Why are today’s senior - and not so senior - decision takers, whether business leaders or politicians, so seemingly unaware of the fast approaching third revolution in Internet access - the Internet of Things?
It is probably because the Internet of Things is the culmination of countless mini-developments creeping up all around us. Among these are the ever smarter mobile phones and the amazing things they can do, the increasingly clever applications of RFID codes, QL codes, facial and gait recognition, tele-medicine, steps towards smart utility metering, Oyster card introduction, car number plate recognition - the list goes on and on.
Right now these are relative silos of activity making an impact by bringing new capabilities and efficiencies to daily life and business. With interoperable standards all these silos will be able to interconnect and intercommunicate. And that’s what the Internet of Things is about.
The Internet of Things goes beyond the millions and millions of machine to machine activity currently conducted via the Internet, with for example mobile phone apps. It is about the billions and billions of tiny chips that’ll flood the world over the next ten years. It’s about those tiny chips being programmable, trackable, findable, and uniquely identified, and with the sensing capabilities currently on mobile phones and more.
The Internet of Things is about the capability of every object - whether a toothbrush or a building - embedded with such chips to have a unique identifier, and, using its sensing, processing and communications capabilities to intercommunicate with its environment, other objects and living things - and, eventually ending up able to make autonomous decisions.
There’ll be major business and social ramifications, opportunities and threats as a result.
Big data is getting personal. People around the globe are monitoring everything from their health, sleep patterns, sex and even toilet habits with articulate detail, aided by mobile technology. Whether users track behavior actively by entering data or passively via sensors and apps, the quantified self (QS) movement has grown to become a global phenomenon, where impassioned users seek context from their big data identities.
Moreover, with services like Saga and Open Sen.se, users can combine multiple streams of data to create insights that inspire broader behavior change than by analyzing a single trait. This reflects a mixed approach design (MAD) research methodology that purposely blends quantitative and qualitative factors in a framework where numbers are driven by nuance. The science of happiness, for example, is now a serious study for business, as organizations combine insights of the head and heart to create environments where workers feel their efforts foster meaningful change.
However it’s studied, the desire to understand monitored behavior has reached a fever pitch, and the QS movement is attempting to meaningfully interpret our daily data.
The Power of Passivity
“We’re moving towards a time when the ability to track and understand data is deeply woven into our daily lives,” says Ernesto Ramirez, community organizer for Quantified Self, the eponymous organization created byKevin Kelly of Wired and Gary Wolf. “Sensors are becoming cheaper and connectivity is more ubiquitous by the day.”
This ever-present nature of data availability will become even more powerful when the general public begins to use apps that require little ongoing attention or input. Passive data collection is especially relevant in the healthcare industry, for example.
“The data quantified self provides is not a replacement of any measurement to date — we haven’t had this type of measurement to date,” says Halle Tecco, co-founder and CEO of Rockhealth, the first seed accelerator for digital health startups. “Patients live very cautiously before trips to doctors, and this causes more trips to doctors. It’s better if physicians can get a more comprehensive view of people’s ongoing health.”
Tecco highlights the importance of passive monitoring. For instance, a mobile app can continuously measure glucose levels or other factors like heart rate over time. Spikes in those readings could immediately trigger a doctor, even remotely. “We can save money and improve outcomes by having data collection embedded in our everyday lives,” she adds.
We can’t just design devices that help us to live better using data; rather, we have to design an entire living environment where those devices communicate with each other and with us. Only by building this interoperable network of humans and computers will we finally be able to exploit the massive potential of Big Data, and of ourselves.
This is the fourth year Newsweek ranked the 500 largest companies on their environmental footprint (45% of score), corporate management (45%) and transparency (10%), using data from Trucost and Sustainalytics.
World’s Top Green Corporations
All the top companies got scores of 82 out of 100 or above - here are some very brief highlights on what makes them stand out.
IBM - which always tops these lists - is the only US company included in the world’s 20 top corporations.
It’s rated #4 in the world for its “Smarter Planet” service that helps clients measure and reduce their own footprint, while saving them money. At its Zurich lab, water that cools a supercomputer is used to warm nearby buildings. Read our profile on IBM.
IBM and Esri, the mapping company, are linking advanced analytics with geographic information by pulling information from previously siloed systems with big data tools.
KDDI, a mobile and fixed line communications company in Japan, has developed extensive information about its customers through the data it collects including URLs visited, type of mobile device, purchase history and buying habits, gender and age group.
Combining data and geo information from Esri, the company is providing the information to firms for marketing campaigns, said Matt Rollender, IBM’s big data technology alliances executive. Now a marketer could make an offer to a potential customer when they approach a company’s retail location.
In the past, Esri focused on mapping and visualization. Now in an alliance with IBM, it can provide advanced analytics on the data prior to visualization to provide greater insights.
Last October Esri acquired SpotOn, a geo-spatial business intelligence software developer which worked with IBM Cognos to provide customers with embedded maps in their reports.
In a key step toward creating a working quantum computer, Princeton University researchers have developed a method that may allow for quick, reliable transfer of quantum information throughout a computing device.
The finding, by a team led by Princeton physicist Jason Petta, could eventually allow engineers to build quantum computers consisting of millions of quantum bits, or qubits. So far, quantum researchers have only been able to manipulate small numbers of qubits.
To make the transfer, Petta’s team used a stream of microwave photons to analyze a pair of electrons trapped in a tiny cage called a quantum dot. The “spin state” of the electrons — information about how they are spinning — serves as the qubit, a basic unit of information. The microwave stream allows the scientists to read that information.
“We create a cavity with mirrors on both ends … that reflect microwave radiation,” Petta said. “We send microwaves in one end, and we look at the microwaves as they come out the other end. The microwaves are affected by the spin states of the electrons in the cavity, and we can read that change.”
In an ordinary sense, the distances involved are very small; the entire apparatus operates over a little more than a centimeter. But on the subatomic scale, they are vast. It is like coordinating the motion of a top spinning on the moon with another on the surface of the earth.
“It’s the most amazing thing,” said Jake Taylor, a physicist at the National Institute of Standards and Technology and the Joint Quantum Institute at the University of Maryland, who worked on the project with the Princeton team. “You have a single electron almost completely changing the properties of an inch-long electrical system.”
The IBM Smarter Planet app integrates content from many spheres of IBM’s Smarter Planet initiative. The Smarter Planet blog is one of the app’s anchors along with the Smarter Planet Tumblr, People for a Smarter Planet on Facebook and the IBM YouTube channel. Also featured are other mobile-optimized sites and sources such as Smarter Planet on m.ibm.com.
A group of researchers have proposed creating a new web-based data network to help researchers and policymakers worldwide turn existing knowledge into real-world applications and technologies and improve science and innovation policy.
Researchers around the world have created datasets that, if interlinked with other datasets and made more broadly available could provide the needed foundation for policy and decision makers. But these datasets are spread across countries, scientific disciplines and data providers, and appear in a variety of inconsistent forms. Writing in the new issue of the journal Science, seven researchers propose a new data network that can help bring this knowledge together and make it available to all.
If you are convinced that your future workplace should look more like a Wirearchy, (a dynamic two-way flow of power and authority based on, knowledge, trust, credibility, a focus on results; enabled by interconnected people and technology) then the best thing you can do now is prepare.
Prepare yourself to be a continuous learner.
Prepare yourself and your team/department to work collaboratively.
Start narrating your work.
Become a knowledge curator and share widely.
Engage in professional social networks and communities of practice.
Model the behaviours you would like to see in others.
Finally, watch for moments of need, when the organization has a problem or crisis and then be ready with the tools and skills to help. It’s like being your own upstart company, developing asymmetrical skills under the radar, inside your organization.
Thanks to Arduino kits and the Raspberry Pi Linux computer, computing now can cost less than LEGOs. So today’s kids — and a generation of enthusiast hackers — are creating a movement that might incubate the next Woz. What will cheap computing build?
The cost of a Raspberry Pi computer you can buy today is $25. It has a 700 MHz CPU with 256 MB RAM. In 2001, the Power Mac G4 Cube, with 450 MHz CPU with 64 MB RAM, cost $1,799. That is how much hardware prices have fallen. Meanwhile, a LEGO X-Wing costs $59.99.
So for $25 anyone can work on a project that uses computers at its heart, and if something breaks, they can just go buy a new one. This makes small Linux computers like the Raspberry Pi and Arduino boards the hardware DIYers’ new LEGO bricks. Last month, tens of thousands of makers from around the world came together at Maker Faire. Kids were begging their parents to help them build RC planes, buy them kits with Arduino boards and learning how to solder.
Will the DIY movement produce the next Apple?
Many of the kits these kids were using weren’t made by billion dollar corporations – they were made by cottage industry electronics businesses, hobbyists, and “fantrepreneurs.” Yes, as Chris Anderson says in his new book “Makers”, we are at the start of a hardware revolution – led from the ground up, in your home.
In the new app economy, organizations no longer own all the data they need to make accurate business decisions. This loss of control requires data marketplaces and data syndication models that few enterprises are currently prepared for. Apigee’s Anant Jhingran looks at three important steps that companies need to take to succeed in the app economy.
Traditional enterprise data sources — be they business systems or even the exhaust from corporate websites — represent the data that is typically captured by an enterprise for analytics and business insight. However, in the new world of APIs and the app economy, organizations no longer own, much less control, all the data they need to make accurate business decisions.
For over 20 years, I’ve led technical strategy and product initiatives for databases, information integration, analytics and big data. Today I work at Apigee, where we help organizations embrace the exploding app economy built on mobile apps, defined by APIs and powered by massive streams of data. I can say that few businesses are prepared to effectively use the new sources of valuable enterprise data that is being generated “outside” the enterprise today in the app economy.
A growing number of businesses are successfully building new channels through APIs and third-party applications that tap their data and Web services. As a result, all kinds of important customer interaction is happening in apps written by other people (partners and developers), far away from the enterprise core. There are three ensuing new sources of data that organizations must be able to capture, measure and analyze to get a complete view of their customers and businesses:
Scientists at the U.S. Department of Energy’s National Renewable Energy Laboratory (NREL) have produced solar cells using nanotechnology techniques at an efficiency – 18.2%—that is competitive. The breakthrough should be a major step toward helping lower the cost of solar energy.
This summer Google set a new landmark in the field of artificial intelligence with software that learned how to recognize cats, people, and other things simply by watching YouTube videos (see “Self-Taught Software”). That technology, modeled on how brain cells operate, is now being put to work making Google’s products smarter, with speech recognition being the first service to benefit.
Google’s learning software is based on simulating groups of connected brain cells that communicate and influence one another. When such a neural network, as it’s called, is exposed to data, the relationships between different neurons can change. That causes the network to develop the ability to react in certain ways to incoming data of a particular kind—and the network is said to have learned something.
Neural networks have been used for decades in areas where machine learning is applied, such as chess-playing software or face detection. Google’s engineers have found ways to put more computing power behind the approach than was previously possible, creating neural networks that can learn without human assistance and are robust enough to be used commercially, not just as research demonstrations.
So remember last month how I told you about Project Sparta? That would be the IBM project aimed at simplifying how companies attack their big-data problems.
Well, it has a name now, and as I suspected, it’s part of the growing Pure line. It has been dubbed the PureData System, and it comes in three flavors: One optimized for transactions, one for operations and one for big-data analytics. IBM announced the system late Monday, in connection with an event in Singapore.
In the announcement, Big Blue included one of those big-picture observations about the state of data and the unceasing struggle to get a handle on it all. According to IBM’s reckoning, 2.5 exabytes of data is created every day. (You know what a gigabyte is; after that are terabytes, then petabytes, then exabytes.) And the amount is growing so fast that 90 percent of the data that now exists has been created in the last two years. What this means is that the amount of data that companies and governments and people are creating is growing like crazy, and that doesn’t even begin to get the point across.
I talked with Arvind Krishna, general manager, IBM Information Management, and he compared the different flavors to a Web site selling stuff: One version of the system can handle all the sales; another handles the analytics one might use to figure out what combinations of products people buy together at different times of the year, or to watch for credit card fraud.
With these new systems, IBM is promising that it can handle problems like these in minutes instead of hours.
A handful of leaders in health data suggest that data-driven personalized health approaches could achieve mainstream adoption in five years, with some saying valuable but intermittent work could happen even sooner.
For now, the applications of personal health data are mostly the stuff of “Quantified Self” hobbyists and experimental research. But some say it may not be too long before personal health data becomes a powerful part of the mainstream clinical experience.
At the Health 2.0 conference in San Francisco on Wednesday, David Ewing Duncan, a journalist and author of “When I’m 164,” asked a panel of health data leaders when data-driven personalized health might reach “escape velocity”.
So, inquiring minds want to know: What is he obsessed with right now?
The answer is “maker subculture,” which is where the latest in digital technology meets the classic do-it-yourself (DIY) world of crafting and small scale construction. Anderson is so engrossed in this world that he decided to write his latest book about it. Makers: The New Industrial Revolution, which hit shelves (and the world of e-books and e-booksellers) last week, makes the argument that what’s happening right now with makers is actually the third wave of the industrial revolution which first initiated back in the 18th century.
newly appointed chairman of I.B.M., answered a mysterious and pressing question on Tuesday: Where is Watson?
Watson is the room-size computer that defeated its human rivals to become a “Jeopardy!” champion. The answer, Ms. Rometty said at Fortune’s Most Powerful Women summit, is that Watson is in medical school.
The computer is working with many health care organizations to learn medical data so it can diagnose cancer, and that is just the beginning. It has so far ingested 80 percent of the world’s medical data.
“Watch it work, and it’s almost as if he’s talking to a colleague,” Ms. Rometty said. “The beauty of it is it tells you what’s right or wrong,” and explains with confidence why it believes what it says.
Watson’s skills – from “Jeopardy!” to oncology to working with banks and call centers – are part of I.B.M.’s bigger aim. It is called cognitive computing, which she described as machines that can learn.
“In this world of huge and big data, you won’t be able to program machines for everything they should know,” said Ms. Rometty. “These machines will have to learn what is right, what is wrong, what is a pattern.”
It is the third wave of computing, she said. At first, computers could count. Today, they are programmed to follow “if this, then that.” Next they will need to discover and learn on their own, she said, not just as a search engine, but proactively. The next era, for all jobs, not just those in computing, will be to help businesses sift big data, she said.
“It’s going to be a whole era to help you get through this, and everyone’s job, from chief marketing officer to chief of police, their jobs are going to be redefined by that,” she said.
Cities around the world are constantly sparking new questions: How can we ensure adequate housing for all? How can we live more sustainably? How can we live harmoniously in increasingly multiethnic environments? The questions are almost limitless, and it can be hard for those in the urban field to keep up.
Through the TEDx initiative, where local organizers host TED-style events, the future of the city will become a global conversation on Saturday, Oct. 13. TEDxDumbo is one of more than 60 TEDx chapters around the world organizing a special “City2.0” event that day.
Enterprise social networks will eventually supplant the traditional intranets and portals currently used to manage internal employee communications, workflows, and files. They have the advantages of being purpose-built for today’s business: they are fluid, immediate, end-user oriented and collaborative.