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A leader who “ums” and “ahs” over too many decisions probably won’t be a leader for very long. Even when uncertainty is warranted, an obvious lack of confidence won’t inspire your team or coworkers. Professionals know that confidence can have tremendous value, but where exactly does it come from? And more to the point: how can we get more of it? After all, when we teach children that “you can be anything you want”, we do so on the premise that confidence is a key driver for success in life and work.

Research shows that most of us are not just confident, but overconfident. One influential study from 1977 that measured knowing and certainty asked participants to answer a series of questions then record their confidence in the accuracy of their answers. What the researchers found was that people who were 70% confident were right 60% of the time; while those who were 90% confident were right just 75% of the time. Confidence only gets you so close to the truth.

A person’s lack of confidence, meanwhile, is generally seen as a problem that needs to be fixed. There are books, courses, and coaches whose goal it is to help people become more confident. For good reason: without confidence, we will never believe in ourselves or have the nerve to try things that test our abilities.

The tricky part is getting your confidence and ability to properly align.

How do we align confidence and ability?

Confidence is most useful when it emerges from a foundation of ability. Research has given us some guidelines for how to make that happen. For starters, we have to be experienced in the domain we’re operating in. Reliable, grounded confidence requires that you already know the ins and outs of the system or specialization you’re attempting to predict or control or explain.

Second, that domain or system must be regular and predictable. You can certainly be confident in your ability to perform heart surgery, but it’s meaningless to say you’re confident you can win the lottery. Twenty years of experience buying lotto tickets doesn’t translate into accurate predictions for your next bet. After all, studies of investors and investing have found that even experienced stock traders often perform at a level just slightly above chance.

One implication of all this is that experts in stable fields can learn to trust their own intuition. A doctor making a snap diagnosis, a pilot navigating rough weather, or a designer gauging next season’s trends are rightly justified in trusting their instincts. But be wary of the experienced sports gambler who claims some underdog will win, or an investor who promises high returns year after year.

When predictions fall flat

Nobel Prize-winning psychologist Daniel Kahneman wrote about the hazards of confidence during his years in the Army. Tasked with identifying troopers with leadership potential, Kahneman put groups of soldiers through an obstacle course where he observed who took charge, who came up with solutions, and who became discouraged when their opinions were rejected by the other soldiers.

By the end, Kahneman believed he had a good idea of who had the qualities of a leader. But when he measured the results of his predictions? Barely above chance. “The story was always the same: our ability to predict performance at the school was negligible. Our forecasts were better than blind guesses, but not by much.”

Despite the discouraging feedback, Kahneman noted that he continued to feel confident in the predictions. “The statistical evidence of our failure should have shaken our confidence in our judgments of particular candidates, but it did not. It should also have caused us to moderate our predictions, but it did not. … I was reminded of visual illusions, which remain compelling even when you know that what you see is false.”

Gut instinct can be powerfully persuasive. Here’s a question: a bat and ball together cost $1.10. The bat costs $1 more than the ball. How much does the ball cost? For most people, $0.10 comes screeching to mind. That intuition is fast, easy, and just feelsright. Except that the correct answer is $0.05.

Confidence is a feeling that needn’t require logical analysis. It can accompany a gut reaction or even just a sense of ease. Take, for instance, the evidence that people are more likely to invest in stocks with easy-to-pronounce names. In Kahneman’s words, “Confidence is a feeling, which reflects the coherence of the information and the cognitive ease of processing it. It is wise to take admissions of uncertainty seriously, but declarations of high confidence mainly tell you that an individual has constructed a coherent story in his mind, not necessarily that the story is true.”

Sorting fact from fiction

What about overconfidence, confidence’s boisterous cousin? It certainly has its advantages: it motivates us, it can be self-fulfilling (believing in yourself makes you more effective), and it can certainly convince others to trust or follow you.

But that’s not to say that overconfidence should be taken to the extreme. Rather, that it’s useful in motivating ourselves and others. Surely we can be ambitious while also aware of our limitations. As we try things, and experience different rates of success and failure, we should learn and adjust our confidence level accordingly. If we don’t believe that our flaws exist, it won’t be easy to fix them.

The bottom line for professionals in leadership roles is this: seek balance. If you come across as overly indecisive, you’ll of course fail to inspire confidence in your colleagues or friends. On the flip side, brash overconfidence coupled with errors and mistakes does its own brand of harm. Instead, aim for an accurate understanding of your abilities coupled with a healthy skepticism for gut instinct. And don’t forget that doubt is a necessary element in effective, inspirational confidence.

Latest patent filings cover new inventions used in developing Entefy’s universal communicator

PALO ALTO, Calif. January 17, 2018. Entefy Inc. has filed an additional 15 patents with the United States Patent and Trademark Office (USPTO). The company’s portfolio of filed and issued patents now stands at 46. These filings cover new inventions in a number of technology domains such as search, artificial intelligence (including natural language processing, data intelligence, and predictive intelligence), blockchain, communication, data privacy, and Internet of Things.

“Our team recognizes the valuable role innovation plays in staying ahead of today’s rapidly evolving market, and this news represents a major milestone in Entefy’s path to deploy the universal communicator,” said Entefy’s CEO, Alston Ghafourifar.

In 2017, Entefy announced that it had completed its Series A financing round at a $150 million valuation, the filing of an additional 13 patents in AI, cybersecurity, and data privacy, the issuance of a patent for encrypted search, and the issuance of another patent covering context awareness in messages.

ABOUT ENTEFY

Entefy is building the first universal communicator—a smart platform that uses artificial intelligence to help you seamlessly interact with the people, services, and smart things in your life—all from a single application that runs beautifully on all your favorite devices. Our patented technology combines digital communication with advanced computer vision and natural language processing to create a lightning fast and secure digital experience for people everywhere. 

On December 14, 2017, the FCC voted to repeal the rules that ensured that Internet Service Providers (ISPs) provide Internet access without restriction, preferences, or prioritization. It was a sad day in the history of the Internet.

The term net neutrality describes the principle of a free, open, democratic Internet where no one website or service is given priority over another. Despite evidence that 83% of Americans opposed its repeal, net neutrality is no longer the law of the land and, instead, ISPs are able to use their pricing power and near-monopoly status in many markets to slice and dice the Internet in whatever ways best boost their profits. Without net neutrality, it’s easy to imagine ISPs charging customers extra for “fast lane” access to certain websites—at their sole discretion—in the same way that cable television providers offer tiered pricing and channel packages.

The good news is that the principle of net neutrality isn’t dead. At least not yet. But it will take Congress changing current laws or favorable rulings in the courts to reinstate a free, open Internet.

In case you’re on the fence about net neutrality, or in need of some motivation to take action and show your support, we’ve assembled 6 reasons why net neutrality is a good thing. Each of these arguments on their own justify enshrining the right to a free Internet into law. Taken together, they’re an unassailable argument in favor of protecting the Internet from the narrow interests of a handful of industry Goliaths.

At the end of the article we’ve shared resources for following the net neutrality fight and expressing your support for the cause.

1. Net neutrality protects consumers choice and free speech. The top 4 broadband ISPs control 75% of the residential market. For fast Internet (100 Mbps) access, 88% of the country has either one or no provider. And many of the largest ISPs are also content producers, which mean their incentives to limit competing content is complicated at best. At its core, this conflict of interest has free speech implications: “In 1776, Thomas Paine didn’t need the permission of any other content creator or distributor to circulate Common Sense. But without rules prohibiting blocking, throttling, and the like, broadband providers would gain the power to limit what unpopular content flows over their networks—to the detriment of consumers and democracy.”  
2. Net neutrality is pro-business. The free availability to any kind of information drives personal and corporate productivity, enables new products and services, and allows for healthy competition among established companies and disruptive upstarts. The only businesses that win without net neutrality are the handful of ISPs. “Without net neutrality rules, prioritization of internet traffic by telecom and cable companies would skew the competition for content, as well as tilt the scales in the dissemination of all political and social views in favor of websites and companies that are able to pay internet access providers.”
3. Net neutrality is pro-freedom. Without net neutrality, ISPs are allowed to take certain actions that directly impact four Internet freedoms that consumers have come to value highly. Michael Powell, the FCC Chairman under President Bush, defined these freedoms in a 2004 speech: The freedom to access any content, so long as it was legal; the freedom to access any service or application; the freedom to use their Internet connections on any computer or device; and the freedom to get detailed, transparent subscription information from their ISP. These freedoms are now in jeopardy.
4. Net neutrality gives consumers the power of choice. In the absence of net neutrality, ISPs have the right—and the financial incentive—to bundle Internet services, including charging more for access to specific websites and services. With few or no options for many, consumers won’t even be able to vote with their pocketbooks when it comes to any new pricing models. ISPs gain the upper hand.
5. Net neutrality preserves the competitiveness of the U.S. technology sector. A free, open, non-preferential Internet has an important feature: every company that relies on the Internet for its products or services can compete on even footing with every other. This enables the quintessentially American ideal of fair competition. Without net neutrality, ISPs are in a powerful position of being able to pick winners and losers through their pricing power. Said one economics professor: “U.S. growth and worldwide dominance of high technology would be significantly challenged without network neutrality.”
6. Without net neutrality, the U.S. joins the out-crowd. Another result of the FCC vote is that the U.S. joins an exclusive group of countries without consumer protections for free, open Internet access. That group includes North Korea, Saudi Arabia, Cuba, and Iran. Also on the list is Portugal, where the “country’s wireless carrier Meo requires users to pay additionally for apps and services they would like to use, like WhatsApp, Facebook, Snapchat, and Messenger. Video apps are also offered as paid add-ons in a variety of bundles.”

Concerned about the future of the Internet? The fight for net neutrality will continue, so there’s still time to make a difference. The nonprofit Fight for the Future maintains a website called www.battleforthenet.com that features an automated tool for contacting your Congressional representatives. It is a good starting point for learning about the issues and taking action to support Internet freedom. 

Always be learning. For professionals today, keeping pace with the changing dynamics of business is an imperative. And the idea that a diploma represents the end of learning is an old fashioned one. Yet knowing that’s true isn’t the same as knowing how to learn effectively, or to manage the growth of your knowledge and skillsets into new specialties and directions.

If continual learning seems daunting, it doesn’t have to be. It’s not necessary—or even desirable—to start from scratch every time you sit down to explore a new subject. There’s one very powerful toolbox on your side: mental models.

The investor Charlie Munger is Warren Buffet’s longtime partner. In a talk at USC Business School in 1994, he laid out the rationale for using mental models to better understand the world. Here’s what he said:

What is elementary, worldly wisdom? Well, the first rule is that you can’t really know anything if you just remember isolated facts and try and bang ’em back. If the facts don’t hang together on a latticework of theory, you don’t have them in a usable form.

You’ve got to have models in your head. And you’ve got to array your experience—both vicarious and direct—on this latticework of models. You may have noticed students who just try to remember and pound back what is remembered. Well, they fail in school and in life. You’ve got to hang experience on a latticework of models in your head.

What are the models? Well, the first rule is that you’ve got to have multiple models—because if you just have one or two that you’re using, the nature of human psychology is such that you’ll torture reality so that it fits your models, or at least you’ll think it does.

He goes on to recommend that your own mental models should come from different areas and disciplines, giving you the intellectual flexibility it takes to foster expertise in a particular subject. Mental models serve a similar function as that of the pilot’s preflight checklist. There are many dozens of things that have to be confirmed to ensure a safe flight. Sure, you can just try and remember the list mentally but consistent use of preflight checklists improves safety dramatically. 

Similarly, when considering a situation, there are many ways of looking at that event. There is a scientific view, an economic view, a social view, a sustainability perspective, and so on. The more complex and consequential the topic, the more worthwhile it is to have a cognitive toolbox of mental models to apply. This becomes your own personal “latticework of theory,” to use Charlie Munger’s term.

You might be surprised by just how many of these mental models you already know about, or even use frequently. As you look over the following list, take note of which models “click” with you—these are the ones that deserve your attention and are likely to be useful to you in the future.

Here are 6 mental models that you can use to deepen your understanding of practically any subject:

1. Occam’s razor. Among competing and equally plausible explanations for a phenomenon, simplicity should be given preference; that is, the explanation requiring the least number of assumptions. When you hear the sound of galloping hooves, first assume horses, not zebras.
2. The map-territory relation. A representation of reality is not necessarily reality itself. Complex systems require abstract representation in order to simplify them sufficiently to be understood. We use maps, pictures, sketches, and measurements to represent something, but those representations are always potentially fallible. There is an imperfect relationship between reality and the models we use to represent and understand reality. This mental model suggests two questions: Is what I am looking at the map or the territory? And, is the map an accurate representation of the territory?
3. Bell curves or normal distribution. You’ve probably heard of bell curves and standard deviations. For many things in life, there is a normal distribution of outcomes that can be represented as a bell curve. Find the average, then expect an equal distribution on either side of that average. Height and IQ are two normally distributed attributes. The average IQ might be 100, but 2% will have scores above 130 and 2% will have scores below 70. This mental model triggers the question: Is what I am looking at the average or the exception? 
4. Feedback loops. Many systems have one or more feedback mechanisms that can impact strategic decisionmaking. Some systems are very simple: A causes B. In complex systems, there are many steps like A causes B, B causes C, and C causes D, ultimately leading to the outcome H. In these cases the feedback mechanisms is so that B is confirmed back to A and C is confirmed back to B, ensuring sustained quality along the way.For example, you move into an office which, unknown to you, has a remotely controlled thermostat. It feels chilly so you bring in a portable electric heater. No matter how warm you set the heater, the room stays cool because the thermostat is a hidden feedback mechanism. The warmer you set the heater, the cooler the ventilation becomes in order to maintain the thermostatic setting. In order to accurately change complex systems, you have to know what all the feedback mechanisms are. 
5. Zero-sum or non-zero-sum. Some systems are zero-sum and others are non-zero-sum, and it is vital to know the difference. Zero-sum systems have winners and losers. Sports games, spelling bees, and chess are all examples where there is a winner and a loser. Non-zero-sum systems are far more desirable because everyone can emerge better off than they were before—everyone can be a winner. Voluntary markets are an example of a non-zero-sum systems where everyone participates and finishes better off than if they had not participated. Zero-sum systems rest on the question: How do I win? Non-zero-sum systems rest on the question: How can we win? 
6. Correlation is not causation. This mental model is one of the most fundamental laws in statistics. When one thing happens and then a second thing happens, you can’t rely on the fact that there was a causal relationship between the two. Many things are correlated that are not causally related. For example, data will show a correlation between the age of Miss America and the number of people who die by hot steam; clearly, there is no cause-and-effect relationship between these two things. Our pattern-seeking brains fall prey to natural confirmation bias all the time, making unrelated correlations one of the most common decisionmaking mistakes. This mental model suggests the question: Am I confident that I know the real causal relationships?

Remember, these are only 6 of many possible mental models. They are listed here as a useful set of perspectives that can help you understand complex situations or get up to speed on a new topic. Using them and discovering your own can quickly deepen your understanding of the world around you.

$57.6 billion is expected to be invested in artificial intelligence and other cognitive technologies by 2021. Across a very diverse set of industries, AI is supporting new products and services, and upsetting longstanding competitive dynamics.

This presentation focuses on AI disruption in industries known for making smart use of emerging technologies. From telecommunications to travel to media, companies large and small are pursuing opportunities created by new AI algorithms. 

You can read more about the research featured in this presentation in Entefy’s article, Making smart use of smart systems: AI’s disruptive impact in 10 industries.

As internally developed artificial intelligence systems move from lab to deployment, the importance of creating unbiased, ethical systems is greater than ever. The challenge is that there is no simple solution to building ethical consideration into AI algorithms. But there are a few things you can do early on that help.

To get a sense of the many issues, let’s check out a hypothetical AI-powered job candidate review system. Designed as an AI chatbot, the system vets potential candidates, analyzing a wide range of objective criteria to determine whether someone should pass through the initial application stage. The company decided to restrict access by the system to data about a candidate’s gender, age, and ethnicity, in order to promote a level playing field for candidates who might otherwise be overlooked. 

The system provides another benefit, reducing the chance that a hiring manager’s bad mood or distracted mindset will hurt a qualified applicant’s prospects of landing the job. Operating without emotion, the AI system evaluates candidates based on their experience, skillsets, and even their empathy levels. But the system doesn’t make decisions, it makes recommendations, passing along the most promising candidates to the company’s human managers, who then rely on their professional judgment to make a final decision.

This simplified example highlights the need to anticipate non-technical factors like data bias in designing AI systems. Decisions made early on in the planning process help ensure your company successfully engineers an ethical AI system.

Indispensable human judgment

Data is vital to decisionmaking, and AI helps gather and parse that information. It can even generate reports and recommendations based on the objectives with which it’s been programmed. But a machine learning algorithm can’t tell you whether a decision is ethical or whether it will irreparably damage morale within your organization. It hasn’t spent years honing its business intuition – the kind of intuition that tells you that even though a decision looks right on paper, it would be a betrayal of your core client base. 

That’s where human judgment enters the picture. There are a number of approaches you can take for integrating AI into your decisionmaking strategies. Depending how high the stakes are and the problem you’re trying to solve, you might outsource the job to AI but insist that a person review its findings before action is taken. Or you might identify key areas that will largely be the domain of AI, relieving you of the need to be involved in every decision related to that particular process. 

But human judgment will remain central to business decisions for some time to come. In fact, judgment and interpersonal skills will be at a premium in the workforce of the future. As AI becomes an increasingly prominent tool in our professional arsenals, we must ensure that we’re using it ethically. Here are some ways to do that: 

1. Identify your company’s core values 

Systematizing your company’s core values starts with identifying and documenting those values. Start a process that captures the values that have become central to your company culture. Writing in Harvard Business Review, one researcher made a useful distinction between “values” as marketing and “values” as deeply held beliefs: “If you’re not willing to accept the pain real values incur, don’t bother going to the trouble of formulating a values statement.”

If an AI program suggests a course of action that makes sense on paper but not in the broader context of your organization’s long-term goals, you’ll need a strong internal compass to make the right call. Data is important, but you’re ultimately responsible for your decisions. When called upon to explain your actions, you can’t default to saying, “The AI made me do it.” Use the tools to gather information and add context to your decisionmaking process. But when you make a choice, human nature should be in the mix. 

2. Establish an AI oversight group 

Machine learning systems are only as good as the data we feed them. Which immediately creates a challenge for AI system developers: humans are biased. Even the most fair-minded person carries unconscious bias, sometimes without being aware of it. And so without meaning to, developers can end up corrupting the systems they’re designing to help us make more objective decisions. 

To get around this problem, create internal AI watchdog groups that periodically review your algorithms’ outputs and can address complaints about discrimination and bias. Then use the group’s findings to refine your AI-assisted approach to leadership. 

3. Use AI to facilitate better experiences for customers and employees alike 

Machine learning systems can generate powerfully personalized experiences—for both customers and employees. The World Economic Forum suggests that using AI ethically includes shifting employee performance metrics from output-based measurements to evaluating the creative value they bring to the company. “Although there are roles under threat, there are also roles that will become needed more than ever. It’s more cost efficient to retrain current employees to fill the roles you need in the future than it is to hire new ones, and they are also more likely to be loyal to your organisation.” 

One great part of the power of AI tools in the office is that people don’t have to do drudge work anymore. As their roles become more dynamic, so, too, should your evaluation standards.

By investigating these 3 areas early on in the development process, your company is better positioned to build new AI systems that reflect—and protect—your company’s values. And improve the experiences of your customers and employees alike. 

In the not-too-distant past, worker happiness seemed to fall pretty far down the priority list at most companies. Our culture celebrated the myth of busyness with little regard for how those long hours and the pressure to perform were impacting people’s well-being and sense of fulfillment.

Flash forward to today and happiness is of paramount importance. Some companies even employ Chief Happiness Officers, whose job is – you guessed it – to foster employee happiness. Their job duties range from organizing morale-boosting team events to conducting emotional check-ins with employees to implementing new happiness-oriented policies. They’re tasked with creating an environment that inspires worker loyalty and drives increased productivity.

Although some people are critical of the concept of having a dedicated happiness officer in the C-suite, few would disagree with the fact that happy workers are more productive workers. Research indicates that productivity jumps 12% among happy employees, and it drops by 10% among their less cheerful colleagues. Harvard University researcher Shawn Anchor, who wrote The Happiness Advantage, found that increased happiness changes the way our brains function. People who are more optimistic are better problem-solvers because they’re more likely to spot new opportunities and potential solutions.

Whether you hire a Chief Happiness Officer or simply integrate happiness-supporting policies into your culture, it’s clear that employee satisfaction isn’t just a nice-to-have. It’s another competitive advantage for successful modern businesses.

Happiness and wellness go hand-in-hand   

What does happiness really mean from a workplace perspective? Certainly, a positive, collegial environment is more attractive than a tense, ulcer-inducing office space. However, discussions of happiness and productivity are a gateway to a broader topic: how employers can foster happiness through the right benefits and perks.

Tech startups in particular became notorious in recent years for offering flashy perks, such as foosball tables and craft beer on tap. But the novelty of having a fun office environment seems to be giving way to desires for more substantial, lifestyle benefits. Interestingly, many of the perks associated with tech startups – such as ping-pong set-ups and unlimited snacks and beer – aren’t as in-demand as more substantial benefits.

One study showed that only 12% of workers surveyed feel that employers should encourage games in the workplace, indicating that fun but superficial perks aren’t relevant to employee happiness. However, people are drawn to companies offering more creative and impactful job perks, such as pet insurance, help paying down student loan debt and covering wedding expenses, and paid time off to volunteer.

That may be in part because Millennials, who represent the largest demographic in the American workforce, are entering new stages in their lives. As they marry or co-habitat with long-term partners, buy houses, and start families, office game rooms and happy hours carry less appeal than flexible work policies, fitness incentives, and robust health insurance benefits. This age group is also adamant about finding work that fulfills and challenges them. An employer that pays you to volunteer (and pays your student loans) checks a lot of those boxes.  

The shift is likely due to a growing cultural awareness around well-being too. Celebrity business leaders such as Arianna Huffington have been leading the charge for a fundamental overhaul of how we think about productivity and work-life integration. Huffington shared her story of collapsing at her desk one evening when she was exhausted and overworked. Since then, she’s made it her mission to sound the alarm about the dangers of burnout and fatigue. Her company, Thrive Global, advises big-name businesses such as JPMorgan on enhancing productivity and improving behavioral patterns through holistic methods. The company recently raised $30 million and is now valued at $120 million, indicating the growing interest in this field.

The business case for happiness

The emphasis on more valuable perks and cultural wellness makes sense from a business perspective. We know that happy workers are more productive workers, and it’s hard to be happy when you’re stressed and exhausted. The person who pulls 12-hour days to impress her bosses but barely has time to see her family isn’t going to be chipper for very long. The stress of her job, combined with constant anxiety about student loans and guilt over not spending more time with her kids – not to mention a lifestyle that involves a lot of take-out and very little exercise – is a recipe for a breakdown, not a professional breakthrough. Physical and emotional well-being directly influence people’s moods and energy lives, which in turn impact their productivity.

Given the correlation between well-being, happiness, and productivity, it’s not surprising that the businesses atop Fortune’s 100 Best Companies to Work For 2017 list offer a range of holistic benefits such as onsite childcare and fitness facilities, flexible work policies, tuition reimbursement, discounted gym membership rates, and other lifestyle-enhancing perks.

Having so many attractive options on-site likely keeps people in the office and working hard. But it also signals that it’s OK to take a few minutes to rest or refuel in healthy ways, rather than working from sun up to sundown. It’s becoming clear that the things that make us happy at work are the same that make us happy in our personal lives. Healthy habits, fulfilling work, and stability for our families leads to improved emotional states across the board.

While companies on the Fortune list have set high standards for employee benefits, you don’t have to have a multi-million-dollar budget to support workers’ happiness. A few core wellness policies can make a tremendous difference to your employees’ satisfaction and productivity.

Consulting powerhouse PwC released projections for the economic value of artificial intelligence. They found that the world’s $75 trillion Gross Domestic Product (or GDP, the measurement of the total economic output of a place) could grow 14% higher by 2030 on the back of advancements in AI. That works out to an additional $15.7 trillion in economic value. PwC projects that China will see the greatest gains, with 26% GDP growth from AI, while North America should see a 14% boost. Big picture, the biggest sector gains will be in retail, financial services, and healthcare.

It’s easy to read a ginormous figure like $15.7 trillion dollars without the size settling in mentally. So here’s some context to this projection of AI’s global impact. Using data on 2016 GDP from the World Bank, $15.7 trillion represents the total economic output of 177 different countries. Ranking the world by GDP size, with U.S. #1, China #2, and so on, $15.7 trillion equates to the output of every country below #17 on the list – from the Netherlands, Switzerland, and Saudi Arabia all the way down to #195 Tuvalu.

Or to put it another way: By 2030, AI will create as much economic value as the current GDP of 177 countries. 

Entefy’s enFacts are illuminating nuggets of information about the intersection of communications, artificial intelligence, security and cyber privacy, and the Internet of Things. Have an idea for an enFact? We would love to hear from you. 

Here’s something to keep in mind when you’re scanning the next “Predictions for 2018” article you come across. Because whether it’s an expert in a magazine or your own company’s market forecast, it’s easy to get caught up in predictions that sound great but have a flimsy evidentiary foundation. The hazard of forecasting is that people struggle to see past their own commitment to a particular vision for the future, and end up blinded by questionable assumptions or their own cognitive biases. 

There’s plenty of data to support the notion that people are terrible forecasters—but there’s more to the story. Philip E. Tetlock is one of the leading researchers in this area of making forecasts. He notes in his book Superforecastingthat how you think matters more than what you think. He goes on to identify a number of traits that are associated with individuals who do a very good job of making usefully accurate forecasts:

• Be cautious
• Be humble
• Be intellectually curious and open-minded
• Value and synthesize diverse views
• Believe it’s possible to improve

With the speed at which the world moves these days, much of what we have great confidence in today has a good chance of being either wrong or irrelevant in a year, five years, or a decade down the line. Caution and humility usually do not come naturally to us, so much so that there’s a term for it: the Dunning-Kruger Effect.

So with the gates of a new year wide open before us, let’s take a pause to look at some very prominent people making very prominent predictions that were so wildly inaccurate as to be comic in hindsight.

• “There’s no chance that the iPhone is going to get any significant market share.” A statement by Steve Ballmer, Microsoft CEO, in April 2007.
• “There’s just not that many videos I want to watch.” The co-founder of YouTube, Steve Chen, shared his concern about the company’s future.
• The Internet will ‘go spectacularly supernova and in 1996 catastrophically collapse.’ A prediction by Bob Metcalf, the founder of 3Com.
• ‘Cellular phones will absolutely not replace local wire systems.’ A statement by Martin Cooper, a pioneer of wireless communications in 1981.
• “Remote shopping, while entirely feasible, will flop.” Time Magazine got it wrong in 1968.
• “There is no reason for any individual to have a computer in his home.” From a speech to the 1977 World Future Society meeting in Boston by Ken Olsen, founder and CEO of Digital Equipment Corporation, an early pioneer in computers.
• “There is practically no chance communications space satellites will be used to provide better telephone, telegraph, television, or radio service inside the United States.” A statement from FCC Commissioner T. A. M. Craven in 1961.   
• “Ignore electric light. When the Paris Exhibition closes, electric light will close with it and no more be heard of.” In 1878 the Oxford University professor Eramus Wilson provided his thoughts on the dim future of electric light.

So what is ahead for 2018? More predictions, of course. Just remember that none of us will know whether they were good or bad for some time to come.

From everyone at Entefy, best wishes for growth and success in 2018!