Brain game: the freaky factor of artificial intelligence

Artificial Intelligence, Technology, Lifestyle
Face facts: what it means to be human. Photograph: Getty Images


The Guardian

The release of Blade Runner 2049 has once again inspired us to imagine what it would be like if the distinction between artificial life and humans all but disappeared. Once something else is almost as ‘real’ as us, the idea of what it means to be human is challenged.

Neuroscientists know already that such a scenario is disturbing to us – thanks to a phenomenon known as Uncanny Valley. In the experiment, when people were faced with robots that looked very robotic (think flashing lights and metal), their response was fine. But the more human the robot became, the stronger their antipathy, discomfort and even revulsion – and the spookier it seemed.

In studies we measure the degree to which anything is human in terms of how it looks, how it moves and how it responds. In all cases the more artificial anything seems, the more easily we cope. Of course, once the difference between us and artificial life is undetectable, our response is exactly the same. At which point, the tables will turn – an enduring theme in Blade Runner – and it will be the robots who struggle with the idea of who they are and what it means to be human.

Dr. Daniel Glaser is director of Science Gallery at King’s College London

Article source: https://www.theguardian.com/lifeandstyle/2017/oct/15/brain-game-the-freaky-factor-of-artificial-intelligence

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Dubai International Airport will replace ID checks with a facial recognition aquarium

Dubai Airport, Technology, Facial Recognition, Innovation
Photo: Satish Kumar for The National

In a world in which people are increasingly willing to trade privacy for convenience, facial recognition seems to be a new frontier. And the foremost pioneers on that frontier now appear to be the folks at Dubai International Airport.

Airport officials plan to install a virtual tunnel-shaped aquarium equipped with 80 supposedly invisible cameras that will identify passengers as they walk through, in lieu of customs agents looking from your passport to your face and back. The first aquarium will be up and running by the end of next summer, according to The National. Emirates customers will be the first to experience the tunnel, but the airport plans to install more until 2020.

Facial recognition is popping up at more and more airports as a way to streamline the process of identifying passengers ahead of boarding, and it has its conveniences. You don’t have to remember your passport or driver’s license or other forms of ID, and the lines will theoretically move more quickly because people don’t have to stop and wait for an official to check those IDs.

Dubai’s aquariums seem to be taking the relaxation idea to a level no one else has thought of, but the aquariums serve a purpose other than to calm passengers as they head to their planes.

“The fish is a sort of entertainment and something new for the traveler but, at the end of the day, it attracts the vision of the travelers to different corners in the tunnel for the cameras to capture his/her face print,” Obaid Al Hameeri, the deputy director general of Dubai residency and foreign affairs, told The National.

The National reports that travelers will be able to register their faces at kiosks, and those scans will presumably be matched up with what the aquarium-tunnel cameras pick up as you pass through.

If the cameras determine you are who you say you are, you’ll get a green light at the end of the aquari-tunnel. If not, you’ll get a red light, and an official will likely conduct extra screening of some kind.

It’s not clear what Dubai airport officials will do with these face scans after they have them. Do they keep them on file, assuming you’ll return? Do they share this information with government officials in the United Arab Emirates? How about with officials in other countries?

And face scans are just part one of a two-part plan. Soon, these aquariums may also have cameras that scan your irises. Just remember that when you’re looking at all the pretty fish.

 

4 Questions Every Small Business Must Ask About Artificial Intelligence

Business, Technology, Artificial Intelligence
Photo credit ODD ANDERSEN/AFP/Getty Images

 Opinions expressed by Forbes Contributors are their own.

This article was developed with Kevin Haaland, CTO of The Better Software Company, a SaaS platform to small business and franchise owners. Haaland was formerly the head of IBM’s Watson Analytics.

From Siri to Alexa, customers are becoming accustomed to AI-powered solutions and soon they will expect the same for their local businesses. Sure, an AI roll-out can be daunting, but by adopting a strategic approach and adding smart software, small businesses will not only be able to differentiate themselves from competitors, but compete with the industry giants as well.

While many over complicate the technology, AI’s behaviors are predictable – it’s merely an advanced system that is trained, not told. AI mimics the human brain in the way that it learns. It starts with no information, and after being given thousands of pieces of information, is able to understand and make predictions about data it has never seen before.

AI will become a threat to small businesses if owners believe it won’t impact them, or isn’t already impacting them. The fact is, AI has the potential to drastically help companies of all sizes work smarter and more efficiently than ever before.

Before acting on an AI roll-out, here are the top four questions small businesses should ask themselves:

What is it you are looking to achieve?

AI can provide great value for sales, marketing, finance, HR, customer service, and more. Hone in on what exactly you are hoping to achieve with the use of AI – where do you need to increase productivity?

By setting highly focused goals, you will be able to develop a plan that prioritizes specific applications for AI technology. This way, small businesses can slowly adapt and familiarize themselves with the software, that will, overtime, drastically enhance the bottom line.

The most immediate benefit of AI is that it will provide immense efficiency. There will be less time entering data and more time getting valuable insight to augment decision making. There’s a mass amount of data waiting to be analyzed and AI will guide businesses on how to act.

 What data is already in a system of record?

You’ll never hear the words “too much data” and “AI” used in the same sentence. AI systems become more accurate and effective as the volume of data increases. The big industry players have been accumulating business intelligence and already moved on to predictive analytics.

The first step in your AI project is to systematize your business . With the widespread adoption of cloud based solutions (SAAS) and the rapid reduction in the cost of storage and processing, the first step is to start instrumenting all elements of your business. Your website, your marketing activities, your sales – including the business that you “win” and “lose.”

Unlike huge, multi-national companies that are able to capture and process peta-bytes of data, small businesses have had access to significantly less data. This is changing with the adoption of cloud-based products and services and the availability of open data sets from governments and other providers. The goal for small business owners is to have the appropriate systems and infrastructure needed to go and analyze data and extract even more business value.

What is your ability to explore your business data and understand what’s going on objectively?

If you’re looking at the raw data it’s easy to “torture the data” to get the answer you want to be there – don’t fall victim to this habit.

Your goal is to generate several hypotheses from the data. Examine outliers and the associations between data elements. Be careful not to draw conclusions too early though, as outliers could be caused by “bad data” that needs to be cleaned up, and the relationships may not be strong enough to make any definitive conclusions. We often allow our personal biases and expectations get in the way of looking at data. The numbers don’t lie, but if we look at them expecting certain results, we may end up manipulating the information to meet our expectations. In order to take full advantage of AI, we need to be able to trust the numbers.

You don’t need to use expensive tools; use the reports and dashboards that are built into the tools you already have and approach the problem with an inquisitive mind. Look for the unexpected and when you detect something that’s interesting, create one or more hypothesis to explain what you’re seeing, and then set about to prove or disprove it.

Are your technology providers able to support these capabilities to provide more meaningful insights?

AI will not provide any benefit if small businesses lack the IT infrastructure to support it. Start by upgrading your approach to IT – move toward a cloud-based resource that can support AI once implemented. Data is a prerequisite to introducing AI into a system, and a paper system is useless when it comes to incorporating AI.

Make sure your goals are aligned with the direction your software is going. If it doesn’t seem as though your software provider is working toward the same future as you, it might be time to consider another option. It’s important to ensure your provider is taking steps to remain relevant in the future of technology.

If you’re just getting started on the business analytics journey, begin by using the reports and dashboards that your systems have today. Become familiar with the digital assistants that are already on your smartphone; explore what they are already able to do and stay current with how these systems are evolving.

By making an effort to understand and embrace AI, small businesses are optimizing operations, improving customer-service, and growing their bottom line. Imagine where your company would be if you didn’t embrace the uncertainty of the internet or didn’t go mobile in the age of the smartphone. Artificial intelligence is the newest technology adding efficiency and intellect to small business – don’t be late to adapt; be better, faster, smarter operators with the use of AI.

Christine Crandell is a B2B cross-organization customer alignment and corporate transformation strategist for New Business Strategies and author of the Sellers’ Compass.

Fab Cab is LCCC innovation on wheels

With receiving freshly printed copies of a Stocker Arts Center 2017 brochure, Tracy Green, vice president of strategic and institutional development at Lorain County Community College, speaks passionately Aug. 15, 2017, about new developments at 1005 N. Abbe Road in Elyria. Carol Harper — The Morning Journal

A maker space on wheels rolls out in the form of a Fab Cab this school year from Lorain County Community College.

The Fab Cab is a mobile Fab Lab destined for Scout meetings, libraries, civic groups and school clubs throughout Lorain County, said Tracy Green, vice president of strategic and institutional development at LCCC at 1005 N. Abbe Road in Elyria.

The Fab Cab is one of many innovations introduced this month as LCCC continues a tradition of introducing locally cutting edge trends academically.

School starts Aug. 28 at LCCC, Green said, adding the crew enjoyed a great Jack Nicklaus golf outing Aug. 14 to raise money for scholarships.

Cindy Kushner, director of marketing and outreach initiatives at LCCC, said she is excited about the opportunity to talk about the first bachelor’s degree at the community college.

The Bachelor’s of Microelectronic Manufacturing meets a need of companies in Northeastern Ohio, Kushner said.

“And right now we have a wonderful associate’s degree we’re recruiting students into,” Green said.

The education stream follows a “learn and earn” model, Kushner said, with a student in class a couple of days a week, and working for an employer in the field and applying what they learned a few days a week.

“It’s very well received by students and employers,” she said.

The college is working with about 20 different companies across the region, such as Synapse Biomedical in Oberlin and Core Technology in Avon, Green said.

“It involves engineering technology jobs,” she said. “They’re working with companies that are making their products what we call, ‘smart.’

“So they’re embedding sensors. There are many, many sensors like what is in that phone. They’re taking those same types of sensors and they’re putting them into new products that can communicate and provide data outside of them.”

Examples are medical devices, workout equipment or athletic gear, Kushner said.

“It’s across the board,” she said. “It’s really everything.”

It’s a pretty significant movement, Green said.

“Now they have sensors embedded in running shoes so you can tell when you should be changing your shoes,” she said. “It will look at any type of wear and tear on the shoe and the stability of the insole, everything is becoming customized and providing data to the consumer so they can make decisions about when they should buy a new pair of shoes.”

“I used to be excited when they would light up,” Kushner said.

“Now they’re talking to our iPhones,” Green said.

The available labs limit starting the class to 12 students a semester for this stream.

Green said when she started her career, she had no idea it would lead to preparing students for these and similar innovations.

“Every day I wonder, ‘What’s going to happen new today?’” she asked. “Everything is different every day.

“You can’t even describe careers now because you have to be able to work across many different areas.”

So, LCCC built a new Campana Center for Ideation and Invention on the south edge of campus, with more new developments to add soon.

“Talking about cutting edge and innovation,” Green said. “When you’re talking about a career, sometimes it’s not working for someone else; it’s working for yourself and creating an entrepreneurial path. We have expanded our Fab Lab significantly. So someone can take that idea and turn it into a product and then get support working with our entrepreneurship program to be able to turn that product into a business.”

That’s what LCCC is excited about, Green said.

“Particularly as you talk about the next generation, we see that movement of folks who prefer to be their own boss, to be an entrepreneur themselves and to grow their own company,” she said. “It’s the ability to give them the tools and the resources to do that.

“Within that building, you can come in and you can design a new product on the computer. Using software you can view it in 3-D form in virtual reality, then go from that concept to a printed part using additive manufacturing and be able to hold that product in a matter of hours.”

Tech savvy can happen any time, but often starts young.

Kushner said she works a lot with students from kindergarten through grade 12.

She said she envisions a grandparent bringing a grandchild to the college to work on a project together.

“We have some very exciting programming for K-12 students that is going to inspire that entrepreneurial spirit in that world of making,” Kushner said.

Soon, the college will take the Fab Lab on the road through the Fab Cab, Green said.

“We can take that to a classroom, to a Girl Scout meeting, community libraries and have that experience out in the community and hopefully, have them come in and use the Campana Center,” Kushner said.

It fits inside a van, Green said, so it’s portable.

“On the partnership side, I’m really excited about the Master’s of Business Administration, the MBA,” Kushner said. “It’s with Lake Erie College. We have a good group — I think there’s room for a couple more — we have a nice group starting this fall on their Parker MBA through Lake Erie College in the Painesville area. They are just wonderful.”

And a new associate degree of Applied Science in Cyber and Information Security will enable students to prevent breeches in Internet access, and viruses, Green said.

For example, they would learn how hackers steal credit card information, and how to prevent hacker access, she said.

Not a new development for LCCC but possibly new information for Lorain residents, Kushner said, are two LCCC learning centers in Lorain.

One learning center is across from Lorain City Hall at 201 W. Erie Ave.

The other center is at Lorain High School at 2600 Ashland Ave.

“There’s confusion at the Lorain High School site,” Kushner said. “It’s for the community, not just for the high school.”

People in neighborhoods around Lorain High are welcome to take college classes there, she said, adding there are college programs designed for high school students that are not open to the general public.

Those are separate programs.

About 35 percent of high school seniors in Lorain County are graduating with some LCCC college credits, Kushner said, adding that’s “substantially higher than the rest of the state.”

“One other really cool thing is our support of veterans,” Green said. “We’re recognizing their knowledge and skills as they come back to civilian life.”

Veterans can access a fast track to civilian careers as paramedics, EMTs, technicians or other areas.

“They have already had a lot of that training,” Green said. “How do we help them translate that into a civilian career?”

Recently, LCCC released a new season schedule for the Stocker Arts Center, Green said.

“While we focus on students, we also know we are the community’s college,” she said.

5 things everyone gets wrong about artificial intelligence and what it means for our future

Artificial Intelligence, Technology, Business, Innovation
The humanoid robot AILA (artificial intelligence lightweight android) operates a switchboard during a demonstration by the German research centre for artificial intelligence at the CeBit computer fair in Hanover March 5, 2013. REUTERS/Fabrizio Bensch

Luis Perez-Brava, MIT Innovation Program

Artificial Intelligence, Luis Perez-Brava, Innovation
Luis Perez-Brava is a Research Scientist at MIT’s School of Engineering. Alex Kingsbury

There are a lot of myths out there about artificial intelligence (AI).

In June, Alibaba founder Jack Ma said AI is not only a massive threat to jobs but could also spark World War III. Because of AI, he told CNBC, in 30 years we’ll work only 4 hours a day, 4 days a week.

Recode founder Kara Swisher told NPR’s “Here and Now” that Ma is “a hundred percent right,” adding that “any job that’s repetitive, that doesn’t include creativity, is finished because it can be digitized” and “it’s not crazy to imagine a society where there’s very little job availability.”

She even suggested only eldercare and childcare jobs will remain because they require “creativity” and “emotion”—something Swisher says AI can’t provide yet.

I actually find that all hard to imagine. I agree it has always been hard to predict new kinds of jobs that’ll follow a technological revolution, largely because they don’t just pop up. We create them. If AI is to become an engine of revolution, it’s up to us to imagine opportunities that will require new jobs. Apocalyptic predictions about the end of the world as we know it are not helpful.

Common confusion

So, what may be the biggest myth—Myth 1: AI is going to kill our jobs—is simply not true.

Ma and Swisher are echoing the rampant hyperbole of business and political commentators and even many technologists—many of whom seem to conflate AI, robotics, machine learning, Big Data, and so on. The most common confusion may be about AI and repetitive tasks. Automation is just computer programming, not AI. When Swisher mentions a future automated Amazon warehouse with only one human, that’s not AI.

We humans excel at systematizing, mechanizing, and automating. We’ve done it for ages. It takes human intelligence to automate something, but the automation that results isn’t itself “intelligence”—which is something altogether different. Intelligence goes beyond most notions of “creativity” as they tend to be applied by those who get AI wrong every time they talk about it. If a job lost to automation is not replaced with another job, it’s lack of human imagination to blame.

In my two decades spent conceiving and making AI systems work for me, I’ve seen people time and again trying to automate basic tasks using computers and over-marketing it as AI. Meanwhile, I’ve made AI work in places it’s not supposed to, solving problems we didn’t even know how to articulate using traditional means.

For instance, several years ago, my colleagues at MIT and I posited that if we could know how a cell’s DNA was being read it would bring us a step closer to designing personalized therapies. Instead of constraining a computer to use only what humans already knew about biology, we instructed an AI to think about DNA as an economic market in which DNA regulators and genes competed—and let the computer build its own model of that, which it learned from data. Then the AI used its own model to simulate genetic behavior in seconds on a laptop, with the same accuracy that took traditional DNA circuit models days of calculations with a supercomputer.

At present, the best AIs are laboriously built and limited to one narrow problem at a time. Competition revolves around research into increasingly sophisticated and general AI toolkits, not yet AIs. The aspiration is to create AIs that partner with humans across multiple domains—like in IBM’s ads for Watson. IBM’s aim is to turn what today’s just a powerful toolkit into an infrastructure for businesses.

The larger objective

The larger objective for AI is to create AIs that partner with us to build new narratives around problems we care to solve and can’t today—new kinds of jobs follow from the ability to solve new problems.

That’s a huge space of opportunity, but it’s difficult to explore with all these myths about AI swirling around. Let’s dispel some more of them.

Myth 2: Robots are AI. Not true.A worker guides the first shipment of an IBM System Z mainframe computer in Poughkeepsie, New York, U.S. March 6, 2015. Picture taken March 6, 2015. Jon Simon/IBM/Handout via REUTERS A worker guides the first shipment of an IBM System Z mainframe computer in PoughkeepsieThomson Reuters

Industrial and other robots, drones, self-organizing shelves in warehouses, and even the machines we’ve sent to Mars are all just machines programmed to move.

Myth 3: Big Data and Analytics are AI. Wrong again. These, along with data mining, pattern recognition, and data science, are all just names for cool things computers do based on human-created models. They may be complex, but they’re not AI. Data are like your senses: just because smells can trigger memories, it doesn’t make smelling itself intelligent, and more smelling is hardly the path to more intelligence.

Myth 4: Machine Learning and Deep Learning are AI. Nope. These are just tools for programming computers to react to complex patterns—like how your email filters out spam by “learning” what millions of users have identified as spam. They’re part of the AI toolkit like an auto mechanic has wrenches. They look smart—sometimes scarily so, like when a computer beats an expert at the game Go—but they’re certainly not AI.

Myth 5: Search engines are AI. They look smart, too, but they’re not AI. You can now search information in ways once impossible, but you—the searcher—contribute the intelligence. All the computer does is spot patterns from what you search and recommend others do the same. It doesn’t actually know any of what it finds; as a system, it’s as dumb as they come.

In my own AI work, I’ve made use of AI whenever a problem we could imagine solving with science became too complex for science’s reductive approaches. That’s because AI allows us to ask questions that are not easy to ask in traditional scientific “terms.” For instance, more than 20 years ago, my colleagues and I used AI to invent a technology to locate cellphones in an emergency faster and more accurately than GPS ever could. Traditional science didn’t help us solve the problem of finding you, so we worked on building an AI that would learn to figure out where you are so emergency services can find you.

By the way, our AI solution actually created jobs.

AI’s most important attribute isn’t processing scores of data or executing programs—all computers do that—but rather learning to fulfill tasks we humans cannot so we can reach further. It’s a partnership: we humans guide AI and learn to ask better questions.

Swisher is right, though: we ought to figure out what the next jobs are, but not by agonizing over how much some current job is creative or repetitive. I would note that the AI toolkit has already created hundreds of thousands of jobs of all kinds—Uber, Facebook, Google, Apple, Amazon, and so on.

Our choice is continuing the dystopian AI narrative about the future of jobs. or having a different conversation about making the AI we want happen so we can address problems that cannot be solved by traditional means, for which the science we have is inadequate, incomplete, or nonexistent—and imagining and creating some new jobs along the way.

Luis Perez-Brava is the head of MIT’s Innovation Program and a Research Scientist at MIT’s School of Engineering. He recently published ‘Innovating: A Doer’s Manifesto for Starting from a Hunch, Prototyping Problems, Scaling Up, and Learning to Be Productively Wrong.’  

How Artificial Intelligence benefits companies and ups their game

Technology, Artificial Intelligence, AI
A file photo of workers at the General Electric Co. (GE ) energy plant in Greenville, South Carolina, US. GE uses machine learning to predict required maintenance for its large industrial machines. Photo: Bloomberg

Jayanth Kolla

After decades of false starts, Artificial Intelligence (AI) is already pervasive in our lives. Although invisible to most people, features such as custom search engine results, social media alerts and notifications, e-commerce recommendations and listings are powered by AI-based algorithms and models. AI is fast turning out to be the key utility of the technology world, much as electricity evolved a century ago. Everything that we formerly electrified, we will now cognitize.

AI’s latest breakthrough is being propelled by machine learning—a subset of AI which includes abstruse techniques that enable machines to improve at tasks through learning and experience.Although in its infancy, the rapid development and impending AI-led technology revolution are expected to impact all the industries and companies (both big and small) in the respective ecosystem/value chains. We are already witnessing examples of how AI-powered new entrants are able to take on incumbents and win—as Uber and Lyft have done to the cab-hailing industry.

Currently, deployed key AI-based solutions, across industry verticals, include:

Predictive analytics, diagnostics and recommendations: Predictive analytics has been in the mainstream for a while, but deep learning changes and improves the whole game. Predictive analytics can be described as the ‘everywhere electricity’—it is not so much a product as it is a new capability that can be added to all the processes in a company. Be it a national bank, a key supplier of raw material and equipment for leading footwear brands, or a real estate company, companies across every industry vertical are highly motivated to adopt AI-based predictive analytics because of proven returns on investment.

Japanese insurance firm Fukoku Mutual Life Insurance is replacing its 34-strong workforce with IBM’s Watson Explorer AI. The AI system calculates insurance policy payouts, which according to the firm’s estimates is expected to increase productivity by 30% and save close to £1 million a year. Be it user-based collaborative filtering used by Spotify and Amazon to content-based collaborative filtering used by Pandora or Frequency Itemset Mining used by Netflix, digital media firms have been using various machine learning algorithms and predictive analytics models for their recommendation engines.

In e-commerce, with thousands of products and multiple factors that impact their sales, an estimate of the price to sales ratio or price elasticity is difficult. Dynamic price optimization using machine learning—correlating pricing trends with sales trends using an algorithm, then aligning with other factors such as category management and inventory levels—is used by almost every leading e-commerce player from Amazon.com to Blibli.com.

Chatbots and voice assistants: Chatbots have evolved mainly on the back of internet messenger platforms, and have hit an inflection point in 2016. As of mid-2016, more than 11,000 Facebook Messenger bots and 20,000 Kik bots had been launched. As of April 2017, 100,000 bots were created for Facebook Messenger alone in the first year of the platform. Currently, chatbots are rapidly proliferating across both the consumer and enterprise domains, with capabilities to handle multiple tasks including shopping, travel search and booking, payments, office management, customer support, and task management.

Royal Bank of Scotland (RBS) launched Luvo, a natural language processing AI bot which answers RBS, Natwest and Ulster bank customer queries and perform simple banking tasks like money transfers.

If Luvo is unable to find the answer it will pass the customer over to a member of staff. While RBS is the first retail bank in the UK to launch such a service, others such as Sweden’s SwedBank and Spain’s BBVA have created similar virtual assistants.

Technology companies and digital natives are investing in and deploying the technology at scale, but widespread adoption among less digitally mature sectors and companies is lagging. However, the current mismatch between AI investment and adoption has not stopped people from imagining a future where AI transforms businesses and entire industries.

The National Health Services (NHS) in the UK has implemented an AI-powered chatbot on the 111 non-emergency helpline. Being trialled in North London, its 1.2 million residents can opt for a chatbot rather than talking to a person on the 111 helpline. The chatbot encourages patients to enter their symptoms into the app. It will, then, consult a large medical database and users will receive tailored responses based on the information they have entered.

Image recognition, processing and diagnostics: On an average, it takes about 19 million images of cats for the current Deep Learning algorithms to recognize an image of a cat, unaided. Compared to the progress of natural language processing solutions, computer vision-based AI solutions are still in developmental stage, primarily due to the lack of large, structured data sets and the significant amount of computational power required to train the algorithms.

That said, we are witnessing adoption of image recognition in healthcare and financial services sectors. Israel-based Zebra Medical Systems uses deep learning techniques in radiology. It has amassed a huge training set of medical images along with categorization technology that will allow computers to predict diseases accurately better than humans.

Chinese technology companies Alipay (the mobile payments arm of Alibaba) and WeChat Pay (the mobile payments unit of Tencent) use advanced mobile-based image and facial recognition techniques for loan disbursement, financing, insurance claims authentication, fraud management and credit history ratings of both retail and enterprise customers.

General Electric (GE) is an example of a large multi-faceted conglomerate that has adopted AI and ML successfully at a large scale, across various functions, to evolve from industrial and consumer products and financial services firm to a ‘digital industrial’ company with a strong focus on the ‘Industrial Internet’. GE uses machine-learning approaches to predict required maintenance for its large industrial machines. The company achieves this by continuously monitoring and learning from new data of its machines ‘digital twins’ (a digital, cloud-based replica of its actual machines in the field) and modifying predictive models over time. Beyond, industrial equipment, the company has also used AI and ML effectively for integrating business data. GE used machine-learning software to identify and normalize differential pricing in its supplier data across business verticals, leading to savings of $80 million.

GE’s successful acquisition and integration of innovative AI startups such as “SmartSignal” (acquired in 2011) to provide supervised learning models for remote diagnostics, “Wise.io” (acquired in 2016) for unsupervised deep learning capabilities and its in-house the data scientists, and of “Bit Stew” (another 2016 acquisition) to integrate data from multiple sensors in industrial equipment has enabled the company to evolve as a leading conglomerate in the AI business.

Industry sector-wise adoption of AI: Sector-by-sector adoption of AI is highly uneven currently, reflecting many characteristics of digital adoption on a broader scale. According to the McKinsey Global Index survey, released in June, larger companies and industries that adopted digital technologies in the past are more likely to adopt AI. For them, AI is the next wave. Other than online and IT companies, which are early adopters and proponents of various AI technologies, banks, financial services and healthcare are the leading non-core technology verticals that are adopting AI. According to the McKinsey survey, there is also clear evidence that early AI adopters are driven to employ AI solutions in order to grow revenue and market share, and the potential for cost reduction is a secondary idea.

AI, thus, can go beyond changing business processes to changing entire business models with winner-takes-all dynamics. Firms that are waiting for the AI dust to settle down risk being left behind.

The author is Founder and Partner of digital technologies research and advisory firm, Convergence Catalyst.

How to Become A 2018 World’s Most Innovative Company

innovation, fast company, business, tech
Image Source: FastCompany.com

 

Innovation is everywhere. So how do we cut through the clutter to name our annual Most Innovative Companies Top 50 and Top 10 industry lists?

Our team of dogged and dedicated reporters and editors spend months culling research on the world’s top companies. But this year—for the first time ever—you can submit your own organizationto become a 2018 Most Innovative Company.

Here’s how you can put together the best possible entry for our team of Most Innovative Companies editors. (And don’t forget to download our MIC special edition and how-to guide here).

  1. Identify Your Innovation Bucket
    Fast Company takes an expansive view of what constitutes innovation: Product innovation: We’re happy to celebrate a successful new entrant in the market that serves a previously unmet need, such as new lifesaving drugs from Gilead Sciences or Casper’s mattresses and bedding. Creative innovation: We gave the nod to the ad agency 72andSunny for breaking through the clutter with great work in a variety of media for clients ranging from Starbucks to Activision to Google. Sometimes, of course, an innovation hits several of these notes or belongs in a category we haven’t mentioned here. Business-model innovation: Warby Parker introduced try-before-you-buy to eyewear and has led the way in marrying real-world retail with e-commerce.
  2. Focus On A Project
    Tell us about a particular initiative. It’s not enough merely to state that your product or strategy is innovative. The key is to isolate the novelty in what you’re doing and delineate how and why it’s different from what’s come before.
  3. Be Concise, Yet Descriptive
    We are not accepting attachments of any kind, including presentation decks or visuals. The more detail you can provide in the space allotted, the greater the case can be made for your innovation. What makes you most excited when you think about what you’ve developed? Which of your features are your customers are buzzing about, either in communicating back to you or among themselves?
  4.  Share Your Completed Work
    If you’re an architecture firm, finished buildings will garner more attention than those in the planning stage. If you’re a pharmaceutical company, an FDA-approved drug matters more than a promising clinical trial. In-progress ideas will certainly be considered, but completing the work counts.
  5. Choose Your Strategic Weapon
    Technology is transforming every aspect of our world. How are you using it to get a leg up on competitors? Or perhaps good design is…Continue reading

Article source: https://www.fastcompany.com/40440722/how-to-become-a-2018-worlds-most-innovative-company