I’ve often thought about swimming pool meditation under water… Surface floating is good, but sometime I just want to noise and not dry off.
Now I can sit at the bottom of the pool, and plan my next great adventure… Exploring the world below the sea
Welcome to the future of underwater breathing
Triton. A state-of-the-art oxygen respirator, that allows you to breathe underwater by utilizing our”artificial gills’ technology. Swim among tropical fish, marvel at exotic coral and experience the serene beauty of marine life − without having to come up for air. Welcome to Triton.
With Triton there’s no heavy equipment, complicated safety procedures or training. It’s easy to use, and no longer than a snorkel. Gently bite into the mouthpiece, breathe normally, and enjoy a sense of underwater freedomunavailable until now. Just imagine exploring gin-clear waters, alongside tropical fish, without bulky equipment or having to surface for air.
Small business has much more data than they realize. Aggregators like MasterCard make it easier to use transaction data with IBM Watson
MasterCard has partnered with IBM to give small to medium merchants access to big data analytics.
The payments giant has integrated IBM Watson Analytics into its platform, along with its own anonymised transaction data gathered through MasterCard Advisors Local Market Intelligence (LMI), to bring artificial intelligence (AI) to its payments platform.
“There is an increasing wealth of data today that merchants can leverage to better understand their market and consumers,” MasterCard Advisors senior vice president Eric Schneider said. “However, smaller merchants often don’t have the resources to maximise the insights. That’s the value of this platform turning big data into smarter data that is easily accessible and actionable.”
The product will be available via subscription for merchants who accept MasterCard in mid-2016 and according to MasterCard, this will see access to real-time, analytics-based market insights on revenue, market share, customer demographics, and competitors in both a specific location as well as across multiple locations.
MasterCard said its Advisors’ LMI focuses on business performance, customer behaviour, and competitive standing, providing SMBs with insights that help drive some of the most important decisions a business can make about operations, marketing, and personnel.
Additionally, the IBM Watson Analytics data discovery tool will provide automatic insights that MasterCard said will enable the user to discover new patterns in their data such as customer buying and behaviour trends.
“We are thrilled to be working hand in hand with MasterCard to help smaller merchants understand their business and competition better, and increase the strength and value of their customer relationships,” said Yashesh Kampani, IBM ASEAN Financial Services Sector Leader.
“Through this new service, merchants will discover just how easy leveraging big data can actually be with the analytical tools that IBM and MasterCard are making available.”
Citing its latest research, MasterCard said that more than seven out of 10 smaller businesses in the Asia-Pacific region are expecting higher business costs in 2016.
MasterCard said the research was based on a survey completed in December last year on 2,806 SME owners, co-owners, or key decision makers across Australia, China, Hong Kong, India, Indonesia, Malaysia, and Singapore, with approximately 400 survey participants per market.
“With this enhanced analytical platform powered by MasterCard Advisors and IBM, merchants will hence be better equipped to make informed decisions that lower costs based on a deeper knowledge of their business’ financial strengths and pitfalls,” the company said in a statement.
More and more people are comfortable ordering groceries online or on a phone.
Amazon bet big from the start on the world moving to ecommerce and ease of deliverablity.. back when most of us thought they were a bookstore. Today that is paying off.
Photographer: Lydia Mulvany/Bloomberg
One Wall Street Firm Says Amazon Is About to ‘Feast’ on the Food and Beverage Market
Amazon top 10 player by 2019.
While Amazon.com Inc. is already a force to be reckoned with when it comes to online retail, one Wall Street firm says it’s about to move up the ranks in yet another industry: U.S. food and beverage.
“Amazon will be a top-10 player in the approximately $795 billion U.S. food and beverage grocery market by 2019,” analysts at Cowen & Company LLC, led by John Blackledge, said in a new note this week. “We are encouraged by Amazon’s growing footprint in this category, which we see as ripe for potential disruption given younger [demographics] increasingly purchasing food and beverage [F&B] grocery items via digital channels.”
Amazon has already been building its grocery offerings and going up against such competitors as Wal-Mart Stores, Inc., Costco Wholesales Corp., and Publix Supermarkets Inc. It now has Amazon Prime, Prime Now, Prime Pantry, and Amazon Fresh that offer delivery times as short as an hour, in some cases.
Reich came to feel that nonprofits were ignoring some of the new insights coming from the world of political and brand campaigns. The whole philanthropic world, he came to feel, was a “philanthropic-industrial complex” stuck in very set patterns of thinking. In particular, there was an obsession with getting people to donate money—an understandably important metric, but perhaps not the only one worth measuring.
“It’s like dating,” Reich says of the typical nonprofit mindset, which asks for money as soon as anyone expresses interest. “In the world of nonprofits, I assume you’re going to sleep with me right away.”Oh, you kind of like me? Sleep with me.’ Over and over again. In a real relationship, you meet the friends, the friends have to like you, you have to not screw up birthdays…”
Reich came to feel in particular that the world of nonprofits was overlooking a key demographic: something like the altruistic equivalent of a swing voter.
To explain, Reich draws a pair of concentric circles. The innermost circle represents the core 10 million Americans who tend to be actively engaged with philanthropy, and open to being solicited. An outer ring represents the next 10-15 million of so-called “lookalikes”—people who resemble the actively philanthropic and are deemed worth targeting by most NGOs.
Then Reich draws a third ring. He calls these “persuadables,” people who are kind of on the fence about being altruistic at all, but could—possibly—be persuaded. Reich came to feel that attending to the behavior of persuadables should be the next frontier in nonprofit development. There’s potentially a huge number of them, after all, and he believes that if you could spend resources persuading the persuadables, then those “lookalikes” one rung in would topple toward do-gooding naturally, like dominoes.
The only problem? No one had really studied persuadables in the nonprofit space. And no one really knew how to study them. It was unlikely that you could get them to donate to a cause right away. But you might get them to become aware and educated about it; you might even get them talking to their friends about it. And years later, Reich posited, these persuadables might blossom into financial donors to a cause.
A few years ago, Reich teamed up with Ari Wallach, who runs a consulting firm called Synthesis Corp., with some clients in the nonprofit world. Wallach shared many of Reich’s feelings about nonprofit sclerosis. But one of Synthesis’s clients, the United Nations High Commissioner for Refugees, had displayed a willingness to experiment with new forms of outreach. Wallach pitched the UNHCR for a significant amount of funding to fuel a startup-like operation experimenting with educating the American public about the current global refugee crisis. The UNHCR bit the bullet, and Reich and Wallach launched The Hive in late 2014, with guaranteed funding for two years.
So what is the Hive, and what does it do? It’s a team of 10 people working out of New York, exploring the universe of “persuadables” in the context of the refugee crisis. Reich and his colleagues are always asking, “Who can we get to engage around this issue that the UNHCR might not otherwise target? And how can we measure their behavior?” Its hiring practices are unorthodox, for the nonprofit world, at least. “We have the first full-time data scientist in a nonprofit in the U.S. focused on engagement,” says Reich.
The Hive has a wide berth to experiment with things that an august body like the UNHCR might not typically try. It made a “Jesus Was a Refugee” bumper sticker, riffing on a line of the Pope’s, and distributed it around the time of his U.S. visit. During the Major League Baseball playoffs, the Hive launched targeted social media ads explaining that the number of people forced to flee their homes each day—42,000—was about the same as those that would fill a baseball stadium. One of the Hive’s biggest successes came from piggy-backing onto the “Straight Outta…” meme that circulated around Facebook during the release of the NWA biopic. The Hive circulated images naming the cities refugees had fled or the camps they’d been resettled in.
If it’s not seeking donations from the people it targets, how does the Hive measure its success? Reich’s first answer is that, for now, it’s too early to tell. Most businesses with vision think in terms of the “lifetime value” of a customer, and Reich thinks it would be smart for nonprofits to think that way, too. Reich and his colleagues are interested to see who clicked or shared, but they’ll be even more interested to see if that person donates or shows up to an event years down the line. The Hive is still slicing the data, teasing out patterns and insights. Each test is scientifically designed, complete with control groups. Science makes progress at its own pace. Recently, the Hive ran an ad campaign that got “literally zero clicks,” says Reich. It was a success, in a sense: “We learned a lot.”
The Hive may find its greatest success if it inspires other large nonprofits and NGOs to invest in similar initiatives—or to invest in the Hive itself. Reich thinks the greatest investment nonprofits could make is to collectively fund research; he speaks of the need for “Manhattan Project for nonprofits,” where pooled resources could usher in a new science of altruism.
“That lack of collective intelligence is so significant, it’s borderline dangerous,” says Reich of the fumbling and sometimes off-putting way many nonprofits interact with potential do-gooders. “If many organizations really cared about solving the issues they were advocating for, most of them would put themselves out of business tomorrow.”
SEOUL, SOUTH KOREA — Google’s artificially intelligent Go-playing computer system has claimed victory in its historic match with Korean grandmaster Lee Sedol after winning a third straight game in this best-of-five series. This Google creation is known as AlphaGo, and with its three-games-to-none triumph, the machine has earned Google a million dollars in prize money, which the company will donate to charity. But the money is merely an afterthought.
Machines have conquered the last games. Now comes the real world.
Over the last twenty-five years, machines have beaten the top humans at checkers and chess and Othello and Scrabble and Jeopardy!. But this is the first time an artificially intelligent system has topped one of the very best at Go, which is exponentially more complex than chess and requires an added level of intuition—at least among humans. This makes the win a major milestone for AI—a moment whose meaning extends well beyond a single game. Considering that many of the machine learning technologies at the heart of AlphaGo are already running services inside some of the world’s largest Internet companies, the victory shows how quickly AI will progress in the years to come.
Just two years ago, most experts believed that another decade would pass before a machine could claim this prize. But then researchers at DeepMind—a London AI lab acquired by Google—changed the equation using two increasingly powerful forms of machine learning, technologies that allow machines to learn largely on their own. Lee Sedol is widely regarded as the best Go player of the past decade. But he was beaten by a machine that taught itself to play the ancient game..
Sergey in the House
Though AlphaGo had won the first two games of the match—and Lee Sedol almost conceded the match after his loss in Game Two—the outcome of Game Three was by no means a certainty, and it was surrounded by the same heightened level of excitement. Unlike in Game Two, Lee Sedol was set to play the black stones in Game Three—a notable advantage, since black moves first. And he could draw on the experience of two complete contests—another advantage, since the Google team doesn’t have the power to tweak AlphaGo in the middle of a match.
In one sense, this is a game. But the match also represents the future of Google.
Among Go aficionados, the rumor was that after Game Two, Lee Sedol and several other top Go players stayed up most of the night analyzing the first two games and looking for weaknesses in AlphaGo’s play. But one of the match’s English language commentators, Michael Redmond, wasn’t convinced this was the best approach. “Lee Sedol has more to gain by playing his own game or playing an opening he likes to play, rather than fooling around and trying to find some weakness in AlphaGo,” he said. But the rumor gave the game an added spice.
For far different reasons, the match is just as important to Lee Sedol. When Game Three began, he seemed to betray the pressure he was feeling to win at least one contest in this five game match. As the referee kicked things off, he leaned forward in his chair, and as if to calm himself, he closed his eyes—keeping them closed for several seconds.
His opening was definite. From the beginning, Lee Sedol played quickly, and he by no means played it safe. According to Redmond, the Korean’s opening was rather unusual, perhaps an indication that he aimed to push AlphaGo in a new direction. Indeed, within a mere 45 minutes, Redmond felt that the game had entered entirely new territory. “It’s already a position we probably haven’t ever seen in a professional game,” he said.
That’s a product not only of Lee Sedol’s opening, but of AlphaGo’s unique approach to the game. The machine plays like no human ever would—quite literally. Using what are called deep neural networks—vast networks of hardware and software that mimic the web of neurons in the human brain—AlphaGo initially learned the game by analyzing thousands of moves from real live Go grandmasters. But then, using a sister technology called reinforcement learning, it reached a new level by playing game after game against itself, coming to recognize moves that give it the highest probability of winning. The result is a machine that often makes the most inhuman of moves.
This happened in Game Two—in a very big way. With its 19th move, AlphaGo made a play that shocked just about everyone, including both the commentators and Lee Sedol, who needed nearly fifteen minutes to choose a response. The commentators couldn’t even begin to evaluate AlphaGo’s move, but it proved effective. Three hours later, AlphaGo had won the match.
Very Lee Sedol-Like
Game Three was different. As it approached the one-hour-and-twenty-minute mark, Redmond called the contest a “very Lee Sedol-like game,” meaning that the Korean was able to play in his characteristic style—a fast and aggressive approach. But AlphaGo was playing just as aggressively—”fighting,” as Redmond described it. He couldn’t judge who was ahead and who was behind.
Such is the nature of Go—a game that’s won by the tiniest of increments. This week’s match is so meaningful because this ancient pastime is so complex. As Google likes to say of Go: there are more possible positions on the board than atoms in a universe. Even for commentator Michael Redmond—a very talented Go player in his own right—judging the progress of a Go match is a difficult thing. That said, there’s one Go player that has made a science of this: AlphaGo. One of the machine’s advantages is that it’s constantly calculating its chances of winning. Every move it makes is an effort to maximize these chances.
There’s one Go player that has made a science of this: AlphaGo.
This was clearly on Redmond’s mind as the game progressed. The play had concentrated in the top left-hand corner of the board, and Redmond said that Lee Sedol had found his way into a “scary” situation. “I would be worried if I was black,” he said, referring to the Korean grandmaster. In other words, the pressure was on Lee Sedol to break out of the top left corner and extend the play into the middle of the board. AlphaGo was simply connecting his lines of white stones—as opposed to playing more strategic moves—and Redmond started to wonder if, even this early in the match, AlphaGo “thinks it’s ahead.”
As Google researcher Thore Graepel explained earlier in the week, because AlphaGo tries to maximize its probability of winning, it doesn’t necessarily maximize its margin of victory. Graepel even went so far as to say that inconsequential or “slack” moves can indicate that the machine believes its probability of winning is quite high. As Redmond saw them, AlphaGo’s latest string of moves were slack.
That said, the game was still very young. “It’s a bit early,” Redmond said. “Generally speaking, it’s dangerous to think you’re ahead at this point in the game. There is so much area on the board that still needs to be filled.”
The Ko Theory
A few minutes later, Lee Sedol had an opportunity to invite what’s called a “ko.” Basically, this is a situation where the game could enter a loop where the two players capture each other’s pieces—and then recapture them over and over again. There’s a rule that prevents this infinite loop. But prior to the game, the theory among Go aficionados was that AlphaGo—like past computer Go programs—was ill-equipped to handle a ko.
During Games One and Two, AlphaGo seemed to avoid the ko scenario. But Redmond played down the theory. He pointed out that even back in October, when a much less proficient version of AlphaGo topped three-time European Go champion Fan Hui during a closed-door match at DeepMind headquarters in London, the machine successfully embraced the ko. “I doubt that would be a major problem,” Redmond said.
In any event, the ko did not play out. Instead, Lee Sedol shored up his position on the left-hand side of the board as AlphaGo continued to play with moves that Redmond and his co-commentator Chris Garlock describes as slack.
Two hours and forty minutes in, the two players approached the end game. And it was still too early to tell who was ahead. But Lee Sedol was beginning to run into time trouble, just as he had in Game Two. Because he found himself in that tight position on the left-hand side of the board, the Korean had used a significant amount of time very early in the game, and his play clock had dropped to under 20 minutes. AlphaGo still had close to an hour. Once their play clocks run out, the players must make each move in under 60 seconds.
Not sure if people truly understand what dedication and commitment it takes to be the Rock. Pretty inspiring.
Mark Webster saw the most ludicrous diet plan on the planet and decided to try it out.
As Iwrote last year, the movie star Dwayne “The Rock” Johnson, in order to maintain his legendary physique, eats more than 5,000 calories a day. The calories, spread over seven meals, include roughly 2.3 pounds of cod, a fish particularly rich in protein. The rest is eggs, steak, chicken, vegetables and potatoes — all told, about 10 pounds of food per day. In one year, The Rock consumes more than one-third of a ton of cod alone.
“Mouthwatering and scrumptious.” These are just two of the words that can be used to describe Jardin’s popular Crispy Buttermilk Chicken Sandwich served with mustard slaw and garlic & herb ranch. #Foodie #Food #Wynn #Vegas
If you are ever in Vegas and looking for a great spot to eat, stop by the new restaurant at Wynn, Jardin, sporting an all-day menu that does not disappoint. Chef Joseph Zanelli has curated all your favorites into a single menu that goes the distance all day long.