Innovation   90164

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Why I remain optimistic about Tesla | Vivek Wadhwa | Pulse | LinkedIn
Tesla is already diversifying its businesses so that it doesn’t sink with the automotive industry when this happens. Underlying the Tesla cars is another technology platform that it is commercializing: the battery. Tesla’s Powerwall is a rechargeable lithium-ion battery that provides homes with the storage of solar-captured energy for use at night or during power outages. This complements the solar roof tiles that Tesla is selling, which look like ordinary tiles and are priced competitively. So what you have is what Musk has called “a smoothly integrated and beautiful solar-roof-with-battery product that just works.” This is the kind of advantage and elegance that came with the Apple iPhone, which integrated music, telephony, and computer applications into one device.

In the same way as Tesla could make car ownership a revenue generator for its drivers, it could do the same for solar power. Homeowners could share their excess energy with other homeowners and provide charging stations for others’ Tesla vehicles. This would also dramatically expand the Tesla supercharger network, enabling charging of cars almost anywhere.
Tesla  car  innovation 
yesterday by dominomaster
Innovation labs: best practice – Made by Many
The second volume of our report on innovation labs presents a broader collection of contemporary best-practice knowledge, gathered from our discussions wit...
innovation  labs  publication  madebymany 
yesterday by andrewn
RT : Today marks the 106th anniversary of IBM’s very first :
innovation  patent  from twitter
yesterday by chrispoole
The Business of Artificial Intelligence
What it can — and cannot — do for your organization

Harvard Business Review
The Business of Artificial Intelligence
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Artificial Intelligence, Real Food
We asked IBM’s AI to create recipes and then had celebrity chef Ming Tsai cook them. Watch what happened.
For more than 250 years the fundamental drivers of economic growth have been technological innovations. The most important of these are what economists call general-purpose technologies — a category that includes the steam engine, electricity, and the internal combustion engine. Each one catalyzed waves of complementary innovations and opportunities. The internal combustion engine, for example, gave rise to cars, trucks, airplanes, chain saws, and lawnmowers, along with big-box retailers, shopping centers, cross-docking warehouses, new supply chains, and, when you think about it, suburbs. Companies as diverse as Walmart, UPS, and Uber found ways to leverage the technology to create profitable new business models.

The most important general-purpose technology of our era is artificial intelligence, particularly machine learning (ML) — that is, the machine’s ability to keep improving its performance without humans having to explain exactly how to accomplish all the tasks it’s given. Within just the past few years machine learning has become far more effective and widely available. We can now build systems that learn how to perform tasks on their own.

ABOVE: What does it mean to be human? What do we recognize as artificial? The art for this series was generated from a series of photographs of humans, but because of an application of distortion, you may not recognize the features they portray. Or you may. (SOURCE: HBR Design Staff)

Why is this such a big deal? Two reasons. First, we humans know more than we can tell: We can’t explain exactly how we’re able to do a lot of things — from recognizing a face to making a smart move in the ancient Asian strategy game of Go. Prior to ML, this inability to articulate our own knowledge meant that we couldn’t automate many tasks. Now we can.

Second, ML systems are often excellent learners. They can achieve superhuman performance in a wide range of activities, including detecting fraud and diagnosing disease. Excellent digital learners are being deployed across the economy, and their impact will be profound.

Erik Brynjolfsson (@erikbryn) is the director of MIT’s Initiative on the Digital Economy, the Schussel Family Professor of Management Science at the MIT Sloan School of Management, and a research associate at NBER. His research examines the effects of information technologies on business strategy, productivity and performance, digital commerce, and intangible assets. At MIT he teaches courses on the economics of information and the Analytics Lab.

In the sphere of business, AI is poised have a transformational impact, on the scale of earlier general-purpose technologies. Although it is already in use in thousands of companies around the world, most big opportunities have not yet been tapped. The effects of AI will be magnified in the coming decade, as manufacturing, retailing, transportation, finance, health care, law, advertising, insurance, entertainment, education, and virtually every other industry transform their core processes and business models to take advantage of machine learning. The bottleneck now is in management, implementation, and business imagination.

Like so many other new technologies, however, AI has generated lots of unrealistic expectations. We see business plans liberally sprinkled with references to machine learning, neural nets, and other forms of the technology, with little connection to its real capabilities. Simply calling a dating site “AI-powered,” for example, doesn’t make it any more effective, but it might help with fundraising. This article will cut through the noise to describe the real potential of AI, its practical implications, and the barriers to its adoption.

The term artificial intelligence was coined in 1955 by John McCarthy, a math professor at Dartmouth who organized the seminal conference on the topic the following year. Ever since, perhaps in part because of its evocative name, the field has given rise to more than its share of fantastic claims and promises. In 1957 the economist Herbert Simon predicted that computers would beat humans at chess within 10 years. (It took 40.) In 1967 the cognitive scientist Marvin Minsky said, “Within a generation the problem of creating ‘artificial intelligence’ will be substantially solved.” Simon and Minsky were both intellectual giants, but they erred badly. Thus it’s understandable that dramatic claims about future breakthroughs meet with a certain amount of skepticism.

Let’s start by exploring what AI is already doing and how quickly it is improving. The biggest advances have been in two broad areas: perception and cognition. In the former category some of the most practical advances have been made in relation to speech. Voice recognition is still far from perfect, but millions of people are now using it — think Siri, Alexa, and Google Assistant. The text you are now reading was originally dictated to a computer and transcribed with sufficient accuracy to make it faster than typing. A study by the Stanford computer scientist James Landay and colleagues found that speech recognition is now about three times as fast, on average, as typing on a cell phone. The error rate, once 8.5%, has dropped to 4.9%. What’s striking is that this substantial improvement has come not over the past 10 years but just since the summer of 2016.


Image recognition, too, has improved dramatically. You may have noticed that Facebook and other apps now recognize many of your friends’ faces in posted photos and prompt you to tag them with their names. An app running on your smartphone will recognize virtually any bird in the wild. Image recognition is even replacing ID cards at corporate headquarters. Vision systems, such as those used in self-driving cars, formerly made a mistake when identifying a pedestrian as often as once in 30 frames (the cameras in these systems record about 30 frames a second); now they err less often than once in 30 million frames. The error rate for recognizing images from a large database called ImageNet, with several million photographs of common, obscure, or downright weird images, fell from higher than 30% in 2010 to about 4% in 2016 for the best systems. (See the exhibit “Puppy or Muffin?”)

The speed of improvement has accelerated rapidly in recent years as a new approach, based on very large or “deep” neural nets, was adopted. The ML approach for vision systems is still far from flawless — but even people have trouble quickly recognizing puppies’ faces or, more embarrassingly, see their cute faces where none exist.

Puppy or Muffin? Progress in Image Recognition
Machines have made real strides in distinguishing among similar-looking categories of images.
Karen Zack/@teenybiscuit
The second type of major improvement has been in cognition and problem solving. Machines have already beaten the finest (human) players of poker and Go — achievements that experts had predicted would take at least another decade. Google’s DeepMind team has used ML systems to improve the cooling efficiency at data centers by more than 15%, even after they were optimized by human experts. Intelligent agents are being used by the cybersecurity company Deep Instinct to detect malware, and by PayPal to prevent money laundering. A system using IBM technology automates the claims process at an insurance company in Singapore, and a system from Lumidatum, a data science platform firm, offers timely advice to improve customer support. Dozens of companies are using ML to decide which trades to execute on Wall Street, and more and more credit decisions are made with its help. Amazon employs ML to optimize inventory and improve product recommendations to customers. Infinite Analytics developed one ML system to predict whether a user would click on a particular ad, improving online ad placement for a global consumer packaged goods company, and another to improve customers’ search and discovery process at a Brazilian online retailer. The first system increased advertising ROI threefold, and the second resulted in a $125 million increase in annual revenue.

Machine learning systems are not only replacing older algorithms in many applications, but are now superior at many tasks that were once done best by humans. Although the systems are far from perfect, their error rate — about 5% — on the ImageNet database is at or better than human-level performance. Voice recognition, too, even in noisy environments, is now nearly equal to human performance. Reaching this threshold opens up vast new possibilities for transforming the workplace and the economy. Once AI-based systems surpass human performance at a given task, they are much likelier to spread quickly. For instance, Aptonomy and Sanbot, makers respectively of drones and robots, are using improved vision systems to automate much of the work of security guards. The software company Affectiva, among others, is using them to recognize emotions such as joy, surprise, and anger in focus groups. And Enlitic is one of several deep-learning startups that use them to scan medical images to help diagnose cancer.

These are impressive achievements, but the applicability of AI-based systems is still quite narrow. For instance, their remarkable performance on the ImageNet database, even with its … [more]
business  technology  innovation  artificial-intelligence  ai  machine-learning  data-science  big-data  leadership  management 
yesterday by enochko
The 4 Types of Innovation and the Problems They Solve
We need to start treating innovation like other business disciplines — as a set of tools that are designed to accomplish specific objectives. Just as we wouldn’t rely on a single marketing tactic or a single source of financing for the entire life of an organization, we need to build up a portfolio of innovation strategies designed for specific tasks.
yesterday by ltalley

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