[youtube https://www.youtube.com/watch?v=07MZVjjuLKw?rel=0]
CTIA-The Wireless Association explains the opportunities the IoT will provide and what is needed in order to meet user demands (hint: spectrum!).
[youtube https://www.youtube.com/watch?v=07MZVjjuLKw?rel=0]
CTIA-The Wireless Association explains the opportunities the IoT will provide and what is needed in order to meet user demands (hint: spectrum!).

You will find more statistics at Statista
Video: http://espn.go.com/video/clip?id=espn:13415984
In our modern age of Tinder, OkCupid and Match.com, we’re used to the idea that algorithms can help us find love. But while the algorithms may have improved as the market for online dating has expanded, the inputs — the questions these computer matchmakers ask dating hopefuls — haven’t changed much since the 1960s, when Compatibility Research Inc. launched the first computerized dating service.
See also the Harvard Crimson article from November 3, 1965, about Compatibility Research’s Operation Match, which includes this ditty:
Well, I filled out my form and I sent it along,
Never hoping I’d get anything like this.
But now when I see her,
Whenever I see her,
I want to give her one great big I.B.M. kiss.
She’s my I.B.M. baby, the ideal lady,
She’s my I.B.M. baby.
From the first time I met her I couldn’t forget her,
She’s my I.B.M. baby.
Well we’ve dated sometime,
Things are going just fine, and I’d like to settle down with her.
Just like birds of a feather
We put 2 and 2 together, and we came one with an I.B.M. affair.
She’s my I.B.M. baby, I don’t mean maybe,
She’s my I.B.M. baby.
Today, one in 10 adults now spends, on average, an hour a day on a dating web site or app, according to Nielsen. Online dating in the U.S. was a $2.2 billion industry last year.
[youtube https://www.youtube.com/watch?v=lszB8muRqQA?rel=0]
Fresh out of topping Gartner’s most hyped technologies list for the second year in a row, the Internet of Things (IoT) has kept its buzz going over the last couple of weeks with a series of announcements and new market analysis reports. First, here’s a sample of recent announcements:
We also learned more this month about the state-of-the-market for IoT and its potential impact from a number of new reports:
IDC (also here) shared the results of its survey of 2,350 IT and business decision makers in (mostly) large and medium-size enterprises worldwide:
Gartner reiterated its forecast of more than 30 billion installed IoT units and estimated it will result in a 20% increase in potential revenue generated from software for manufacturers running ‘intelligent devices’. The Internet of Things (IoT), in Gartner’s view, turns every manufacturer into a software provider, a transformation which will have profound impact on application strategy, architecture, development and integration.
Gartner recommends that manufacturers differentiate with software, increase the intelligence in their devices by adding software, and ensure they have the licensing and entitlements tools to manage the software.
Accenture estimates that based on current policy and investment trends, the IoT could add about $500 billion to China’s cumulative GDP by 2030. This would result in China’s GDP being 0.3 percent higher in that year compared with current projections. However, by taking additional measures to improve its capacity to absorb IoT technologies and increase IoT investment, China could boost its annual GDP by 1.3 percent by 2030, cumulatively adding $1.8 trillion to the economy by that time.
Last but not least, Harvard Business School professor Michael Porter and PTC CEO Jim Heppelmann published in the Harvard Business Review “How Smart, Connected Products Are Transforming Companies.” They describe the impact of the IoT on the organizational structure of manufacturing companies and conclude that “Smart, connected products reshape not only competition, as we detailed in our previous article, but the very nature of the manufacturing firm, its work, and how it is organized. They are creating the first true discontinuity in the organization of manufacturing firms in modern business history.” They also see broader benefits of the IoT, including changing consumption patterns: “Smart, connected products will free us to purchase only the goods and services we need, to share products that we do not use much, and to get more out of the products that we already have. Instead of tossing out old products for the next generation, we will hold on to products that are continually improved, upgraded, and modernized.”
Originally published on Forbes.com
We used CB Insights’ valuation data to look at how the rise of Uber’s valuation correlates with the market capitalization of Medallion Financial Corp (NASDAQ: TAXI). Medallion Financial is a publicly-traded company that originates, acquires, and services loans used to purchase taxi medallions in several large US urban markets that Uber is also active in, including New York. We charted the stock price of TAXI versus the valuations for many of Uber’s rounds since 2010.
We found that TAXI has also been hammered by an “Uber Effect,” with its price down even more than the decline seen by New York City medallions. TAXI’s stock price is down nearly 49% since Uber raised its breakout $258M Series C at a $3.5B valuation. (The NASDAQ is up ~26% in the same time period.)
Uber’s valuation is up over 13x.
Mark J. Perry at the American Enterprise Institute:
In 1942, economist Joseph Schumpeter described “creative destruction” as a “process of industrial mutation that incessantly revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one.” There probably hasn’t been a better example of Schumpeterian creative destruction in the last decade or more than the recent ascendance of app-based ride-sharing services like Uber (and Lyft, Sidecar, Gett, Via, etc.) challenging traditional, legacy taxi cartels in cities like New York, San Francisco, Chicago and more than 160 other US cities. Market-based evidence of the gale of creative destruction in the transportation industry is displayed in the two charts above. The top chart above shows how the increasing popularity of ride-sharing apps like Uber has caused the price of New York City individual taxi medallions to collapse by at least 37%, from a peak of more than $1 million in August 2013 to only about $650,000 in recent months (based on advertised asking prices here, not actual sales).
Further evidence of the “Uber effect” is displayed in the bottom chart above, showing the collapse in the stock price of Medallion Financial Corporation, from $16.45 in November 2013 to below $7 per share in the last few days. Medallion Financial Corporation (NASDAQ: TAXI) is a NYC-based specialty finance company that originates, acquires, and services loans that finance taxicab medallions. Just as the sky-high taxi medallion prices have been significantly eroded due to competition from the upstart ride-sharing services, so has the value of Medallion Financial Corporation’s stock price been significantly dropping. After tracking the SP&500 Index closely for many decades, the share price of Medallion Financial has fallen by a whopping 58% from its November 2013 peak, during a time when the S&P 500 has increased by 7.1%.
As the traditional, legacy taxi industry continues to collapse under the Schumpeterian forces of market disruption, the taxi cartels like the one in NYC are asking for taxpayer bailouts, or at least taxpayer-supported guarantees for taxi medallion loans. Consumers are the obvious winners from the creative destruction in the transportation industry – we now have more choice, better and faster service, friendlier drivers, cleaner cars, and maybe most importantly — lower prices. Traditional taxi drivers and medallion owners, after being protected from competition by government regulations for many generations, are the obvious losers from the “Uber effect.” Medallion prices will continue to fall as the taxi cartels continue to crumble and collapse.
NPR Planet Money: Listen to Episode 643, July 31, 2015, on Gene Freidman, the “Taxi King” and how his empire is starting to crumble. Also, “Why Does A Taxi Medallion Cost $1 Million?” from 2011.
Gartner released today its top predictions for “the digital future… an algorithmic and smart machine-driven world where people and machines must define harmonious relationships”:
1) By 2018, 20 percent of business content will be authored by machines.
Technologies with the ability to proactively assemble and deliver information through automated composition engines are fostering a movement from human- to machine-generated business content. Data-based and analytical information can be turned into natural language writing using these emerging tools. Business content, such as shareholder reports, legal documents, market reports, press releases, articles and white papers, are all candidates for automated writing tools.
2) By 2018, six billion connected things will be requesting support.
In the era of digital business, when physical and digital lines are increasingly blurred, enterprises will need to begin viewing things as customers of services — and to treat them accordingly. Mechanisms will need to be developed for responding to significantly larger numbers of support requests communicated directly by things. Strategies will also need to be developed for responding to them that are distinctly different from traditional human-customer communication and problem-solving. Responding to service requests from things will spawn entire service industries, and innovative solutions will emerge to improve the efficiency of many types of enterprise.
3) By 2020, autonomous software agents outside of human control will participate in five percent of all economic transactions.
Algorithmically driven agents are already participating in our economy. However, while these agents are automated, they are not fully autonomous, because they are directly tethered to a robust collection of mechanisms controlled by humans — in the domains of our corporate, legal, economic and fiduciary systems. New autonomous software agents will hold value themselves, and function as the fundamental underpinning of a new economic paradigm that Gartner calls the programmable economy. The programmable economy has potential for great disruption to the existing financial services industry. We will see algorithms, often developed in a transparent, open-source fashion and set free on the blockchain, capable of banking, insurance, markets, exchanges, crowdfunding — and virtually all other types of financial instruments
4) By 2018, more than 3 million workers globally will be supervised by a “robo-boss.”
Robo-bosses will increasingly make decisions that previously could only have been made by human managers. Supervisory duties are increasingly shifting into monitoring worker accomplishment through measurements of performance that are directly tied to output and customer evaluation. Such measurements can be consumed more effectively and swiftly by smart machine managers tuned to learn based on staffing decisions and management incentives.
5) By year-end 2018, 20 percent of smart buildings will have suffered from digital vandalism.
Inadequate perimeter security will increasingly result in smart buildings being vulnerable to attack. With exploits ranging from defacing digital signage to plunging whole buildings into prolonged darkness, digital vandalism is a nuisance, rather than a threat. There are, nonetheless, economic, health and safety, and security consequences. The severity of these consequences depend on the target. Smart building components cannot be considered independently, but must be viewed as part of the larger organizational security process. Products must be built to offer acceptable levels of protection and hooks for integration into security monitoring and management systems.
6) By 2018, 45 percent of the fastest-growing companies will have fewer employees than instances of smart machines.
Gartner believes the initial group of companies that will leverage smart machine technologies most rapidly and effectively will be startups and other newer companies. The speed, cost savings, productivity improvements and ability to scale of smart technology for specific tasks offer dramatic advantages over the recruiting, hiring, training and growth demands of human labor. Some possible examples are a fully automated supermarket or a security firm offering drone-only surveillance services. The “old guard” (existing) companies, with large amounts of legacy technologies and processes, will not necessarily be the first movers, but the savvier companies among them will be fast followers, as they will recognize the need for competitive parity for either speed or cost.
7) By year-end 2018, customer digital assistant will recognize individuals by face and voice across channels and partners.
The last mile for multichannel and exceptional customer experiences will be seamless two-way engagement with customers and will mimic human conversations, with both listening and speaking, a sense of history, in-the-moment context, timing and tone, and the ability to respond, add to and continue with a thought or purpose at multiple occasions and places over time. Although facial and voice recognition technologies have been largely disparate across multiple channels, customers are willing to adopt these technologies and techniques to help them sift through increasing large amounts of information, choice and purchasing decisions. This signals an emerging demand for enterprises to deploy customer digital assistants to orchestrate these techniques and to help “glue” continual company and customer conversations.
8) By 2018, two million employees will be required to wear health and fitness tracking devices as a condition of employment.
The health and fitness of people employed in jobs that can be dangerous or physically demanding will increasingly be tracked by employers via wearable devices. Emergency responders, such as police officers, firefighters and paramedics, will likely comprise the largest group of employees required to monitor their health or fitness with wearables. The primary reason for wearing them is for their own safety. Their heart rates and respiration, and potentially their stress levels, could be remotely monitored and help could be sent immediately if needed. In addition to emergency responders, a portion of employees in other critical roles will be required to wear health and fitness monitors, including professional athletes, political leaders, airline pilots, industrial workers and remote field workers.
9) By 2020, smart agents will facilitate 40 percent of mobile interactions, and the postapp era will begin to dominate.
Smart agent technologies, in the form of virtual personal assistants (VPAs) and other agents, will monitor user content and behavior in conjunction with cloud-hosted neural networks to build and maintain data models from which the technology will draw inferences about people, content and contexts. Based on these information-gathering and model-building efforts, VPAs can predict users’ needs, build trust and ultimately act autonomously on the user’s behalf.
10) Through 2020, 95 percent of cloud security failures will be the customer’s fault
Security concerns remain the most common reason for avoiding the use of public cloud services. However, only a small percentage of the security incidents impacting enterprises using the cloud have been due to vulnerabilities that were the provider’s fault. This does not mean that organizations should assume that using a cloud means that whatever they do within that cloud will necessarily be secure. The characteristics of the parts of the cloud stack under customer control can make cloud computing a highly efficient way for naive users to leverage poor practices, which can easily result in widespread security or compliance failures. The growing recognition of the enterprise’s responsibility for the appropriate use of the public cloud is reflected in the growing market for cloud control tools. By 2018, 50 percent of enterprises with more than 1,000 users will use cloud access security broker products to monitor and manage their use of SaaS and other forms of public cloud, reflecting the growing recognition that although clouds are usually secure, the secure use of public clouds requires explicit effort on the part of the cloud customer.
[youtube https://www.youtube.com/watch?v=f4-pFZEv3QQ?rel=0]
On September 14, 2015, GE announced the creation of GE Digital, “a transformative move that brings together all of the digital capabilities from across the company into one organization.” It integrates GE’s Software Center, the expertise of GE’s global IT and commercial software teams, and the industrial security strength of Wurldtech. This “new model” (not a business unit, apparently) is led by Bill Ruh, chief digital officer.
In the video above, Ruh talked briefly about GE Digital, preceded by GE Digital’s CTO Harel Kodesh talking about Predix, GE’s software platform for the “Industrial Internet” or IoT.
[youtube https://www.youtube.com/watch?v=dpzxW6buh9Y]
Owen Zhang is ranked #1 on Kaggle, the online stadium for data science competitions. An engineer by training, Zhang says that data science is finding “practical solutions to not very well-defined problems,” similar to engineering. He believes that good data scientists, “otherwise known as unicorn data scientists,” have three types of expertise. Since data science deals with practical problems, the first one is being familiar with a specific domain and knowing how to solve a problem in that domain. The second is the ability to distinguish signal from noise, or understanding statistics. The third skill is software engineering.
[youtube https://www.youtube.com/watch?v=7YnVZrabTA8]
Zhang, Chief Product Officer at DataRobot, shares in this talk his experience with open source tools in data science competitions. Slides here.