper staff powering AI, employees are able to focus on other critical activities and boost productivity creating a larger ROI. If an enterprise’s digital transformation goal is for AI to become a business accelerator, it needs to be an amplifier of its people. “It’s going to take work to give everyone access to the fundamental knowledge and skills in problem-finding and remove the elitism around advanced technology, but the boost to productivity and ROI will be worth it in the end,” says Joshi. Businesses that haven’t yet allocated budget for AI should start small by manually auditing the organization to streamline processes and free up employees’ bandwidth. This allows decision makers to clearly see which systems aren’t utilized effectively and which areas could benefit from technology down the road.

Shifting Roles

Anthony Macciola, Chief Innovation Officer and is responsible for AI initiatives at Moscow, Russia-based global giant ABBYY, a company that uses machine learning, robotic process automation and text analytics to improve business outcomes. He says that the introduction of AI into the general workplace will result in more tasks being addressed by system of record applications and shift knowledge workers’ roles from a control to an expertise standpoint.

He cites an example of how this will work in the mortgage lending market. The dependency on a loan origination officer to drive the loan process will diminish over time due to the loan origination system being able to make intelligent decisions based on past funding behavior. This will leave only rules-based exceptions to require a loan processor’s attention. As a result, this will lighten the overall workload for loan officers, allowing them to be more responsive when an exception rises and should allow mortgage lenders to increase the productivity of their operations.

“As software gets smarter, dependency on the workforce shrinks and knowledge workers who have typically conducted manual input tasks or controlled processes in fintech, healthcare, transportation and logistics, and government customer/constituent engagement scenarios will become more narrowly focused from a role and responsibility standpoint,” he says.

Read the source article at CMSWire.com.

AI World Teams Up with Fortune to Produce Branded Content on AI in the Enterprise

AI World Conference & Expo, part of Cambridge Innovation Institute, has announced a collaboration with FORTUNE to produce a branded content section focused on Artificial Intelligence in the Enterprise.  The branded content section will be published in a fall 2018 issue of FORTUNE, and will be available at the Third Annual AI World Conference & […]

AI World Conference & Expo, part of Cambridge Innovation Institute, has announced a collaboration with FORTUNE to produce a branded content section focused on Artificial Intelligence in the Enterprise.  The branded content section will be published in a fall 2018 issue of FORTUNE, and will be available at the Third Annual AI World Conference & Expo which is taking place in Boston, December 3-5 at the Seaport World Trade Center.

The Exhibitors & Sponsors of the AI World Conference & Expo are invited to be part of the Artificial Intelligence in the Enterprise section in FORTUNE.  The Editorial Advisor for this venture will be Eliot Weinman, who also serves as the AI World Conference Chair.

According to Mr. Weinman, “We are honored to be working with FORTUNE to produce and promote a branded content section on Artificial Intelligence in the Enterprise.  AI technologies are fundamentally transforming many industries, businesses and society at large.  AI, machine learning software and service providers who wish to participate in this publication will benefit in reaching the extensive readership of FORTUNE as well as AI Trends and AI World marketing.  FORTUNE is number one for business, and has the highest concentration of affluent and influential business decision-makers vs. the competition, more than Forbes, Business Insider, Bloomberg, CNBC, The Wall Street Journal and Wired.  Marketing and promotion through FORTUNE will reach a total audience of more than 3.3 million business professionals.  This targeted business reach will be further expanded by the integrated media and marketing plan being produced by AI World.”

According to Joel Baboolal, Business Development Manager of Branded Content, FORTUNE, “We are pleased to be collaborating with Mr. Weinman and his team at Cambridge Innovation Institute.  AI World is the preeminent conference brand in the industry and FORTUNE is committed to covering the leading edge of business and technology, and clearly artificial intelligence is one of the fastest growing technology markets today.”

For more information about being part of the AI World and FORTUNE branded content section focused on Artificial Intelligence in the Enterprise, please contact Eliot Weinman at ew@AIWorld.com.

About Cambridge Innovation Institute (www.CambridgeInnovationInstitute.com)

A vision since 1992: Cambridge Innovation Institute (CII) delivers cutting edge information through events, publishing, and training to leading commercial, academic, government and research institutes across the life science and energy industries. Cambridge Innovation Institute consists of two business areas: our coverage of advances in life sciences under the well-established Cambridge Healthtech Institute (CHI) brand, and coverage of rechargeable batteries under the newly established Cambridge EnerTech (CET) brand. We focus on high technology fields where research and development are essential for the advancement of innovation.

About AI World Conference & Expo (www.AIWorld.com)

AI World Conference and Expo, part of Cambridge Innovation Institute, is focused on the business and technology of artificial intelligence in the enterprise.  AI World has become is the “must attend” event for enterprise executives and decision makers from Global 2000 organizations and business leaders from across the entire artificial intelligence and machine learning ecosystem.  The 3-day conference and expo is designed for business and technology executives who want to learn about the state-of-the-practice of AI in the enterprise.

 

AI and Business Strategy: Think Big, Start Small and Scale Fast

It’s time to take a step back from the noise and hype surrounding artificial intelligence (AI). Businesses have been inundated with AI sales pitches promoting the technology’s potential to automate tasks, increase speed and accuracy and cut costs. But what’s the long-term plan? Most businesses lack a vision of how AI will transform their operations. Developing […]

It’s time to take a step back from the noise and hype surrounding artificial intelligence (AI). Businesses have been inundated with AI sales pitches promoting the technology’s potential to automate tasks, increase speed and accuracy and cut costs. But what’s the long-term plan? Most businesses lack a vision of how AI will transform their operations.

Developing an overarching AI strategy

Rather than the piecemeal adoption of AI systems, some believe businesses need to develop an overarching strategy for how to embed AI in their organisation over time.

“The most important thing is having a comprehensive and holistic view of AI sourcing within the organisation,” says Mohammed Chaara, a former Lenovo strategist who is now “an evangelist” for AI. Working out what kind of AI is needed for different processes and whether these will be carried out in-house, outsourced or in partnership is an important step to developing a strategy, he says.

Mr. Chaara believes businesses need to base their AI strategy on four areas. Low-risk, low-value AI involves automating low-level repetitive tasks, such as data processing, which can be outsourced.

Low-value, high-risk AI includes audience-targeting driven by machine-learning, which can cut the costs of identifying and reaching audiences, and boost profits by targeting the right customers. But this risks wasting the marketing budget, if the targeting is wrong, and so is high risk. It should be developed in tandem with external partners, he says.

The third area is high-value, low-risk tasks, such as the credit scoring models in financial services. This offers high value, but is low risk and so complex no one else can decipher it.

Finally, there is high-risk and high-value activity, the uncharted territory where there is no solution on the market, for instance using AI systems to fuel new product development.

The last two categories could involve building in-house solutions or buying in the expertise through acquisitions.

If you can adopt these four frameworks, you can have an idea of when you need to migrate AI from one point to another,” says Mr Chaara. “Without having that comprehensive view, the organisation will not be able to move quickly and in an agile way from one category to another.”

The key to a successful AI strategy: think big, start small and scale fast

Some doubt that businesses have the time to develop in-depth strategies for AI and are best off implementing it as needed. Dr Lee Howells, an automation and AI expert at PA Consulting Group, says companies should use AI to solve a specific business problem rather than being led by the technology. He fears an AI strategy could be outdated before it gets implemented as developments are moving so fast.

“When organisations try and introduce AI, they need to find a balance between their strategic ambition for AI and getting started quickly. You can never have a strategy that is 100 per cent correct, and with AI people deliberate about it and the greatest problem is inertia; they don’t want to get started,” he says, adding that the best approach is to “think big, start small and scale fast”.

AI goals should be long-term and realistic

One of the fastest growing areas of AI is robotic process automation (RPA), which uses software to automate repetitive tasks, such as replying to standard emails, processing customer orders and updating payrolls.

Guy Kirkwood, chief evangelist at RPA company UiPath, says: “When companies say they want AI, what they actually mean is they want the tools necessary to automate more of their processes, because that is what the real strength of AI is.”

But he adds: “RPA is a tool not a panacea.” Mr Kirkwood believes organisations are adopting RPA so quickly because it cuts across all geographies, industries and services. “There is no limit to where it can spread,” he says. “The addressable market for RPA is unlimited. It is one robot per employee everywhere – that’s the potential, which is pretty staggering.”

Read the source article at Raconteur.

New Breed of AI Weeders Could Disrupt the $100 Billion Pesticides Industry

A new solar-powered weed killer robot that looks like a table on wheels, can scan rows and rows of crops with its camera and zap weeds with jets of blue liquid as soon as they are identified. The liquid will be replaced by a weed killer spray as soon as final tests are complete. This […]

A new solar-powered weed killer robot that looks like a table on wheels, can scan rows and rows of crops with its camera and zap weeds with jets of blue liquid as soon as they are identified. The liquid will be replaced by a weed killer spray as soon as final tests are complete.

This is a new breed of AI weeders that could disrupt the $100 billion pesticides and seeds industry by reducing the need for herbicides and modified crops. “Some of the profit pools that are now in the hands of the big agrochemical companies will shift, partly to the farmer and partly to the equipment manufacturers,” said Cedric Lecamp, who runs the $1 billion Pictet-Nutrition fund that invests in companies along the food supply chain.

Even though the technology is still in the initial phase, ot marks a shift from the standard methods of crop production to plant-by-plant approach. ecoRobotix, the developer of the weed killer robot, believes that the design can reduce the amount of herbicide farmers use by 20 times.

Blue River, a Silicon Valley startup bought by US tractor company Deere & Co last year, has also developed a machine to distinguish weeds by using onboard cameras and squirt herbicides only where necessary. German engineering company Robert Bosch is also working on a similar precision spraying kits.

Other startups like Denmark’s Agrointelli are also working on similar technology and show how it is gaining traction. ROBO Global, an advisory firm that runs a robotics and automation investment index tracked by funds worth a combined $4 billion, believes plant-by-plant precision spraying will only gain importance.

“A lot of the technology is already available. It’s just a question of packaging it together at the right cost for the farmers,” said Richard Lightbound, Robo’s CEO for Europe, the Middle East, and Africa. “If you can reduce herbicides by the factor of 10 it becomes very compelling for the farmer in terms of productivity. It’s also eco-friendly and that’s clearly going to be very popular, if not compulsory, at some stage.”

Jeneiv Shah, deputy manager of the $212 million Sarasin Food & Agriculture opportunities fund, said that it will put crop business could be at risk as the new technology provides a simpler solution. “The fact that a tractor and row-crop oriented company such as John Deere did this means it won’t be long before corn or soybean farmers in the U.S. Midwest will start using precision spraying.”

The weed killer robot, with its precision spraying definitely a cause for worry for the giants that have a monopoly in the pesticides and genetically modified seeds. The new technology not only offers a cheaper alternative, it is much more efficient.

Read the source article in WonderfulEngineering.com.

Digital Strategies Are Passe: Time For An Artificial Intelligence Strategy

There’s nothing new about organizations and their leaders fumbling around for a coherent, business-relevant strategy any time a new technology appears on the scene. We’ve been seeing this in recent years with the rise of digital, raising issues from defining what exactly digital is, to defining what it means to succeed. Now, such is the case […]

There’s nothing new about organizations and their leaders fumbling around for a coherent, business-relevant strategy any time a new technology appears on the scene. We’ve been seeing this in recent years with the rise of digital, raising issues from defining what exactly digital is, to defining what it means to succeed. Now, such is the case with the constellation of cognitive solutions — artificial intelligence, machine learning and so forth — that are now starting to be embraced.

With AI and cognitive computing the flavor of the month (or year), it’s time to start exploring what, exactly, it can do for business growth, and how to go about achieving it. Some good news: AI isn’t quite as amorphous and squishy as digital. More good news: many of the bread-and-butter issues arising from previous generations of technology apply with AI as well — starting with the most fundamental of fundamental principles: don’t implement technology for technology’s sake, have a business goal in mind.

The not-so-good news is that a lot of money is starting to be poured into AI and cognitive technologies, vendors are hyper-ventilating about it, and analysts are telling us that if we don’t do it we will all be quickly put out of misery. So, with more and more money being invested in it, it’s really important to strategize things a bit more, to give it a broader purpose in the enterprise. As Thomas Davenport and Vikram Mahidhar, recently observed in MIT Sloan Management Review,  “few companies are yet getting value from their investments. Many of the projects companies undertake aren’t targeted at important business problems or opportunities. Most organizations don’t have a strategy for cognitive technologies.”

So what are the essential components of an AI strategy, or something close to a strategy. Here are a few pointers from leading voices in the field.

Step back and look at AI from an industry perspective: “Many companies that develop or provide AI to others have considerable strength in the technology itself and the data scientists needed to make it work, but they can lack a deep understanding of end markets,” as pointed out by Michael Chui and a team of fellow McKinsey analysts. Companies that seek to provide AI-driven offerings should not only analyze the value of their AI initiative, but also AI adoption across their industries.

Make AI about people and empowerment. AI may lead to many autonomous processes, but people will decide how it will drive the business. “The vision of AI should always be about empowering technical professionals and the business citizens to build a better user experience,” says Carlton Sapp, analyst with Gartner. “Technology cannot do this alone, and neither can AI. ”

Leverage data. This is the fuel that powers AI outputs. “Part of the reason machine learning has been so successful is that of its ability to train models based on data—as opposed to traditional methods that explicitly defined how the application would behave,” says Sapp. “Leveraging machine learning in your organization tells the world that you are truly data-driven.” Chui suggests building a “data plan” built on use cases and capable of producing “results and predictions, which can be fed either into designed interfaces for humans to act on or into transaction systems.” This includes mapping out how data is created, acquired, managed and delivered to AI engines.

Read the source article in Forbes.

Executive Interview: Richard Soley, CEO of OMG

Industrial Internet Consortium Members “Invent the Future” by Anticipating Disruptions from AI, Pursue Standard Testbeds to Accelerate Adoption Dr. Richard Soley is Chairman and CEO of the Object Management Group (OMG) and Executive Director of the Industrial Internet Consortium (IIC). In a career in the computer industry approaching 40 years, he has overseen a range […]

Industrial Internet Consortium Members

“Invent the Future” by Anticipating Disruptions from AI,

Pursue Standard Testbeds to Accelerate Adoption

Dr. Richard Soley is Chairman and CEO of the Object Management Group (OMG) and Executive Director of the Industrial Internet Consortium (IIC). In a career in the computer industry approaching 40 years, he has overseen a range of software standard collaboration efforts. These include the CORBA specification, the Unified Modeling Language (UML) and Model Driven Architecture (MDA), which permeate critical software today. He began his professional career at Honeywell Computer Systems working on the Multics operating systems. He was later co-founder and CEO of A.I. Architects, maker of the 386 HummingBoard. Dr. Soley holds bachelor’s, master’s and doctoral degrees in Computer Science and Engineering from MIT. He recently spoke with AI Trends Editor John P. Desmond.

Q. Please describe the mission of the IIC today and give us your historical perspective on AI?

A. The IIC mission continues to focus on industrial applications and market verticals including smart cities, transportation, and agriculture among others. What I find interesting is this sudden recognition that artificial intelligence (AI), machine learning, and even 3D printing, all help in the adoption of IoT and industrial IoT systems.

I was CEO of AI Architects in the mid to late ’80s and worked for Symbolics, which was an AI company making expert system tools and AI hardware. This was around the same time when “Time” magazine announced its 1984 Man of the Year award as the computer, which I believe generated the AI Winter. The magazine created the expectation that machines think. I remember computer companies like Thinking Machines Corporation, whose motto was, “We want to make a computer that’s proud of us,” and that was never the function of AI. Expectations were way over-hyped and couldn’t possibly be met.

The ubiquitous access today to a large number of connected cloud computers that have far more compute power, far more memory, and access to open source software for data analysis, that’s something that we couldn’t even dream of in 1982. It created an opportunity to put more intelligence in systems and bring us to where we are today. It remains overhyped, because that’s the nature of the IT industry, but it will deliver a lot more. Voice recognition experienced this struggle in 1982. And now, we all carry a voice recognition system in our pockets, whether we call it Siri or Google Assistant.

Q. Can you share how IIC is organized and funded?

A. The Industrial Internet Consortium (IIC) is a major part of the Object Management Group, which is, itself, nearly a 30-year old standards organization. The IIC is accelerating the adoption of industrial IoT by building testbeds. That requires agreement on a shared architecture, agreed security framework, analytics framework, vocabulary, and so forth.

The test beds are IIC’s major differentiator. The largest funding source for IIC is membership with the balance coming mostly from events. Funding of the IIC parallels the funding of OMG.

Q. What are some of the most important IIC initiatives today?

A. The most important initiatives are the test beds run by our nearly 300 member companies. Roughly 30 testbeds are currently running and I’ll give examples of a few compelling ones.

The Track and Trace testbed was initiated by Bosch Software Innovations in Germany. It started with a very simple idea that factories could be made more efficient and safer if you know where everything in the factory was located within a meter. The 3-year old testbed utilizes Cisco Wi-Fi routers to triangulate position of things – people, parts, works-in-progress, and tools – inside the factory and overhead cameras to provide about five centimeters of resolution. Results from this testbed started publishing last year, which are informing the requirements for new standards, new concepts of training, retraining, and hiring. It’s a great opportunity to learn more about IoT in manufacturing.

But we’re not limited to manufacturing. Our Infinite Testbed is managed by Dell Technologies in Southern Island, County Cork, Ireland. In over two and a half years of use, they have integrated national and provincial information resources to optimize ambulance delivery and information delivered to and from ambulances to assist in saving lives.

The Smart Building Testbed is a project between Dell and Toshiba in Yokohama, Japan. The companies outfitted a brand-new building with 35,000 sensors collecting between 0.5-1.0 terabyte of data every day. The sensor data collects information about light, temperature, people movement, telephone calls, and more. And it’s learning how the building is used, which enables optimization for the comfort of the users and predictive maintenance of the building.

Major testbed results are being published in the IIC “Journal of Innovation.”

Q. You’ve been involved in several collaborative efforts to define computing standards, including CORBA, Unified Modeling Language and Model-Driven Architecture. How effective has the effort to collaborate on software standards been?

A. Very effective. There are about five billion corporate systems running today, including every smartphone in the world, every telco switch, every banking system, every robotic system, and so forth. Our oldest standard, now more than 28 years old, is the corporate standard Common Object Request Broker Architecture (CORBA), built into every Java virtual machine.

According to Gartner, 71% of all software development organizations use UML today. Our model-driven approach to building software has been extremely effective.

We’re now in about two dozen vertical markets. And OMG standards drive every retail point of sale, every NATO military radio, and every middleware system. And we have new standards coming out in CubeSats, (a miniature model for space research). We’ve run our standards process over a thousand times, and all those standards are implemented. Implementation is a requirement of our standards process.

Q.Could you contrast the Industrial IoT opportunity with its Consumer IoT counterpart?

A. First of all, there’s plenty of good work going on in the consumer space and IIC does not need to get involved in it. We see bigger opportunities in the industrial space, where IoT can have huge, disruptive effects on markets including agriculture, healthcare, transportation, smart cities, manufacturing, and production. We’re trying to learn what that disruption will be.

And, in terms of the technology, standards and security are mission-critical in the industrial space. If you hit a switch and nothing goes on or off in your house, you just hit the switch again. That’s not the end of the world. But if the factory stops working for a couple of days, that might be the business equivalent to the end of the world.

Q. How does IIC differentiate the testbeds from a use case?

A. A use case is a use of technology. One of the limitations with standards and with test beds is a focus on the technology, instead of the application of that technology. Some of the testbeds that we’ve developed at the IIC, such as the Time Sensitive Network (TSN) Testbed, are necessarily focused on technology availability, integration, and portability.

The use case approach is driven by understanding the desired outcome as opposed to what technology is currently available. For example, the world’s largest copper mining company had a unique need for high-reliability wireless networks to make mining operations safer. As a result, we’re putting together a stack of technology from our members to deliver industry-specific requirements.

Q. Looking ahead, what does the intersection of AI and industrial IoT suggest for innovation?

A. Without question, AI and IoT are going to disrupt enormous existing markets, including transportation, manufacturing, and healthcare. With access to more intelligence, the huge amounts of data generated by industrial IoT can actually be analyzed, generating insights that lead us to better efficiencies, better productivity, and improved safety.

When you have systems that can ingest massive amounts of unstructured data and generate new insights, you are outperforming the human capacity. So, we’re going to see huge disruptions driven by the combination of AI and IoT.

For more information, go to the Industrial Internet Consortium.

These 4 Apps Powered by AI Will Strengthen Your Business

Leaders know they need technology to advance their businesses and boost their teams — but many of them are scared to use it. They’re not alone; Americans as a whole fear technology more than death. Christopher Bader, a sociology professor at Chapman University and one of the authors of the study that found Americans find robots more terrifying […]

Leaders know they need technology to advance their businesses and boost their teams — but many of them are scared to use it. They’re not alone; Americans as a whole fear technology more than death.

Christopher Bader, a sociology professor at Chapman University and one of the authors of the study that found Americans find robots more terrifying than dying, said, “People tend to express the highest level of fear for things they’re dependent on but that they don’t have any control over, and that’s almost a perfect definition of technology.”

The great thing is that leaders don’t necessarily need to know how the technology works to benefit from it. Take these four platforms, which are powered by artificial intelligence but do the work for businesses:

1. Spiro.ai

Spiro.ai is an AI-powered CRM that empowers salespeople to nurture leads and close sales without the distraction of data entry. Spiro uses AI to proactively build a to-do list for sales reps and is the only CRM with a built-in email assistant. The email assistant provides sales reps and managers all the information they need, without requiring them to log in to their CRM.

The biggest selling point for the tech-averse may be that the CRM relies on an algorithm built by salespeople for salespeople, with the aim of creating a CRM people will actually use.

Because the CRM cuts down on the need to organize between calls, it fuels sales productivity and pushes salespeople to follow up in the quick timeline needed to close sales. And it offers another tech advantage in the form of reporting: Because Spiro self-populates with data, the reports it sends to managers are eight times richer with data than many competing platforms’ reports.

2. Pi

Pi is an AI-powered social marketing tool to help companies boost their social strategies by analyzing followers’ interactions. After connecting with a company’s Twitter or Facebook account, Pi evaluates followers’ profiles, posts, and comments (through natural language processing and sentiment analysis) to develop a list of relevant topics and tones. The app can recommend when to publish an associated post and use performance to continually assess and adjust its suggestions.

For those who don’t have the time to look at others’ accounts, the AI’s biggest benefit is that it can create a pool of links to consider posting, and it can predict how well a drafted post will perform once posted. It also offers a marketplace for sponsored posts.

3. Legal Robot

Legal issues are perhaps as terrifying for some as technology itself; enter Legal Robot, an AI-powered “legal advisor” that helps both lawyers and consumers build contracts. Built to overcome the difficulty of understanding legal language, the app uses deep learning and natural language processing to create models of contracts for various scenarios and uses. It can then translate the terminology into layman’s terms, compare documents to create a language benchmark for consistency, and ensure compliance.

The app aims to help businesses identify risks and pinpoint their specific blind spots in creating contracts, and its ability to learn and transform its understanding boosts its likelihood of doing that.

4. Learn Chinese

Microsoft’s Learn Chinese may specifically aim to teach users how to speak Chinese, but the AI-powered language app is a precursor of things to come. The app utilizes deep neural networks to use speech to help people learn to actually speak Chinese, not simply learn its grammar rules.

Learn Chinese uses speech clues to anticipate what a learner wants to say and then scores the speaker’s attempt compared to native speakers and synthesized examples. This allows users to practice language skills they may need to use on business trips or with visiting companies without face-to-face teacher availability.

The AI shines when users consider that people can learn either language featured– Chinese or English–through the app. Learn Chinese also identifies individual words needing more practice and offers audio samples of how the words should be pronounced for future reference, saving business leaders everywhere from the risk of dying of embarrassment.

Being dependent upon something without being comfortable with it is a scary prospect. Spending more time with AI-powered tools, however, can not only make leaders less scared of technology, but it can also improve their business–and the possibility of a stronger company should outweigh any fear.

Read the source article at Inc.com.