“The glass table surface contains a dye synthesized solar cell; based on photosynthesis it uses color properties to create an electrical current”
Like photosynthesis, it uses color properties to create an electrical current, unlike classic solar cells however, the colored cells do not require direct sunlight for power, they function under diffused light.
A dye-sensitized solar cell (DSSC, DSC or DYSC) is a low-cost solar cell belonging to the group of thin film solar cells. The DSSC has a number of attractive features; it is simple to make using conventional roll-printing techniques, is semi-flexible and semi-transparent which offers a variety of uses not applicable to glass-based systems, and most of the materials used are low-cost.
Dutch designer Marjan van Aubel has created ‘the energy collection’ a collection of everyday objects that absorb energy from daylight.
Improving the game! Cambridge Consultants minitor realtime trayectory.
The ArcAid system, demonstrated at CES 2014, consists of three cameras mounted behind a basketball backboard — two on the upper sides of the basket and one behind it.
The two upper cameras analyze the arc, speed, and angle of a basketball in mid-air. The third one checks to see if the user sinks the basket. A large screen displays the data and the adjustments needed for the user to make the basket, like aim farther to the left or shoot harder.” via PSFK
What is big data? Roughly defined, it refers to massive data sets that can be used to predict or model future events. That can include everything from the online purchase history of millions of Americans (to predict what they’re about to buy) to where people in San Francisco are most likely to jog (according to GPS) to Facebook posts and Twitter trends and 100 year storm records.
With that in mind, here’s the three most important things you need to know about big data right now:
1. The data experts are organizing and they want a revolution!
Data mining, (the primordial ancestor to what is today called predictive analytics) used to be considered a company or organization-specific problem. The data and the people who worked on it were “siloed” in effect. What would a statistics expert in the military and a number cruncher working in retail marketing have to talk about?
These days, it turns out, there’s a lot to discuss. First, new open source data crunching tools like Hadoop (a distributed operating system that lets you gang together thousands of computers to solve problems) are helping organizations big and small develop their own data departments at a small fraction of how much specialized software used to cost a few years ago. That means that the skills that miners are acquiring in one industry like retail are increasingly applicable across sectors, like in government. Second, combining data sets yields new insights, and the number of available sets (in some easily crunchable form like XML or just Excel) is growing. “Over the last couple of years we’ve seen the horizentalization of data scientist,” says Alistair Croll of Bitcurrent, one of the organizers of the conference.
There was a considerable (but not surprising) consensus among attendees that data and analytics should drive a lot more decision-making within organizations, even if that better-informed strategizing comes at the expense of traditional managers, who will argue that their hard-won expertise is much more valuable than any model based on statistics. More and more often, they’ll be shown to be wrong.
There’s plenty of debate over whether everyone who works with large data sets in a technical way should get to call themselves a “data scientist.” It may be a matter of the uniqueness of the research, or just a price point.
2. You’re going to be asked to opt-in to sharing your data a lot more.
A major topic for discussion this week was the Target Snafu. As originally reported in The New York Times (reg req.), Target raised a lot of eyebrows when the company used customer data and predictive analytics to figure out that one of their customers was pregnant, and, more remarkably, what trimester she was in. They emailed her some promotional material and the girl’s father discovered his daughter was pregnant based on the coupons she started receinving from a big box retailer, which gave rise to an awkward conversation, no doubt.
Big organizations are just beginning to realize the huge upside potential of using massive amounts of data to predict everything from what their customers are going to start buying to which of their employees will complete a certain project on time. More importantly, that data is getting increasingly easy and cheap to collect, and there’s already an enormous storehouse of it to aid in pattern extrapolation.
So where is the middle ground? According to many of the folks here, it’s the point where people knowingly agree to contribute data. As one programmer put it, “Spying is the act of collecting data secret. Transparent data collection with defined boundries is NOT spying.”
What that means: more companies will look to make the case that allowing them to track your behavior will benefit you. If enough people buy the pitch, societal attitudes about data tracking will change. There are a lot of things organizations can do to make the offer a good one for consumers, but they haven’t yet.
As Alistair Croll of Bitcurrent put it, “Imagine if that [New York Times] article had said, Target figured out that 1% of its customer base had cancer and it told them. I would sign up for a program that tracked my purchases to let me know if there was a correlation between what I bought and what people that got colon cancer bought.”
3. The stuff you can predict is amazing, the stuff you can’t is frustrating.
This conference was full of amazing case examples of people using big data to predict things. According to Google’s Hal Varian, unemployment query volume on “Sign up for unemployment” can predict future unemployment claims with a high degree of accuracy one week before official numbers are released from the U.S. government. Coupon and rebate search queries are an excellent predictor of weak economic times ahead.
Having said that, the hype on big data is likely to grow faster than the actual capabilities, as are incidents of “data washing” or making some especially considering how early we are on the hype cycle.
“The most prevalent model in the industry to address this problem is MCU, make crap up,” according to marketing guru Avinash Kaushik.
What that means: Too many organizations are too focused on collecting data without a clear sense of what to do with it. The order should be reversed, according to several presenters. If you want to get started with data-driven decision making first set goals and then start amazing and crunching data sets around those goals.
Most importantly, many agreed that having great data collection and analysis capability is useless if an organization doesn’t have internal processes in place to allow people to use the new info, and not just at the top of the corporate pyramid.
“You’ve got to empower every person to make decisions with data” according to Kaushik. “Say, ‘You, Janitor! You will be in charge of using Data to make your job better!”
Botton line: Big data is going to change the way organizations and individuals deal with information and plan ahead. Many of those transitions will be difficult; but, ten years from now, we’ll wonder how we got along without it. Even after the hype cycle on big data goes from peak to valley, there’s still a lot to look forward to.
Between global warming, a global financial crisis and global food shortages, you can’t blame folks for being a tad depressed AND on top of all that, if you believe the Mayan Calendar, 2012 is supposed to be the year it all ends for all of us.
But in their new book, “Abundance: The Future is Better Than You Think,” the authors offer a boldly contrarian and optimistic book for today’s cynical times. They make the case that we are indeed on the cusp of a new era, an era when the lives of millions are improved.
Think of this book as the ultimate “Yes, we can.”
PersistenceFor Jonas Eliasson to bring the Me-Mover to market, all it took was one eureka moment. And then 13 years of painstaking improvement.
The planning began in 1998. He thought on the train, on his walk to the office, and at night instead of sleeping. He kept thinking after he moved to Denmark and co-founded an optical touch-screen developer. Ten years later, after several versions of sketches and plans, Jonas built the first crude prototype of an upright three-wheeled scooter powered by a driver pushing pedals. Now three years–and several more prototypes later — Jonas has taken delivery of the production-ready version of the “Me-Mover.”
- Company: Me-Mover
- Headquarters: Copenhagen, Denmark
- Founder: Jonas Eliasson
- Previous startups: Co-founder, O-pen 2002, a developer of optical touch-screen solutions
- Revenues: Pre-revenue
- Employees: 5
- Raised: US$700,000
- From: SDTI, Denmark
This post reviews basic terminology commonly used in the venture world.
First, the entities into which capital sources are aggregated for purposes of making investments are usually referred to as “funds,” “venture companies,” or “venture partnerships.” They resemble mutual funds in a sense but are not, with rare exceptions (AR&D was one), registered under the Investment Company Act of 1940 because they are not publicly held and do not offer to redeem their shares frequently or at all. The paradigmatic venture fund is an outgrowth of the Greylock model, a partnership with a limited group of investors, or limited partners, and an even more limited group of managers who act as general partners, the managers enjoying a so-called carried interest, entitling them to a share in the profits of the partnership in ratios disproportionate to their capital contributions. Venture funds include federally assisted Small Business Investment Companies (which can be either corporations or partnerships) and, on occasion, a publicly held corporation along the AR&D model, styled since 1980 as “business development corporations.” This post, following common usage, will refer to any managed pool of capital as a “fund” or “partnership.”
via VC Experts.
Singularity University challenges its graduates to change the lives of a billion people over a decade.
Its latest class projects include drones delivering medicine to isolated African villages, an automated “personal coach” advising people about health risks based on their genetic profiles, and online games for homeowners to slash their electricity bills. Yet theyre much more than just academic exercises; each idea has led to new startups backed by some of Silicon Valleys biggest investors.The effort embodies the philosophy of Peter Diamandis, cofounder of Singularity University and a leading entrepreneur with degrees in medicine and aerospace engineering.
As CEO and Chairman of the X Prize Foundation, Diamandis has already transformed challenges — ranging from private spaceflight to oil spill cleanup— into prize opportunities.But when Diamandis read Ray Kurzweils book, “The Singularity Is Near,” about technologies capable of transforming life on Earth within a few decades, he gained new inspiration. He decided to convene some of the worlds most brilliant young minds in the heart of Silicon Valley , give them a crash course on the fastest-changing technologies of our time, and then unleash them like smart missiles to solve the world’s biggest problems.
Read the interview.