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by Sharyl Attkisson

I’ve done quite a bit of reporting about how Wikipedia is definitely not “the encyclopedia anyone can edit.” It’s become a vehicle for special interests to control information. Agenda editors are able to prevent or revert edits and sourcing on selected issues and people in order to control the narrative.

Watch Sharyl Attkisson’s TedX talk on Wikipedia and other Astroturf tools

My own battle with Wikipedia included being unable to correct provably false facts such as incorrect job history, incorrect birth place and incorrect birth date.

What’s worse is that agenda editors related to pharmaceutical interests and the partisan blog Media Matters control my Wikipedia biographical page, making sure that slanted or false information stays on it. For example, they falsely refer to my reporting as “anti-vaccine,” and imply my reporting on the topic has been discredited. In fact, my vaccine and medical reporting has been recognized by top national journalism awards organizations, and has even been cited as a source in a peer-reviewed scientific publication. However, anyone who tries to edit this factual context and footnotes onto my page finds it is quickly removed.

What persists on my page, however, are sources that are supposedly disallowed by Wikipedia’s policies. They include citations by Media Matters, with no disclosure that it’s a partisan blog.

Another entity quoted on my Wikipedia biographical page to disparage my work is the vaccine industry’s Dr. Paul Offit. But there’s no mention of the lawsuits filed against Offit for libel (one prompted him to apologize and correct his book), or the fact that he provided false information about his work and my reporting to the Orange County Register, which later corrected its article. Obviously, these facts would normally make Offit an unreliable source, but for Wikipedia, he’s presented as if an unconflicted expert. In fact, Wikipedia doesn’t even mention that’s Offit is a vaccine industry insider who’s made millions of dollars off of vaccines.

Meantime, turn to Dr. Offit’s own Wikipedia biography and– at last look– it also omitted all mention of his countless controversies. Instead, it’s written like a promotional resume– in violation of Wikipedia’s supposed politics on neutrality.

Watch Sharyl Attkisson’s TedX talk on Fake News

These biographies are just two examples of ones that blatantly violate Wikipedia’s strict rules, yet they are set in stone. The powerful interests that “watch” and control the pages make sure Offit’s background is whitewashed and that mine is subtly tarnished. They will revert or change any edits that attempt to correct the record.

This, in a nutshell, exemplifies Wikipedia’s problems across the platform as described by its co-founder Larry Sanger.

Watch “Wikipedia: The Dark Side,” a Full Measure investigation

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By Viviane Callier

Researchers are beginning to understand the ways in which viruses strategically manipulate and cooperate with one another.

Social organisms come in all shapes and sizes, from the obviously gregarious ones like mammals and birds down to the more cryptic socializers like bacteria. Evolutionary biologists often puzzle over altruistic behaviors among them, because self-sacrificing individuals would at first seem to be at a severe disadvantage under natural selection. William D. Hamilton, one of the 20th century’s most prominent evolutionary theorists, developed a mathematical theory to explain the evolution of altruism through kin selection — for instance, why most individual ants, bees and wasps forgo the ability to reproduce and instead pour all their efforts into raising their siblings. Bacteriologists developed game-theory models to explain why bacteria in groups produce metabolites for their neighbors, even though some cheaters take advantage of the situation.

But until recently, no one had considered that simple viruses, too, have social lives that influence their fitness and their evolution. “From a theoretical perspective, there is clearly huge potential for viruses to interact socially, leading to possibilities for cooperation and conflict,” wrote Stuart West, a biologist at Oxford University who studies the evolution of social behaviors, in an email to Quanta. “However, there has been relatively little attempt to tackle this empirically.”

In a recent study published in Nature Microbiology, Rafael Sanjuán, an evolutionary geneticist at the University of Valencia in Spain, and his colleagues used a combination of theory and experiments to explore viral cooperation and conflict. They found that the spatial structure of a viral infection — the way that different sets of viruses can be isolated in separate compartments of the infected body — matters tremendously. In an evenly mixed system, altruistic viruses fall victim to “cheaters” that take advantage of their sacrifices, but if pockets in the body can isolate and shelter the altruists, they have a shot at survival.

Consider the vesicular stomatitis virus (VSV), a less dangerous member of the same viral family as rabies. Viral infections usually stimulate the cells of their mammalian hosts to produce interferons, the signaling proteins that raise neighboring cells’ antiviral defenses and interfere with viral replication. The wild-type VSV has evolved ways to suppress its host’s innate immune system, but at the cost of reproducing more slowly. Still, that ability enables the population of suppressive viruses to thrive — unless a “cheater” variant comes along.

The cheater does not have the ability to suppress its host’s defenses; in fact, its presence stimulates the release of interferons. But it still reaps the benefit of a lowered immune response because of the nearby VSVs that suppress interferon release. Because the cheaters don’t pay the reproductive cost of interferon suppression, they can outcompete the wild-type virus in the short term. From a social behavior standpoint, as Sanjuán and his colleagues pointed out in their paper, the wild-type VSV’s suppression of interferon qualifies as an altruistic act because in effect the wild type sacrifices itself for the cheater.

Eventually, the host’s interferon response overwhelms both types of viruses and kills them. It might seem like natural selection would therefore always weed out the ability to suppress interferon because its altruism would perversely leave viruses that had it at a disadvantage.

Sanjuán’s modeling study shows, however, that is not necessarily the case: The altruistic interferon-suppressing virus can still evolve and thrive if it and the cheater are physically segregated. Structures and barriers in the body can create havens where the interferon-suppressing viruses can survive, safe from the damage that cheaters would otherwise bring down upon them.

To model the specific conditions in which innate immune suppression can occur, the researchers used the theoretical framework that Hamilton developed. According to Hamilton’s rule, altruism evolves when $latex r~×~B~>~C $, where B is the benefit to the recipient, r is the recipient’s relatedness to the giver, and C is the cost to the giver.

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By Katherine Prince

Continuous learning, cultural awareness, change expertise, adaptable and effective communication and the ability to learn from failure. These are just some of the capabilities that participants in KnowledgeWorks’ convenings on the future of work identified as being important for graduates. Finding resources to solve problems, time and project management, reflective leadership and a sense of responsibility to the broader community also promised to help all young people thrive no matter what future of work emerges.

That question – what future of work will emerge – is unanswerable, making it critical to help young people, along with other education and employment stakeholders, plan for multiple possible futures. From today’s vantage point, we can identify two critical drivers of change shaping the future of readiness for further learning, work and life: the rise of smart machines and the decline of full-time employment. But we cannot yet know what extent of technological unemployment we will face or how much support individuals will have in navigating the changing employment landscape.

A New Foundation for Readiness

In the face of such uncertainties, stakeholders need to help people develop our uniquely human attributes along with developing flexible skills that we can apply across settings. Putting social-emotional skill development at the center of learning promises to help individuals develop the foundation necessary to navigate uncertainty throughout their lives. The new foundation for readiness shown below illustrates how redefining readiness from the inside out – focusing on human development rather than attempting to prepare learners for any particular future of work – can provide a platform for future success.

This new foundation for readiness is grounded in the human qualities that are most central to our relationships with one another and which are most difficult to code. Social-emotional skill development will need to be supported in integrated ways alongside the mastery of content and the application of skills and knowledge to specific contexts. Education institutions will need to balance supporting learners in preparing for their first-careers while also helping them develop the adaptability and resilience needed to navigate the changing economy and the ways of thinking necessary to address complex problems.

Flipping Education’s Focus

Establishing a new focus on feeling and relating will help education institutions and systems align with a future of readiness in which foundational skills and practices will be more important and enduring than specific content or job- and task-related skills.

For K-12 education, flipping the focus of learning to whole-person development could mean that:

  • Curriculum needs to be inverted, with core social-emotional competencies shaping the design of inquiry projects and the school and classroom rituals that anchor the learning climate and culture.
  • Students need to be grouped in new ways to follow flexible learning pathways.
  • Classrooms need to become more fluid and open, enabling new ways of structuring learning.
  • School schedules need to be transformed to allow for more interdisciplinary collaboration, deep reflection, and personalized learning.
  • Educators’ roles need to be reconfigured to focus less on content or grade specialization and more on foundational skills and practices, as well as on interdisciplinary, phenomenological or challenge-based learning.
  • Community partners need to become key assets for introducing new kinds of learning experiences that stretch students’ comfort zones and expand their aspirations.
  • K-12 schools and districts need to explore where and when it may be more appropriate for them to serve as brokers, rather than direct providers, of learning experiences.

At the postsecondary level, institutions might need to:

  • Focus more on supporting deep personal development as well as context- and discipline-specific skills and knowledge.
  • Diversify offerings and business models, with a multitude of formats and structures engaging learners and increasing access.
  • Contribute to student-driven and student-designed ecosystems of support that evolve over time and reflect students’ strengths, weaknesses, and needs.
  • Help students plan for both their careers and their lives and respond to changing conditions.
  • Enable learners to weave in and out of learning experiences as their career development needs dictate.
  • Collaborate more extensively with workplace partners.
  • Shift the focus of faculty professional development toward supporting students’ development of foundational skills and practices and attaining ongoing learning related to relevant workplace skills.

Strategies for Redefining Readiness

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China’s state-run press agency has welcomed its first female AI anchor who will join its growing team of virtual presenters. The female AI newsreader will make her professional debut during the upcoming meetings of the country’s national legislature and top political advisory body in March, according to Xinhua at a press conference on Tuesday. Modeled after the agency’s flesh-and-blood journalist Qu Meng, the AI newsreader was jointly developed by Xinhua and search engine company Sogou.com and can ‘read texts as naturally as a professional anchor’.

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by Kumar Mehta

Despite the countless books and articles written about innovation, we don’t have a good understanding about what drives innovation within organizations. Instead, we have multiple, often conflicting, theories about what makes innovation happen. We follow the one that resonates best with us or is in vogue. As a result, innovation efforts at many companies suffocate as they stumble along, thinking they are on the right path but not really knowing where they are headed.

The way to build an environment where innovation happens consistently is to make certain that the foundational building blocks that drive innovation are present. Few companies have built such an environment, what I call an innovation biome . The companies that have built this environment are the ones we admire as they are the ones that bring the most innovative offerings to the world.

In the rest of the companies, though, the most common reason that innovations fail is this: others reject an idea because they don’t have the right tools to evaluate the idea.

Overcoming disbelievers and naysayers

In most companies, when someone has an idea it has to climb up several layers of management, requiring a yes at every level for it to keep climbing.

A single no going up the management staircase can kill an idea. Since no one is ever penalized for saying no (you only get hurt for saying yes to the wrong thing), companies often develop cultures that are conservative and not conducive to innovation.

Just about every major innovation in history was rejected by the experts of the time.

Whether it was the earliest people who believed the earth is round, or Darwin’s theory of evolution, or Pasteur’s theory of germs, disbelievers and naysayers have always shown up in full force. This has never stopped. Experts thought the Personal Computer would not be successful, nor the automobile, nor the telephone (presumably the telephone was never supposed to catch on because there was no shortage of messenger boys).

This still happens every day in companies around the world.

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by Michell Zappa

“Education lies at a peculiar crossroad in society. On one hand it has the responsibility of anticipating real-life skills by preparing us for an increasingly complex world – but education methodologies can only be formalized after practices have been defined. This dichotomy is particularly aggravated when it comes to technology, where fast-paced innovation and perpetual change is the only constant.

This visualization attempts to organize a series of emerging technologies that are likely to influence education in the upcoming decades. Despite its inherently speculative nature, the driving trends behind the technologies can already be observed, meaning it’s a matter of time before these scenarios start panning out in learning environments around the world.”

Envisioning The 6 Domains of Future Technology In Education

The Future of Education Technology

6 Domains Of Education Technology

1. Digitized Classrooms: Rather than considering IT a standalone tool or skill, digitization tends to disperse throughout every facet of the classroom.
Examples: tablets, electronic screens, interactive whiteboards, data projectors, 

2. Tangible Computing: Embedding computation to the physical via intelligent objects, the internet of things, and connectivity with a profound impact on learning mechanisms.

Examples: reactive materials, reactive furniture, 3D printers, digitally intermediated field trips

3. Gamification: Billed as an evolution in grading mechanisms, gamification brings instant feedback to acquired knowledge through achievements and points systems.

Examples: student-developed apps, educational games, educational programming tools, achievement badges, self-paced learning

4. Virtual/Physical Studios: Bridging the online-offline gap, these future technologies offer a potential future where embodiment is secondary to information access.

Examples: eyewear/HUDs, retinal screens, holography, neuroinformatics, immersive virtual reality

5. Disintermediation: Undoing the traditional teacher-student model, these technologies offer a scenario where AI handles personalization while teachers focus on teaching

Examples: telepresence, algo-generated lessons, mobile learning platforms, task-assignment algorithms, S2S teaching platforms, assessment algorithms, student-designed learning mechanics

6. Opening of Information: Dissemination of information outside the physical silos of schools and classrooms, offering feedback and assessment to students anywhere.

Examples: portable academic histories, flipped classrooms, inter-school teaching platforms, digitization of books, open courseware, education app stores, online school communities, video lessons, formal communication backchannels

Image attribution flickr user radarcommunication; Envisioning The Future Of Technology In Education

by FirstRound

In 1989, Liz Wiseman took her first job out of business school at a mid-size startup called Oracle. With no previous experience, she was recruited as a technical trainer, charged with teaching programming to all of the company’s new engineering recruits. She admits she barely knew what the company did, much less how to teach engineers. A year later, she was promoted to manage the training department and make CEO Larry Ellison‘s vision for what he called ‘Oracle University’ a reality. She was 24.

“I really didn’t know what I was doing. All I knew was that this was a grown-up job and I wasn’t quite grown up yet, but no one seemed to be bothered by that but me,” says Wiseman. It was scary then, but looking back, she sees clearly how being a rookie made her an ideal candidate for the blue-sky project. “My real value didn’t come from having fresh ideas. It was having no ideas at all. When you know nothing you’re forced to create something.”

Little did she know that she’d spend the next 17 years leading the University effort and Oracle’s global human resources. Since then, Wiseman has written three books about what makes people effective as employees and leaders, and has conducted extensive research on how management can maximize performance inside organizations. Now president of the Wiseman Group, training executives around the world, she recently spoke at Stanford’s Entrepreneurship Corner and shared her findings about the advantages of the rookie mindset, how knowing too much can be dangerous for innovation, and what leaders can do to help everyone around them achieve their potential.

The Power of Being a Rookie

Harkening back to her experience spearheading Oracle University, Wiseman breaks down why her beginner’s mind was such a strength: “When you’re forced to create something and you don’t know how to do it, you go out and you ask,” she says. “All I knew was how important product knowledge transfer was inside of the company, so I went out and talked to every single one of the product bosses. I asked them what problems they had getting people to learn. I talked to the people who needed the knowledge.”

Based on these interviews, she knew she needed to keep things simple to lower the barrier to entry, and she needed to leverage the incredible amount of knowledge existing employees already had. “Instead of hiring instructors and technical experts that we’d need to bring up to speed, we went back to the product bosses and asked them to take on the additional responsibility of teaching trainings. We knew they’d be the best at it. It was the best, most accurate, fastest solution.”

Eventually, the program got so big that management made moves to replace Wiseman with a more seasoned executive, but the experts she had recruited to teach stepped in and demanded that she stay at her post. “It turned out that by not really knowing anything myself, I was able to stay much closer to all of the important stakeholders. And, because I needed to prove myself, we operated in thin slices — what we would today probably call a lean or agile approach — to deliver quick wins. We were giving people what they needed when they needed it because I knew no other way of working.”

When you’re a rookie, you’re also a pioneer. You’re out there on the frontier without confidence, so you have to focus on the basics. You end up operating very lean.

Rookie Smarts

While researching her book ‘Rookie Smarts,’ Wiseman studied 400 different scenarios where people were new to something or not: Taking on pieces of work, debugging a program, writing a proposal, teaching a class, etc. Her team looked at how experienced people handled tasks and compared it to people doing them for the very first time. Here’s what she found:

Experience creates a number of blind spots. “With time, we obviously gain knowledge, wisdom and more data points to inform our power of intuition. We build confidence and networks, but we’re also creating blind spots.” When your mind recognizes a pattern, it tends to stop innovating. You’re no longer looking for outlier possibilities, you miss opportunities. Generally speaking, you stop making things up. You gloss over the gaps.

Studies have shown that if misspelled words are strung together in a sentence, people can still read them with ease because all that matter is the first and last letter of a word. Our brains fill in the rest. The same thing happens when we face situations where we have experience. Our automatic response is to reach for what we already know. “We start answering questions before they’ve been asked. We stop seeing new data points or contrary points of you. We stop seeking feedback and input from others.”

We develop scar tissue. The more experience you gain, the more likely you’ll have some bad experiences that will leave scars behind, continually reminding you of your mistakes. “I have a whole set of scars that remind me not to do things that didn’t seem to work out very well the first time,” Wiseman says. “You also have to realize that you will have ideas that touch on other people’s scar tissue. They will quickly say, ‘No, no, we tried that and it didn’t work.’ This is a major way that experience can create a number of troubling blind spots.”

Ignorance can drive top performance. “If you envision a really steep learning curve, it starts in a phase of ignorance, this really gentle part of the curve. This is where, even when we’re given important and hard tasks, we can say to ourselves, ‘How hard can this be? I can do it,’” says Wiseman. “It’s only when we start to dig in and become more aware that we realize how hard something is. We start seeing the gap between what we can do and what the people around us can do. Then we move into a state of desperation. We start to panic. We look around for someone who knows what they’re doing who can help. This is where we start to reach out.”

The most powerful form of learning comes when we’re desperate. When we have no choice but to learn.

Wiseman’s research showed that in fields requiring specialized knowledge, inexperienced people tend to outperform their experienced peers by a small margin. “But where they really outperform is when the work is innovative in nature,” she says. “Rookies are a lot faster than people with experience because they are desperate and uncomfortable. When we get comfortable, that’s when we start to teach and mentor other people.” But it’s also where people slow down and stop contributing as much.

“As I looked at top performing rookies, I found this really interesting type of person: the perpetual rookie. These are people who are successful professionals, leaders, entrepreneurs with years of mastery who, despite that, maintain their rookie smarts — their ability to think and approach their work as if they were doing it for the first time.” As she investigated the attributes of perpetual rookies, Wiseman identified several traits they have in common:

  • They are risk mitigators, not risk takers. They learn how to operate in thin slices, test, and de-risk their progress.
  • They are never satisfied. “There’s an abhorrence of mediocrity that they share.”
  • They are curious. They always want to learn about everything, even if it’s not related to their job or immediate challenges.
  • They are humble. “I don’t mean in the sense of low self-esteem. I mean willing to learn from anyone and everyone no matter where they are in the hierarchy.”
  • They are playful. “It’s not like they try to create fun amid the work. For them, their work is just fun.”

So how can someone go about holding on to their rookie smarts? “As I looked across so many of these leaders and professionals, they all had a deliberate ritual — something that helped them go back to their rookie roots,” Wiseman says.

She cites Bob Hurley, founder of a surf company called Hurley Sports that eventually sold to Nike. “He said that at every juncture of building his business he had no idea what he was doing, and it turned out to be an advantage.”

When Hurley finds himself stuck in a rut, he thinks back to something that happened many years ago on Huntington Beach when he was an avid surfer himself. He ran into Wayne Bartholomew, the reigning world champion surfer at the time, who said he preferred surfing with beginners because they gave him energy. “So Bob told me, ‘Now when I have bad days, I go out and surf with the amateurs,'” Wiseman says. “He spends his time talking to them, hanging out with them, and he says it revitalizes his point of view.”

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By Bruce Schneier for Wired.com

In his 2008 white paper that first proposed bitcoin, the anonymous Satoshi Nakamoto concluded with: “We have proposed a system for electronic transactions without relying on trust.” He was referring to blockchain, the system behind bitcoin cryptocurrency. The circumvention of trust is a great promise, but it’s just not true. Yes, bitcoin eliminates certain trusted intermediaries that are inherent in other payment systems like credit cards. But you still have to trust bitcoin—and everything about it.

Much has been written about blockchains and how they displace, reshape, or eliminate trust. But when you analyze both blockchain and trust, you quickly realize that there is much more hype than value. Blockchain solutions are often much worse than what they replace.

First, a caveat. By blockchain, I mean something very specific: the data structures and protocols that make up a public blockchain. These have three essential elements. The first is a distributed (as in multiple copies) but centralized (as in there’s only one) ledger, which is a way of recording what happened and in what order. This ledger is public, meaning that anyone can read it, and immutable, meaning that no one can change what happened in the past.

The second element is the consensus algorithm, which is a way to ensure all the copies of the ledger are the same. This is generally called mining; a critical part of the system is that anyone can participate. It is also distributed, meaning that you don’t have to trust any particular node in the consensus network. It can also be extremely expensive, both in data storage and in the energy required to maintain it. Bitcoin has the most expensive consensus algorithm the world has ever seen, by far.

Finally, the third element is the currency. This is some sort of digital token that has value and is publicly traded. Currency is a necessary element of a blockchain to align the incentives of everyone involved. Transactions involving these tokens are stored on the ledger.

Private blockchains are completely uninteresting. (By this, I mean systems that use the blockchain data structure but don’t have the above three elements.) In general, they have some external limitation on who can interact with the blockchain and its features. These are not anything new; they’re distributed append-only data structures with a list of individuals authorized to add to it. Consensus protocols have been studied in distributed systems for more than 60 years. Append-only data structures have been similarly well covered. They’re blockchains in name only, and—as far as I can tell—the only reason to operate one is to ride on the blockchain hype.

All three elements of a public blockchain fit together as a single network that offers new security properties. The question is: Is it actually good for anything? It’s all a matter of trust.

Trust is essential to society. As a species, humans are wired to trust one another. Society can’t function without trust, and the fact that we mostly don’t even think about it is a measure of how well trust works.

The word “trust” is loaded with many meanings. There’s personal and intimate trust. When we say we trust a friend, we mean that we trust their intentions and know that those intentions will inform their actions. There’s also the less intimate, less personal trust—we might not know someone personally, or know their motivations, but we can trust their future actions. Blockchain enables this sort of trust: We don’t know any bitcoin miners, for example, but we trust that they will follow the mining protocol and make the whole system work.

Most blockchain enthusiasts have a unnaturally narrow definition of trust. They’re fond of catchphrases like “in code we trust,” “in math we trust,” and “in crypto we trust.” This is trust as verification. But verification isn’t the same as trust.

In 2012, I wrote a book about trust and security, Liars and Outliers. In it, I listed four very general systems our species uses to incentivize trustworthy behavior. The first two are morals and reputation. The problem is that they scale only to a certain population size. Primitive systems were good enough for small communities, but larger communities required delegation, and more formalism.

The third is institutions. Institutions have rules and laws that induce people to behave according to the group norm, imposing sanctions on those who do not. In a sense, laws formalize reputation. Finally, the fourth is security systems. These are the wide varieties of security technologies we employ: door locks and tall fences, alarm systems and guards, forensics and audit systems, and so on.

These four elements work together to enable trust. Take banking, for example. Financial institutions, merchants, and individuals are all concerned with their reputations, which prevents theft and fraud. The laws and regulations surrounding every aspect of banking keep everyone in line, including backstops that limit risks in the case of fraud. And there are lots of security systems in place, from anti-counterfeiting technologies to internet-security technologies.

In his 2018 book, Blockchain and the New Architecture of Trust, Kevin Werbach outlines four different “trust architectures.” The first is peer-to-peer trust. This basically corresponds to my morals and reputational systems: pairs of people who come to trust each other. His second is leviathan trust, which corresponds to institutional trust. You can see this working in our system of contracts, which allows parties that don’t trust each other to enter into an agreement because they both trust that a government system will help resolve disputes. His third is intermediary trust. A good example is the credit card system, which allows untrusting buyers and sellers to engage in commerce. His fourth trust architecture is distributed trust. This is emergent trust in the particular security system that is blockchain.

What blockchain does is shift some of the trust in people and institutions to trust in technology. You need to trust the cryptography, the protocols, the software, the computers and the network. And you need to trust them absolutely, because they’re often single points of failure.

When that trust turns out to be misplaced, there is no recourse. If your bitcoin exchange gets hacked, you lose all of your money. If your bitcoin wallet gets hacked, you lose all of your money. If you forget your login credentials, you lose all of your money. If there’s a bug in the code of your smart contract, you lose all of your money. If someone successfully hacks the blockchain security, you lose all of your money. In many ways, trusting technology is harder than trusting people. Would you rather trust a human legal system or the details of some computer code you don’t have the expertise to audit?

Blockchain enthusiasts point to more traditional forms of trust—bank processing fees, for example—as expensive. But blockchain trust is also costly; the cost is just hidden. For bitcoin, that’s the cost of the additional bitcoin mined, the transaction fees, and the enormous environmental waste.

Blockchain doesn’t eliminate the need to trust human institutions. There will always be a big gap that can’t be addressed by technology alone. People still need to be in charge, and there is always a need for governance outside the system. This is obvious in the ongoing debate about changing the bitcoin block size, or in fixing the DAO attack against Ethereum. There’s always a need to override the rules, and there’s always a need for the ability to make permanent rules changes. As long as hard forks are a possibility—that’s when the people in charge of a blockchain step outside the system to change it—people will need to be in charge.

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by Shana Lebowitz

  • Will networking help you build a successful career? I’ve never been sure.
  • Mostly, traditional networking seems to me like it takes a lot of time and effort.
  • Some experts say building connections is a practical strategy, in case you ever lose your job.
  • Other experts say you’re better off working and developing concrete skills than schmoozing.

A few weeks ago, one of my coworkers at Business Insider created a Slack channel called #lunch-buddy. Anyone who joined the channel would be randomly paired with another BI employee; the two would then meet for lunch, or coffee, or maybe just a walk, and get to know each other.

This initiative seemed to me a brilliant idea. Generally speaking, my coworkers are lovely people, but I know only a sliver personally. And when it comes to employees in other departments — say, product or finance — I’m curious to know what they do all day because, as it stands, I have no clue. (I imagine the feeling is mutual.)

I typed “#lunch-buddy” into the Slack search bar. And then I closed out of it. It was a Monday morning and, already, I was behind on work. I imagined that, by the time my buddy and I arranged to meet up, I’d be even farther behind. Inevitably, I’d wind up nibbling nervously on a sandwich while sneaking glances at my phone to make sure no one was Slacking me. This buddy business was not going to work out, at least not for me.

I should mention that, when the email about the lunch-buddy program went out, I was in the middle of reporting a story about networking. My specific goal was to figure out whether networking was good for your career, as so many influencers would have it, or bad. Good because you meet interesting new people who can introduce you to interesting new job opportunities, clients, and projects. Bad because you spend so much time schmoozing that you forget to, you know, work.

I wasn’t sure where I stood on the subject. As the lunch-buddy incident had made clear, I theoretically supported networking, but wasn’t very adept at practicing it. On LinkedIn, I posed the question to my connections. Unsurprisingly for a networking website, several people who commented said their relationships had always benefited them in their career.

And maybe they’d benefited mine, too. A few years ago, I was looking for a new job and mentioned as much to an old coworker (who’d become a friend) when we got together for drinks. Days later, she emailed me a Business Insider job posting that I’d missed in my search and, well, the rest is history.

Does that count as networking? I’m not sure. I like to think it’s better defined as being a human being with human friends who are willing to help you out.

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by TeachThought Staff

Critical Thinking

As an organization, critical thinking is at the core of what we do, from essays and lists to models and teacher training. (You can check out What It Means To Think Critically for a wordier survey of the intent of critical thinking.)

For this post, we’ve gathered various critical thinking resources. As you’ll notice, conversation is a fundamental part of critical thinking, if for no other reason than the ability to identify a line of reasoning, analyze, evaluate, and respond to it accurately and thoughtfully is among the most common opportunities for critical thinking for students in everyday life. Who is saying what? What’s valid and what’s not? How should I respond?

This varied and purposely broad collection includes resources for teaching critical thinking, from books and videos to graphics and models, rubrics and taxonomies to presentations and debate communities. Take a look, and let us know in the comments which you found the most–or least–useful.

And for something in the way of specific training for staff, there’s always Professional Development on Critical Thinking provided by TeachThought.

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25 Of The Best Resources For Teaching Critical Thinking

1. The TeachThought Taxonomy for Understanding, a taxonomy of thinking tasks broken up into 6 categories, with 6 tasks per category

2. A Collection Of Research On Critical Thinking by criticalthinking.org

3. It’s difficult to create a collection of critical thinking resources without talking about failures in thinking, so here’s A Logical Fallacies Primer in PowerPoint format.

4. The Watson-Glaser Critical Thinking Test (it’s not free, but you can check out some samples here)

5. 6 Hats Thinking, a model for divergent thinking.

6. 4 Strategies for Teaching With Bloom’s Taxonomy 

7. An Intro To Critical Thinking, a 10-minute video from wireless philosophy that takes given premises, and walks the viewer through valid and erroneous conclusions

8. Why Questions Are More Important Than Answers by Terry Heick

9. A Printable Flip Chart For Critical Thinking Questions (probably easier to buy one for a few bucks, but there it is nonetheless)

11. A Collection Of Bloom’s Taxonomy Posters

12. 6 Facets of Understanding by Grant Wiggins and Jay McTighe

13. A 3D Model of Bloom’s Taxonomy

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