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Given the demands of the modern world, many people find solace and relaxation when they disconnect from their smart phones, computers and email. But what if you could improve your overall happiness simply by playing games on your phone? In a recent interview, tech entrepreneur and co-founder of Happify Ofer Leidner said gamification can make people “happier”, and that the development of technology that improves well-being is only just getting beginning.

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It should be noted that not just any game on your phone can help one live a happier, healthier life. Instead, Happify and other comparable platforms use science-based games to drive behavior and to help people learn skills for generally improving their outlook on life. It’s still gaming and gamification, but gaming done with a meaningful purpose.

“After telling us a little bit about themselves, we recommend a certain track, which is a topic around which (Happify users) want to build those skills for greater emotional fitness. We then prescribe for them a set of activities and interventions that have been transformed into an interactive app,” Leidner said. “You can do them on your phone, when you’re commuting, or you can do it at night. What we’re doing, in terms of the measurement of improved outcome, is we’re actually measuring them based on scientific event reports.”

Leidner said that Happify and other apps like it aren’t inventing the science, but rather translating existing interventions, studies, and research; this data suggests that an overall happiness index is determined by one’s ability to experience positive emotions and overall life satisfaction. While that may seem like a nebulous target, he said there’s plenty of research to back it up.

In addition to the behavioral science aspect of gamification technology, Leidner also cites evidence of its efficacy in a neuroscientific approach. Seeing the notable changes in functional MRI brain scans as a result of gamification-driven behavior is what led him to make applications that could engage and benefit society on a grander scale.

Leidner acknowledges that happiness is a charged term that can mean many things to different people. But in a world with both objective and self-reported measurements, it’s what the user does with the feedback from those gaming measurements that will make the difference. “To give you a simple example, you will not be able to be happy if you’re sleep deprived. It doesn’t matter. Sleep deprivation is the number one technique to make you unhappy,” Leidner said. “I think the important thing is not just to measure, but what do you do with the measurements.”

Looking to the future, Leidner sees more gamification-driven applications and hardware on the horizon that will help people learn to live happier, healthier lives, including existing health applications like HealthKeep. Future app technologies will likely also collaborate with sensors in your body to help calibrate self-reported information (like mental state) with objective physical measurements, in turn improving recommended activities and better tailoring apps to enhance an individual user’s happiness and well-being.

Beyond that, Leidner also predicts that augmented and virtual reality will play a big role in improving people’s lives in the future. Such technologies, he said, will help people escape from their lives and emotions while helping them learn how to use more of their “mindfulness muscle”.

“There is a theme that says technology is not the way. If you want to live well and live happier lives, disconnect from technology, shut down your devices,” Leidner said. “We’re basically saying, ‘We’re not gonna’ shut down our devices. We’re just gonna’ turn the focus to apps and technology and services that help us create more meaningful lives. Augmented reality can play a very important role (in that) I think.” As with anything else, our new apps are a tool, one that can be used for ill or for good; eliminating this technology from our lives may not be realistic, but choosing how we use these technologies is within every free person’s realm of personal choice.


“When programmers at the MIT Instrumentation Laboratory set out to develop the flight software for the Apollo 11 space program in the mid-1960s, the necessary technology did not exist. They had to invent it.”

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“HoloLens … is not just a headset. It’s also an API – called Windows Holographic — built by Microsoft to let developers code programs from the HoloLens itself. The company’s announcement that it’s opening Windows Holographic to partners means that they, too, will be able to build devices for its API platform. Anything that’s developed using that API should work as well on partner devices as on the HoloLens itself.”

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Anyone who has heard of Bitcoin knows that it is built on a mechanism called The Blockchain. Most of us who follow the topic are also aware that Bitcoin and the blockchain were unveiled—together—in a whitepaper by a mysterious developer, under the pseudonym Satoshi Nakamoto.

That was eight years ago. Bitcoin is still the granddaddy of all blockchain-based networks, and most of the others deal with alternate payment coins of one type or another. Since Bitcoin is king, the others are collectively referred to as ‘Altcoins’.

But the blockchain can power so much more than coins and payments. And so—as you might expect—investors are paying lots of attention to blockchain startups or blockchain integration into existing services. Not just for payments, but for everything under the sun.

Think of Bitcoin as a product and the blockchain as a clever network architecture that enables Bitcoin and a great many future products and institutions to do more things—or to do these things better, cheaper, more robust and more blockchain-01secure than products and institutions built upon legacy architectures.

When blockchain developers talk about permissionless, peer-to-peer ledgers, or decentralized trust, or mining and “the halving event”, eyes glaze over. That’s not surprising. These things refer to advantages and minutiae in abstract ways, using a lexicon of the art. But—for many—they don’t sum up the benefits or provide a simple listing of products that can be improved, and how they will be better.

I am often asked “What can the Blockchain be used for—other than digital currency?” It may surprise some readers to learn that the blockchain is already redefining the way we do banking and accounting, voting, land deeds and property registration, health care proxies, genetic research, copyright & patents, ticket sales, and many proof-of-work platforms. All of these things existed in the past, but they are about to serve society better because of the blockchain. And this impromptu list barely scratches the surface.

I address the question of non-coin blockchain applications in other articles. But today, I will focus on a subtle but important tangent. I call it “A blockchain in name only”

Question: Can a blockchain be a blockchain if it is controlled by the issuing authority? That is, can we admire the purpose and utility, if it was released in a fashion that is not is open-source, fully distributed—and permissionless to all users and data originators?

Answer: Unmask the Charlatans
Many of the blockchains gaining attention from users and investors are “blockchains” in name only. So, what makes a blockchain a blockchain?

Everyone knows that it entails distributed storage of a transaction ledger. But this fact alone could be handled by a geographically redundant, cloud storage service. The really beneficial magic relies on other traits. Each one applies to Bitcoin, which is the original blockchain implementation:

▪Fully distributed among all users.
▪ Any user can also be a node to the ledger
▪Permissionless to all users and data originators
▪Access from anywhere data is generated or analyzed

A blockchain designed and used within Santander Bank, the US Post Office, or even MasterCard might be a nifty tool to increase internal redundancy or immunity from hackers. These potential benefits over the legacy mechanism are barely worth mentioning. But if a blockchain pretender lacks the golden facets listed above, then it lacks the critical and noteworthy benefits that make it a hot topic at the dinner table and in the boardroom of VCs that understand what they are investing in.

Some venture financiers realize this, of course. But, I wonder how many Wall Street pundits stay laser-focused on what makes a blockchain special, and know how to ascertain which ventures have a leg up in their implementations.

Perhaps more interesting and insipid is that even for users and investors who are versed in this radical and significant new methodology—and even for me—there is a subtle bias to assume a need for some overseer; a nexus; a trusted party. permissioned-vs-permissionlessAfter all, doesn’t there have to be someone who authenticates a transaction, guarantees redemption, or at least someone who enforces a level playing field?

That bias comes from our tendency to revert to a comfort zone. We are comfortable with certain trusted institutions and we feel assured when they validate or guarantee a process that involves value or financial risk, especially when we deal with strangers. A reputable intermediary is one solution to the problem of trust. It’s natural to look for one.

So, back to the question. True or False?…

In a complex value exchange with strangers and at a distance, there must be someone or some institution who authenticates a transaction, guarantees redemption, or at least enforces the rules of engagement (a contract arbiter).

Absolutely False!

No one sits at the middle of a blockchain transaction, nor does any institution guarantee the value exchange. Instead, trust is conveyed by math and by the number of eyeballs. Each transaction is personal and validation is crowd-sourced. More importantly, with a dispersed, permissionless and popular blockchain, transactions are more provably accurate, more robust, and more immune from hacking or government interference.

What about the protections that are commonly associated with a bank-brokered transaction? (For example: right of rescission, right to return a product and get a refund, a shipping guaranty, etc). These can be built into a blockchain transaction. That’s what the Cryptocurrency Standards Association is working on right now. Their standards and practices are completely voluntary. Any missing protection that might be expected by one party or the other is easily revealed during the exchange set up.

For complex or high value transactions, some of the added protections involve a trusted authority. blockchain-02But not the transaction itself. (Ah-hah!). These outside authorities only become involved (and only tax the system), when there is a dispute.

Sure! The architecture must be continuously tested and verified—and Yes: Mechanisms facilitating updates and scalability need organizational protocol—perhaps even a hierarchy. Bitcoin is a great example of this. With ongoing growing pains, we are still figuring out how to manage disputes among the small percentage of users who seek to guide network evolution.

But, without a network that is fully distributed among its users as well as permissionless, open-source and readily accessible, a blockchain becomes a blockchain in name only. It bestows few benefits to its creator, none to its users—certainly none of the dramatic perks that have generated media buzz from the day Satoshi hit the headlines.


Philip Raymond is co-chair of The Cryptocurrency Standards Association,
host & MC for The Bitcoin Event and editor at A Wild Duck.

If you’ve ever seen a “recommended item” on eBay or Amazon that was just what you were looking for (or maybe didn’t know you were looking for), it’s likely the suggestion was powered by a recommendation engine. In a recent interview, Co-founder of machine learning startup Delvv, Inc., Raefer Gabriel, said these applications for recommendation engines and collaborative filtering algorithms are just the beginning of a powerful and broad-reaching technology.

Raefer Gabriel, Delvv, Inc.
Raefer Gabriel, Delvv, Inc.

Gabriel noted that content discovery on services like Netflix, Pandora, and Spotify are most familiar to people because of the way they seem to “speak” to one’s preferences in movies, games, and music. Their relatively narrow focus of entertainment is a common thread that has made them successful as constrained domains. The challenge lies in developing recommendation engines for unbounded domains, like the internet, where there is more or less unlimited information.

“Some of the more unbounded domains, like web content, have struggled a little bit more to make good use of the technology that’s out there. Because there is so much unbounded information, it is hard to represent well, and to match well with other kinds of things people are considering,” Gabriel said. “Most of the collaborative filtering algorithms are built around some kind of matrix factorization technique and they definitely tend to work better if you bound the domain.”

Of all the recommendation engines and collaborative filters on the web, Gabriel cites Amazon as the most ambitious. The eCommerce giant utilizes a number of strategies to make item-to-item recommendations, complementary purchases, user preferences, and more. The key to developing those recommendations is more about the value of the data that Amazon is able to feed into the algorithm initially, hence reaching a critical mass of data on user preferences, which makes it much easier to create recommendations for new users.

“In order to handle those fresh users coming into the system, you need to have some way of modeling what their interest may be based on that first click that you’re able to extract out of them,” Gabriel said. “I think that intersection point between data warehousing and machine learning problems is actually a pretty critical intersection point, because machine learning doesn’t do much without data. So, you definitely need good systems to collect the data, good systems to manage the flow of data, and then good systems to apply models that you’ve built.”

Beyond consumer-oriented uses, Gabriel has seen recommendation engines and collaborative filter systems used in a narrow scope for medical applications and in manufacturing. In healthcare for example, he cited recommendations based on treatment preferences, doctor specialties, and other relevant decision-based suggestions; however, anything you can transform into a “model of relationships between items and item preferences” can map directly onto some form of recommendation engine or collaborative filter.

One of the most important elements that has driven the development of recommendation engines and collaborative filtering algorithms is the Netflix Prize, Gabriel said. The competition, which offered a $1 million prize to anyone who could design an algorithm to improve upon the proprietary Netflix’s recommendation engine, allowed entrants to use pieces of the company’s own user data to develop a better algorithm. The competition spurred a great deal of interest in the potential applications of collaborative filtering and recommendation engines, he said.

In addition, relative ease of access to an abundant amount of cheap memory is another driving force behind the development of recommendation engines. An eCommerce company like Amazon with millions of items needs plenty of memory to store millions of different of pieces of item and correlation data while also storing user data in potentially large blocks.

“You have to think about a lot of matrix data in memory. And it’s a matrix, because you’re looking at relationships between items and other items and, obviously, the problems that get interesting are ones where you have lots and lots of different items,” Gabriel said. “All of the fitting and the data storage does need quite a bit of memory to work with. Cheap and plentiful memory has been very helpful in the development of these things at the commercial scale.”

Looking forward, Gabriel sees recommendation engines and collaborative filtering systems evolving more toward predictive analytics and getting a handle on the unbounded domain of the internet. While those efforts may ultimately be driven by the Google Now platform, he foresees a time when recommendation-driven data will merge with search data to provide search results before you even search for them.

“I think there will be a lot more going on at that intersection between the search and recommendation space over the next couple years. It’s sort of inevitable,” Gabriel said. “You can look ahead to what someone is going to be searching for next, and you can certainly help refine and tune into the right information with less effort.”

While “mind-reading” search engines may still seem a bit like science fiction at present, the capabilities are evolving at a rapid pace, with predictive analytics at the bow.


“A little over a year ago, Apple had a problem: The iPad Pro was behind schedule. Elements of the hardware, software, and accompanying stylus weren’t going to be ready for a release in the spring. Chief Executive Officer Tim Cook and his top lieutenants had to delay the unveiling until the fall. That gave most of Apple’s engineers more time. It gave a little-known executive named Johny Srouji much less.”

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In professional cycling, it’s well known that a pack of 40 or 50 riders can ride faster and more efficiently than a single rider or small group. As such, you’ll often see cycling teams with different goals in a race work together to chase down a breakaway before the finish line.

This analogy is one way to think about collaborative multi-agent intelligent systems, which are poised to change the technology landscape for individuals, businesses, and governments, says Dr. Mehdi Dastani, a computer scientist at Utrecht University. The proliferation of these multi-agent systems could lead to significant systemic changes across society in the next decade.

Image credit: ResearchGate
Image credit: ResearchGate

“Multi-agent systems are basically a kind of distributed system with sets of software. A set can be very large. They are autonomous, they make their own decisions, they can perceive their environment, “Dastani said. “They can perceive other agents and they can communicate, collaborate or compete to get certain resources. A multi-agent system can be conceived as a set of individual softwares that interact.”

As a simple example of multi-agent systems, Dastani cited Internet mail servers, which connect with each other and exchange messages and packets of information. On a larger scale, he noted eBay’s online auctions, which use multi-agent systems to allow one to find an item they want to buy, enter their maximum price and then, if needed, up the bid on the buyer’s behalf as the auction closes. Driverless cars are another great example of a multi-agent system, where many softwares must communicate to make complicated decisions.

Dastani noted that multi-agent systems dovetail nicely with today’s artificial intelligence. In the early days of AI, intelligence was a property of one single entity of software that could, for example, understand human language or perceive visual inputs to make its decisions, interact, or perform an action. As multi-agent systems have been developed, those single agents interact and receive information from other agents that they may lack, which allows them to collectively create greater functionality and more intelligent behavior.

“When we consider (global) trade, we basically define a kind of interaction in terms of action. This way of interacting among individuals might make their market more efficient. Their products might get to market for a better price, as the amount of time (to produce them) might be reduced,” Dastani said. “When we get into multi-agent systems, we consider intelligence as sort of an emergent phenomena that can be very functional and have properties like optimal global decision or situations of state.”

Other potential applications of multi-agent systems include designs for energy-efficient appliances, such as a washing machine that can contact an energy provider so that it operates during off-peak hours or a factory that wants to flatten out its peak energy use, he said. Municipal entities can also use multi-agent systems for planning, such as simulating traffic patterns to improve traffic efficiency.

Looking to the future, Dastani notes the parallels between multi-agent systems and Software as a Service (SaaS) computing, which could shed light on how multi-agent systems might evolve. Just as SaaS combines various applications for on-demand use, multi-agent systems can combine functionalities of various software to provide more complex solutions. The key to those more complex interactions, he added, is to develop a system that will govern the interactions of multi-agent systems and overcome the inefficiencies that can be created on the path toward functionality.

“The idea is the optimal interaction that we can design or we can have. Nevertheless, that doesn’t mean that multi-agent systems are by definition, efficient,” Dastani said. “We can have many processes that communicate, make an enormous number of messages and use a huge amount of resources and they still can not have a sort of interesting functionality. The whole idea is, how can we understand and analyze the interactions? How can we decide which interaction is better than the other interactions or more efficient or more productive?”


“The money pouring into ed tech tells a different story, however. Despite the volume of novel products aimed at schools, the biggest investments are largely going to start-ups focused on higher education or job-related skills — businesses that feed a market of colleges, companies and consumers willing to spend to promote career advancement.”

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Ex-NSA boss says FBI director is wrong on encryption


Encryption protects everyone’s communications, including terrorists. The FBI director wants to undermine that. The ex-NSA director says that’s a terrible idea.

The FBI director wants the keys to your private conversations on your smartphone to keep terrorists from plotting secret attacks.

But on Tuesday, the former head of the U.S. National Security Agency…

Read the full article at CNN Money


““Your quest stands upon the edge of a knife. Stray but a little and it will fail, to the ruin of all.” So says Galadrial to the fellowship sent to destroy the One Ring in The Lord of the Rings. But that advice might as well be directed to the burgeoning virtual reality industry. Early optimism that the second coming of VR, after a false start in the 1990s, will blossom into a new mainstream medium could collapse into despair, with the technology joining 3D television as another misfire.”

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