Tag Archives: Static

#433892 The Spatial Web Will Map Our 3D ...

The boundaries between digital and physical space are disappearing at a breakneck pace. What was once static and boring is becoming dynamic and magical.

For all of human history, looking at the world through our eyes was the same experience for everyone. Beyond the bounds of an over-active imagination, what you see is the same as what I see.

But all of this is about to change. Over the next two to five years, the world around us is about to light up with layer upon layer of rich, fun, meaningful, engaging, and dynamic data. Data you can see and interact with.

This magical future ahead is called the Spatial Web and will transform every aspect of our lives, from retail and advertising, to work and education, to entertainment and social interaction.

Massive change is underway as a result of a series of converging technologies, from 5G global networks and ubiquitous artificial intelligence, to 30+ billion connected devices (known as the IoT), each of which will generate scores of real-world data every second, everywhere.

The current AI explosion will make everything smart, autonomous, and self-programming. Blockchain and cloud-enabled services will support a secure data layer, putting data back in the hands of users and allowing us to build complex rule-based infrastructure in tomorrow’s virtual worlds.

And with the rise of online-merge-offline (OMO) environments, two-dimensional screens will no longer serve as our exclusive portal to the web. Instead, virtual and augmented reality eyewear will allow us to interface with a digitally-mapped world, richly layered with visual data.

Welcome to the Spatial Web. Over the next few months, I’ll be doing a deep dive into the Spatial Web (a.k.a. Web 3.0), covering what it is, how it works, and its vast implications across industries, from real estate and healthcare to entertainment and the future of work. In this blog, I’ll discuss the what, how, and why of Web 3.0—humanity’s first major foray into our virtual-physical hybrid selves (BTW, this year at Abundance360, we’ll be doing a deep dive into the Spatial Web with the leaders of HTC, Magic Leap, and High-Fidelity).

Let’s dive in.

What is the Spatial Web?
While we humans exist in three dimensions, our web today is flat.

The web was designed for shared information, absorbed through a flat screen. But as proliferating sensors, ubiquitous AI, and interconnected networks blur the lines between our physical and online worlds, we need a spatial web to help us digitally map a three-dimensional world.

To put Web 3.0 in context, let’s take a trip down memory lane. In the late 1980s, the newly-birthed world wide web consisted of static web pages and one-way information—a monumental system of publishing and linking information unlike any unified data system before it. To connect, we had to dial up through unstable modems and struggle through insufferably slow connection speeds.

But emerging from this revolutionary (albeit non-interactive) infodump, Web 2.0 has connected the planet more in one decade than empires did in millennia.

Granting democratized participation through newly interactive sites and applications, today’s web era has turbocharged information-sharing and created ripple effects of scientific discovery, economic growth, and technological progress on an unprecedented scale.

We’ve seen the explosion of social networking sites, wikis, and online collaboration platforms. Consumers have become creators; physically isolated users have been handed a global microphone; and entrepreneurs can now access billions of potential customers.

But if Web 2.0 took the world by storm, the Spatial Web emerging today will leave it in the dust.

While there’s no clear consensus about its definition, the Spatial Web refers to a computing environment that exists in three-dimensional space—a twinning of real and virtual realities—enabled via billions of connected devices and accessed through the interfaces of virtual and augmented reality.

In this way, the Spatial Web will enable us to both build a twin of our physical reality in the virtual realm and bring the digital into our real environments.

It’s the next era of web-like technologies:

Spatial computing technologies, like augmented and virtual reality;
Physical computing technologies, like IoT and robotic sensors;
And decentralized computing: both blockchain—which enables greater security and data authentication—and edge computing, which pushes computing power to where it’s most needed, speeding everything up.

Geared with natural language search, data mining, machine learning, and AI recommendation agents, the Spatial Web is a growing expanse of services and information, navigable with the use of ever-more-sophisticated AI assistants and revolutionary new interfaces.

Where Web 1.0 consisted of static documents and read-only data, Web 2.0 introduced multimedia content, interactive web applications, and social media on two-dimensional screens. But converging technologies are quickly transcending the laptop, and will even disrupt the smartphone in the next decade.

With the rise of wearables, smart glasses, AR / VR interfaces, and the IoT, the Spatial Web will integrate seamlessly into our physical environment, overlaying every conversation, every road, every object, conference room, and classroom with intuitively-presented data and AI-aided interaction.

Think: the Oasis in Ready Player One, where anyone can create digital personas, build and invest in smart assets, do business, complete effortless peer-to-peer transactions, and collect real estate in a virtual world.

Or imagine a virtual replica or “digital twin” of your office, each conference room authenticated on the blockchain, requiring a cryptographic key for entry.

As I’ve discussed with my good friend and “VR guru” Philip Rosedale, I’m absolutely clear that in the not-too-distant future, every physical element of every building in the world is going to be fully digitized, existing as a virtual incarnation or even as N number of these. “Meet me at the top of the Empire State Building?” “Sure, which one?”

This digitization of life means that suddenly every piece of information can become spatial, every environment can be smarter by virtue of AI, and every data point about me and my assets—both virtual and physical—can be reliably stored, secured, enhanced, and monetized.

In essence, the Spatial Web lets us interface with digitally-enhanced versions of our physical environment and build out entirely fictional virtual worlds—capable of running simulations, supporting entire economies, and even birthing new political systems.

But while I’ll get into the weeds of different use cases next week, let’s first concretize.

How Does It Work?
Let’s start with the stack. In the PC days, we had a database accompanied by a program that could ingest that data and present it to us as digestible information on a screen.

Then, in the early days of the web, data migrated to servers. Information was fed through a website, with which you would interface via a browser—whether Mosaic or Mozilla.

And then came the cloud.

Resident at either the edge of the cloud or on your phone, today’s rapidly proliferating apps now allow us to interact with previously read-only data, interfacing through a smartphone. But as Siri and Alexa have brought us verbal interfaces, AI-geared phone cameras can now determine your identity, and sensors are beginning to read our gestures.

And now we’re not only looking at our screens but through them, as the convergence of AI and AR begins to digitally populate our physical worlds.

While Pokémon Go sent millions of mobile game-players on virtual treasure hunts, IKEA is just one of the many companies letting you map virtual furniture within your physical home—simulating everything from cabinets to entire kitchens. No longer the one-sided recipients, we’re beginning to see through sensors, creatively inserting digital content in our everyday environments.

Let’s take a look at how the latest incarnation might work. In this new Web 3.0 stack, my personal AI would act as an intermediary, accessing public or privately-authorized data through the blockchain on my behalf, and then feed it through an interface layer composed of everything from my VR headset, to numerous wearables, to my smart environment (IoT-connected devices or even in-home robots).

But as we attempt to build a smart world with smart infrastructure, smart supply chains and smart everything else, we need a set of basic standards with addresses for people, places, and things. Just like our web today relies on the Internet Protocol (TCP/IP) and other infrastructure, by which your computer is addressed and data packets are transferred, we need infrastructure for the Spatial Web.

And a select group of players is already stepping in to fill this void. Proposing new structural designs for Web 3.0, some are attempting to evolve today’s web model from text-based web pages in 2D to three-dimensional AR and VR web experiences located in both digitally-mapped physical worlds and newly-created virtual ones.

With a spatial programming language analogous to HTML, imagine building a linkable address for any physical or virtual space, granting it a format that then makes it interchangeable and interoperable with all other spaces.

But it doesn’t stop there.

As soon as we populate a virtual room with content, we then need to encode who sees it, who can buy it, who can move it…

And the Spatial Web’s eventual governing system (for posting content on a centralized grid) would allow us to address everything from the room you’re sitting in, to the chair on the other side of the table, to the building across the street.

Just as we have a DNS for the web and the purchasing of web domains, once we give addresses to spaces (akin to granting URLs), we then have the ability to identify and visit addressable locations, physical objects, individuals, or pieces of digital content in cyberspace.

And these not only apply to virtual worlds, but to the real world itself. As new mapping technologies emerge, we can now map rooms, objects, and large-scale environments into virtual space with increasing accuracy.

We might then dictate who gets to move your coffee mug in a virtual conference room, or when a team gets to use the room itself. Rules and permissions would be set in the grid, decentralized governance systems, or in the application layer.

Taken one step further, imagine then monetizing smart spaces and smart assets. If you have booked the virtual conference room, perhaps you’ll let me pay you 0.25 BTC to let me use it instead?

But given the Spatial Web’s enormous technological complexity, what’s allowing it to emerge now?

Why Is It Happening Now?
While countless entrepreneurs have already started harnessing blockchain technologies to build decentralized apps (or dApps), two major developments are allowing today’s birth of Web 3.0:

High-resolution wireless VR/AR headsets are finally catapulting virtual and augmented reality out of a prolonged winter.

The International Data Corporation (IDC) predicts the VR and AR headset market will reach 65.9 million units by 2022. Already in the next 18 months, 2 billion devices will be enabled with AR. And tech giants across the board have long begun investing heavy sums.

In early 2019, HTC is releasing the VIVE Focus, a wireless self-contained VR headset. At the same time, Facebook is charging ahead with its Project Santa Cruz—the Oculus division’s next-generation standalone, wireless VR headset. And Magic Leap has finally rolled out its long-awaited Magic Leap One mixed reality headset.

Mass deployment of 5G will drive 10 to 100-gigabit connection speeds in the next 6 years, matching hardware progress with the needed speed to create virtual worlds.

We’ve already seen tremendous leaps in display technology. But as connectivity speeds converge with accelerating GPUs, we’ll start to experience seamless VR and AR interfaces with ever-expanding virtual worlds.

And with such democratizing speeds, every user will be able to develop in VR.

But accompanying these two catalysts is also an important shift towards the decentralized web and a demand for user-controlled data.

Converging technologies, from immutable ledgers and blockchain to machine learning, are now enabling the more direct, decentralized use of web applications and creation of user content. With no central point of control, middlemen are removed from the equation and anyone can create an address, independently interacting with the network.

Enabled by a permission-less blockchain, any user—regardless of birthplace, gender, ethnicity, wealth, or citizenship—would thus be able to establish digital assets and transfer them seamlessly, granting us a more democratized Internet.

And with data stored on distributed nodes, this also means no single point of failure. One could have multiple backups, accessible only with digital authorization, leaving users immune to any single server failure.

Implications Abound–What’s Next…
With a newly-built stack and an interface built from numerous converging technologies, the Spatial Web will transform every facet of our everyday lives—from the way we organize and access our data, to our social and business interactions, to the way we train employees and educate our children.

We’re about to start spending more time in the virtual world than ever before. Beyond entertainment or gameplay, our livelihoods, work, and even personal decisions are already becoming mediated by a web electrified with AI and newly-emerging interfaces.

In our next blog on the Spatial Web, I’ll do a deep dive into the myriad industry implications of Web 3.0, offering tangible use cases across sectors.

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#433807 The How, Why, and Whether of Custom ...

A digital afterlife may soon be within reach, but it might not be for your benefit.

The reams of data we’re creating could soon make it possible to create digital avatars that live on after we die, aimed at comforting our loved ones or sharing our experience with future generations.

That may seem like a disappointing downgrade from the vision promised by the more optimistic futurists, where we upload our consciousness to the cloud and live forever in machines. But it might be a realistic possibility in the not-too-distant future—and the first steps have already been taken.

After her friend died in a car crash, Eugenia Kuyda, co-founder of Russian AI startup Luka, trained a neural network-powered chatbot on their shared message history to mimic him. Journalist and amateur coder James Vlahos took a more involved approach, carrying out extensive interviews with his terminally ill father so that he could create a digital clone of him when he died.

For those of us without the time or expertise to build our own artificial intelligence-powered avatar, startup Eternime is offering to take your social media posts and interactions as well as basic personal information to build a copy of you that could then interact with relatives once you’re gone. The service is so far only running a private beta with a handful of people, but with 40,000 on its waiting list, it’s clear there’s a market.

Comforting—Or Creepy?
The whole idea may seem eerily similar to the Black Mirror episode Be Right Back, in which a woman pays a company to create a digital copy of her deceased husband and eventually a realistic robot replica. And given the show’s focus on the emotional turmoil she goes through, people might question whether the idea is a sensible one.

But it’s hard to say at this stage whether being able to interact with an approximation of a deceased loved one would be a help or a hindrance in the grieving process. The fear is that it could make it harder for people to “let go” or “move on,” but others think it could play a useful therapeutic role, reminding people that just because someone is dead it doesn’t mean they’re gone, and providing a novel way for them to express and come to terms with their feelings.

While at present most envisage these digital resurrections as a way to memorialize loved ones, there are also more ambitious plans to use the technology as a way to preserve expertise and experience. A project at MIT called Augmented Eternity is investigating whether we could use AI to trawl through someone’s digital footprints and extract both their knowledge and elements of their personality.

Project leader Hossein Rahnama says he’s already working with a CEO who wants to leave behind a digital avatar that future executives could consult with after he’s gone. And you wouldn’t necessarily have to wait until you’re dead—experts could create virtual clones of themselves that could dispense advice on demand to far more people. These clones could soon be more than simple chatbots, too. Hollywood has already started spending millions of dollars to create 3D scans of its most bankable stars so that they can keep acting beyond the grave.

It’s easy to see the appeal of the idea; imagine if we could bring back Stephen Hawking or Tim Cook to share their wisdom with us. And what if we could create a digital brain trust combining the experience and wisdom of all the world’s greatest thinkers, accessible on demand?

But there are still huge hurdles ahead before we could create truly accurate representations of people by simply trawling through their digital remains. The first problem is data. Most peoples’ digital footprints only started reaching significant proportions in the last decade or so, and cover a relatively small period of their lives. It could take many years before there’s enough data to create more than just a superficial imitation of someone.

And that’s assuming that the data we produce is truly representative of who we are. Carefully-crafted Instagram profiles and cautiously-worded work emails hardly capture the messy realities of most peoples’ lives.

Perhaps if the idea is simply to create a bank of someone’s knowledge and expertise, accurately capturing the essence of their character would be less important. But these clones would also be static. Real people continually learn and change, but a digital avatar is a snapshot of someone’s character and opinions at the point they died. An inability to adapt as the world around them changes could put a shelf life on the usefulness of these replicas.

Who’s Calling the (Digital) Shots?
It won’t stop people trying, though, and that raises a potentially more important question: Who gets to make the calls about our digital afterlife? The subjects, their families, or the companies that hold their data?

In most countries, the law is currently pretty hazy on this topic. Companies like Google and Facebook have processes to let you choose who should take control of your accounts in the event of your death. But if you’ve forgotten to do that, the fate of your virtual remains comes down to a tangle of federal law, local law, and tech company terms of service.

This lack of regulation could create incentives and opportunities for unscrupulous behavior. The voice of a deceased loved one could be a highly persuasive tool for exploitation, and digital replicas of respected experts could be powerful means of pushing a hidden agenda.

That means there’s a pressing need for clear and unambiguous rules. Researchers at Oxford University recently suggested ethical guidelines that would treat our digital remains the same way museums and archaeologists are required to treat mortal remains—with dignity and in the interest of society.

Whether those kinds of guidelines are ever enshrined in law remains to be seen, but ultimately they may decide whether the digital afterlife turns out to be heaven or hell.

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#433486 This AI Predicts Obesity ...

A research team at the University of Washington has trained an artificial intelligence system to spot obesity—all the way from space. The system used a convolutional neural network (CNN) to analyze 150,000 satellite images and look for correlations between the physical makeup of a neighborhood and the prevalence of obesity.

The team’s results, presented in JAMA Network Open, showed that features of a given neighborhood could explain close to two-thirds (64.8 percent) of the variance in obesity. Researchers found that analyzing satellite data could help increase understanding of the link between peoples’ environment and obesity prevalence. The next step would be to make corresponding structural changes in the way neighborhoods are built to encourage physical activity and better health.

Training AI to Spot Obesity
Convolutional neural networks (CNNs) are particularly adept at image analysis, object recognition, and identifying special hierarchies in large datasets.

Prior to analyzing 150,000 high-resolution satellite images of Bellevue, Seattle, Tacoma, Los Angeles, Memphis, and San Antonio, the researchers trained the CNN on 1.2 million images from the ImageNet database. The categorizations were correlated with obesity prevalence estimates for the six urban areas from census tracts gathered by the 500 Cities project.

The system was able to identify the presence of certain features that increased likelihood of obesity in a given area. Some of these features included tightly–packed houses, being close to roadways, and living in neighborhoods with a lack of greenery.

Visualization of features identified by the convolutional neural network (CNN) model. The images on the left column are satellite images taken from Google Static Maps API (application programming interface). Images in the middle and right columns are activation maps taken from the second convolutional layer of VGG-CNN-F network after forward pass of the respective satellite images through the network. From Google Static Maps API, DigitalGlobe, US Geological Survey (accessed July 2017). Credit: JAMA Network Open
Your Surroundings Are Key
In their discussion of the findings, the researchers stressed that there are limitations to the conclusions that can be drawn from the AI’s results. For example, socio-economic factors like income likely play a major role for obesity prevalence in a given geographic area.

However, the study concluded that the AI-powered analysis showed the prevalence of specific man-made features in neighborhoods consistently correlating with obesity prevalence and not necessarily correlating with socioeconomic status.

The system’s success rates varied between studied cities, with Memphis being the highest (73.3 percent) and Seattle being the lowest (55.8 percent).

AI Takes To the Sky
Around a third of the US population is categorized as obese. Obesity is linked to a number of health-related issues, and the AI-generated results could potentially help improve city planning and better target campaigns to limit obesity.

The study is one of the latest of a growing list that uses AI to analyze images and extrapolate insights.

A team at Stanford University has used a CNN to predict poverty via satellite imagery, assisting governments and NGOs to better target their efforts. A combination of the public Automatic Identification System for shipping, satellite imagery, and Google’s AI has proven able to identify illegal fishing activity. Researchers have even been able to use AI and Google Street View to predict what party a given city will vote for, based on what cars are parked on the streets.

In each case, the AI systems have been able to look at volumes of data about our world and surroundings that are beyond the capabilities of humans and extrapolate new insights. If one were to moralize about the good and bad sides of AI (new opportunities vs. potential job losses, for example) it could seem that it comes down to what we ask AI systems to look at—and what questions we ask of them.

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#433288 The New AI Tech Turning Heads in Video ...

A new technique using artificial intelligence to manipulate video content gives new meaning to the expression “talking head.”

An international team of researchers showcased the latest advancement in synthesizing facial expressions—including mouth, eyes, eyebrows, and even head position—in video at this month’s 2018 SIGGRAPH, a conference on innovations in computer graphics, animation, virtual reality, and other forms of digital wizardry.

The project is called Deep Video Portraits. It relies on a type of AI called generative adversarial networks (GANs) to modify a “target” actor based on the facial and head movement of a “source” actor. As the name implies, GANs pit two opposing neural networks against one another to create a realistic talking head, right down to the sneer or raised eyebrow.

In this case, the adversaries are actually working together: One neural network generates content, while the other rejects or approves each effort. The back-and-forth interplay between the two eventually produces a realistic result that can easily fool the human eye, including reproducing a static scene behind the head as it bobs back and forth.

The researchers say the technique can be used by the film industry for a variety of purposes, from editing facial expressions of actors for matching dubbed voices to repositioning an actor’s head in post-production. AI can not only produce highly realistic results, but much quicker ones compared to the manual processes used today, according to the researchers. You can read the full paper of their work here.

“Deep Video Portraits shows how such a visual effect could be created with less effort in the future,” said Christian Richardt, from the University of Bath’s motion capture research center CAMERA, in a press release. “With our approach, even the positioning of an actor’s head and their facial expression could be easily edited to change camera angles or subtly change the framing of a scene to tell the story better.”

AI Tech Different Than So-Called “Deepfakes”
The work is far from the first to employ AI to manipulate video and audio. At last year’s SIGGRAPH conference, researchers from the University of Washington showcased their work using algorithms that inserted audio recordings from a person in one instance into a separate video of the same person in a different context.

In this case, they “faked” a video using a speech from former President Barack Obama addressing a mass shooting incident during his presidency. The AI-doctored video injects the audio into an unrelated video of the president while also blending the facial and mouth movements, creating a pretty credible job of lip synching.

A previous paper by many of the same scientists on the Deep Video Portraits project detailed how they were first able to manipulate a video in real time of a talking head (in this case, actor and former California governor Arnold Schwarzenegger). The Face2Face system pulled off this bit of digital trickery using a depth-sensing camera that tracked the facial expressions of an Asian female source actor.

A less sophisticated method of swapping faces using a machine learning software dubbed FakeApp emerged earlier this year. Predictably, the tech—requiring numerous photos of the source actor in order to train the neural network—was used for more juvenile pursuits, such as injecting a person’s face onto a porn star.

The application gave rise to the term “deepfakes,” which is now used somewhat ubiquitously to describe all such instances of AI-manipulated video—much to the chagrin of some of the researchers involved in more legitimate uses.

Fighting AI-Created Video Forgeries
However, the researchers are keenly aware that their work—intended for benign uses such as in the film industry or even to correct gaze and head positions for more natural interactions through video teleconferencing—could be used for nefarious purposes. Fake news is the most obvious concern.

“With ever-improving video editing technology, we must also start being more critical about the video content we consume every day, especially if there is no proof of origin,” said Michael Zollhöfer, a visiting assistant professor at Stanford University and member of the Deep Video Portraits team, in the press release.

Toward that end, the research team is training the same adversarial neural networks to spot video forgeries. They also strongly recommend that developers clearly watermark videos that are edited through AI or otherwise, and denote clearly what part and element of the scene was modified.

To catch less ethical users, the US Department of Defense, through the Defense Advanced Research Projects Agency (DARPA), is supporting a program called Media Forensics. This latest DARPA challenge enlists researchers to develop technologies to automatically assess the integrity of an image or video, as part of an end-to-end media forensics platform.

The DARPA official in charge of the program, Matthew Turek, did tell MIT Technology Review that so far the program has “discovered subtle cues in current GAN-manipulated images and videos that allow us to detect the presence of alterations.” In one reported example, researchers have targeted eyes, which rarely blink in the case of “deepfakes” like those created by FakeApp, because the AI is trained on still pictures. That method would seem to be less effective to spot the sort of forgeries created by Deep Video Portraits, which appears to flawlessly match the entire facial and head movements between the source and target actors.

“We believe that the field of digital forensics should and will receive a lot more attention in the future to develop approaches that can automatically prove the authenticity of a video clip,” Zollhöfer said. “This will lead to ever-better approaches that can spot such modifications even if we humans might not be able to spot them with our own eyes.

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#431925 How the Science of Decision-Making Will ...

Neuroscientist Brie Linkenhoker believes that leaders must be better prepared for future strategic challenges by continually broadening their worldviews.
As the director of Worldview Stanford, Brie and her team produce multimedia content and immersive learning experiences to make academic research and insights accessible and useable by curious leaders. These future-focused topics are designed to help curious leaders understand the forces shaping the future.
Worldview Stanford has tackled such interdisciplinary topics as the power of minds, the science of decision-making, environmental risk and resilience, and trust and power in the age of big data.
We spoke with Brie about why understanding our biases is critical to making better decisions, particularly in a time of increasing change and complexity.

Lisa Kay Solomon: What is Worldview Stanford?
Brie Linkenhoker: Leaders and decision makers are trying to navigate this complex hairball of a planet that we live on and that requires keeping up on a lot of diverse topics across multiple fields of study and research. Universities like Stanford are where that new knowledge is being created, but it’s not getting out and used as readily as we would like, so that’s what we’re working on.
Worldview is designed to expand our individual and collective worldviews about important topics impacting our future. Your worldview is not a static thing, it’s constantly changing. We believe it should be informed by lots of different perspectives, different cultures, by knowledge from different domains and disciplines. This is more important now than ever.
At Worldview, we create learning experiences that are an amalgamation of all of those things.
LKS: One of your marquee programs is the Science of Decision Making. Can you tell us about that course and why it’s important?
BL: We tend to think about decision makers as being people in leadership positions, but every person who works in your organization, every member of your family, every member of the community is a decision maker. You have to decide what to buy, who to partner with, what government regulations to anticipate.
You have to think not just about your own decisions, but you have to anticipate how other people make decisions too. So, when we set out to create the Science of Decision Making, we wanted to help people improve their own decisions and be better able to predict, understand, anticipate the decisions of others.

“I think in another 10 or 15 years, we’re probably going to have really rich models of how we actually make decisions and what’s going on in the brain to support them.”

We realized that the only way to do that was to combine a lot of different perspectives, so we recruited experts from economics, psychology, neuroscience, philosophy, biology, and religion. We also brought in cutting-edge research on artificial intelligence and virtual reality and explored conversations about how technology is changing how we make decisions today and how it might support our decision-making in the future.
There’s no single set of answers. There are as many unanswered questions as there are answered questions.
LKS: One of the other things you explore in this course is the role of biases and heuristics. Can you explain the importance of both in decision-making?
BL: When I was a strategy consultant, executives would ask me, “How do I get rid of the biases in my decision-making or my organization’s decision-making?” And my response would be, “Good luck with that. It isn’t going to happen.”
As human beings we make, probably, thousands of decisions every single day. If we had to be actively thinking about each one of those decisions, we wouldn’t get out of our house in the morning, right?
We have to be able to do a lot of our decision-making essentially on autopilot to free up cognitive resources for more difficult decisions. So, we’ve evolved in the human brain a set of what we understand to be heuristics or rules of thumb.
And heuristics are great in, say, 95 percent of situations. It’s that five percent, or maybe even one percent, that they’re really not so great. That’s when we have to become aware of them because in some situations they can become biases.
For example, it doesn’t matter so much that we’re not aware of our rules of thumb when we’re driving to work or deciding what to make for dinner. But they can become absolutely critical in situations where a member of law enforcement is making an arrest or where you’re making a decision about a strategic investment or even when you’re deciding who to hire.
Let’s take hiring for a moment.
How many years is a hire going to impact your organization? You’re potentially looking at 5, 10, 15, 20 years. Having the right person in a role could change the future of your business entirely. That’s one of those areas where you really need to be aware of your own heuristics and biases—and we all have them. There’s no getting rid of them.
LKS: We seem to be at a time when the boundaries between different disciplines are starting to blend together. How has the advancement of neuroscience help us become better leaders? What do you see happening next?
BL: Heuristics and biases are very topical these days, thanks in part to Michael Lewis’s fantastic book, The Undoing Project, which is the story of the groundbreaking work that Nobel Prize winner Danny Kahneman and Amos Tversky did in the psychology and biases of human decision-making. Their work gave rise to the whole new field of behavioral economics.
In the last 10 to 15 years, neuroeconomics has really taken off. Neuroeconomics is the combination of behavioral economics with neuroscience. In behavioral economics, they use economic games and economic choices that have numbers associated with them and have real-world application.
For example, they ask, “How much would you spend to buy A versus B?” Or, “If I offered you X dollars for this thing that you have, would you take it or would you say no?” So, it’s trying to look at human decision-making in a format that’s easy to understand and quantify within a laboratory setting.
Now you bring neuroscience into that. You can have people doing those same kinds of tasks—making those kinds of semi-real-world decisions—in a brain scanner, and we can now start to understand what’s going on in the brain while people are making decisions. You can ask questions like, “Can I look at the signals in someone’s brain and predict what decision they’re going to make?” That can help us build a model of decision-making.
I think in another 10 or 15 years, we’re probably going to have really rich models of how we actually make decisions and what’s going on in the brain to support them. That’s very exciting for a neuroscientist.
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