10 Principles of Change Management

Tools and techniques to help companies transform quickly.

Way back when (pick your date), senior executives in large companies had a simple goal for themselves and their organizations: stability. Shareholders wanted little more than predictable earnings growth. Because so many markets were either closed or undeveloped, leaders could deliver on those expectations through annual exercises that offered only modest modifications to the strategic plan. Prices stayed in check; people stayed in their jobs; life was good.

Market transparency, labor mobility, global capital flows, and instantaneous communications have blown that comfortable scenario to smithereens. In most industries — and in almost all companies, from giants on down — heightened global competition has concentrated management’s collective mind on something that, in the past, it happily avoided: change. Successful companies, as Harvard Business School professor Rosabeth Moss Kanter told s+b in 1999, develop “a culture that just keeps moving all the time.”

This presents most senior executives with an unfamiliar challenge. In major transformations of large enterprises, they and their advisors conventionally focus their attention on devising the best strategic and tactical plans. But to succeed, they also must have an intimate understanding of the human side of change management — the alignment of the company’s culture, values, people, and behaviors — to encourage the desired results. Plans themselves do not capture value; value is realized only through the sustained, collective actions of the thousands — perhaps the tens of thousands — of employees who are responsible for designing, executing, and living with the changed environment.

Long-term structural transformation has four characteristics: scale (the change affects all or most of the organization), magnitude (it involves significant alterations of the status quo), duration (it lasts for months, if not years), and strategic importance. Yet companies will reap the rewards only when change occurs at the level of the individual employee.

Many senior executives know this and worry about it. When asked what keeps them up at night, CEOs involved in transformation often say they are concerned about how the work force will react, how they can get their team to work together, and how they will be able to lead their people. They also worry about retaining their company’s unique values and sense of identity and about creating a culture of commitment and performance. Leadership teams that fail to plan for the human side of change often find themselves wondering why their best-laid plans have gone awry.

No single methodology fits every company, but there is a set of practices, tools, and techniques that can be adapted to a variety of situations. What follows is a “Top 10” list of guiding principles for change management. Using these as a systematic, comprehensive framework, executives can understand what to expect, how to manage their own personal change, and how to engage the entire organization in the process.

1. Address the “human side” systematically. Any significant transformation creates “people issues.” New leaders will be asked to step up, jobs will be changed, new skills and capabilities must be developed, and employees will be uncertain and resistant. Dealing with these issues on a reactive, case-by-case basis puts speed, morale, and results at risk. A formal approach for managing change — beginning with the leadership team and then engaging key stakeholders and leaders — should be developed early, and adapted often as change moves through the organization. This demands as much data collection and analysis, planning, and implementation discipline as does a redesign of strategy, systems, or processes. The change-management approach should be fully integrated into program design and decision making, both informing and enabling strategic direction. It should be based on a realistic assessment of the organization’s history, readiness, and capacity to change.

2. Start at the top. Because change is inherently unsettling for people at all levels of an organization, when it is on the horizon, all eyes will turn to the CEO and the leadership team for strength, support, and direction. The leaders themselves must embrace the new approaches first, both to challenge and to motivate the rest of the institution. They must speak with one voice and model the desired behaviors. The executive team also needs to understand that, although its public face may be one of unity, it, too, is composed of individuals who are going through stressful times and need to be supported.

Executive teams that work well together are best positioned for success. They are aligned and committed to the direction of change, understand the culture and behaviors the changes intend to introduce, and can model those changes themselves. At one large transportation company, the senior team rolled out an initiative to improve the efficiency and performance of its corporate and field staff before addressing change issues at the officer level. The initiative realized initial cost savings but stalled as employees began to question the leadership team’s vision and commitment. Only after the leadership team went through the process of aligning and committing to the change initiative was the work force able to deliver downstream results.

3. Involve every layer. As transformation programs progress from defining strategy and setting targets to design and implementation, they affect different levels of the organization. Change efforts must include plans for identifying leaders throughout the company and pushing responsibility for design and implementation down, so that change “cascades” through the organization. At each layer of the organization, the leaders who are identified and trained must be aligned to the company’s vision, equipped to execute their specific mission, and motivated to make change happen.

A major multiline insurer with consistently flat earnings decided to change performance and behavior in preparation for going public. The company followed this “cascading leadership” methodology, training and supporting teams at each stage. First, 10 officers set the strategy, vision, and targets. Next, more than 60 senior executives and managers designed the core of the change initiative. Then 500 leaders from the field drove implementation. The structure remained in place throughout the change program, which doubled the company’s earnings far ahead of schedule. This approach is also a superb way for a company to identify its next generation of leadership.

4. Make the formal case. Individuals are inherently rational and will question to what extent change is needed, whether the company is headed in the right direction, and whether they want to commit personally to making change happen. They will look to the leadership for answers. The articulation of a formal case for change and the creation of a written vision statement are invaluable opportunities to create or compel leadership-team alignment.

Three steps should be followed in developing the case: First, confront reality and articulate a convincing need for change. Second, demonstrate faith that the company has a viable future and the leadership to get there. Finally, provide a road map to guide behavior and decision making. Leaders must then customize this message for various internal audiences, describing the pending change in terms that matter to the individuals.

A consumer packaged-goods company experiencing years of steadily declining earnings determined that it needed to significantly restructure its operations — instituting, among other things, a 30 percent work force reduction — to remain competitive. In a series of offsite meetings, the executive team built a brutally honest business case that downsizing was the only way to keep the business viable, and drew on the company’s proud heritage to craft a compelling vision to lead the company forward. By confronting reality and helping employees understand the necessity for change, leaders were able to motivate the organization to follow the new direction in the midst of the largest downsizing in the company’s history. Instead of being shell-shocked and demoralized, those who stayed felt a renewed resolve to help the enterprise advance.

5. Create ownership. Leaders of large change programs must overperform during the transformation and be the zealots who create a critical mass among the work force in favor of change. This requires more than mere buy-in or passive agreement that the direction of change is acceptable. It demands ownership by leaders willing to accept responsibility for making change happen in all of the areas they influence or control. Ownership is often best created by involving people in identifying problems and crafting solutions. It is reinforced by incentives and rewards. These can be tangible (for example, financial compensation) or psychological (for example, camaraderie and a sense of shared destiny).

At a large health-care organization that was moving to a shared-services model for administrative support, the first department to create detailed designs for the new organization was human resources. Its personnel worked with advisors in cross-functional teams for more than six months. But as the designs were being finalized, top departmental executives began to resist the move to implementation. While agreeing that the work was top-notch, the executives realized they hadn’t invested enough individual time in the design process to feel the ownership required to begin implementation. On the basis of their feedback, the process was modified to include a “deep dive.” The departmental executives worked with the design teams to learn more, and get further exposure to changes that would occur. This was the turning point; the transition then happened quickly. It also created a forum for top executives to work as a team, creating a sense of alignment and unity that the group hadn’t felt before.

6. Communicate the message. Too often, change leaders make the mistake of believing that others understand the issues, feel the need to change, and see the new direction as clearly as they do. The best change programs reinforce core messages through regular, timely advice that is both inspirational and practicable. Communications flow in from the bottom and out from the top, and are targeted to provide employees the right information at the right time and to solicit their input and feedback. Often this will require overcommunication through multiple, redundant channels.

In the late 1990s, the commissioner of the Internal Revenue Service, Charles O. Rossotti, had a vision: The IRS could treat taxpayers as customers and turn a feared bureaucracy into a world-class service organization. Getting more than 100,000 employees to think and act differently required more than just systems redesign and process change. IRS leadership designed and executed an ambitious communications program including daily voice mails from the commissioner and his top staff, training sessions, videotapes, newsletters, and town hall meetings that continued through the transformation. Timely, constant, practical communication was at the heart of the program, which brought the IRS’s customer ratings from the lowest in various surveys to its current ranking above the likes of McDonald’s and most airlines.

7. Assess the cultural landscape. Successful change programs pick up speed and intensity as they cascade down, making it critically important that leaders understand and account for culture and behaviors at each level of the organization. Companies often make the mistake of assessing culture either too late or not at all. Thorough cultural diagnostics can assess organizational readiness to change, bring major problems to the surface, identify conflicts, and define factors that can recognize and influence sources of leadership and resistance. These diagnostics identify the core values, beliefs, behaviors, and perceptions that must be taken into account for successful change to occur. They serve as the common baseline for designing essential change elements, such as the new corporate vision, and building the infrastructure and programs needed to drive change.

8. Address culture explicitly. Once the culture is understood, it should be addressed as thoroughly as any other area in a change program. Leaders should be explicit about the culture and underlying behaviors that will best support the new way of doing business, and find opportunities to model and reward those behaviors. This requires developing a baseline, defining an explicit end-state or desired culture, and devising detailed plans to make the transition.

Company culture is an amalgam of shared history, explicit values and beliefs, and common attitudes and behaviors. Change programs can involve creating a culture (in new companies or those built through multiple acquisitions), combining cultures (in mergers or acquisitions of large companies), or reinforcing cultures (in, say, long-established consumer goods or manufacturing companies). Understanding that all companies have a cultural center — the locus of thought, activity, influence, or personal identification — is often an effective way to jump-start culture change.

A consumer goods company with a suite of premium brands determined that business realities demanded a greater focus on profitability and bottom-line accountability. In addition to redesigning metrics and incentives, it developed a plan to systematically change the company’s culture, beginning with marketing, the company’s historical center. It brought the marketing staff into the process early to create enthusiasts for the new philosophy who adapted marketing campaigns, spending plans, and incentive programs to be more accountable. Seeing these culture leaders grab onto the new program, the rest of the company quickly fell in line.

9. Prepare for the unexpected. No change program goes completely according to plan. People react in unexpected ways; areas of anticipated resistance fall away; and the external environment shifts. Effectively managing change requires continual reassessment of its impact and the organization’s willingness and ability to adopt the next wave of transformation. Fed by real data from the field and supported by information and solid decision-making processes, change leaders can then make the adjustments necessary to maintain momentum and drive results.

A leading U.S. health-care company was facing competitive and financial pressures from its inability to react to changes in the marketplace. A diagnosis revealed shortcomings in its organizational structure and governance, and the company decided to implement a new operating model. In the midst of detailed design, a new CEO and leadership team took over. The new team was initially skeptical, but was ultimately convinced that a solid case for change, grounded in facts and supported by the organization at large, existed. Some adjustments were made to the speed and sequence of implementation, but the fundamentals of the new operating model remained unchanged.

10. Speak to the individual. Change is both an institutional journey and a very personal one. People spend many hours each week at work; many think of their colleagues as a second family. Individuals (or teams of individuals) need to know how their work will change, what is expected of them during and after the change program, how they will be measured, and what success or failure will mean for them and those around them. Team leaders should be as honest and explicit as possible. People will react to what they see and hear around them, and need to be involved in the change process. Highly visible rewards, such as promotion, recognition, and bonuses, should be provided as dramatic reinforcement for embracing change. Sanction or removal of people standing in the way of change will reinforce the institution’s commitment.

Most leaders contemplating change know that people matter. It is all too tempting, however, to dwell on the plans and processes, which don’t talk back and don’t respond emotionally, rather than face up to the more difficult and more critical human issues. But mastering the “soft” side of change management needn’t be a mystery.

Author Profiles:

  • John Jones is a vice president with Booz Allen Hamilton in New York. Mr. Jones is a specialist in organization design, process reengineering, and change management.
  • DeAnne Aguirre (deanne.aguirre@strategyand.us.pwc.com) is an advisor to executives on organizational topics for Strategy&, PwC’s strategy consulting business, and a principal with PwC US. Based in San Francisco, she specializes in culture, leadership, talent effectiveness, and organizational change management.
  • Matthew Calderone is a senior associate with Booz Allen Hamilton in the New York Office. He specializes in organization transformation, people issues, and change management.

http://www.strategy-business.com/article/rr00006?gko=643d0

Definition of Change Management

A useful definition of change management that I use is:

‚the coordination of a structured period of transition from situation A to situation B in order to achieve lasting change within an organization‘.
(BNET Business Dictionary)

To help you in your search for a definition of change management here are others I’ve found to be useful:

The systematic approach and application of knowledge, tools and resources to deal with change. Change management means defining and adopting corporate strategies, structures, procedures and technologies to deal with changes in external conditions and the business environment.
SHRM Glossary of Human Resources Terms, http://www.shrm.org.

Change management is the process, tools and techniques to manage the people-side of business change to achieve the required business outcome, and to realize that business change effectively within the social infrastructure of the workplace.
Change Management Learning Center

Change Management: activities involved in (1) defining and instilling new values, attitudes, norms, and behaviors within an organization that support new ways of doing work and overcome resistance to change; (2) building consensus among customers and stakeholders on specific changes designed to better meet their needs; and (3) planning, testing, and implementing all aspects of the transition from one organizational structure or business process to another.
http://www.gao.gov/special.pubs/bprag/bprgloss.htm

…a systematic approach to dealing with change, both from the perspective of an organization and on the individual level…proactively addressing adapting to change, controlling change, and effecting change.
Case Western Reserve University

Change management is a systematic approach to dealing with change, both from the perspective of an organization and on the individual level.
searchsmb.com

Change Management is an organized, systematic application of the knowledge, tools, and resources of change that provides organizations with a key process to achieve their business strategy.
Lamarsh

The systematic management of a new business model integration into an organization and the ability to adapt this change into the organization so that the transformation enhances the organizational relationships with all its constituents.
bitpipe.com

Change Management: the process, tools and techniques to manage the people-side of change processes, to achieve the required outcomes, and to realize the change effectively within individuals, teams, and the wider systems.

Change management is a structured approach to transitioning individuals, teams, and organizations from a current state to a desired future state. The current definition of Change Management includes both organizational change management processes and individual change management models, which together are used to manage the people side of change.
Wikipedia

Minimizing resistance to organizational change through involvement of key players and stakeholders.
BusinessDictionary.com

Change management is a style of management that aims to encourage organizations and individuals to deal effectively with the changes taking place in their work.
English Collins Dictionary

quote: http://www.change-management-coach.com/definition-of-change-management.html

Artificial intelligence assistants are taking over

It was a weeknight, after dinner, and the baby was in bed. My wife and I were alone—we thought—discussing the sorts of things you might discuss with your spouse and no one else. (Specifically, we were critiquing a friend’s taste in romantic partners.) I was midsentence when, without warning, another woman’s voice piped in from the next room. We froze.

“I HELD THE DOOR OPEN FOR A CLOWN THE OTHER DAY,” the woman said in a loud, slow monotone. It took us a moment to realize that her voice was emanating from the black speaker on the kitchen table. We stared slack-jawed as she—it—continued: “I THOUGHT IT WAS A NICE JESTER.”

“What. The hell. Was that,” I said after a moment of stunned silence. Alexa, the voice assistant whose digital spirit animates the Amazon Echo, did not reply. She—it—responds only when called by name. Or so we had believed.

We pieced together what must have transpired. Somehow, Alexa’s speech recognition software had mistakenly picked the word Alexa out of something we said, then chosen a phrase like “tell me a joke” as its best approximation of whatever words immediately followed. Through some confluence of human programming and algorithmic randomization, it chose a lame jester/gesture pun as its response.

In retrospect, the disruption was more humorous than sinister. But it was also a slightly unsettling reminder that Amazon’s hit device works by listening to everything you say, all the time. And that, for all Alexa’s human trappings—the name, the voice, the conversational interface—it’s no more sentient than any other app or website. It’s just code, built by some software engineers in Seattle with a cheesy sense of humor.

But the Echo’s inadvertent intrusion into an intimate conversation is also a harbinger of a more fundamental shift in the relationship between human and machine. Alexa—and Siri and Cortana and all of the other virtual assistants that now populate our computers, phones, and living rooms—are just beginning to insinuate themselves, sometimes stealthily, sometimes overtly, and sometimes a tad creepily, into the rhythms of our daily lives. As they grow smarter and more capable, they will routinely surprise us by making our lives easier, and we’ll steadily become more reliant on them.

Even as many of us continue to treat these bots as toys and novelties, they are on their way to becoming our primary gateways to all sorts of goods, services, and information, both public and personal. When that happens, the Echo won’t just be a cylinder in your kitchen that sometimes tells bad jokes. Alexa and virtual agents like it will be the prisms through which we interact with the online world.

It’s a job to which they will necessarily bring a set of biases and priorities, some subtler than others. Some of those biases and priorities will reflect our own. Others, almost certainly, will not. Those vested interests might help to explain why they seem so eager to become our friends.

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ibmAP

In the beginning, computers spoke only computer language, and a human seeking to interact with one was compelled to do the same. First came punch cards, then typed commands such as run, print, and dir.

The 1980s brought the mouse click and the graphical user interface to the masses; the 2000s, touch screens; the 2010s, gesture control and voice. It has all been leading, gradually and imperceptibly, to a world in which we no longer have to speak computer language, because computers will speak human language—not perfectly, but well enough to get by.

Alexa and software agents like it will be the prisms through which we interact with the online world.
We aren’t there yet. But we’re closer than most people realize. And the implications—many of them exciting, some of them ominous—will be tremendous.

Like card catalogs and AOL-style portals before it, Web search will begin to fade from prominence, and with it the dominance of browsers and search engines. Mobile apps as we know them—icons on a home screen that you tap to open—will start to do the same. In their place will rise an array of virtual assistants, bots, and software agents that act more and more like people: not only answering our queries, but acting as our proxies, accomplishing tasks for us, and asking questions of us in return.

This is already beginning to happen—and it isn’t just Siri or Alexa. As of April, all five of the world’s dominant technology companies are vying to be the Google of the conversation age. Whoever wins has a chance to get to know us more intimately than any company or machine has before—and to exert even more influence over our choices, purchases, and reading habits than they already do.

So say goodbye to Web browsers and mobile home screens as our default portals to the Internet. And say hello to the new wave of intelligent assistants, virtual agents, and software bots that are rising to take their place.

No, really, say “hello” to them. Apple’s Siri, Google’s mobile search app, Amazon’s Alexa, Microsoft’s Cortana, and Facebook’s M, to name just five of the most notable, are diverse in their approaches, capabilities, and underlying technologies. But, with one exception, they’ve all been programmed to respond to basic salutations in one way or another, and it’s a good way to start to get a sense of their respective mannerisms. You might even be tempted to say they have different personalities.

Siri’s response to “hello” varies, but it’s typically chatty and familiar:

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Alexa is all business:

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Google is a bit of an idiot savant: It responds by pulling up a YouTube video of the song “Hello” by Adele, along with all the lyrics.

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Cortana isn’t interested in saying anything until you’ve handed her the keys to your life:

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Once those formalities are out of the way, she’s all solicitude:

160331_CS_cortanaScreen.CROP.promo xlarge2.jpgSlate/Screenshot

Then there’s Facebook M, an experimental bot, available so far only to an exclusive group of Bay Area beta-testers, that lives inside Facebook Messenger and promises to answer almost any question and fulfill almost any (legal) request. If the casual, what’s-up-BFF tone of its text messages rings eerily human, that’s because it is: M is powered by an uncanny pairing of artificial intelligence and anonymous human agents.

160331_CS_mScreen.CROP.promo xlarge2.jpgSlate/Screenshot

You might notice that most of these virtual assistants have female-sounding names and voices. Facebook M doesn’t have a voice—it’s text-only—but it was initially rumored to be called Moneypenny, a reference to a secretary from the James Bond franchise. And even Google’s voice is female by default. This is, to some extent, a reflection of societal sexism. But these bots’ apparent embrace of gender also highlights their aspiration to be anthropomorphized: They want—that is, the engineers that build them want—to interact with you like a person, not a machine. It seems to be working: Already people tend to refer to Siri, Alexa, and Cortana as “she,” not “it.”

That Silicon Valley’s largest tech companies have effectively humanized their software in this way, with little fanfare and scant resistance, represents a coup of sorts. Once we perceive a virtual assistant as human, or at least humanoid, it becomes an entity with which we can establish humanlike relations. We can like it, banter with it, even turn to it for companionship when we’re lonely. When it errs or betrays us, we can get angry with it and, ultimately, forgive it. What’s most important, from the perspective of the companies behind this technology, is that we trust it.

Should we?

* * *

Siri wasn’t the first digital voice assistant when Apple introduced it in 2011, and it may not have been the best. But it was the first to show us what might be possible: a computer that you talk to like a person, that talks back, and that attempts to do what you ask of it without requiring any further action on your part. Adam Cheyer, co-founder of the startup that built Siri and sold it to Apple in 2010, has said he initially conceived of it not as a search engine, but as a “do engine.”

If Siri gave us a glimpse of what is possible, it also inadvertently taught us about what wasn’t yet. At first, it often struggled to understand you, especially if you spoke into your iPhone with an accent, and it routinely blundered attempts to carry out your will. Its quick-witted rejoinders to select queries (“Siri, talk dirty to me”) raised expectations for its intelligence that were promptly dashed once you asked it something it hadn’t been hard-coded to answer. Its store of knowledge proved trivial compared with the vast information readily available via Google search. Siri was as much an inspiration as a disappointment.

Five years later, Siri has gotten smarter, if perhaps less so than one might have hoped. More importantly, the technology underlying it has drastically improved, fueled by a boom in the computer science subfield of machine learning. That has led to sharp improvements in speech recognition and natural language understanding, two separate but related technologies that are crucial to voice assistants.

siriReuters/Suzanne PlunkettLuke Peters demonstrates Siri, an application which uses voice recognition and detection on the iPhone 4S, outside the Apple store in Covent Garden, London Oct. 14, 2011.

If Siri gave us a glimpse of what is possible, it also inadvertently taught us about what wasn’t yet.

If a revolution in technology has made intelligent virtual assistants possible, what has made them inevitable is a revolution in our relationship to technology. Computers began as tools of business and research, designed to automate tasks such as math and information retrieval. Today they’re tools of personal communication, connecting us not only to information but to one another. They’re also beginning to connect us to all the other technologies in our lives: Your smartphone can turn on your lights, start your car, activate your home security system, and withdraw money from your bank. As computers have grown deeply personal, our relationship with them has changed. And yet the way they interact with us hasn’t quite caught up.

“It’s always been sort of appalling to me that you now have a supercomputer in your pocket, yet you have to learn to use it,” says Alan Packer, head of language technology at Facebook. “It seems actually like a failure on the part of our industry that software is hard to use.”

Packer is one of the people trying to change that. As a software developer at Microsoft, he helped to build Cortana. After it launched, he found his skills in heavy demand, especially among the two tech giants that hadn’t yet developed voice assistants of their own. One Thursday morning in December 2014, Packer was on the verge of accepting a top job at Amazon—“You would not be surprised at which team I was about to join,” he says—when Facebook called and offered to fly him to Menlo Park, California, for an interview the next day. He had an inkling of what Amazon was working on, but he had no idea why Facebook might be interested in someone with his skill set.

As it turned out, Facebook wanted Packer for much the same purpose that Microsoft and Amazon did: to help it build software that could make sense of what its users were saying and generate intelligent responses. Facebook may not have a device like the Echo or an operating system like Windows, but its own platforms are full of billions of people communicating with one another every day. If Facebook can better understand what they’re saying, it can further hone its News Feed and advertising algorithms, among other applications. More creatively, Facebook has begun to use language understanding to build artificial intelligence into its Messenger app. Now, if you’re messaging with a friend and mention sharing an Uber, a software agent within Messenger can jump in and order it for you while you continue your conversation.

In short, Packer says, Facebook is working on language understanding because Facebook is a technology company—and that’s where technology is headed. As if to underscore that point, Packer’s former employer this year headlined its annual developer conference by announcing plans to turn Cortana into a portal for conversational bots and integrate it into Skype, Outlook, and other popular applications. Microsoft CEO Satya Nadella predicted that bots will be the Internet’s next major platform, overtaking mobile apps the same way they eclipsed desktop computing.

* * *Amazon Echo DotAP

Siri may not have been very practical, but people immediately grasped what it was. With Amazon’s Echo, the second major tech gadget to put a voice interface front and center, it was the other way around. The company surprised the industry and baffled the public when it released a device in November 2014 that looked and acted like a speaker—except that it didn’t connect to anything except a power outlet, and the only buttons were for power and mute. You control the Echo solely by voice, and if you ask it questions, it talks back. It was like Amazon had decided to put Siri in a black cylinder and sell it for $179. Except Alexa, the virtual intelligence software that powers the Echo, was far more limited than Siri in its capabilities. Who, reviewers wondered, would buy such a bizarre novelty gadget?

That question has faded as Amazon has gradually upgraded and refined the Alexa software, and the five-star Amazon reviews have since poured in. In the New York Times, Farhad Manjoo recently followed up his tepid initial review with an all-out rave: The Echo “brims with profound possibility,” he wrote. Amazon has not disclosed sales figures, but the Echo ranks as the third-best-selling gadget in its electronics section. Alexa may not be as versatile as Siri—yet—but it turned out to have a distinct advantage: a sense of purpose, and of its own limitations. Whereas Apple implicitly invites iPhone users to ask Siri anything, Amazon ships the Echo with a little cheat sheet of basic queries that it knows how to respond to: “Alexa, what’s the weather?” “Alexa, set a timer for 45 minutes.” “Alexa, what’s in the news?”

The cheat sheet’s effect is to lower expectations to a level that even a relatively simplistic artificial intelligence can plausibly meet on a regular basis. That’s by design, says Greg Hart, Amazon’s vice president in charge of Echo and Alexa. Building a voice assistant that can respond to every possible query is “a really hard problem,” he says. “People can get really turned off if they have an experience that’s subpar or frustrating.” So the company began by picking specific tasks that Alexa could handle with aplomb and communicating those clearly to customers.

At launch, the Echo had just 12 core capabilities. That list has grown steadily as the company has augmented Alexa’s intelligence and added integrations with new services, such as Google Calendar, Yelp reviews, Pandora streaming radio, and even Domino’s delivery. The Echo is also becoming a hub for connected home appliances: “ ‘Alexa, turn on the living room lights’ never fails to delight people,” Hart says.

When you ask Alexa a question it can’t answer or say something it can’t quite understand, it fesses up: “Sorry, I don’t know the answer to that question.” That makes it all the more charming when you test its knowledge or capabilities and it surprises you by replying confidently and correctly. “Alexa, what’s a kinkajou?” I asked on a whim one evening, glancing up from my laptop while reading a news story about an elderly Florida woman who woke up one day with a kinkajou on her chest. Alexa didn’t hesitate: “A kinkajou is a rainforest mammal of the family Procyonidae … ” Alexa then proceeded to list a number of other Procyonidae to which the kinkajou is closely related. “Alexa, that’s enough,” I said after a few moments, genuinely impressed. “Thank you,” I added.

“You’re welcome,” Alexa replied, and I thought for a moment that she—it—sounded pleased.

As delightful as it can seem, the Echo’s magic comes with some unusual downsides. In order to respond every time you say “Alexa,” it has to be listening for the word at all times. Amazon says it only stores the commands that you say after you’ve said the word Alexa and discards the rest. Even so, the enormous amount of processing required to listen for a wake word 24/7 is reflected in the Echo’s biggest limitation: It only works when it’s plugged into a power outlet. (Amazon’s newest smart speakers, the Echo Dot and the Tap, are more mobile, but one sacrifices the speaker and the other the ability to respond at any time.)

Even if you trust Amazon to rigorously protect and delete all of your personal conversations from its servers—as it promises it will if you ask it to—Alexa’s anthropomorphic characteristics make it hard to shake the occasional sense that it’s eavesdropping on you, Big Brother–style. I was alone in my kitchen one day, unabashedly belting out the Fats Domino song “Blueberry Hill” as I did the dishes, when it struck me that I wasn’t alone after all. Alexa was listening—not judging, surely, but listening all the same. Sheepishly, I stopped singing.

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The notion that the Echo is “creepy” or “spying on us” might be the most common criticism of the device so far. But there’s a more fundamental problem. It’s one that is likely to haunt voice assistants, and those who rely on them, as the technology evolves and bores it way more deeply into our lives.

The problem is that conversational interfaces don’t lend themselves to the sort of open flow of information we’ve become accustomed to in the Google era. By necessity they limit our choices—because their function is to make choices on our behalf.

For example, a search for “news” on the Web will turn up a diverse and virtually endless array of possible sources, from Fox News to Yahoo News to CNN to Google News, which is itself a compendium of stories from other outlets. But ask the Echo, “What’s in the news?” and by default it responds by serving up a clip of NPR News’s latest hourly update, which it pulls from the streaming radio service TuneIn. Which is great—unless you don’t happen to like NPR’s approach to the news, or you prefer a streaming radio service other than TuneIn. You can change those defaults somewhere in the bowels of the Alexa app, but Alexa never volunteers that information. Most people will never even know it’s an option. Amazon has made the choice for them.

And how does Amazon make that sort of choice? The Echo’s cheat sheet doesn’t tell you that, and the company couldn’t give me a clear answer.

Alexa does take care to mention before delivering the news that it’s pulling the briefing from NPR News and TuneIn. But that isn’t always the case with other sorts of queries.

Let’s go back to our friend the kinkajou. In my pre-Echo days, my curiosity about an exotic animal might have sent me to Google via my laptop or phone. Just as likely, I might have simply let the moment of curiosity pass and not bothered with a search. Looking something up on Google involves just enough steps to deter us from doing it in a surprising number of cases. One of the great virtues of voice technology is to lower that barrier to the point where it’s essentially no trouble at all. Having an Echo in the room when you’re struck by curiosity about kinkajous is like having a friend sitting next to you who happens to be a kinkajou expert. All you have to do is say your question out loud, and Alexa will supply the answer. You literally don’t have to lift a finger.

That is voice technology’s fundamental advantage over all the human-computer interfaces that have come before it: In many settings, including the home, the car, or on a wearable gadget, it’s much easier and more natural than clicking, typing, or tapping. In the logic of today’s consumer technology industry, that makes its ascendance in those realms all but inevitable.

But consider the difference between Googling something and asking a friendly voice assistant. When I Google “kinkajou,” I get a list of websites, ranked according to an algorithm that takes into account all sorts of factors that correlate with relevance and authority. I choose the information source I prefer, then visit its website directly—an experience that could help to further shade or inform my impression of its trustworthiness. Ultimately, the answer does come not from Google, per se, but directly from some third-party authority, whose credibility I can evaluate as I wish.

A voice-based interface is different. The response comes one word at a time, one sentence at a time, one idea at a time. That makes it very easy to follow, especially for humans who have spent their whole lives interacting with one another in just this way. But it makes it very cumbersome to present multiple options for how to answer a given query. Imagine for a moment what it would sound like to read a whole Google search results page aloud, and you’ll understand no one builds a voice interface that way.

That’s why voice assistants tend to answer your question by drawing from a single source of their own choosing. Alexa’s confident response to my kinkajou question, I later discovered, came directly from Wikipedia, which Amazon has apparently chosen as the default source for Alexa’s answers to factual questions. The reasons seem fairly obvious: It’s the world’s most comprehensive encyclopedia, its information is free and public, and it’s already digitized. What it’s not, of course, is infallible. Yet Alexa’s response to my question didn’t begin with the words, “Well, according to Wikipedia … ” She—it—just launched into the answer, as if she (it) knew it off the top of her (its) head. If a human did that, we might call it plagiarism.

The sin here is not merely academic. By not consistently citing the sources of its answers, Alexa makes it difficult to evaluate their credibility. It also implicitly turns Alexa into an information source in its own right, rather than a guide to information sources, because the only entity in which we can place our trust or distrust is Alexa itself. That’s a problem if its information source turns out to be wrong.

The constraints on choice and transparency might not bother people when Alexa’s default source is Wikipedia, NPR, or TuneIn. It starts to get a little more irksome when you ask Alexa to play you music, one of the Echo’s core features. “Alexa, play me the Rolling Stones” will queue up a shuffle playlist of Rolling Stones songs available through Amazon’s own streaming music service, Amazon Prime Music—provided you’re paying the $99 a year required to be an Amazon Prime member. Otherwise, the most you’ll get out of the Echo are 20-second samples of songs available for purchase. Want to guess what one choice you’ll have as to which online retail giant to purchase those songs from?

When you say “Hello” to Alexa, you’re signing up for her party.

Amazon’s response is that Alexa does give you options and cite its sources—in the Alexa app, which keeps a record of your queries and its responses. When the Echo tells you what a kinkajou is, you can open the app on your phone and see a link to the Wikipedia article, as well as an option to search Bing. Amazon adds that Alexa is meant to be an “open platform” that allows anyone to connect to it via an API. The company is also working with specific partners to integrate their services into Alexa’s repertoire. So, for instance, if you don’t want to be limited to playing songs from Amazon Prime Music, you can now take a series of steps to link the Echo to a different streaming music service, such as Spotify Premium. Amazon Prime Music will still be the default, though: You’ll only get Spotify if you specify “from Spotify” in your voice command.

What’s not always clear is how Amazon chooses its defaults and its partners and what motivations might underlie those choices. Ahead of the 2016 Super Bowl, Amazon announced that the Echo could now order you a pizza. But that pizza would come, at least for the time being, from just one pizza-maker: Domino’s. Want a pizza from Little Caesars instead? You’ll have to order it some other way.

To Amazon’s credit, its choice of pizza source is very transparent. To use the pizza feature, you have to utter the specific command, “Alexa, open Domino’s and place my Easy Order.” The clunkiness of that command is no accident. It’s Amazon’s way of making sure that you don’t order a pizza by accident and that you know where that pizza is coming from. But it’s unlikely Domino’s would have gone to the trouble of partnering with Amazon if it didn’t think it would result in at least some number of people ordering Domino’s for their Super Bowl parties rather than Little Caesars.

None of this is to say that Amazon and Domino’s are going to conspire to monopolize the pizza industry anytime soon. There are obviously plenty of ways to order a pizza besides doing it on an Echo. Ditto for listening to the news, the Rolling Stones, a book, or a podcast. But what about when only one company’s smart thermostat can be operated by Alexa? If you come to rely on Alexa to manage your Google Calendar, what happens when Amazon and Google have a falling out?
When you say “Hello” to Alexa, you’re signing up for her party. Nominally, everyone’s invited. But Amazon has the power to ensure that its friends and business associates are the first people you meet.

* * *

google now speak now screenBusiness Insider, William Wei

These concerns might sound rather distant—we’re just talking about niche speakers connected to niche thermostats, right? The coming sea change feels a lot closer once you think about the other companies competing to make digital assistants your main portal to everything you do on your computer, in your car, and on your phone. Companies like Google.

Google may be positioned best of all to capitalize on the rise of personal A.I. It also has the most to lose. From the start, the company has built its business around its search engine’s status as a portal to information and services. Google Now—which does things like proactively checking the traffic and alerting you when you need to leave for a flight, even when you didn’t ask it to—is a natural extension of the company’s strategy.

If something is going to replace Google’s on-screen services, Google wants to be the one that does it.
As early as 2009, Google began to work on voice search and what it calls “conversational search,” using speech recognition and natural language understanding to respond to questions phrased in plain language. More recently, it has begun to combine that with “contextual search.” For instance, as Google demonstrated at its 2015 developer conference, if you’re listening to Skrillex on your Android phone, you can now simply ask, “What’s his real name?” and Google will intuit that you’re asking about the artist. “Sonny John Moore,” it will tell you, without ever leaving the Spotify app.

It’s no surprise, then, that Google is rumored to be working on two major new products—an A.I.-powered messaging app or agent and a voice-powered household gadget—that sound a lot like Facebook M and the Amazon Echo, respectively. If something is going to replace Google’s on-screen services, Google wants to be the one that does it.

So far, Google has made what seems to be a sincere effort to win the A.I. assistant race without

sacrificing the virtues—credibility, transparency, objectivity—that made its search page such a dominant force on the Web. (It’s worth recalling: A big reason Google vanquished AltaVista was that it didn’t bend its search results to its own vested interests.) Google’s voice search does generally cite its sources. And it remains primarily a portal to other sources of information, rather than a platform that pulls in content from elsewhere. The downside to that relatively open approach is that when you say “hello” to Google voice search, it doesn’t say hello back. It gives you a link to the Adele song “Hello.” Even then, Google isn’t above playing favorites with the sources of information it surfaces first: That link goes not to Spotify, Apple Music, or Amazon Prime Music, but to YouTube, which Google owns. The company has weathered antitrust scrutiny over allegations that this amounted to preferential treatment. Google’s defense was that it puts its own services and information sources first because its users prefer them.

* * *

HerYouTube

If there’s a consolation for those concerned that intelligent assistants are going to take over the world, it’s this: They really aren’t all that intelligent. Not yet, anyway.

The 2013 movie Her, in which a mobile operating system gets to know its user so well that they become romantically involved, paints a vivid picture of what the world might look like if we had the technology to carry Siri, Alexa, and the like to their logical conclusion. The experts I talked to, who are building that technology today, almost all cited Her as a reference point—while pointing out that we’re not going to get there anytime soon.

Google recently rekindled hopes—and fears—of super-intelligent A.I. when its AlphaGo software defeated the world champion in a historic Go match. As momentous as the achievement was, designing an algorithm to win even the most complex board game is trivial compared with designing one that can understand and respond appropriately to anything a person might say. That’s why, even as artificial intelligence is learning to recommend songs that sound like they were hand-picked by your best friend or navigate city streets more safely than any human driver, A.I. still has to resort to parlor tricks—like posing as a 13-year-old struggling with a foreign language—to pass as human in an extended conversation. The world is simply too vast, language too ambiguous, the human brain too complex for any machine to model it, at least for the foreseeable future.

But if we won’t see a true full-service A.I. in our lifetime, we might yet witness the rise of a system that can approximate some of its capabilities—comprising not a single, humanlike Her, but a million tiny hims carrying out small, discrete tasks handily. In January, the Verge’s Casey Newton made a compelling argument that our technological future will be filled not with websites, apps, or even voice assistants, but with conversational messaging bots. Like voice assistants, these bots rely on natural language understanding to carry on conversations with us. But they will do so via the medium that has come to dominate online interpersonal interaction, especially among the young people who are the heaviest users of mobile devices: text messaging. For example, Newton points to “Lunch Bot,” a relatively simple agent that lived in the wildly popular workplace chat program Slack and existed for a single, highly specialized purpose: to recommend the best place for employees to order their lunch from on a given day. It soon grew into a venture-backed company called Howdy.

A world of conversational machines is one in which we treat software like humans, letting them deeper into our lives and confiding in them more than ever.

I have a bot in my own life that serves a similarly specialized yet important role. While researching this story, I ran across a company called X.ai whose mission is to build the ultimate virtual scheduling assistant. It’s called Amy Ingram, and if its initials don’t tip you off, you might interact with it several times before realizing it’s not a person. (Unlike some other intelligent assistant companies, X.ai gives you the option to choose a male name for your assistant instead: Mine is Andrew Ingram.) Though it’s backed by some impressive natural language tech, X.ai’s bot does not attempt to be a know-it-all or do-it-all; it doesn’t tell jokes, and you wouldn’t want to date him. It asks for access to just one thing—your calendar. And it communicates solely by email. Just cc it on any thread in which you’re trying to schedule a meeting or appointment, and it will automatically step in and take over the back-and-forth involved in nailing down a time and place. Once it has agreed on a time with whomever you’re meeting—or, perhaps, with his or her own assistant, whether human or virtual—it will put all the relevant details on your calendar. Have your A.I. cc my A.I.

For these bots, the key to success is not growing so intelligent that they can do everything. It’s staying specialized enough that they don’t have to.

“We’ve had this A.I. fantasy for almost 60 years now,” says Dennis Mortensen, X.ai’s founder and CEO. “At every turn we thought the only outcome would be some human-level entity where we could converse with it like you and I are [conversing] right now. That’s going to continue to be a fantasy. I can’t see it in my lifetime or even my kids’ lifetime.” What is possible, Mortensen says, is “extremely specialized, verticalized A.I.s that understand perhaps only one job, but do that job very well.”

Yet those simple bots, Mortensen believes, could one day add up to something more. “You get enough of these agents, and maybe one morning in 2045 you look around and that plethora—tens of thousands of little agents—once they start to talk to each other, it might not look so different from that A.I. fantasy we’ve had.”

That might feel a little less scary. But it still leaves problems of transparency, privacy, objectivity, and trust—questions that are not new to the world of personal technology and the Internet but are resurfacing in fresh and urgent forms. A world of conversational machines is one in which we treat software like humans, letting them deeper into our lives and confiding in them more than ever. It’s one in which the world’s largest corporations know more about us, hold greater influence over our choices, and make more decisions for us than ever before. And it all starts with a friendly “Hello.”

 

www.businessinsider.com/ai-assistants-are-taking-over-2016-4

Most Important Job Positions For Creating A Culture Of Innovation

If you’re changing the world, you’re working on important things. You’re excited to get up in the morning. You want to be working on meaningful, impactful projects.

The role of the recruiter is no longer just to find talent. It is to understand and empathize with their applicants, and then help make their experience better.

Netflix believes the best thing they can do for employees is hire only „A“ players to work alongside them. Excellent colleagues trump everything else.

At Pixar he conceived of a program that lets employees choose from 14 classes per week, including ballet, improv, drawing, and painting skills, computer programming, belly dancing, and color theory. This keeps Pixar’s talent learning and growing, thus making better films.

the shift toward culture may be gaining prominence as small businesses struggle to compete for talent with larger counterparts in the areas of plump paychecks and generous benefits packages. They’re beginning to recognize culture for the trump card it is, particularly with millennials used to interactive, stimulating environments.“

We all fear the job that looks great on paper and is a nightmare in practice. What makes some companies great to work for and others a disaster? The answer: good workplace culture. It’s the difference between Google and Yahoo, Costco, and the Department of Corrections. Studies have shown that office culture is one of the most revealing indicators of workplace satisfaction. How can companies be intentional about building and nurturing a good workplace culture?

The short answer: Hire for the right roles. Some people believe that founders are the only ones who can create company culture. It’s true that founders are usually responsible for creating the original values. Consider how Larry Page and Sergey Brin from Google defined the way they wanted their first dozen employees to feel at work. In fact many of the best-loved parts of the culture started before Google had 50 employees.

But as a company grows, there are still opportunities for cultural recalibration. Here are seven roles of people who help define, harness, reflect, and embody culture. Think of them as the new faces of organizational culture.

1. The Gardener

Exemplar: Company Founder(s)
Google cofounders Larry Page and Sergey Brin had one intention for Google’s culture: They wanted to create a company that felt like their experience at Stanford’s graduate school. Page and Brin famously hired a chef to cook for Google’s first employees, a perk they had grown to love at Stanford University’s dining halls.

Beyond the free food, Google tried to mimic the actual experience and culture of being a grad student. Page observed, „When you’re a grad student, you can work on whatever you want. And the projects that were really good got a lot of people really wanting to work on them. We’ve taken that learning to Google, and it’s been really, really helpful. If you’re changing the world, you’re working on important things. You’re excited to get up in the morning. You want to be working on meaningful, impactful projects.“

Page and Brin are examples of founders who have helped nurture Google’s culture. Several culture theorists use the term „gardening“ when talking about fostering good workplace culture. A few years ago, the New York Times columnist Thomas Friedman spoke with Amazon CEO Jeff Bezos about trends in technology and leadership. Friedman wrote that Bezos believes „the job of the company leader is now changing fast. ‘You have to think of yourself not as a designer but as a gardener’—seeding, nurturing, inspiring, cultivating the ideas coming from below, and then making sure people execute them.“

Google has retained many parts of its unique startup culture because Page and Brin understood that the company’s culture needed care and nurturing. The most important thing the gardener can do is to write down the company’s cultural values and share them within the company. Page and Brin wrote Google’s values when the company was just a few years old.

2. The Sage

Exemplar: Venture Capitalist
It was 2012, and Airbnb cofounders Brian Chesky, Joe Gebbia, and Nathan Blecharczyk had just closed their Series C funding round with investor Peter Thiel. Thiel cofounded PayPal and now runs Palantir Technologies. Thiel is known for his business aptitude, and he frequently writes about his philosophical (and sometimes controversial) viewpoints, including his support of libertarianism and his belief that companies should operate with a flat structure and share company data with all employees. The Airbnb cofounders invited Thiel to their office in San Francisco. To help you picture this scene, it’s important to know that the conference rooms in the Airbnb office are designed to look like real Airbnb apartment listings from cities including New York, Berlin, and Hong Kong. They invited Thiel into the „Berlin Room“ (decorated with ornate baroque wallpaper and a rococo couch) to show him various metrics. In the middle of the conversation, Chesky asked Thiel what his single most important piece of advice for them was.

Thiel replied, „Don’t fuck up the culture.“

Thiel is an example of a sage, a wise veteran who has been through the trenches of starting a company before. The most important thing a sage can do is to remind a company to focus on culture. Founders often feel like they have no time to focus on company culture, when it is actually the most important thing they should be focused on. Jerry Colonna, a former venture capitalist at Flatiron Partners and current CEO coach, is known as the Yoda of Silicon Valley. His Yoda wisdom? „Even if you are not intentional about your culture, you will still have one.“

3. The Empathizer

Exemplar: The Recruiter
Let’s say I’ve just stayed at an Airbnb, and I’ve had a great experience using the service. I’m on the website, leaving a review, and I navigate down to the careers page. I already have a job I love, but I’m just curious (we’ve all been there). Within two minutes, I have learned what Airbnb’s cultural values are and specifically what traits and values Airbnb is looking for in candidates. But what really hooks me is learning what it’s like to work at Airbnb. I see that employees get to have „fireside chats“ with industry leaders and musicians like Will.I.Am. I learn what it’s like for employees to work at Airbnb every day. And not just employees, in the generic sense. Specifically, videos on what it’s like for interns, developers, and even moms to work at Airbnb. There’s also a huge section on what the engineering culture is like (hilariously, at nerds.airbnb.com).

Airbnb has carefully mapped out what comes next. Jill Riopelle, head of recruiting, used a design technique called journey mapping to gain empathy for the candidates going through Airbnb’s job application process. She hosted a brainstorming session in the Airbnb lunch room, and asked Airbnb employees to reflect on their own best and worst hiring moments. Next, they brainstormed how they wanted applicants to feel at each point and mapped out the ideal process for both the applicant and the hiring team. Based on these ideas for an „ideal process“ Airbnb made the communication process with applicants more high touch (applicants get more updates, and aren’t wondering when the company will respond). When applicants first apply, they receive a warm acknowledgement message via email. The email „outlines next steps and suggests what candidates could do in the interim (watch company culture videos, read our FAQs, and more).“ Airbnb started offering rejected applicants a chance to get feedback on the phone, which helps applicants stay positive toward the company, even if they didn’t get a job. The result? The company has more people applying a second time around, but with more experience and understanding of which roles would be right for them. It’s as if the recruiter is offering an olive branch, or playing the role of a coach or therapist.

The role of the recruiter is no longer just to find talent. It is to understand and empathize with their applicants, and then help make their experience better.

4. The Talent Guru

Exemplar: HR Manager
In 2009, Netflix published its seminal culture deck. Ostensibly an internal-only document that was slipped out to Slideshare, it immediately drew massive attention from the business world. The person behind this deck was former chief talent officer Patty McCord. McCord was willing to question all the old-school rules of traditional HR. In 2014, Harvard Business Review published McCord’s approach to „reinventing“ human resources. McCord helped Netflix attract top talent by coming up with a new approach: „Hire, reward, and tolerate only fully formed adults.“ Netflix believes the best thing they can do for employees is hire only „A“ players to work alongside them. Excellent colleagues trump everything else.

McCord let go of people whose skills no longer fit with the company. To keep their „A“ players happy, she instituted policies that provided freedom, responsibility, and excellent benefits that are flexible around employees’ needs. Instead of creating benefits packages based on HR rules and policies, McCord created benefits packages based on the company’s values and employees‘ core motivations.

The role of the chief talent officer is no longer to blindly push through outdated HR policies. Instead, talent gurus must reinvent the company’s policies to match the company’s cultural values and employees‘ personal values. Most importantly, talent gurus create the narrative that defines how a company aligns its actions (the what) with its values (the how).

5. The Dean

Exemplar: Learning and Developing Leader
Here’s how Randy Nelson describes himself on his LinkedIn page: „I work with organizations to make the best use of the people, wisdom, and skills they already have, want to attract, and need to develop. I help build amplifiers out of groups, using appropriate and dynamic mixtures of training and education, traditional skills, and innovative technology, flexible programs, and high standards. In addition to increased productivity and effectiveness, hijinks and shenanigans sometimes result.“

Nelson was the dean of Pixar University for 12 years, and now he is the director of Apple University. Nelson’s role is creating professional development programs that not only train employees but actually help educate employees. At Pixar, he conceived of a program that lets employees choose from 14 classes per week, including ballet, improv, drawing, and painting skills, computer programming, belly dancing, and color theory. This keeps Pixar’s talent learning and growing, thus making better films.

Pixar president Ed Catmull writes in his book, Creativity, Inc., that the magic of Pixar University „wasn’t that the class material directly enhanced our employees’ job performance. Instead, there was something about an apprentice lighting technician sitting alongside an experienced animator, who in turn was sitting next to someone who worked in legal or accounting or security—that proved immensely valuable. In the classroom setting, people interacted in a way they didn’t in the workplace. They felt free to be goofy, relaxed, open, vulnerable. Hierarchy did not apply, and as a result, communication thrived. Simply by providing an excuse for us all to toil side by side, humbled by the challenge of sketching a self-portrait or writing computer code or taming a lump of clay, Pixar University changed the culture for the better.“

Deans can play the role of fostering collaboration and creativity. They can help employees regain the mindset of being students again. When we’re learning, we retain a sense of possibility— and that sense of possibility is integral to Pixar’s whimsical success.

6. The Storyteller

Exemplar: Collective Voice of Individual Storytellers
How does Ideo attract talent and clients? Ideo has widely shared stories about the firm’s „special sauce“—its processes, rituals, and values. Ideo’s leaders have published a dozen books, several sets of tools, and countless videos and articles about what it’s like to work at Ideo. You might think that by sharing its proprietary methods Ideo could lose business. Who is to say that another consulting firm won’t just copy what Ideo is doing?

In reality, publishing this information has only attracted more talent and more clients to Ideo. In fact, most of Ideo’s talent and clients come to Ideo, rather than Ideo seeking them out. The company has a very small recruiting budget, and only has a few recruiters (for a firm of 700 people, this is unusual). Instead of constantly attending recruiting fairs, Ideo aims to reach potential talent through stories. Ideo’s currency is in stories. Thus, it understands the tremendous value of having more than just one set of individuals sharing stories. Ideo encourages all employees to be storytellers.

Stories humanize companies. The role of the storyteller is to share his or her company’s inner personality and narrative with the broader world. At Ideo, there is room for many different voices, all sharing their own personal story as it relates to Ideo’s larger narrative. Ideo’s most well-known stories include books by Ideo founder David Kelley and his brother, Tom Kelley, CEO Tim Brown, and partner Jane Fulton Suri. But many other Ideo authors tell stories through videos, articles, case studies, and podcasts, including Fred Dust, David Aycan, Diego Rodriguez, Paul Bennett, Sina Mossayeb, Joe Brown, Roshi Givechi, Ingrid Fetell Lee, Ashlea Powell, and Duane Bray, to name a few. Each story has helped clarify and exemplify Ideo’s collective culture, mindsets, and values.

7. The Questioner

Exemplar: The Diversity Lead
A huge part of a company’s organizational culture is its hiring practices. Trying to increase diversity is like trying to change culture: Both are much harder to do when a company has become a midsize or large company. If a company is intentional about its culture and diversity when it’s small, the value of diversity will be baked into the company’s DNA.

Enter Slack: a company that started thinking about diversity when it was only two years old. Slack is a workplace communication and messaging app used by organizations like the New York Times, NASA, Airbnb, Buzzfeed, and Spotify. Slack was cofounded by four white men, but CEO Stewart Butterfield made diversity a priority once the company reached 40 employees. Slack was growing quickly, and they needed to hire quickly. When companies need to hire quickly, there is a tendency to hire people who come from employee networks, which leads to hiring people who are similar to current employees.

Slack’s vice president for people and policy, Anne Toth, is trying to break that cycle. She released a diversity report, which is unusual for such a young company. At the time of the release in September 2015, the company only had 250 employees. Its first strategy for breaking the cycle is to continuously look at its diversity hiring numbers and then make immediate adjustments, rather than waiting until the end of the year. According to PBS, Toth asks questions like, „Are we promoting women and people of color at the same rate? Are we retaining them at the same rate? Are we paying them equitably? Are they as engaged as other employees across the board?“

Slack’s second strategy is to change the types of questions interviewers ask candidates. Slack has stopped asking questions that produce answers that cannot be objectively evaluated. For example, one problematic question is, „What do you do for fun?“ What a candidate does for fun isn’t relevant to that candidate’s ability to do work. Not only is this question not helpful for an interview, but it can actually lead to unconscious bias.

Conclusion

The richness of these personas is that anyone can adopt them, regardless of specific job function. Any of these approaches would benefit almost any role or organization. For example, applying a gardening approach is a useful mindset that extends beyond founders; many other roles can help build a narrative around how the company puts its values into practice.

What are the takeaway lessons that we can learn from all of these roles, regardless of your own role? One idea is to look for ways to take on one of these faces for an activity, a project, or just a day. Alternatively you can look for ways to engage with your organization at a larger level with a new lens. At Ideo, a group of creative employees came together as „gardeners“ to create a series of videos designed to bring to life each of Ideo’s values. These videos helped plant seeds for new employees to learn about Ideo’s culture in a cinematic way. Regardless of your role, you can play a role in shaping your company’s culture. Anyone can adopt the lens of a dean, questioner, or storyteller in some capacity.

Why do these new faces matter? Workplace culture is becoming increasingly important, and increasingly shaped by a wider group of employees. Steelcase explains that „the shift toward culture may be gaining prominence as small businesses struggle to compete for talent with larger counterparts in the areas of plump paychecks and generous benefits packages. They’re beginning to recognize culture for the trump card it is, particularly with millennials used to interactive, stimulating environments.“ According to a recent study from Workforce Institute and WorkplaceTrends, employees feel they have more influence over culture than ever before (although managers and HR professionals disagree). Millennials, in particular, feel that the power to shape culture lies not with the executive leaders or the HR team but with the people doing the work. It’s clear that in order to attract, retain, and engage the modern workforce, companies need to focus on culture. Thankfully, there are an expanding number of roles and people who can help with this.

We’re curious: What other faces of culture does your organization have? How are your employees adopting these new roles and faces?

https://www.fastcodesign.com/3059062/from-ideo-7-people-you-need-to-create-a-culture-of-innovation

Elon Musk: we’ll ultimately be in the position where almost everyone will be able to afford a Tesla

The Internet is still waking up from the madness that was the Model 3 unveiling, but Tesla CEO is – as always – looking towards the future. While in Norway recently, Musk talked about Tesla’s upcoming EV. No, not the 3, but the even cheaper and smaller electric vehicle that will be coming out after the 3 debuts. Musk said the following, talking about the Model 3 (to start):

I’m super excited about being able to produce a car that most people can afford. And there will be future cars that are even more affordable down the road, but, with something like the Model 3, it’s designed such that roughly half of the people will be able to afford the car. Then, with fourth generation and smaller cars, we’ll ultimately be in the position where almost everyone will be able to afford the car.

You can hear it for yourself at about 12 minutes into the video above. It’s worth watching the whole thing, because Musk also mentions fossil fuel subsidies, that mysterious mass transit solution thing and
dying on Mars.

What’s most interesting about Musk’s comments about Tesla’s future is that he may not be around to steer the ship when this next EV arrives. Musk has said that he will remain the Tesla CEO at least until the Model 3 production has ramped up, but after that, who knows. As he said a year and a half ago, „I will never leave Tesla forever, but I may not be CEO forever. Nobody should be CEO forever.“

Silicon Valley legend Bill Campbell – leadership advice

Bill Campbell, widely known in Silicon Valley as „The Coach,“ died on Tuesday after a long battle with cancer.

Before entering the tech industry, Campbell served as head football coach at Columbia University and maintained a pep-talk approach when dealing with executives. Campbell’s illustrious career included a stint as an Apple executive and board member, and he served as CEO and chairman of Intuit.

He became not only an adviser to but also a close friend of power players like late Apple CEO Steve Jobs, Google cofounders Larry Page and Sergey Brin, and Twitter and Square CEO Jack Dorsey.

As Kleiner Perkins Caufield & Byers partner Randy Komisar said in an episode of his „Ventured“ podcast, Campbell’s executive-coaching style was akin to that of a psychiatrist, asking the right questions to steer his subjects to their own conclusions rather than giving mandates.

Campbell preferred to stay out of the spotlight, but we’ve collected some of his best leadership advice from relatively recent interviews.

These lessons shed light on why he was such a valuable coach to have.

Know that great products drive success. Everything else is a supporting function

Campbell was adamant that the greatest marketing in the world was useless if it didn’t advertise an excellent product. It’s why he was a fierce advocate for granting engineers creative freedom.

Source: Intuit

Trust your managers, and make sure they trust their subordinates

At companies Campbell worked at, he would aim to eliminate tensions between product managers and engineers by building a culture of trust, where managers knew that engineers were in the best position to find a solution and engineers knew managers were in the best position to guide them to that goal.

Source: Intuit

Experiment, but never at the cost of your existing business

Campbell was close friends with Ron Johnson, the Apple executive whose attempt at relaunching J.C. Penney in 2012-2013 failed miserably because, as Campbell said, he tried starting from scratch.

Source: Intuit

Spend your days doing, not planning

„Writing a list of things and checking dates and all that, that’s a bunch of bulls—, you can take the last 10 minutes of your day and do that,“ he said.

The vast majority of your day as a leader should be spent working with your team.

Source: Intuit

Your company must have unifying product principles

Even while evolving, you must ensure that your company retains its unique identity by sticking to fundamental creative principles.

„That’s what Apple does brilliantly,“ Campbell said. „Everyone knows where the design principles are trending.“

Source: Intuit

It is imperative that you stop infighting as soon as it arises

Campbell said that internal warfare „brings companies to their knees“ and that it is the CEO’s job to end tensions immediately. He said that Apple under CEO John Sculley, before Steve Jobs was brought back in to lead his company, was marked by turf wars and power grabs.

„The political problem just goes down through the organization,“ Campbell said. „Everybody’s paralyzed by the fighting that top executives have, all the time.“

He recommended that CEOs bring their warring parties into the same room and give them a deadline for settling their disputes, or else they would step in and make the decision for them.

Source: Intuit

Determine cultural values from the outset and then model them

Values allow employees to hold each other accountable, and the CEO must embody the values, or else no one will follow them.

Source: „Venture“ podcast

Evaluate your managers by what their employees think of them

Regularly survey your employees to ensure that their managers are upholding the company’s values and guiding, rather than interfering with, their work.

Source: „Venture“ podcast

Maintain a culture of respect

Campbell placed prime importance on respect when leading or consulting with a company.

For example, he said, „Larry Page takes great, great pride in making sure that [executives he hires] are humble about what they do.“

If someone continuously disrespects their colleagues to the point where they feel their opinions aren’t heard, then that person needs to be let go.

Source: „Venture“ podcast

Be honest with your team

The reason why Campbell was not only greatly respected in the Valley but also deeply admired on a personal level was because he spent time building relationships with those he worked with.

To him, the best leaders are straightforward with their praise and criticism, so that there are no illusions holding someone back from success.

Source: Fortune

http://www.businessinsider.de/bill-campbells-leadership-advice-2016-4

How to Successfully Manage Teams

Managing a team is a rewarding task that offers unique benefits and challenges. What follows are six ways to ensure you are successful at managing any kind of team.

Careful Selection

If possible, screen candidates based on a fair and standard format. This could be an entrance interview with basic questions about motivation, work styles and core competencies. Even if there are no choices, an initial interview with existing team members will allow everyone to understand preferences and backgrounds, which will help new team members better fit in and adapt to the subculture.

Proper Training

Some leaders have unrealistic expectations about employee performance and learning capacity. This means they expect employees to instantly become proficient with complex tasks and technologies that may take weeks to master. Regardless of competency, employees must be given time to process information and ask questions. This is a great way to introduce new ideas and to challenge existing, inefficient processes.

Team leaders can continue their professional development by getting an advanced degree, like a master’s degree that pertains to their career field. No matter what your field, a master’s degree can help you gain the necessary leadership experience to make a difference.

Learn Project Management

Every supervisor and team leader should be familiar with the basic principles of project management. However, they must also be prepared to train and help team members master these project management techniques and systems. One good solution is to use popular project management software. This will help team leaders better manage assignments and scheduling, as well as increase productivity and accountability.

Empower Employees

Employees need to be empowered to perform their jobs without direct supervision. This will reduce the supervisors’ work load, but can only occur when there are formal procedures and parameters that guide employees through the decision making process. Avoid micromanaging and setting up employees to fail through setting unrealistic standards.

Set Goals

Some team leaders only focus on daily operations, so they lose focus on the big picture. This can be remedied through quarterly goals collectively created by team members. These goals should be reviewed during every meeting  to realign focus and energy. Be sure to offer team members rewards for reaching the goals.

Set an Open Door Policy

New employees naturally make more mistakes if they are uncomfortable asking questions or for feedback. Team leaders can continue their professional development by getting a degree like a Master’s of Civil Engineering. No matter what your field, a master’s degree can help you gain the necessary leadership experience to make a difference.

Finally, build a team subculture that welcomes change and innovation. If you want your team to be successful, you need to take steps to better yourself and improve your skills too. These tips can help you be a more effective leader.

http://switchandshift.com/6-tips-successfully-manage-teams

The brightest minds in AI research – Machine Learning

In AI research,  brightest minds aren’t driven by the next product cycle or profit margin – They want to make AI better, and making AI better doesn’t happen when you keep your latest findings to yourself.

http://www.wired.com/2016/04/openai-elon-musk-sam-altman-plan-to-set-artificial-intelligence-free/

Inside OpenAI, Elon Musk’s Wild Plan to Set Artificial Intelligence Free

ElonMusk201604

THE FRIDAY AFTERNOON news dump, a grand tradition observed by politicians and capitalists alike, is usually supposed to hide bad news. So it was a little weird that Elon Musk, founder of electric car maker Tesla, and Sam Altman, president of famed tech incubator Y Combinator, unveiled their new artificial intelligence company at the tail end of a weeklong AI conference in Montreal this past December.

But there was a reason they revealed OpenAI at that late hour. It wasn’t that no one was looking. It was that everyone was looking. When some of Silicon Valley’s most powerful companies caught wind of the project, they began offering tremendous amounts of money to OpenAI’s freshly assembled cadre of artificial intelligence researchers, intent on keeping these big thinkers for themselves. The last-minute offers—some made at the conference itself—were large enough to force Musk and Altman to delay the announcement of the new startup. “The amount of money was borderline crazy,” says Wojciech Zaremba, a researcher who was joining OpenAI after internships at both Google and Facebook and was among those who received big offers at the eleventh hour.

How many dollars is “borderline crazy”? Two years ago, as the market for the latest machine learning technology really started to heat up, Microsoft Research vice president Peter Lee said that the cost of a top AI researcher had eclipsed the cost of a top quarterback prospect in the National Football League—and he meant under regular circumstances, not when two of the most famous entrepreneurs in Silicon Valley were trying to poach your top talent. Zaremba says that as OpenAI was coming together, he was offered two or three times his market value.

OpenAI didn’t match those offers. But it offered something else: the chance to explore research aimed solely at the future instead of products and quarterly earnings, and to eventually share most—if not all—of this research with anyone who wants it. That’s right: Musk, Altman, and company aim to give away what may become the 21st century’s most transformative technology—and give it away for free.

Zaremba says those borderline crazy offers actually turned him off—despite his enormous respect for companies like Google and Facebook. He felt like the money was at least as much of an effort to prevent the creation of OpenAI as a play to win his services, and it pushed him even further towards the startup’s magnanimous mission. “I realized,” Zaremba says, “that OpenAI was the best place to be.”

That’s the irony at the heart of this story: even as the world’s biggest tech companies try to hold onto their researchers with the same fierceness that NFL teams try to hold onto their star quarterbacks, the researchers themselves just want to share. In the rarefied world of AI research, the brightest minds aren’t driven by—or at least not only by—the next product cycle or profit margin. They want to make AI better, and making AI better doesn’t happen when you keep your latest findings to yourself.

OpenAI is a billion-dollar effort to push AI as far as it will go.
This morning, OpenAI will release its first batch of AI software, a toolkit for building artificially intelligent systems by way of a technology called “reinforcement learning”—one of the key technologies that, among other things, drove the creation of AlphaGo, the Google AI that shocked the world by mastering the ancient game of Go. With this toolkit, you can build systems that simulate a new breed of robot, play Atari games, and, yes, master the game of Go.

But game-playing is just the beginning. OpenAI is a billion-dollar effort to push AI as far as it will go. In both how the company came together and what it plans to do, you can see the next great wave of innovation forming. We’re a long way from knowing whether OpenAI itself becomes the main agent for that change. But the forces that drove the creation of this rather unusual startup show that the new breed of AI will not only remake technology, but remake the way we build technology.

AI Everywhere
Silicon Valley is not exactly averse to hyperbole. It’s always wise to meet bold-sounding claims with skepticism. But in the field of AI, the change is real. Inside places like Google and Facebook, a technology called deep learning is already helping Internet services identify faces in photos, recognize commands spoken into smartphones, and respond to Internet search queries. And this same technology can drive so many other tasks of the future. It can help machines understand natural language—the natural way that we humans talk and write. It can create a new breed of robot, giving automatons the power to not only perform tasks but learn them on the fly. And some believe it can eventually give machines something close to common sense—the ability to truly think like a human.

But along with such promise comes deep anxiety. Musk and Altman worry that if people can build AI that can do great things, then they can build AI that can do awful things, too. They’re not alone in their fear of robot overlords, but perhaps counterintuitively, Musk and Altman also think that the best way to battle malicious AI is not to restrict access to artificial intelligence but expand it. That’s part of what has attracted a team of young, hyper-intelligent idealists to their new project.

OpenAI began one evening last summer in a private room at Silicon Valley’s Rosewood Hotel—an upscale, urban, ranch-style hotel that sits, literally, at the center of the venture capital world along Sand Hill Road in Menlo Park, California. Elon Musk was having dinner with Ilya Sutskever, who was then working on the Google Brain, the company’s sweeping effort to build deep neural networks—artificially intelligent systems that can learn to perform tasks by analyzing massive amounts of digital data, including everything from recognizing photos to writing email messages to, well, carrying on a conversation. Sutskever was one of the top thinkers on the project. But even bigger ideas were in play.

Sam Altman, whose Y Combinator helped bootstrap companies like Airbnb, Dropbox, and Coinbase, had brokered the meeting, bringing together several AI researchers and a young but experienced company builder named Greg Brockman, previously the chief technology officer at high-profile Silicon Valley digital payments startup called Stripe, another Y Combinator company. It was an eclectic group. But they all shared a goal: to create a new kind of AI lab, one that would operate outside the control not only of Google, but of anyone else. “The best thing that I could imagine doing,” Brockman says, “was moving humanity closer to building real AI in a safe way.”

Musk is one of the loudest voices warning that we humans could one day lose control of systems powerful enough to learn on their own.
Musk was there because he’s an old friend of Altman’s—and because AI is crucial to the future of his various businesses and, well, the future as a whole. Tesla needs AI for its inevitable self-driving cars. SpaceX, Musk’s other company, will need it to put people in space and keep them alive once they’re there. But Musk is also one of the loudest voices warning that we humans could one day lose control of systems powerful enough to learn on their own.

The trouble was: so many of the people most qualified to solve all those problems were already working for Google (and Facebook and Microsoft and Baidu and Twitter). And no one at the dinner was quite sure that these thinkers could be lured to a new startup, even if Musk and Altman were behind it. But one key player was at least open to the idea of jumping ship. “I felt there were risks involved,” Sutskever says. “But I also felt it would be a very interesting thing to try.”

Breaking the Cycle
Emboldened by the conversation with Musk, Altman, and others at the Rosewood, Brockman soon resolved to build the lab they all envisioned. Taking on the project full-time, he approached Yoshua Bengio, a computer scientist at the University of Montreal and one of founding fathers of the deep learning movement. The field’s other two pioneers—Geoff Hinton and Yann LeCun—are now at Google and Facebook, respectively, but Bengio is committed to life in the world of academia, largely outside the aims of industry. He drew up a list of the best researchers in the field, and over the next several weeks, Brockman reached out to as many on the list as he could, along with several others.

Many of these researchers liked the idea, but they were also wary of making the leap. In an effort to break the cycle, Brockman picked the ten researchers he wanted the most and invited them to spend a Saturday getting wined, dined, and cajoled at a winery in Napa Valley. For Brockman, even the drive into Napa served as a catalyst for the project. “An underrated way to bring people together are these times where there is no way to speed up getting to where you’re going,” he says. “You have to get there, and you have to talk.” And once they reached the wine country, that vibe remained. “It was one of those days where you could tell the chemistry was there,” Brockman says. Or as Sutskever puts it: “the wine was secondary to the talk.”

By the end of the day, Brockman asked all ten researchers to join the lab, and he gave them three weeks to think about it. By the deadline, nine of them were in. And they stayed in, despite those big offers from the giants of Silicon Valley. “They did make it very compelling for me to stay, so it wasn’t an easy decision,” Sutskever says of Google, his former employer. “But in the end, I decided to go with OpenAI, partly of because of the very strong group of people and, to a very large extent, because of its mission.”

The deep learning movement began with academics. It’s only recently that companies like Google and Facebook and Microsoft have pushed into the field, as advances in raw computing power have made deep neural networks a reality, not just a theoretical possibility. People like Hinton and LeCun left academia for Google and Facebook because of the enormous resources inside these companies. But they remain intent on collaborating with other thinkers. Indeed, as LeCun explains, deep learning research requires this free flow of ideas. “When you do research in secret,” he says, “you fall behind.”

As a result, big companies now share a lot of their AI research. That’s a real change, especially for Google, which has long kept the tech at the heart of its online empire secret. Recently, Google open sourced the software engine that drives its neural networks. But it still retains the inside track in the race to the future. Brockman, Altman, and Musk aim to push the notion of openness further still, saying they don’t want one or two large corporations controlling the future of artificial intelligence.

The Limits of Openness
All of which sounds great. But for all of OpenAI’s idealism, the researchers may find themselves facing some of the same compromises they had to make at their old jobs. Openness has its limits. And the long-term vision for AI isn’t the only interest in play. OpenAI is not a charity. Musk’s companies that could benefit greatly the startup’s work, and so could many of the companies backed by Altman’s Y Combinator. “There are certainly some competing objectives,” LeCun says. “It’s a non-profit, but then there is a very close link with Y Combinator. And people are paid as if they are working in the industry.”

According to Brockman, the lab doesn’t pay the same astronomical salaries that AI researchers are now getting at places like Google and Facebook. But he says the lab does want to “pay them well,” and it’s offering to compensate researchers with stock options, first in Y Combinator and perhaps later in SpaceX (which, unlike Tesla, is still a private company).

Brockman insists that OpenAI won’t give special treatment to its sister companies.
Nonetheless, Brockman insists that OpenAI won’t give special treatment to its sister companies. OpenAI is a research outfit, he says, not a consulting firm. But when pressed, he acknowledges that OpenAI’s idealistic vision has its limits. The company may not open source everything it produces, though it will aim to share most of its research eventually, either through research papers or Internet services. “Doing all your research in the open is not necessarily the best way to go. You want to nurture an idea, see where it goes, and then publish it,” Brockman says. “We will produce lot of open source code. But we will also have a lot of stuff that we are not quite ready to release.”

Both Sutskever and Brockman also add that OpenAI could go so far as to patent some of its work. “We won’t patent anything in the near term,” Brockman says. “But we’re open to changing tactics in the long term, if we find it’s the best thing for the world.” For instance, he says, OpenAI could engage in pre-emptive patenting, a tactic that seeks to prevent others from securing patents.

But to some, patents suggest a profit motive—or at least a weaker commitment to open source than OpenAI’s founders have espoused. “That’s what the patent system is about,” says Oren Etzioni, head of the Allen Institute for Artificial Intelligence. “This makes me wonder where they’re really going.”

The Super-Intelligence Problem
When Musk and Altman unveiled OpenAI, they also painted the project as a way to neutralize the threat of a malicious artificial super-intelligence. Of course, that super-intelligence could arise out of the tech OpenAI creates, but they insist that any threat would be mitigated because the technology would be usable by everyone. “We think its far more likely that many, many AIs will work to stop the occasional bad actors,” Altman says.

But not everyone in the field buys this. Nick Bostrom, the Oxford philosopher who, like Musk, has warned against the dangers of AI, points out that if you share research without restriction, bad actors could grab it before anyone has ensured that it’s safe. “If you have a button that could do bad things to the world,” Bostrom says, “you don’t want to give it to everyone.” If, on the other hand, OpenAI decides to hold back research to keep it from the bad guys, Bostrom wonders how it’s different from a Google or a Facebook.

If you share research without restriction, bad actors could grab it before anyone has ensured that it’s safe.
He does say that the not-for-profit status of OpenAI could change things—though not necessarily. The real power of the project, he says, is that it can indeed provide a check for the likes of Google and Facebook. “It can reduce the probability that super-intelligence would be monopolized,” he says. “It can remove one possible reason why some entity or group would have radically better AI than everyone else.”

But as the philosopher explains in a new paper, the primary effect of an outfit like OpenAI—an outfit intent on freely sharing its work—is that it accelerates the progress of artificial intelligence, at least in the short term. And it may speed progress in the long term as well, provided that it, for altruistic reasons, “opts for a higher level of openness than would be commercially optimal.”

“It might still be plausible that a philanthropically motivated R&D funder would speed progress more by pursuing open science,” he says.

Like Xerox PARC
In early January, Brockman’s nine AI researchers met up at his apartment in San Francisco’s Mission District. The project was so new that they didn’t even have white boards. (Can you imagine?) They bought a few that day and got down to work.

Brockman says OpenAI will begin by exploring reinforcement learning, a way for machines to learn tasks by repeating them over and over again and tracking which methods produce the best results. But the other primary goal is what’s called “unsupervised learning”—creating machines that can truly learn on their own, without a human hand to guide them. Today, deep learning is driven by carefully labeled data. If you want to teach a neural network to recognize cat photos, you must feed it a certain number of examples—and these examples must be labeled as cat photos. The learning is supervised by human labelers. But like many others researchers, OpenAI aims to create neural nets that can learn without carefully labeled data.

“If you have really good unsupervised learning, machines would be able to learn from all this knowledge on the Internet—just like humans learn by looking around—or reading books,” Brockman says.

He envisions OpenAI as the modern incarnation of Xerox PARC, the tech research lab that thrived in the 1970s. Just as PARC’s largely open and unfettered research gave rise to everything from the graphical user interface to the laser printer to object-oriented programing, Brockman and crew seek to delve even deeper into what we once considered science fiction. PARC was owned by, yes, Xerox, but it fed so many other companies, most notably Apple, because people like Steve Jobs were privy to its research. At OpenAI, Brockman wants to make everyone privy to its research.

This month, hoping to push this dynamic as far as it will go, Brockman and company snagged several other notable researchers, including Ian Goodfellow, another former senior researcher on the Google Brain team. “The thing that was really special about PARC is that they got a bunch of smart people together and let them go where they want,” Brockman says. “You want a shared vision, without central control.”

Giving up control is the essence of the open source ideal. If enough people apply themselves to a collective goal, the end result will trounce anything you concoct in secret. But if AI becomes as powerful as promised, the equation changes. We’ll have to ensure that new AIs adhere to the same egalitarian ideals that led to their creation in the first place. Musk, Altman, and Brockman are placing their faith in the wisdom of the crowd. But if they’re right, one day that crowd won’t be entirely human.