Network Connectivity Problems Quotes

People that hold onto hate for so long do so because they want to avoid dealing with their pain. They falsely believe if they forgive they are letting their enemy believe they are a doormat. What they don’t understand is hatred can’t be isolated or turned off. It manifests in their health, choices and belief systems. Their values and religious beliefs make adjustments to justify their negative emotions. Not unlike malware infesting a hard drive, their spirit slowly becomes corrupted and they make choices that don’t make logical sense to others. Hatred left unaddressed will crash a person’s spirit. The only thing he or she can do is to reboot, by fixing him or herself, not others. This might require installing a firewall of boundaries or parental controls on their emotions. Regardless of the approach, we are all connected on this "network of life" and each of us is responsible for cleaning up our spiritual registry.

In retrospect, Euler's unintended message is very simple: Graphs or networks have properties, hidden in their construction, that limit or enhance our ability to do things with them. For more than two centuries the layout of Konigsberg's graph limited its citizens' ability to solve their coffeehouse problem. But a change in the layout, the addition of only one extra link, suddenly removed this constraint.

short term always leaves us in a place worse off than when we started. — To properly heal from addiction, we need a holistic approach. We need to create a life we don’t need to escape. We need to address the root causes that made us turn outside ourselves in the first place. This means getting our physical health back, finding a good therapist, ending or leaving abusive relationships, learning to reinhabit our bodies, changing our negative thought patterns, building support networks, finding meaning and connecting to something greater than ourselves, and so on. To break the cycle of addiction, we need to learn to deal with cravings, break old habits, and create new ones. To address all of this is an overwhelming task, but there is a sane, empowering, and balanced approach. But before we discuss how to implement solutions to the Two-Part Problem, we need to address one of the bigger issues that women and other historically oppressed folks need to consider, which is how patriarchal structures affect the root causes of addiction, how they dominate the recovery landscape, and what that means for how we experience recovery. If we are sick from sexism, homophobia, racism, classism, microaggressions, misogyny, ableism, American capitalism, and so on—and we are—then we need to understand how recovery frameworks that were never built with us in mind can actually work against us, further pathologizing characteristics, attributes, and behaviors that have been used to keep us out of our power for millennia. We need to examine what it means for us individually and collectively when a structure built by and for upper-class white men in the early twentieth century dominates the treatment landscape.

But the leader’s access to information is not the problem. We can work harder, but how much can we actually take in? Attention studies have shown that most people can thoughtfully consider only one thing at a time, and that multitasking dramatically degrades our ability to accomplish tasks requiring cognitive concentration. Given these limitations, the idea that a “heroic leader” enabled with an über-network of connectivity can simultaneously control a thousand marionettes on as many stages is unrealistic.

A fungal computer may sound fantastical, but biocomputing is a fast-growing field. Adamatzky has spent years developing ways to use slime molds as sensors and computers. These prototype biocomputers use slime molds to solve a range of geometrical problems. The slime mold networks can be modified—for instance, by cutting a connection—to alter the set of “logical functions” implemented by the network. Adamatzky’s idea of a “fungal computer” is just an application of slime-mold computing to another type of network-based organism. As Adamatzky observes, the mycelial networks of some species of fungus are more convenient for computing than slime molds.

It was this situation that led mathematician Chris Hauert and his colleagues to consider another possibility in an important evolutionary model published in Science in 2002. In Axelrod's study and in most previous theoretical models, individuals were forced to interact with each other. But what if they could choose not to interact? Rather than attempting to cooperate and risking being taken advantage of, a person could fend for herself. In other words, she could sever her connections to others in the network. Hauert called the people who adopt this strategy "loners." Using some beautiful mathematics, Hauert and his colleagues showed that in a world full of loners it is easy for cooperation to evolve because there are no people to take advantage of the cooperators that appear. The loners fend for themselves, and the cooperators form networks with other cooperators. Soon, the cooperators take over the population because they always do better together than the loners. But once the world is full of cooperators, it is very easy for free riders to evolve and enjoy the fruits of cooperation without contributing (like parasites). As the free riders become the dominant type in the population, there is no one left for them to take advantage of; then, the loners once again take over -- because they want nothing to do, as it were, with those bastards. In short, cooperating can emerge because we can do more together than we can apart. But because of the free-rider problem, cooperation is not guaranteed to succeed.

It’s All about Scaling Most of the current learning algorithms were discovered more than twenty-five years ago, so why did it take so long for them to have an impact on the real world? With the computers and labeled data that were available to researchers in the 1980s, it was only possible to demonstrate proof of principle on toy problems. Despite some promising results, we did not know how well network learning and performance would scale as the number of units and connections increased to match the complexity of real-world problems. Most algorithms in AI scale badly and never went beyond solving toy problems. We now know that neural network learning scales well and that performance continues to increase with the size of the network and the number of layers. Backprop, in particular, scales extremely well. Should we be surprised? The cerebral cortex is a mammalian invention that mushroomed in primates and especially in humans. And as it expanded, more capacity became available and more layers were added in association areas for higher-order representations. There are few complex systems that scale this well. The Internet is one of the few engineered systems whose size has also been scaled up by a million times. The Internet evolved once the protocols were established for communicating packets, much like the genetic code for DNA made it possible for cells to evolve. Training many deep learning networks with the same set of data results in a large number of different networks that have roughly the same average level of performance.

Two years later, DeepMind engineers used what they had learned from game playing to solve an economic problem of vital interest: How should Google optimize the management of its computer servers? The artificial neural network remained similar; the only things that changed were the inputs (date, time, weather, international events, search requests, number of people connected to each server, etc.), the outputs (turn on or off this or that server on various continents), and the reward function (consume less energy). The result was an instant drop in power consumption. Google reduced its energy bill by up to 40 percent and saved tens of millions of dollars—even after myriad specialized engineers had already tried to optimize those very servers. Artificial intelligence has truly reached levels of success that can turn whole industries upside down.

The computer scientists Jeff Clune, Jean-Baptiste Mouret, and Hod Lipson did what computer scientists do: they designed computer simulations.23 They used well-studied networks that had sensory inputs and produced outputs. What those outputs were determined how well the network performed when faced with environmental problems. They simulated twenty-five thousand generations of evolution, programming in a direct selection pressure to either maximize performance alone or maximize performance and minimize connection costs. And voilà! Once wiring-cost-minimization was added, in both changing and unchanging environments, modules immediately began to appear, whereas without the stipulation of minimizing costs, they didn’t. And when the three looked at the highest-performing networks that evolved, those networks were modular. Among that group, they found that the lower the costs were, the greater the modularity that resulted. These networks also evolved much quicker—in markedly fewer generations—whether in stable or changing environments. These simulation experiments provide strong evidence that selection pressures to maximize network performance and minimize connection costs will yield networks that are significantly more modular and more evolvable.

businesses that could benefit from the way networks behave, and this approach yielded some notable successes. Richard came from a different slant. For twenty years, he was a ‘strategy consultant’, using economic analysis to help firms become more profitable than their rivals. He ended up co-founding LEK, the fastest-growing ‘strategy boutique’ of the 1980s, with offices in the US, Europe and Asia. He also wrote books on business strategy, and in particular championed the ‘star business’ idea, which stated that the most valuable venture was nearly always a ‘star’, defined as the biggest firm in a high-growth market. In the 1990s and 2000s, Richard successfully invested the money he had made as a management consultant in a series of star ventures. He also read everything available about networks, feeling intuitively that they were another reason for business success, and might also help explain why some people’s careers took off while equally intelligent and qualified people often languished. So, there were good reasons why Greg and Richard might want to write a book together about networks. But the problem with all such ‘formal’ explanations is that they ignore the human events and coincidences that took place before that book could ever see the light of day. The most

CHOOSE CREATIVITY: To be more creative, the first step is to decide you want to make it happen. 2. THINK LIKE A TRAVELER: Like a visitor to a foreign land, try turning fresh eyes on your surroundings, no matter how mundane or familiar. Don’t wait around for a spark to magically appear. Expose yourself to new ideas and experiences. 3. ENGAGE RELAXED ATTENTION: Flashes of insight often come when your mind is relaxed and not focused on completing a specific task, allowing the mind to make new connections between seemingly unrelated ideas. 4. EMPATHIZE WITH YOUR END USER: You come up with more innovative ideas when you better understand the needs and context of the people you are creating solutions for. 5. DO OBSERVATIONS IN THE FIELD: If you observe others with the skills of an anthropologist, you might discover new opportunities hidden in plain sight. 6. ASK QUESTIONS, STARTING WITH “WHY?”: A series of “why?” questions can brush past surface details and get to the heart of the matter. For example, if you ask someone why they are still using a fading technology (think landline phones), the answers might have more to do with psychology than practicality. 7. REFRAME CHALLENGES: Sometimes, the first step toward a great solution is to reframe the question. Starting from a different point of view can help you get to the essence of a problem. 8. BUILD A CREATIVE SUPPORT NETWORK: Creativity can flow more easily and be more fun when you have others to collaborate with and bounce ideas off.

This British practice, previously unknown in India, caused long-lasting damage. The historian Jon Wilson has argued that India had a dynamic economic and political order—‘a society of little societies’—where constant negotiation between the rulers and the ruled was the norm. India’s villages were not self-reliant republics that lived in blissful isolation. They were networked and connected, and it was the destruction of Indian industry that forced people to retreat and focus on farming, creating both a more agrarian society and the problem of peasant dispossession.

This book should never have happened. If it wasn’t for the most bizarre and twisted sequence of events involving a diverse array of people it wouldn’t have. Let us explain. If someone we, the authors, had wanted to impress - a publisher, say, or a book reviewer - had asked us how it had emerged, we could have come up with all kinds of things to establish our credentials for writing it. But they would have been only a small part of the story of how it came about, and not the interesting bit either. The truth is much more human and fascinating - and it also gets to the heart of the book and shows how networks really work. Greg has always been fascinated by ‘network theory’ - the findings of sociologists, mathematicians and physicists, which seemed to translate to the real world of links between people. Early in his professional life at Auto Trader magazine in Canada he got to see an extraordinary network of buyers and sellers in operation. Later, when he became a venture capitalist - someone who invests in new or young companies, hoping that some of them will become very valuable - he applied what he’d learned. He invested in businesses that could benefit from the way networks behave, and this approach yielded some notable successes. Richard came from a different slant. For twenty years, he was a ‘strategy consultant’, using economic analysis to help firms become more profitable than their rivals. He ended up co-founding LEK, the fastest-growing ‘strategy boutique’ of the 1980s, with offices in the US, Europe and Asia. He also wrote books on business strategy, and in particular championed the ‘star business’ idea, which stated that the most valuable venture was nearly always a ‘star’, defined as the biggest firm in a high-growth market. In the 1990s and 2000s, Richard successfully invested the money he had made as a management consultant in a series of star ventures. He also read everything available about networks, feeling intuitively that they were another reason for business success, and might also help explain why some people’s careers took off while equally intelligent and qualified people often languished. So, there were good reasons why Greg and Richard might want to write a book together about networks. But the problem with all such ‘formal’ explanations is that they ignore the human events and coincidences that took place before that book could ever see the light of day. The most

Neither Duncan nor I could see how to solve that problem by pure mathematics, so we used a computer to simulate the morph on networks of large but manageable size, starting from pristine rings with 1,000 nodes and 10 links per node. To chart the structural changes in the middle ground, we graphed both the average path length and the clustering as functions of the proportion of links that were randomly rewired. What we found amazed us. The slightest bit of randomness contracted the network tremendously. The average path length plummeted at first—with only 1 percent rewiring (meaning that only 1 out of every 100 links was randomized), the graph dropped by 85 percent from its original level. Further rewiring had only a minimal effect; the curve leveled off onto a low-lying plateau, indicating that the network had already gotten about as small as it could possibly get, as if it were completely random. Meanwhile, the clustering barely budged. With 1 percent rewiring, the clustering dropped by only 3 percent. Connections were being yanked out of well-ordered neighborhoods, yet the clustering hardly noticed. Only much later in the morph, long after the crash in path length, did clustering begin to drop significantly.

The heart is essentially a muscular pump connected to an elaborate network of branching tubes. Although there are several kinds of cardiovascular disease, almost all arise from something going wrong in either the tubes or the pump. Most problems start with the tubes, primarily the arteries that carry blood from the heart to every nook and cranny of the body. Like the pipes in a building, arteries are vulnerable to getting clogged with unwanted deposits. This hardening of the arteries, termed atherosclerosis, starts with the buildup of plaque—a gloppy mixture of fat, cholesterol, and calcium—within the walls of arteries. Plaques, however, don’t simply accumulate in arteries like crud settling in a pipe. Instead, they are dynamic, changing, growing, shifting, and sometimes breaking. They develop when white blood cells in arteries trigger inflammation by reacting to damage usually caused by a combination of high blood pressure and so-called bad cholesterol that irritates the walls of the artery. In an effort to repair the damage, white blood cells produce a foamy mixture that incorporates cholesterol and other stuff and then hardens. As plaque accumulates, arteries stiffen and narrow, sometimes preventing enough blood from flowing to the tissues and organs that need it and further driving up blood pressure. One potentially lethal scenario is when plaques block an artery completely or detach and obstruct a smaller artery elsewhere. When this happens, tissues are starved of blood (also called ischemia) and die. Plaques can also cause the artery wall to dilate, weaken, and bulge (an aneurysm) or to tear apart (a rupture), which can lead to massive bleeding (a hemorrhage). Blocked and ruptured arteries create trouble anywhere in the body, but the most vulnerable locations are the narrow coronary arteries that supply the heart muscle itself. Heart attacks, caused by blocked coronary arteries, may damage the heart’s muscle, leading to less effective pumping of blood or triggering an electrical disturbance that can stop the heart altogether. Other highly vulnerable arteries are in the brain, which cause strokes when blocked by blood clots or when they rupture and bleed. To this list of more susceptible locations we should also add the retinas, kidneys, stomach, and intestines. The most extreme consequence of coronary artery disease is a heart attack, which, if one survives, leaves behind a weakened heart unable to pump blood as effectively as before, leading to heart failure.

Now Where Do You Find Customers? When novice entrepreneurs search for opportunities, they too often look beyond their Zone of Influence. They think the action is happening somewhere else, in some other location or industry. But seasoned entrepreneurs almost always find and create opportunities within the context of who they are, what they know, and especially who they know. In each of the examples above, the business validation process begins with potential customers in the entrepreneur’s orbit. Actual people with names. Tribes you belong to or are interested in, most of whom are already self-organized online. People you know how to reach, today. Though it’s rarely a part of their official origin stories, the biggest companies in the world—even the viral apps now worth billions—started through personal networks and real human connections. Mark Zuckerberg started Facebook in a weekend by emailing friends to use it. Version 1 did well, validating it. And Microsoft started with Bill Gates building software for a guy in Albuquerque. He had a CUSTOMER FIRST. In the beginning, founders should reach out to their friends, their former colleagues, their communities. You may think your business is unique, but trust me, it’s not. Every successful business can start this way. For example, Anahita loves her dogs and wanted healthier snacks for them. She started taking her homemade organic dog treats to her local dog park. She would sell out every time. A year later she now has a store called the Barkery, a dog bakery. Before you even think about picking a business idea, make sure you have easy access to the people you want to help. An easy way to do this is to think about where you have easy access to a targeted group of people whom you really want to help—like, say, new moms in Austin, cyclists, freelance writers, and taco obsessives (like me!). CHALLENGE Top three groups. Let’s write out your top three groups to target. Who do you have easy access to that you’d be EXCITED to help? This can be your neighbors, colleagues, religious friends, golf buddies, cooking friends, etc. The better you understand your target group, the better you can speak to them. The more specifically you can speak to their problems, the better and easier you can sell (or test products). Note how this process prioritizes communication with people, through starting (taking the first iteration of your solution straight to customers) and asking (engaging them in a conversation to determine how your solution can best fix their problem). Business creation should always be a conversation! Nearly every impulse we have is to be tight with our ideas by doing more research, going off alone to build the perfect product—anything and everything to avoid the discomfort of asking for money. This is the validation shortcut. You have to learn to fight through this impulse. It won’t be easy, but it’ll be worth it.

Outsourcing requires a tight integration of suppliers, making sure that all pieces arrive just in time. Therefore, when some suppliers were unable to deliver certain basic components like capacitors and flash memory, Compaq's network was paralyzed. The company was looking at 600,000 to 700,000 unfilled orders in handheld devices. The $499 Pocket PCs were selling for $700 to $800 at auctions on eBay and Amazon.com. Cisco experienced a different but equally damaging problem: When orders dried up, Cisco neglected to turn off its supply chain, resulting in a 300 percent ballooning of its raw materials inventory. The final numbers are frightening: The aggregate market value loss between March 2000 and March 2001 of the twelve major companies that adopted outsourcing-Cisco, Dell, Compaq, Gateway, Apple, IBM, Lucent, Hewlett-Packard, Motorola, Ericsson, Nokia, and Nortel-exceeded $1.2 trillion. The painful experience of these companies and their investors is a vivid demonstration of the consequences of ignoring network effects. A me attitude, where the company's immediate financial balance is the only factor, limits network thinking. Not understanding how the actions of one node affect other nodes easily cripples whole segments of the network. Experts agree that such rippling losses are not an inevitable downside of the network economy. Rather, these companies failed because they outsourced their manufacturing without fully understanding the changes required in their business models. Hierarchical thinking does not fit a network economy. In traditional organizations, rapid shifts can be made within the organization, with any resulting losses being offset by gains in other parts of the hierarchy. In a network economy each node must be profitable. Failing to understand this, the big players of the network game exposed themselves to the risks of connectedness without benefiting from its advantages. When problems arose, they failed to make the right, tough decisions, such as shutting down the supply line in Cisco's case, and got into even bigger trouble. At both the macro- and the microeconomic level, the network economy is here to stay. Despite some high-profile losses, outsourcing will be increasingly common. Financial interdependencies, ignoring national and continental boundaries, will only be strengthened with globalization. A revolution in management is in the making. It will take a new, network-oriented view of the economy and an understanding of the consequences of interconnectedness to smooth the way.

Language both required additional cognitive capacities and made new ones possible, and these changes took space and connections to achieve. The space problem was solved, as we saw earlier, by moving some things around in existing cortical space, and also by adding more space. But the connection problem was only partially solved. The part that was solved, connectivity within cortical processing networks, made the enhanced cognitive capacities of the hominid brain possible. But the part that hasn't been fully solved is connectivity between cognitive systems and other parts of the mental trilogy-emotional and motivational systems. This is why a brilliant mathematician or artist, or a successful entrepreneur, can like anyone else fall victim to sexual seduction, road rage, or jealousy, or be a child abuser or rapist, or can have a crippling depression or anxiety. Our brain has not evolved to the point where the new systems that make complex thinking possible can easily control the old systems that give rise to our base needs and motives, and emotional reactions. This doesn't mean that we're simply victims of our brains and should just give in to our urges. It means that downward causation is sometimes hard work. Doing the right thing doesn't always flow naturally from knowing what the right thing to do is. In the end, then, the self is maintained by systems that function both explicitly and implicitly. Through explicit systems, we try to willfully dictate who we are, and how we will behave. But we are only partially effective in doing so, since we have imperfect conscious access to emotional systems, which play such a crucial role in coordinating learning by other systems. In spite of their importance, though, emotion systems are not always active and have only episodic influence on what other brain systems learn and store. Furthermore, because there are multiple independent emotion systems, the episodic influence of any one system is itself but a component of the total impact of emotions on self-development.

SCANDALS AND MISMANAGEMENT If Secretary Clinton’s political career had ended with her defeat for the Democratic presidential nomination in 2008, her skills as a manager would have been judged by her disorganized and drama-filled campaign for the presidency and her disastrous Health Care Task Force as First Lady. President Obama, who defeated her calamitously run campaign, should have been wary of nominating Clinton to a post that was responsible for tens of thousands of federal employees throughout the world. While her tenure in Foggy Bottom didn’t have the highly publicized backstabbing element that tarnished her presidential campaign, Secretary Clinton’s deficiencies as a manager were no less evident. There was one department within State that Secretary Clinton oversaw with great care: the Global Partnerships Initiative (GPI), which was run by long-time Clinton family aide Kris Balderston. Balderston was known in political circles for creating a “hit list” that ranked members of Congress based on loyalty to the Clintons during the 2008 presidential primaries.[434] Balderston was brought to Foggy Bottom to “keep the Clinton political network humming at State.”[435] He focused his efforts on connecting CEOs and business interests—all potential Clinton 2016 donors—to State Department public/private partnerships. Balderston worked alongside Clinton’s long-time aide Huma Abedin, who was given a “special government employee” waiver, allowing her to work both as Secretary Clinton’s deputy chief of staff, and for other private sector clients. With the arrangement, Abedin would serve as a consultant to the top Clinton allied firm, Teneo, in a role in which, as the New York Times reported, “the lines were blurred between Ms. Abedin’s work in the high echelons of one of the government’s most sensitive executive departments and her role as a Clinton family insider.”[436] Secretary Clinton and her allies have placed great emphasis on the secretary of state’s historic role in promoting American business interests overseas, dubbing the effort “economic statecraft.”[437] The efforts of the GPI, Abedin, and Balderston ensured that Secretary Clinton’s “economic statecraft” agenda would be rife with the potential for conflicts of interest reminiscent of the favor-trading scandals that emanated from her husband’s White House. While the political office and donor maintenance program was managed with extreme meticulousness, Secretary Clinton ignored her role as manager of the rest of the sprawling government agency.[438] When it came to these more mundane tasks, Secretary Clinton was not on top of what was really going on in the department she ran. While Secretary Clinton was preoccupied with being filmed and photographed all around the world, the State Department was plagued by chronic management problems and scandals, from visa programs to security contractors. And when Secretary Clinton did weigh in on management issues, it was almost always after a raft of bad press forced her to, and not from any proactive steps she took. In fact, she and her department’s first reaction in certain instances was to silence critics or intimidate whistleblowers, rather than get to the bottom of what was actually going on. The events that unfolded in Benghazi were the worst example of Secretary Clinton neglecting her managerial responsibilities. This pattern of behavior, which led to the tragedy, was characteristic of her management style throughout her four years at Foggy Bottom. “Economic Statecraft” A big part of Secretary Clinton’s record-breaking travel—112 countries visited—was her work as a salesperson for select U.S. business interests.[439] Today, her supporters would have us believe her “economic statecraft” agenda was a major accomplishment.[440] Yet, as always seems to be the case with the Clintons, there was one family that benefited more than any other from all this economic statecraft—the Clinton family.

Where once an educated person might have assumed she was at least conversant with the relevant knowledge on a particular field of study, the explosion of information has rendered that assumption laughable. One solution to information overload is to increase a leader’s access to information, fitting him with two smartphones, multiple computer screens, and weekend updates. But the leader’s access to information is not the problem. We can work harder, but how much can we actually take in? Attention studies have shown that most people can thoughtfully consider only one thing at a time, and that multitasking dramatically degrades our ability to accomplish tasks requiring cognitive concentration. Given these limitations, the idea that a “heroic leader” enabled with an über-network of connectivity can simultaneously control a thousand marionettes on as many stages is unrealistic.

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Where I live, on the West Coast, most churches tend to be small and to have little influence in the culture. Stark and Finke explain, “A major reason for the lack of church membership in the West is high rates of mobility, which decrease the ability of all voluntary organizations, not just churches, to maintain membership. That is, people move so often that they lack the social ties needed to affiliate with churches.”25 To address this problem, one of the most effective church-planting networks in the United States began in Tacoma, Washington, by using a method of developing intensive community in neighborhoods. Soma Communities fosters deep and intense relationships by teaching church planters to get closely involved in their neighborhoods, opening their homes to neighbors, gathering friends together on a regular basis, and forming “missional communities” focused on discovering and meeting the needs of neighbors and the community. It is these relational bonds that make someone unfamiliar with Christianity want to try it out. Rick Richardson, who directs the evangelism and leadership program at Wheaton College Graduate School, argues that “belonging comes before believing.” He contrasts older methods of evangelism that focused on asking individuals to make a set of commitments. Today, asserts Richardson, presenting four spiritual laws and inviting people to make decisions for Christ is less effective. “Evangelism is about helping people belong so that they can come to believe. So our communities need to be places where people can connect before they have to commit.”26 The idea is held up by social science research showing that converts tend to sign on to a new faith only after their social ties become stronger to those in the new faith than to others outside it. “This often occurs before a convert knows much about what the group believes.

Marc Goodman is a cyber crime specialist with an impressive résumé. He has worked with the Los Angeles Police Department, Interpol, NATO, and the State Department. He is the chief cyber criminologist at the Cybercrime Research Institute, founder of the Future Crime Institute, and now head of the policy, law, and ethics track at SU. When breaking down this threat, Goodman sees four main categories of concern. The first issue is personal. “In many nations,” he says, “humanity is fully dependent on the Internet. Attacks against banks could destroy all records. Someone’s life savings could vanish in an instant. Hacking into hospitals could cost hundreds of lives if blood types were changed. And there are already 60,000 implantable medical devices connected to the Internet. As the integration of biology and information technology proceeds, pacemakers, cochlear implants, diabetic pumps, and so on, will all become the target of cyber attacks.” Equally alarming are threats against physical infrastructures that are now hooked up to the net and vulnerable to hackers (as was recently demonstrated with Iran’s Stuxnet incident), among them bridges, tunnels, air traffic control, and energy pipelines. We are heavily dependent on these systems, but Goodman feels that the technology being employed to manage them is no longer up to date, and the entire network is riddled with security threats. Robots are the next issue. In the not-too-distant future, these machines will be both commonplace and connected to the Internet. They will have superior strength and speed and may even be armed (as is the case with today’s military robots). But their Internet connection makes them vulnerable to attack, and very few security procedures have been implemented to prevent such incidents. Goodman’s last area of concern is that technology is constantly coming between us and reality. “We believe what the computer tells us,” says Goodman. “We read our email through computer screens; we speak to friends and family on Facebook; doctors administer medicines based upon what a computer tells them the medical lab results are; traffic tickets are issued based upon what cameras tell us a license plate says; we pay for items at stores based upon a total provided by a computer; we elect governments as a result of electronic voting systems. But the problem with all this intermediated life is that it can be spoofed. It’s really easy to falsify what is seen on our computer screens. The more we disconnect from the physical and drive toward the digital, the more we lose the ability to tell the real from the fake. Ultimately, bad actors (whether criminals, terrorists, or rogue governments) will have the ability to exploit this trust.

One of the surprising lessons of this research is that trying to force an insight can actually prevent the insight. While it’s commonly assumed that the best way to solve a difficult problem is to relentlessly focus, this clenched state of mind comes with a hidden cost: it inhibits the sort of creative connections that lead to breakthroughs. We suppress the very type of brain activity that should be encouraged. For instance, many stimulants taken to increase attention, such as caffeine, Adderall, and Ritalin, seem to make epiphanies much less likely. According to a recent online poll conducted by Nature, nearly 20 percent of scientists and researchers regularly take prescription drugs in order to improve mental performance. The most popular reason given was “to enhance concentration. Because these stimulants shift attention away from the networks of the right hemisphere, they cause people to ignore those neurons that might provide the solution. “People assume that increased focus is always better,” says Martha Farah, a neuroscientist at the University of Pennsylvania. “But what they don’t realize is that intense focus comes with real tradeoffs. You might be able to work for eight hours straight on these drugs, but you’re probably not going to have many big insights.” Marijuana, by contrast, seems to make insights more likely. It not only leads to states of relaxation but also increases brain activity in the right hemisphere.

The comparison between the previous two figures shows one of the most classic mistakes when troubleshooting networks. Sometimes, the temptation is to connect to a router and ping the host on the attached LAN, and it works. So, the engineer moves on, thinking that the network layer issues between the router and host work fine, when the problem still exists with the host’s default router setting.

MAKING THE CALL Suppose you had a successful social encounter at a party. Last night went fine. But now you sit by the phone, the person’s phone number in hand, afraid to make that call you know you want to make. Maybe the person doesn’t really want you to call. (Then why did she give you her phone number?) Maybe she’s changed her mind. (There’s only one way to find out!) If you have a problem following up, you need to internalize this self-coaching advice: Dread, then do. If you feel anxious, use relaxation techniques to ready yourself to make the call. Then make it. No matter what, you will feel relieved and even proud of yourself once you’ve done it. Appropriate follow-up is crucial; otherwise, all the groundwork you’ve laid in your initial conversation will go to waste. When you call someone on the phone, remember all the skills you’ve practiced so far. And be sure to call when you say you are going to call. Imagine how you’d feel if someone whose company you’d enjoyed promised to call you on Tuesday and the call didn’t come until Friday, if at all. And finally, remember to ask about things the person told you in previous conversation. This is your chance to broaden your new friendship, so make plans and follow through on them soon. (Remember: friendship first. It’s okay, especially at this stage, for a woman to initiate a social engagement with a man, whether it leads to romance or not). If you would like to follow up with someone in your company or outside it who could become a valuable part of your career network, the procedure is much the same. Stay in touch in whatever ways are appropriate for your workplace. A clipping of a work-related article with a simple note—“Bill: Thought this would interest you,” and your name—lets the person know you appreciated his knowledge and insight. If you like, you could follow up on an outside contact with a brief note saying you enjoyed meeting the person, and then call later, perhaps with an invitation for a business lunch or a lecture. Developing contacts inside your workplace and beyond could help you build job opportunities. And feeling connected to the business community in which you work can be fulfilling too. People may soon want to begin networking with you!

New users were asked to import their email contacts to invite more people. After each connection request, users were shown screens of even more suggestions. New users who appeared in other people’s contacts—even if they skipped importing it themselves—had suggested connections right after sign-up.

A telephone without a connection at the other end of the line is not even a toy or a scientific instrument. It is one of the most useless things in the world. Its value depends on the connection with the other telephone and increases with the number of connections.

One notable example of this is the ever present “People You May Know” or “Friend suggestions” feature. Every social platform at scale has some kind of implementation of it for a reason: it works incredibly well. My friend Aatif Awan, formerly vice president of growth at LinkedIn—who helped them scale to hundreds of millions of users and spearheaded their acquisition by Microsoft—explains how their algorithm works: People You May Know was a key part of LinkedIn’s success, generating billions of connections within the network. It started with “completing the triangle”—if a bunch of your friends have all connected with Alice but you haven’t yet, then there’s a good chance you might know Alice, too. Later, we incorporated implicit signals—maybe Alice just updated her profile to say she works at your same company. Maybe she’s viewed your profile multiple times over several days. Putting all of these inputs into a machine learning model continued to give us mileage on this feature over many years.77

Your Competition Has Network Effects, Too To figure out a response, it’s important to acknowledge a common myth about defensibility and moats: that somehow, network effects will magically help you fend off competition. This is a myth repeated again and again in startup pitch presentations to investors and entrepreneurs. It’s a lie that entrepreneurs tell to themselves. It isn’t true—simply having network effects is not enough, because if your product has them, it’s likely that your competitors have them, too. Whether you are a marketplace, social network, workplace collaboration tool, or app store, you are in a “networked category.” It’s intrinsic in these categories that every player is a multi-sided network that connects people, and is governed under the dynamics of Cold Start Theory. Effective competitive strategy is about who scales and leverages their network effects in the best way possible. No wonder we often see smaller players upend larger ones, in an apparent violation of Metcalfe’s Law. If every product in a category can rely on their network, then it’s not about who’s initially the largest. Instead, the question is, who is doing the best job amplifying and scaling their Acquisition, Engagement, and Economic effects. It’s what we see repeatedly over time: MySpace was the biggest social network in the mid-2000s and lost to Facebook, then a smaller, newer entrant with a focus on college networks with stronger product execution. HipChat was ahead in workplace communication, but was upended by Slack. Grubhub created a successful, profitable multibillion-dollar food-ordering company, but has rapidly lost ground to Uber Eats and DoorDash.

My friend Bangaly Kaba, formerly head of growth at Instagram, called this idea the theory of “Adjacent Users.” He describes his experience at Instagram, which several years post-launch was growing fast but not at rocketship speed: When I joined Instagram in 2016, the product had over 400 million users, but the growth rate had slowed. We were growing linearly, not exponentially. For many products, that would be viewed as an amazing success, but for a viral social product like Instagram, linear growth doesn’t cut it. Over the next 3 years, the growth team and I discovered why Instagram had slowed, developed a methodology to diagnose our issues, and solved a series of problems that reignited growth and helped us get to over a billion users by the time I left. Our success was anchored on what I now call The Adjacent User Theory. The Adjacent Users are aware of a product and possibly tried using it, but are not able to successfully become an engaged user. This is typically because the current product positioning or experience has too many barriers to adoption for them. While Instagram had product-market fit for 400+ million people, we discovered new groups of billions of users who didn’t quite understand Instagram and how it fit into their lives.67 In my conversations with Bangaly on this topic, he described his approach as a systematic evaluation of the network of networks that constituted Instagram. Rather than focusing on the core network of Power Users—the loud and vocal minority that often drive product decisions—instead the approach was to constantly figure out the adjacent set of users whose experience was subpar. There might be multiple sets of nonfunctional adjacent networks at any given time, and it might require different approaches to fix each one. For some networks, it might be the features of the product, like Instagram not having great support for low-end Android apps. Or it might be because of the quality of their networks—if the right content creators or celebrities hadn’t yet arrived. You fix the experience for these users, then ask yourself again, who are the adjacent users? Then repeat. Bangaly describes this approach: When I started at Instagram, the Adjacent User was women 35–45 years old in the US who had a Facebook account but didn’t see the value of Instagram. By the time I left Instagram, the Adjacent User was women in Jakarta, on an older 3G Android phone with a prepaid mobile plan. There were probably 8 different types of Adjacent Users that we solved for in-between those two points. To solve for the needs of the Adjacent User, the Instagram team had to be nimble, focusing first on pulling the audience of US women from the Facebook network. This required the team to build algorithmic recommendations that utilized Facebook profiles and connections, so that Instagram could surface friends and family on the platform—not just influencers. Later on, targeting users in Jakarta and in other developing countries might involve completely different approaches—refining apps for low-end Android phones with low data connections. As the Adjacent User changes, the strategy has to change as well.

Switching over Entire Networks Part of why cherry picking can be dangerous for the incumbent is that the upstart networks can reach over and directly acquire an entire set of users who have been conveniently aggregated on your network. It’s just software, after all, and users can spread competitors within an incumbent’s network by using all the convenient communication and social tools. Airbnb is again an example of this. The company not only unbundled Craigslist and turned the shared rooms idea into an entire product, but they actually used Craiglist users to advertise Airbnb to other users. How? Early on, Airbnb added functionality so that when a host was done setting up their listing, they could publish it to Craigslist, with photos, details, and an “Interested? Got a question? Contact me here” link that drove Craigslist users back to Airbnb. These features were accomplished not by using APIs provided by Craigslist, but by reverse-engineering the platform and creating a bot to do it automatically—clever! I first wrote about this in 2012 on my blog, in a post titled “Growth Hacker is the new VP Marketing” with this example in mind. By the time Craigslist decided it didn’t like this functionality and disabled it, months had passed and Airbnb had formed its atomic network. The same thing happened in the early days of social networks, when Facebook, LinkedIn, Skype, and others grew on the back of email contacts importing from Hotmail, Yahoo Mail, and other mail clients. They used libraries like Octazen—later acquired by Facebook—to scrape contacts, helping the social networks grow and connect their users. At the time, these new social networks didn’t look like direct threats to email. They were operating within niche parts of messaging overall, focused on college and professional networks. It took several years for the email providers to shut down access after recognizing their importance. When an incumbent has its network cherry-picked, it’s extra painful along two dimensions: First, any network that is lost is unlikely to be regained, as anti-network effects kick back in. And second, the decline in market share hits doubly hard, which has implications for being able to raise money.

First, does the product have a network? Does it connect people with each other, whether for commerce, collaboration, communication, or something else at the core of the experience? And second, does the ability to attract new users, or to become stickier, or to monetize, become even stronger as its network grows larger?

A successful network effect requires both a product and its network, and that was true in the age of the American Telephone & Telegraph Company, and true today. For Uber, the “product” is the app that people run on their phones, and the “network” refers to all the active users at any given time who are connecting with Uber to drive or ride.

Africans began life in the Americas as subjects profoundly shaped by their Atlantic experience, and the communities they created in the Americas were organized around solutions to the specific problems they faced. The cultures they produced do not reflect the simple transfer and continuation of Africa in the Americas but rather reflect the elaboration of specific cultural content and its transformation to meet the particular needs of slave life in the Atlantic system: their need to reassert some kind of healthy relationship to ancestors; to manage death; to produce social networks, communities, and relations of kinship; to address the imbalance of power between black and white; to stake a claim to their bodies to counter the plantation economy’s claim to ownership. In this sense, the cultural practices of diasporic Africa could have meaning only outside Africa. Shared Atlantic experience and memory served as a touchstone for new cultural practices that emerged in the New World diaspora. Only through the capacity and willingness to invent and experiment—to grow and change the cultural tools carried in memory and create new ones to meet the demands of this new world—could Africans hope to remain recognizable to themselves as human beings in a system that held so much of their humanity in callous and calculated disregard. African immigrants retained that foothold in ways determined by the varied circumstances of their slavery: the immigrant slave might adapt a remembered ritual practice to new applications in American slavery or explore and perhaps ultimately adopt an entirely novel practice. The means were extraordinarily diverse because of the great variety of settings and conditions in which the colonial economies of the Americas enslaved human beings. The continuity Africans needed was not the static, ossifying connection of conformity of practice—doing things in the present as they had been done in the past, even when the context of past cultural forms no longer corresponded to the needs and circumstances of the present. Rather, the connection Africans needed was a narrative continuity between past and present—an epistemological means of connecting the dots between there and here, then and now, to craft a coherent story out of incoherent experience.

The question then becomes how to get these users to take the various actions that will make them higher value. This usually happens in the form of educating users—with content or otherwise—or simply introducing and promoting new features. In LinkedIn’s case, a new feature might be a prominent suggestion to an early user to connect with people from their own company, to help them form their initial network. Content and communications might be a series of how-to videos teaching effective use of LinkedIn’s connection features. And an incentive might look like a free subscription when the user completes certain actions. A product road map can be generated with hundreds of these ideas, large and small, and then prioritized. In Dropbox’s case, this segmentation revealed that a user who has installed the product across multiple devices—home and work computers, or on their mobile devices—is more valuable than someone who just has a single device and uses the service for backup. Better yet, high-value users often share folders and collaborate with other users, particularly for work purposes. As I described earlier, Dropbox segmented their users by value—High-Value Actives and Low-Value Actives, as opposed to LinkedIn’s frequency-oriented segmentation. To encourage users to take these high-value actions, Dropbox could improve the functionality of syncing and sharing. It could send or show educational content, showing users the fastest way to get set up on multiple devices. Or it could use incentives—free storage, for instance—to compel users to properly set up their accounts.

For a social or communication product, the loop often starts with a content creator posting or sending content. The content is then sent to everyone they are connected to, and depending on the size of the network, they get a nice stream of likes and comments back. That’s the payoff that keeps them going.

LSD profoundly alters cognitive unity. Many people feel that the separation between the self and world dissolves when on LSD, and they begin to feel at one with everything. Conscious experience as a unified whole also breaks down on LSD, especially during the acute phase at high doses, so that perceptions that originate from inside are difficult to disentangle from those originating from outside. Experience itself becomes like movie frames slowed down so that each frame is perceivable. We know now that there are neurobiological reasons for this; hallucinogens have profound effects on global brain activity. Psilocybin, for example, decreases the connections between visual and sensorimotor networks, while it seems to increase the connectivity between the resting-state networks. Temporal integration is related to one’s sense of the current moment. Conscious experience is somehow located in time. We feel like we occupy an omnipresent widthless temporal point—the now. As Riccardo Manzotti says: Every conscious process is instantiated by patterns of neural activity extended in time. This apparently innocuous hypothesis hides a possible problem. If neural activity spans in time (as it has to do since neural activity consists in trains of temporally distributed spikes), something that takes place in different instants of time has to belong to the same cognitive or conscious process. For instance, what glues together the first and the last spike of neural activity underpinning the perception of a face? We know that neuronal oscillations at different frequencies act as this temporal glue. However, when you’re on LSD, this glue seems to dissolve. As Albert Hofmann and many others report, your normal sense of time vanishes on psychedelics. The famous bicycle trip on acid during which Hofmann reported that he felt he was not moving, and yet he arrived at home somehow, illustrates this distortion of the brain mechanisms that support our normal perception of the flow of time.

Chatroulette pairs random people from around the world for webcam conversations. People can leave a conversation at any time by initiating a new connection or simply by quitting. The strangely addictive site grew from twenty people at launch in late 2009 to more than 1.5 million users six months later. Initially, Chatroulette had no registration requirement and no controls of any kind, leading to what became known as the Naked Hairy Men problem. As the network grew without policing, a growing number of naked hairy men showed up to chat, leading many of the non-naked, non-hairy others to abandon the network. As legitimate users fled, the noise level on the platform increased, setting a negative feedback loop in motion.

The fundamental dilemma underlying the major problems of our time seems to be the illusion that unlimited growth is possible on a finite planet. This, in turn, reflects the clash between linear thinking and the nonlinear patterns in our biosphere—the ecological networks and cycles that constitute the web of life. This highly nonlinear global network contains countless feedback loops through which the planet balances and regulates itself. Our current economic system, by contrast, is fueled by materialism and greed that do not seem to recognize any limits.

The Emory researchers who identified the four phases of meditation found that when meditators slip out of the focused attention of the TPN and into Mind Wandering, the DMN activates. The wandering mind of the DMN has a “me” orientation, focusing on the self. It may flit from what’s going on at the moment (“Is that a mosquito buzzing?”) to future worries (“I’m nervous about next week’s exam”) to the past (“I’m so mad at my brother Jim for calling me a sissy at my fifth birthday party”). The precuneus contributes to both self-referential focus and episodic memory. Disturbing memories are played and replayed. The idle brain defaults to what is bothering us, both recent and long-past events. These egocentric musings of the wandering mind form the fabric of our sense of self. When you quiet your TPN in meditation, you open up a big empty space in consciousness. For a few moments, the brain is quiet, and you feel inner peace. Then the engine starts revving. The DMN kicks in, bringing with it a cascade of worries and random thoughts. You’re doing 2,000 RPM in Park, but going nowhere. And it gets worse. The DMN has a rich neural network connecting it with other brain regions. Through this, it busily starts recruiting other brain regions to go along with its whining self-absorption. It commandeers the brain’s CEO, the prefrontal cortex. This impairs executive functions like memory, attention, flexibility, inhibition, planning, and problem-solving. 2.5. Nerves from the Default Mode Network reach out to communicate with many other parts of the brain. The DMN also recruits the insula, a region that integrates information from other parts of the brain. It has special neurons triggered by emotions that we feel toward other people, such as resentment, embarrassment, lust, and contempt. We don’t just think negative thoughts; we feel them emotionally too. At this stage, the meditator isn’t just wallowing in a whirlwind of self-centered thoughts. The DMN has taken the brain’s CEO hostage, while through the insula it starts replaying all the slights, insults, and disappointments we’ve experienced in our relationships. The quiet meditative space we experienced just a few moments before has been destroyed. This drives meditators absolutely nuts. No sooner do they achieve nirvana, the still, quiet place of Bliss Brain, than the DMN serves up a smorgasbord of self-absorbed fantasies. It pulls us into negative emotional states—then drags the rest of the brain along behind it. The DMN. Hmm . . . that acronym reminds me of something: “the DeMoN.” The DMN is the demon that robs me of the inner peace I’m seeking through meditation

And don’t think of networking as a means to an end—as a tit-for-tat exchange where if you do someone a favor they may do you one in return. Nobody wants to feel like they’re being used. You should talk to people and make connections because you’re naturally curious. You want to know how other teams at your company work and what people do. You want to talk to your competitors because you’re all working to solve the same problems and they’re taking a different approach. You want your projects to be successful, so you don’t just talk to your immediate teammates at lunch—you grab lunch with your partners, your customers, their customers, their partners. You talk to everyone: get their ideas and their perspectives. In doing so you may be able to help someone or make a friend or strike up an interesting conversation.

A somewhat provocative example of the interconnections between the gaming industry and finance. A technologist working for a large London hedge fund hinted this to me in interview. Trained in computer science and engineering, this interviewee first worked as a network programmer for large online multiplayer games. His greatest challenge was the fact that the Internet is not instantaneous: when a player sends a command to execute in action, it takes time for the signal to reach the computer server and interact with the commands of other players. For the game to be realistic, such delays have to be taken into account when rendering reality on the screen. The challenge for the network programmer is to make these asymmetries as invisible as possible so that the game seem 'equitable to everyone.' The problem is similar in finance, where the physical distance from the stock exchange's matching engines matters tremendously, requiring a similar solution to the problem of latency: simulating the most likely state of the order book on the firm's computers in order to estimate the most advantageous strategies or the firm's trading algorithms. Gaming and finance are linked not through an institutional imperative of culture or capital - or even a strategy, as such - but rather through the more mundane and lowly problems of how to fairly manage latency and connectivity.

An important aspect of the power of neural networks is that during training, as the weights on the connections within the network are set, the network is in effect learning a decomposition of the larger problem, and the individual neurons are learning how to solve and combine solutions to the components within this problem decomposition.

My problem is that if I engage in political activism, then the ultimate conclusion is always revolution. I'm a big Guy Fawkes fan, what can I say. I see the "democratic" system here as obsolete and wide open to corruption. I do have a solution, and it qualifies as a response to the degree of connection that has developed since the creation of parliament. Back then, people were obliged to have a representative (albeit a corrupt one) at the seat of power to ensure their best interests were being looked after. We don't suffer from distance like we used to, and the ubiquity of the internet means that people, close to the entire population are connected in communicative union that lends itself to a sort of hive government. As a citizen of a country your duty would be to engage in a nominal percentage of votes a year, with anyone with the support of a given number of voters able to table bills, which everyone then votes on. The next step would be an AI administrator to this networked hub....no, wait, a quantum AI administrator, call it Mother, a dynamic of algorithms that bears no consideration to a ten million pounds backhander, or the ethnicity of the citizen, but only serves self governance and the welfare of the populace. It sounds like a crazy sci-fi plotline, but it's absolutely doable.

In The Rational Optimist Matt Ridley shows how human beings have always been at their most innovative when connected to large, diverse networks.

During the run-up to the 2014 election for prime minister that he won in a landslide, Narendra Modi in India managed to be at many rallies at the same time by using full-scale, three-dimensional holograms that many voters took to be real. He also managed to be at more than one place in ideological terms. To the generation of globally connected ambitious young urban Indians, he was the embodiment of political modernization (emphasizing innovation, venture capital, and a slick pro-business attitude, and so on); the new entrants into the expanding middle class saw him as the one most likely to uphold their vision of nationalism rooted in Hindu tradition; for the economically threatened upper castes, he was the rampart against the (largely imagined) growing influence of Muslims and lower castes. If members of these groups had met together and each had been asked to describe “their” Modi, their answers would probably have been largely unrecognizable to the others. But the networks in which these three groups operated were sufficiently separate that there was no need for internal consistency.

Surveillance capitalism’s command of the division of learning in society begins with what I call the problem of the two texts. The specific mechanisms of surveillance capitalism compel the production of two “electronic texts,” not just one. When it comes to the first text, we are its authors and readers. This public-facing text is familiar and celebrated for the universe of information and connection it brings to our fingertips. Google Search codifies the informational content of the world wide web. Facebook’s News Feed binds the network. Much of this public-facing text is composed of what we inscribe on its pages: our posts, blogs, videos, photos, conversations, music, stories, observations, “likes,” tweets, and all the great massing hubbub of our lives captured and communicated.

This progression is a manifestation of synaptic pruning. When our brains are growing, we make more neurons than we need. As we navigate our lives, solving problems and adapting to different environments, we use certain networks of neurons more than others. These commonly used networks become more numerous and better able to compute information; then they streamline their connections and become more efficient. By the time we are adults, our brain networks are stripped down and specialized. We lose plasticity, but our cognition becomes better at solving the problems we are most likely to face.

Several connections form a mesh network – a group of independent actors capable of connecting one another to a larger system. As users enter and exit the mesh network, logic in the system automatically re-routes traffic through the connected network of users. Structured networks (such as cellphone services) are able to be destroyed, overwhelmed, and degraded in their performance. Mesh networks are not prone to these problems because they are natively built to withstand them.

How to Fix Roku Error 014.30 Resolving this issue is generally straightforward, although it can be caused by problems outside of your control. Test your Wi-Fi network with another device, such as your cell phone or computer. Make sure your internet is working properly before you proceed. If not, stop and contact your internet service provider. Using your Roku remote, go to Settings > System > System restart on the Roku menu or Settings > System > Power > System restart if you have a Roku television to restart your Roku system. Reboot your modem and router. Rebooting the modem and router may reset your wireless signal to normal if there is other interference that has caused the Roku to lose signal. When all devices are powered on again, test the Roku to see if the error occurs. If the error still appears, use the Roku remote to go to Settings > Network > Setup connection and select Wireless. Choose your network and enter the network password to ensure it is entered correctly. Select Connect to proceed. If you use MAC address filtering on your network your Roku device may be blocked because the MAC address isn’t recognized. Add the MAC address provided on the error message screen to your router to see if that unblocks your Roku device and returns your service to normal. If you have determined that your Wi-Fi network is working efficiently and all other efforts to resolve error 014.30 have failed, performing a factory reset on your Roku device will restore it to its original settings. Then you can set the Roku up as if it were new and reconnect it to your network to see if it is operating properly. For further information contact :- +1 855-203-0401

If you do enough threat modeling, you start noticing all kinds of instances where people get the threat profoundly wrong: * The cell phone industry spent a lot of money designing their systems to detect fraud, but they misunderstood the threat. They thought the criminals would steal cell phone service to avoid paying the charge. Actually, what the criminals wanted was anonymity; they didn't want cell phone calls traced back to them. Cell phone identities are stolen off the air, used a few times, and then thrown away. The antifraud system wasn't designed to catch this kind of fraud. * The same cell phone industry, back in the analog days, didn't bother securing the connection because (as they said): 'scanners are expensive, and rare.' Over the years, scanners became cheap and plentiful. Then, in a remarkable display of not getting it, the same industry didn't bother securing digital cell phone connections because 'digital scanners are expensive, and rare.' Guess what? They're getting cheaper, and more plentiful. * Hackers often trade hacking tools on Web sites and bulletin boards. Some of those hacking tools are themselves infected with Back Orifice, giving the tool writer access to the hacker's computer. Aristotle called this kind of thing 'poetic justice.' [...] These attacks are interesting not because of flaws in the countermeasures, but because of flaws in the threat model. In all of these cases, there were countermeasures in place; they just didn't solve the correct problem. Instead, they solved some problem near the correct problem. And in some cases, the solutions created worse problems than they solved.

Conventional medicine doesn't explore the complex connections between closely networked systems of the body (digestive, immune, nervous, endocrine). Most docs aren't looking at problems in a big-picture way and are trained to see things only within their area of specialty; asthma is a lung problem; eczema is a skin problem; constipation is a gut problem; ADHD is a brain problem. But the body is one complete system. Nothing happens in isolation.

For consumers, most of these problems are invisible. That is by design. You’re not supposed to know that the trending topics on Twitter were sifted through by a few destitute people making pennies. You’re not supposed to realize that Facebook can process the billions of photos, links, and shareable items that pass through its network each day only because it recruits armies of content moderators through digital labor markets. Or that these moderators spend hours numbly scrolling through grisly photos that people around the world are trying to upload to the network. Uber’s selling point is convenience: press a button on your phone and a car will arrive in minutes, maybe seconds, to take you anywhere you want to go. As long as that’s what happens, what do consumers have to complain about? Now joined by a host of start-up delivery services, ride-sharing companies are in the business of taking whomever or whatever from point A to point B with minimal fuss or waiting time. That this self-indulgent convenience ultimately comes at the expense of others is easily brushed off or shrouded in the magical promise that anything you want can be produced immediately.

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