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

New robotic exosuit could push the limits of human performance

Engineering, Wearable Technology, Innovation
Image Source: http://news.harvard.edu Credit: Wyss Institute at Harvard University A system of actuation wires attached to the back of the exosuit provides assistive force to the hip joint during running.

 

What if you could improve your average running pace from 9:14 minutes/mile to 8:49 minutes/mile without weeks of training?

Researchers at Harvard’s Wyss Institute and the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) at Harvard University have demonstrated that a tethered soft exosuit can reduce the metabolic cost of running on a treadmill by 5.4 percent, bringing those dreams of high performance closer to reality.

Homo sapiens has evolved to become very good at distance running, but our results show that further improvements to this already extremely efficient system are possible,” says corresponding author Philippe Malcolm, former postdoctoral research fellow at the Wyss Institute and SEAS, and now assistant professor at the University of Nebraska, Omaha, where he continues to collaborate on this work. The study appears today in Science Robotics.

Running is a naturally more costly form of movement than walking, so any attempt to reduce its strain on the body must impose a minimal additional burden. The soft exosuit technology developed in the lab of Wyss core faculty member Conor Walsh represents an ideal platform for assisted running, as its textile-based design is lightweight and moves with the body. A team of scientists in Walsh’s lab, led by Wyss postdoctoral fellow Giuk Lee, performed the study with an exosuit that incorporated flexible wires connecting apparel anchored to the back of the thigh and waist belt to an external actuation unit. As subjects ran on a treadmill wearing the exosuit, the unit pulled on the wires, which acted as a second pair of hip extensor muscles applying force to the legs with each stride. The metabolic cost was measured by analyzing the subjects’ oxygen consumption and carbon dioxide production while running.

The team tested two different “assistance profiles,” or patterns of wire-pulling: one based on human biology that applied force starting at the point of maximum hip extension observed in normal running, and one based on a simulation of exoskeleton-assisted running from a group at Stanford University that applied force slightly later in the running stride and suggested that the optimal point to provide assistive force might not be the same as the biological norm. Confirming this suspicion, Lee and colleagues found that the simulation-based profile outperformed the…Continue Reading

Article Source: http://news.harvard.edu/gazette/story/2017/06/new-robotic-exosuit-could-push-the-limits-of-human-performance/

The Modern Cyclist: 14 Bold Bike Ideas & Innovations

Bicycle Innovation, Bicycles, Cycles, Bikes
Image Source: http://weburbanist.com

Article by , filed under Products & Packaging in the Design category

Minimalist frames, technology-equipped accessories, 3D printing and lots of multi-functionality make bikes more convenient, safe, fun and beautiful, as proven by these 14 cycling concepts and innovations. With modular parts, commuter-friendly features and designs that make racing more fun for casual cyclists, bikes get a functional makeover for the modern age.

Archont Electro E-Bike
bikes archont

bikes archont 2

bikes archont 3

Isn’t this bike a beauty? The Archont by Ono features the profile of a vintage motorcycle, but it’s an electric bicycle with a handcrafted stainless steel frame and 29-inch front wheel. The curvaceous cruiser has a 72-volt battery with a range of 99 kilometers and can go up to 80 km/h.

fUCI Bike: Fast Road Bike for Non-Racers
bike fuci

bikef uci 2

bike fuci 3

bike fuci 4

Most racing bikes are designed to the standards of the UCI (Union Cycliste Internationle), the governing body of every major bike tour in the world, to keep the races fair. But not everyone who wants a fast bike wants to compete in official races, and there are lots of fun features their bikes could have without these regulations. Designer Robert Egger presents fUCI (eff UCI), which has a larger back wheel, electric motor in the hub, a storage space in the wheel and a smartphone mount.

Recoiling Plume Mudguard
bike plume mudguard

bike plume mudguard 2

bike plume mudguard 3

This mudguard has literally got your back when it starts raining, keeping you from getting splattered. With a rubber mount stretching to fit any standard seat post size, the simple add-on absorbs shock so it won’t automatically fold up when you hit a bump. Resistant to rust and corrosion, it suspends over the real wheel or retracts within seconds.

Sno-Bike
bike snow

bike snow 2

Combining two entirely separate sports, the Sno Bike concept by Venn Industrial Design Consultancy features a Z-shaped tensile frame linking a rear wheel to a single ski controlled by the handlebars. How would it actually handle in real-life conditions? It’s impossible to say, since it’s just a concept, but it looks like fun.

Shibusa Bicycle with Swappable Electric-Assisted Parts
bikes shibusa

bikes shibusa 2

This sleek black modular bike can be boosted with electric components or made back into a regular bicycle just by swapping a few parts. The award-winning Shibusa design eliminates the bulkiness associated with many electric bikes for a “hassle-free commuter” offering plenty of flexibility. Modular components include a stand-alone bike light, battery pack, storage rack and charge monitor.

Continue reading
Article source: http://weburbanist.com/2016/02/10/the-modern-cyclist-14-bold-bike-ideas-innovations/

Thought for Today: Being Human in an Android Society

Android, Technology, Science

Advanced technology gives us the ability to live better and the opportunity to get things done faster. On the flipside, how and when do you draw the line between accelerated technological progress, while avoiding human obsolescence.

As we embrace new technological phases of progress more fervently, scientifically and compassionately than before, we must be careful to avoid allowing our human value to depreciate. Modern technology is great, as long as we do not allow it to make us lazy and useless. (Taken from the April 11, 2009, article, Blinding “Me” With Science – Tips For Preventing Human Obsolescence)