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#435614 3 Easy Ways to Evaluate AI Claims

When every other tech startup claims to use artificial intelligence, it can be tough to figure out if an AI service or product works as advertised. In the midst of the AI “gold rush,” how can you separate the nuggets from the fool’s gold?

There’s no shortage of cautionary tales involving overhyped AI claims. And applying AI technologies to health care, education, and law enforcement mean that getting it wrong can have real consequences for society—not just for investors who bet on the wrong unicorn.

So IEEE Spectrum asked experts to share their tips for how to identify AI hype in press releases, news articles, research papers, and IPO filings.

“It can be tricky, because I think the people who are out there selling the AI hype—selling this AI snake oil—are getting more sophisticated over time,” says Tim Hwang, director of the Harvard-MIT Ethics and Governance of AI Initiative.

The term “AI” is perhaps most frequently used to describe machine learning algorithms (and deep learning algorithms, which require even less human guidance) that analyze huge amounts of data and make predictions based on patterns that humans might miss. These popular forms of AI are mostly suited to specialized tasks, such as automatically recognizing certain objects within photos. For that reason, they are sometimes described as “weak” or “narrow” AI.

Some researchers and thought leaders like to talk about the idea of “artificial general intelligence” or “strong AI” that has human-level capacity and flexibility to handle many diverse intellectual tasks. But for now, this type of AI remains firmly in the realm of science fiction and is far from being realized in the real world.

“AI has no well-defined meaning and many so-called AI companies are simply trying to take advantage of the buzz around that term,” says Arvind Narayanan, a computer scientist at Princeton University. “Companies have even been caught claiming to use AI when, in fact, the task is done by human workers.”

Here are three ways to recognize AI hype.

Look for Buzzwords
One red flag is what Hwang calls the “hype salad.” This means stringing together the term “AI” with many other tech buzzwords such as “blockchain” or “Internet of Things.” That doesn’t automatically disqualify the technology, but spotting a high volume of buzzwords in a post, pitch, or presentation should raise questions about what exactly the company or individual has developed.

Other experts agree that strings of buzzwords can be a red flag. That’s especially true if the buzzwords are never really explained in technical detail, and are simply tossed around as vague, poorly-defined terms, says Marzyeh Ghassemi, a computer scientist and biomedical engineer at the University of Toronto in Canada.

“I think that if it looks like a Google search—picture ‘interpretable blockchain AI deep learning medicine’—it's probably not high-quality work,” Ghassemi says.

Hwang also suggests mentally replacing all mentions of “AI” in an article with the term “magical fairy dust.” It’s a way of seeing whether an individual or organization is treating the technology like magic. If so—that’s another good reason to ask more questions about what exactly the AI technology involves.

And even the visual imagery used to illustrate AI claims can indicate that an individual or organization is overselling the technology.

“I think that a lot of the people who work on machine learning on a day-to-day basis are pretty humble about the technology, because they’re largely confronted with how frequently it just breaks and doesn't work,” Hwang says. “And so I think that if you see a company or someone representing AI as a Terminator head, or a big glowing HAL eye or something like that, I think it’s also worth asking some questions.”

Interrogate the Data

It can be hard to evaluate AI claims without any relevant expertise, says Ghassemi at the University of Toronto. Even experts need to know the technical details of the AI algorithm in question and have some access to the training data that shaped the AI model’s predictions. Still, savvy readers with some basic knowledge of applied statistics can search for red flags.

To start, readers can look for possible bias in training data based on small sample sizes or a skewed population that fails to reflect the broader population, Ghassemi says. After all, an AI model trained only on health data from white men would not necessarily achieve similar results for other populations of patients.

“For me, a red flag is not demonstrating deep knowledge of how your labels are defined.”
—Marzyeh Ghassemi, University of Toronto

How machine learning and deep learning models perform also depends on how well humans labeled the sample datasets use to train these programs. This task can be straightforward when labeling photos of cats versus dogs, but gets more complicated when assigning disease diagnoses to certain patient cases.

Medical experts frequently disagree with each other on diagnoses—which is why many patients seek a second opinion. Not surprisingly, this ambiguity can also affect the diagnostic labels that experts assign in training datasets. “For me, a red flag is not demonstrating deep knowledge of how your labels are defined,” Ghassemi says.

Such training data can also reflect the cultural stereotypes and biases of the humans who labeled the data, says Narayanan at Princeton University. Like Ghassemi, he recommends taking a hard look at exactly what the AI has learned: “A good way to start critically evaluating AI claims is by asking questions about the training data.”

Another red flag is presenting an AI system’s performance through a single accuracy figure without much explanation, Narayanan says. Claiming that an AI model achieves “99 percent” accuracy doesn’t mean much without knowing the baseline for comparison—such as whether other systems have already achieved 99 percent accuracy—or how well that accuracy holds up in situations beyond the training dataset.

Narayanan also emphasized the need to ask questions about an AI model’s false positive rate—the rate of making wrong predictions about the presence of a given condition. Even if the false positive rate of a hypothetical AI service is just one percent, that could have major consequences if that service ends up screening millions of people for cancer.

Readers can also consider whether using AI in a given situation offers any meaningful improvement compared to traditional statistical methods, says Clayton Aldern, a data scientist and journalist who serves as managing director for Caldern LLC. He gave the hypothetical example of a “super-duper-fancy deep learning model” that achieves a prediction accuracy of 89 percent, compared to a “little polynomial regression model” that achieves 86 percent on the same dataset.

“We're talking about a three-percentage-point increase on something that you learned about in Algebra 1,” Aldern says. “So is it worth the hype?”

Don’t Ignore the Drawbacks

The hype surrounding AI isn’t just about the technical merits of services and products driven by machine learning. Overblown claims about the beneficial impacts of AI technology—or vague promises to address ethical issues related to deploying it—should also raise red flags.

“If a company promises to use its tech ethically, it is important to question if its business model aligns with that promise,” Narayanan says. “Even if employees have noble intentions, it is unrealistic to expect the company as a whole to resist financial imperatives.”

One example might be a company with a business model that depends on leveraging customers’ personal data. Such companies “tend to make empty promises when it comes to privacy,” Narayanan says. And, if companies hire workers to produce training data, it’s also worth asking whether the companies treat those workers ethically.

The transparency—or lack thereof—about any AI claim can also be telling. A company or research group can minimize concerns by publishing technical claims in peer-reviewed journals or allowing credible third parties to evaluate their AI without giving away big intellectual property secrets, Narayanan says. Excessive secrecy is a big red flag.

With these strategies, you don’t need to be a computer engineer or data scientist to start thinking critically about AI claims. And, Narayanan says, the world needs many people from different backgrounds for societies to fully consider the real-world implications of AI.

Editor’s Note: The original version of this story misspelled Clayton Aldern’s last name as Alderton. Continue reading

Posted in Human Robots

#435145 How Big Companies Can Simultaneously Run ...

We live in the age of entrepreneurs. New startups seem to appear out of nowhere and challenge not only established companies, but entire industries. Where startup unicorns were once mythical creatures, they now seem abundant, not only increasing in numbers but also in the speed with which they can gain the minimum one-billion-dollar valuations to achieve this status.

But no matter how well things go for innovative startups, how many new success stories we hear, and how much space they take up in the media, the story that they are the best or only source of innovation isn’t entirely accurate.

Established organizations, or legacy organizations, can be incredibly innovative too. And while innovation is much more difficult in established organizations than in startups because they have much more complex systems—nobody is more likely to succeed in their innovation efforts than established organizations.

Unlike startups, established organizations have all the resources. They have money, customers, data, suppliers, partners, and infrastructure, which put them in a far better position to transform new ideas into concrete, value-creating, successful offerings than startups.

However, for established organizations, becoming an innovation champion in these times of rapid change requires new rules of engagement.

Many organizations commit the mistake of engaging in innovation as if it were a homogeneous thing that should be approached in the same way every time, regardless of its purpose. In my book, Transforming Legacy Organizations, I argue that innovation in established organizations must actually be divided into three different tracks: optimizing, augmenting, and mutating innovation.

All three are important, and to complicate matters further, organizations must execute all three types of innovation at the same time.

Optimizing Innovation
The first track is optimizing innovation. This type of innovation is the majority of what legacy organizations already do today. It is, metaphorically speaking, the extra blade on the razor. A razor manufacturer might launch a new razor that has not just three, but four blades, to ensure an even better, closer, and more comfortable shave. Then one or two years later, they say they are now launching a razor that has not only four, but five blades for an even better, closer, and more comfortable shave. That is optimizing innovation.

Adding extra blades on the razor is where the established player reigns.

No startup with so much as a modicum of sense would even try to beat the established company in this type of innovation. And this continuous optimization, both on the operational and customer facing sides, is important. In the short term. It pays the rent. But it’s far from enough. There are limits to how many blades a razor needs, and optimizing innovation only improves upon the past.

Augmenting Innovation
Established players must also go beyond optimization and prepare for the future through augmenting innovation.

The digital transformation projects that many organizations are initiating can be characterized as augmenting innovation. In the first instance, it is about upgrading core offerings and processes from analog to digital. Or, if you’re born digital, you’ve probably had to augment the core to become mobile-first. Perhaps you have even entered the next augmentation phase, which involves implementing artificial intelligence. Becoming AI-first, like the Amazons, Microsofts, Baidus, and Googles of the world, requires great technological advancements. And it’s difficult. But technology may, in fact, be a minor part of the task.

The biggest challenge for augmenting innovation is probably culture.

Only legacy organizations that manage to transform their cultures from status quo cultures—cultures with a preference for things as they are—into cultures full of incremental innovators can thrive in constant change.

To create a strong innovation culture, an organization needs to thoroughly understand its immune systems. These are the mechanisms that protect the organization and operate around the clock to keep it healthy and stable, just as the body’s immune system operates to keep the body healthy and stable. But in a rapidly changing world, many of these defense mechanisms are no longer appropriate and risk weakening organizations’ innovation power.

When talking about organizational immune systems, there is a clear tendency to simply point to the individual immune system, people’s unwillingness to change.

But this is too simplistic.

Of course, there is human resistance to change, but the organizational immune system, consisting of a company’s key performance indicators (KPIs), rewards systems, legacy IT infrastructure and processes, and investor and shareholder demands, is far more important. So is the organization’s societal immune system, such as legislative barriers, legacy customers and providers, and economic climate.

Luckily, there are many culture hacks that organizations can apply to strengthen their innovation cultures by upgrading their physical and digital workspaces, transforming their top-down work processes into decentralized, agile ones, and empowering their employees.

Mutating Innovation
Upgrading your core and preparing for the future by augmenting innovation is crucial if you want success in the medium term. But to win in the long run and be as or more successful 20 to 30 years from now, you need to invent the future, and challenge your core, through mutating innovation.

This requires involving radical innovators who have a bold focus on experimenting with that which is not currently understood and for which a business case cannot be prepared.

Here you must also physically move away from the core organization when you initiate and run such initiatives. This is sometimes called “innovation on the edges” because the initiatives will not have a chance at succeeding within the core. It will be too noisy as they challenge what currently exists—precisely what the majority of the organization’s employees are working to optimize or augment.

Forward-looking organizations experiment to mutate their core through “X divisions,” sometimes called skunk works or innovation labs.

Lowe’s Innovation Labs, for instance, worked with startups to build in-store robot assistants and zero-gravity 3D printers to explore the future. Mutating innovation might include pursuing partnerships across all imaginable domains or establishing brand new companies, rather than traditional business units, as we see automakers such as Toyota now doing to build software for autonomous vehicles. Companies might also engage in radical open innovation by sponsoring others’ ingenuity. Japan’s top airline ANA is exploring a future of travel that does not involve flying people from point A to point B via the ANA Avatar XPRIZE competition.

Increasing technological opportunities challenge the core of any organization but also create unprecedented potential. No matter what product, service, or experience you create, you can’t rest on your laurels. You have to bring yourself to a position where you have a clear strategy for optimizing, augmenting, and mutating your core and thus transforming your organization.

It’s not an easy job. But, hey, if it were easy, everyone would be doing it. Those who make it, on the other hand, will be the innovation champions of the future.

Image Credit: rock-the-stock / Shutterstock.com

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Posted in Human Robots

#434655 Purposeful Evolution: Creating an ...

More often than not, we fall into the trap of trying to predict and anticipate the future, forgetting that the future is up to us to envision and create. In the words of Buckminster Fuller, “We are called to be architects of the future, not its victims.”

But how, exactly, do we create a “good” future? What does such a future look like to begin with?

In Future Consciousness: The Path to Purposeful Evolution, Tom Lombardo analytically deconstructs how we can flourish in the flow of evolution and create a prosperous future for humanity. Scientifically informed, the books taps into themes that are constructive and profound, from both eastern and western philosophies.

As the executive director of the Center for Future Consciousness and an executive board member and fellow of the World Futures Studies Federation, Lombardo has dedicated his life and career to studying how we can create a “realistic, constructive, and ethical future.”

In a conversation with Singularity Hub, Lombardo discussed purposeful evolution, ethical use of technology, and the power of optimism.

Raya Bidshahri: Tell me more about the title of your book. What is future consciousness and what role does it play in what you call purposeful evolution?

Tom Lombardo: Humans have the unique capacity to purposefully evolve themselves because they possess future consciousness. Future consciousness contains all of the cognitive, motivational, and emotional aspects of the human mind that pertain to the future. It’s because we can imagine and think about the future that we can manipulate and direct our future evolution purposefully. Future consciousness empowers us to become self-responsible in our own evolutionary future. This is a jump in the process of evolution itself.

RB: In several places in the book, you discuss the importance of various eastern philosophies. What can we learn from the east that is often missing from western models?

TL: The key idea in the east that I have been intrigued by for decades is the Taoist Yin Yang, which is the idea that reality should be conceptualized as interdependent reciprocities.

In the west we think dualistically, or we attempt to think in terms of one end of the duality to the exclusion of the other, such as whole versus parts or consciousness versus physical matter. Yin Yang thinking is seeing how both sides of a “duality,” even though they appear to be opposites, are interdependent; you can’t have one without the other. You can’t have order without chaos, consciousness without the physical world, individuals without the whole, humanity without technology, and vice versa for all these complementary pairs.

RB: You talk about the importance of chaos and destruction in the trajectory of human progress. In your own words, “Creativity frequently involves destruction as a prelude to the emergence of some new reality.” Why is this an important principle for readers to keep in mind, especially in the context of today’s world?

TL: In order for there to be progress, there often has to be a disintegration of aspects of the old. Although progress and evolution involve a process of building up, growth isn’t entirely cumulative; it’s also transformative. Things fall apart and come back together again.

Throughout history, we have seen a transformation of what are the most dominant human professions or vocations. At some point, almost everybody worked in agriculture, but most of those agricultural activities were replaced by machines, and a lot of people moved over to industry. Now we’re seeing that jobs and functions are increasingly automated in industry, and humans are being pushed into vocations that involve higher cognitive and artistic skills, services, information technology, and so on.

RB: You raise valid concerns about the dark side of technological progress, especially when it’s combined with mass consumerism, materialism, and anti-intellectualism. How do we counter these destructive forces as we shape the future of humanity?

TL: We can counter such forces by always thoughtfully considering how our technologies are affecting the ongoing purposeful evolution of our conscious minds, bodies, and societies. We should ask ourselves what are the ethical values that are being served by the development of various technologies.

For example, we often hear the criticism that technologies that are driven by pure capitalism degrade human life and only benefit the few people who invented and market them. So we need to also think about what good these new technologies can serve. It’s what I mean when I talk about the “wise cyborg.” A wise cyborg is somebody who uses technology to serve wisdom, or values connected with wisdom.

RB: Creating an ideal future isn’t just about progress in technology, but also progress in morality. How we do decide what a “good” future is? What are some philosophical tools we can use to determine a code of ethics that is as objective as possible?

TL: Let’s keep in mind that ethics will always have some level of subjectivity. That being said, the way to determine a good future is to base it on the best theory of reality that we have, which is that we are evolutionary beings in an evolutionary universe and we are interdependent with everything else in that universe. Our ethics should acknowledge that we are fluid and interactive.

Hence, the “good” can’t be something static, and it can’t be something that pertains to me and not everybody else. It can’t be something that only applies to humans and ignores all other life on Earth, and it must be a mode of change rather than something stable.

RB: You present a consciousness-centered approach to creating a good future for humanity. What are some of the values we should develop in order to create a prosperous future?

TL: A sense of self-responsibility for the future is critical. This means realizing that the “good future” is something we have to take upon ourselves to create; we can’t let something or somebody else do that. We need to feel responsible both for our own futures and for the future around us.

Another one is going to be an informed and hopeful optimism about the future, because both optimism and pessimism have self-fulfilling prophecy effects. If you hope for the best, you are more likely to look deeply into your reality and increase the chance of it coming out that way. In fact, all of the positive emotions that have to do with future consciousness actually make people more intelligent and creative.

Some other important character virtues are discipline and tenacity, deep purpose, the love of learning and thinking, and creativity.

RB: Are you optimistic about the future? If so, what informs your optimism?

I justify my optimism the same way that I have seen Ray Kurzweil, Peter Diamandis, Kevin Kelly, and Steven Pinker justify theirs. If we look at the history of human civilization and even the history of nature, we see a progressive motion forward toward greater complexity and even greater intelligence. There’s lots of ups and downs, and catastrophes along the way, but the facts of nature and human history support the long-term expectation of continued evolution into the future.

You don’t have to be unrealistic to be optimistic. It’s also, psychologically, the more empowering position. That’s the position we should take if we want to maximize the chances of our individual or collective reality turning out better.

A lot of pessimists are pessimistic because they’re afraid of the future. There are lots of reasons to be afraid, but all in all, fear disempowers, whereas hope empowers.

Image Credit: Quick Shot / Shutterstock.com

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Posted in Human Robots

#434492 Black Mirror’s ‘Bandersnatch’ ...

When was the last time you watched a movie where you could control the plot?

Bandersnatch is the first interactive film in the sci fi anthology series Black Mirror. Written by series creator Charlie Brooker and directed by David Slade, the film tells the story of young programmer Stefan Butler, who is adapting a fantasy choose-your-own-adventure novel called Bandersnatch into a video game. Throughout the film, viewers are given the power to influence Butler’s decisions, leading to diverging plots with different endings.

Like many Black Mirror episodes, this film is mind-bending, dark, and thought-provoking. In addition to innovating cinema as we know it, it is a fascinating rumination on free will, parallel realities, and emerging technologies.

Pick Your Own Adventure
With a non-linear script, Bandersnatch is a viewing experience like no other. Throughout the film viewers are given the option of making a decision for the protagonist. In these instances, they have 10 seconds to make a decision until a default decision is made. For example, in the early stage of the plot, Butler is given the choice of accepting or rejecting Tuckersoft’s offer to develop a video game and the viewer gets to decide what he does. The decision then shapes the plot accordingly.

The video game Butler is developing involves moving through a graphical maze of corridors while avoiding a creature called the Pax, and at times making choices through an on-screen instruction (sound familiar?). In other words, it’s a pick-your-own-adventure video game in a pick-your-own-adventure movie.

Many viewers have ended up spending hours exploring all the different branches of the narrative (though the average viewing is 90 minutes). One user on reddit has mapped out an entire flowchart, showing how all the different decisions (and pseudo-decisions) lead to various endings.

However, over time, Butler starts to question his own free will. It’s almost as if he’s beginning to realize that the audience is controlling him. In one branch of the narrative, he is confronted by this reality when the audience indicates to him that he is being controlled in a Netflix show: “I am watching you on Netflix. I make all the decisions for you”. Butler, as you can imagine, is horrified by this message.

But Butler isn’t the only one who has an illusion of choice. We, the seemingly powerful viewers, also appear to operate under the illusion of choice. Despite there being five main endings to the film, they are all more or less the same.

The Science Behind Bandersnatch
The premise of Bandersnatch isn’t based on fantasy, but hard science. Free will has always been a widely-debated issue in neuroscience, with reputable scientists and studies demonstrating that the whole concept may be an illusion.

In the 1970s, a psychologist named Benjamin Libet conducted a series of experiments that studied voluntary decision making in humans. He found that brain activity imitating an action, such as moving your wrist, preceded the conscious awareness of the action.

Psychologist Malcom Gladwell theorizes that while we like to believe we spend a lot of time thinking about our decisions, our mental processes actually work rapidly, automatically, and often subconsciously, from relatively little information. In addition to this, thinking and making decisions are usually a byproduct of several different brain systems, such as the hippocampus, amygdala, and prefrontal cortex working together. You are more conscious of some information processes in the brain than others.

As neuroscientist and philosopher Sam Harris points out in his book Free Will, “You did not pick your parents or the time and place of your birth. You didn’t choose your gender or most of your life experiences. You had no control whatsoever over your genome or the development of your brain. And now your brain is making choices on the basis of preferences and beliefs that have been hammered into it over a lifetime.” Like Butler, we may believe we are operating under full agency of our abilities, but we are at the mercy of many internal and external factors that influence our decisions.

Beyond free will, Bandersnatch also taps into the theory of parallel universes, a facet of the astronomical theory of the multiverse. In astrophysics, there is a theory that there are parallel universes other than our own, where all the choices you made are played out in alternate realities. For instance, if today you had the option of having cereal or eggs for breakfast, and you chose eggs, in a parallel universe, you chose cereal. Human history and our lives may have taken different paths in these parallel universes.

The Future of Cinema
In the future, the viewing experience will no longer be a passive one. Bandersnatch is just a glimpse into how technology is revolutionizing film as we know it and making it a more interactive and personalized experience. All the different scenarios and branches of the plot were scripted and filmed, but in the future, they may be adapted real-time via artificial intelligence.

Virtual reality may allow us to play an even more active role by making us participants or characters in the film. Data from your history of preferences and may be used to create a unique version of the plot that is optimized for your viewing experience.

Let’s also not underestimate the social purpose of advancing film and entertainment. Science fiction gives us the ability to create simulations of the future. Different narratives can allow us to explore how powerful technologies combined with human behavior can result in positive or negative scenarios. Perhaps in the future, science fiction will explore implications of technologies and observe human decision making in novel contexts, via AI-powered films in the virtual world.

Image Credit: andrey_l / Shutterstock.com

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Posted in Human Robots

#433872 Breaking Out of the Corporate Bubble ...

For big companies, success is a blessing and a curse. You don’t get big without doing something (or many things) very right. It might start with an invention or service the world didn’t know it needed. Your product takes off, and growth brings a whole new set of logistical challenges. Delivering consistent quality, hiring the right team, establishing a strong culture, tapping into new markets, satisfying shareholders. The list goes on.

Eventually, however, what made you successful also makes you resistant to change.

You’ve built a machine for one purpose, and it’s running smoothly, but what about retooling that machine to make something new? Not so easy. Leaders of big companies know there is no future for their organizations without change. And yet, they struggle to drive it.

In their new book, Leading Transformation: How to Take Charge of Your Company’s Future, Kyle Nel, Nathan Furr, and Thomas Ramsøy aim to deliver a roadmap for corporate transformation.

The book focuses on practical tools that have worked in big companies to break down behavioral and cognitive biases, envision radical futures, and run experiments. These include using science fiction and narrative to see ahead and adopting better measures of success for new endeavors.

A thread throughout is how to envision a new future and move into that future.

We’re limited by the bubbles in which we spend the most time—the corporate bubble, the startup bubble, the nonprofit bubble. The mutually beneficial convergence of complementary bubbles, then, can be a powerful tool for kickstarting transformation. The views and experiences of one partner can challenge the accepted wisdom of the other; resources can flow into newly co-created visions and projects; and connections can be made that wouldn’t otherwise exist.

The authors call such alliances uncommon partners. In the following excerpt from the book, Made In Space, a startup building 3D printers for space, helps Lowe’s explore an in-store 3D printing system, and Lowe’s helps Made In Space expand its vision and focus.

Uncommon Partners
In a dingy conference room at NASA, five prototypical nerds, smelling of Thai food, laid out the path to printing satellites in space and buildings on distant planets. At the end of their four-day marathon, they emerged with an artifact trail that began with early prototypes for the first 3D printer on the International Space Station and ended in the additive-manufacturing future—a future much bigger than 3D printing.

In the additive-manufacturing future, we will view everything as transient, or capable of being repurposed into new things. Rather than throwing away a soda bottle or a bent nail, we will simply reprocess these things into a new hinge for the fence we are building or a light switch plate for the tool shed. Indeed, we might not even go buy bricks for the tool shed, but instead might print them from impurities pulled from the air and the dirt beneath our feet. Such a process would both capture carbon in the air to make the bricks and avoid all the carbon involved in making and then transporting traditional bricks to your house.

If it all sounds a little too science fiction, think again. Lowe’s has already been honored as a Champion of Change by the US government for its prototype system to recycle plastic (e.g., plastic bags and bottles). The future may be closer than you have imagined. But to get there, Lowe’s didn’t work alone. It had to work with uncommon partners to create the future.

Uncommon partners are the types of organizations you might not normally work with, but which can greatly help you create radical new futures. Increasingly, as new technologies emerge and old industries converge, companies are finding that working independently to create all the necessary capabilities to enter new industries or create new technologies is costly, risky, and even counterproductive. Instead, organizations are finding that they need to collaborate with uncommon partners as an ecosystem to cocreate the future together. Nathan [Furr] and his colleague at INSEAD, Andrew Shipilov, call this arrangement an adaptive ecosystem strategy and described how companies such as Lowe’s, Samsung, Mastercard, and others are learning to work differently with partners and to work with different kinds of partners to more effectively discover new opportunities. For Lowe’s, an adaptive ecosystem strategy working with uncommon partners forms the foundation of capturing new opportunities and transforming the company. Despite its increased agility, Lowe’s can’t be (and shouldn’t become) an independent additive-manufacturing, robotics-using, exosuit-building, AR-promoting, fill-in-the-blank-what’s-next-ing company in addition to being a home improvement company. Instead, Lowe’s applies an adaptive ecosystem strategy to find the uncommon partners with which it can collaborate in new territory.

To apply the adaptive ecosystem strategy with uncommon partners, start by identifying the technical or operational components required for a particular focus area (e.g., exosuits) and then sort these components into three groups. First, there are the components that are emerging organically without any assistance from the orchestrator—the leader who tries to bring together the adaptive ecosystem. Second, there are the elements that might emerge, with encouragement and support. Third are the elements that won’t happen unless you do something about it. In an adaptive ecosystem strategy, you can create regular partnerships for the first two elements—those already emerging or that might emerge—if needed. But you have to create the elements in the final category (those that won’t emerge) either with an uncommon partner or by yourself.

For example, when Lowe’s wanted to explore the additive-manufacturing space, it began a search for an uncommon partner to provide the missing but needed capabilities. Unfortunately, initial discussions with major 3D printing companies proved disappointing. The major manufacturers kept trying to sell Lowe’s 3D printers. But the vision our group had created with science fiction was not for vendors to sell Lowe’s a printer, but for partners to help the company build a system—something that would allow customers to scan, manipulate, print, and eventually recycle additive-manufacturing objects. Every time we discussed 3D printing systems with these major companies, they responded that they could do it and then tried to sell printers. When Carin Watson, one of the leading lights at Singularity University, introduced us to Made In Space (a company being incubated in Singularity University’s futuristic accelerator), we discovered an uncommon partner that understood what it meant to cocreate a system.

Initially, Made In Space had been focused on simply getting 3D printing to work in space, where you can’t rely on gravity, you can’t send up a technician if the machine breaks, and you can’t release noxious fumes into cramped spacecraft quarters. But after the four days in the conference room going over the comic for additive manufacturing, Made In Space and Lowe’s emerged with a bigger vision. The company helped lay out an artifact trail that included not only the first printer on the International Space Station but also printing system services in Lowe’s stores.

Of course, the vision for an additive-manufacturing future didn’t end there. It also reshaped Made In Space’s trajectory, encouraging the startup, during those four days in a NASA conference room, to design a bolder future. Today, some of its bold projects include the Archinaut, a system that enables satellites to build themselves while in space, a direction that emerged partly from the science fiction narrative we created around additive manufacturing.

In summary, uncommon partners help you succeed by providing you with the capabilities you shouldn’t be building yourself, as well as with fresh insights. You also help uncommon partners succeed by creating new opportunities from which they can prosper.

Helping Uncommon Partners Prosper
Working most effectively with uncommon partners can require a shift from more familiar outsourcing or partnership relationships. When working with uncommon partners, you are trying to cocreate the future, which entails a great deal more uncertainty. Because you can’t specify outcomes precisely, agreements are typically less formal than in other types of relationships, and they operate under the provisions of shared vision and trust more than binding agreement clauses. Moreover, your goal isn’t to extract all the value from the relationship. Rather, you need to find a way to share the value.

Ideally, your uncommon partners should be transformed for the better by the work you do. For example, Lowe’s uncommon partner developing the robotics narrative was a small startup called Fellow Robots. Through their work with Lowe’s, Fellow Robots transformed from a small team focused on a narrow application of robotics (which was arguably the wrong problem) to a growing company developing a very different and valuable set of capabilities: putting cutting-edge technology on top of the old legacy systems embedded at the core of most companies. Working with Lowe’s allowed Fellow Robots to discover new opportunities, and today Fellow Robots works with retailers around the world, including BevMo! and Yamada. Ultimately, working with uncommon partners should be transformative for both of you, so focus more on creating a bigger pie than on how you are going to slice up a smaller pie.

The above excerpt appears in the new book Leading Transformation: How to Take Charge of Your Company’s Future by Kyle Nel, Nathan Furr, and Thomas Ramsøy, published by Harvard Business Review Press.

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