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#433301 ‘Happiness Tech’ Is On the Rise. Is ...

We often get so fixated on technological progress that we forget it’s merely one component of the entirety of human progress. Technological advancement does not necessarily correlate with increases in human mental well-being.

While cleaner energy, access to education, and higher employment rates can improve quality of life, they do not guarantee happiness and inner peace. Amid what appears to be an increasing abundance of resources and ongoing human progress, we are experiencing a mental health epidemic, with high anxiety and depression rates. This is especially true in the developed world, where we have access to luxuries our ancestors couldn’t even dream of—all the world’s information contained in a device we hold in the palm of our hands, for example.

But as you may have realized through your own experience, technology can make us feel worse instead of better. Social media can become a tool for comparison and a source of debilitating status anxiety. Increased access to goods and services, along with the rise of consumerism, can lead people to choose “stuff” over true sources of meaning and get trapped in a hedonistic treadmill of materialism. Tools like artificial intelligence and big data could lead to violation of our privacy and autonomy. The digital world can take us away from the beauty of the present moment.

Understanding Happiness
How we use technology can significantly impact our happiness. In this context, “happiness” refers to a general sense of well-being, gratitude, and inner peace. Even with such a simple definition, it is a state of mind many people will admit they lack.

Eastern philosophies have told us for thousands of years that the problem of human suffering begins with our thoughts and perceptions of the circumstances we are in, as opposed to beginning with the circumstances themselves. As Derren Brown brilliantly points out in Happy: Why More or Less Everything Is Absolutely Fine, “The problem with the modern conception of happiness is that it is seen as some kind of commodity. There is this fantasy that simply by believing in yourself and setting goals you can have anything. But that simply isn’t how life works. The ancients had a much better view of it. They offered an approach of not trying to control things you can’t control, and of lessening your desires and your expectations so you achieve a harmony between what you desire and what you have.”

A core part of feeling more happy is about re-wiring our minds to adjust our expectations, exercise gratitude, escape negative narratives, and live in the present moment.

But can technology help us do that?

Applications for Mental Well-Being
Many doers are asking themselves how they can leverage digital tools to contribute to human happiness.

Meditation and mindfulness are examples of practices we can use to escape the often overwhelming burden of our thoughts and ground our minds into the present. They have become increasingly democratized with the rise of meditation mobile apps, such as Headspace, Gaia, and Calm, that allow millions of people globally to use their phones to learn from experts at a very low cost.

These companies have also partnered with hospitals, airlines, athletic teams, and others that could benefit from increased access to mindfulness and meditation. The popularity of these apps continues to rise as more people recognize their necessity. The combination of mass technology and ancient wisdom is one that can lead to a transformation of the collective consciousness.

Sometimes merely reflecting on the sources of joy in our lives and practicing gratitude can contribute to better well-being. Apps such as Happier encourage users to reflect upon and share pleasant everyday moments in their daily lives. Such exercises are based on the understanding that being happy is a “skill” one can build though practice and through scientifically-proven activities, such as writing down a nice thought and sharing your positivity with the world. Many other tools such as Track Your Happiness and Happstr allow users to track their happiness, which often serves as a valuable source of data to researchers.

There is also a growing body of knowledge that tells us we can achieve happiness by helping others. This “helper’s high” is a result of our brains producing endorphins after having a positive impact on the lives of others. In many shapes and forms, technology has made it easier now more than ever to help other people no matter where they are located. From charitable donations to the rise of social impact organizations, there is an abundance of projects that leverage technology to positively impact individual lives. Platforms like GoVolunteer connect nonprofits with individuals from a variety of skill sets who are looking to gift their abilities to those in need. Kiva allows for fundraising loans that can change lives. These are just a handful of examples of a much wider positive paradigm shift.

The Future of Technology for Well-Being
There is no denying that increasingly powerful and immersive technology can be used to better or worsen the human condition. Today’s leaders will not only have to focus on their ability to use technology to solve a problem or generate greater revenue; they will have to ask themselves if their tech solutions are beneficial or detrimental to human well-being. They will also have to remember that more powerful technology does not always translate to happier users. It is also crucial that future generations be equipped with the values required to use increasingly powerful tools responsibly and ethically.

In the Education 2030 report, the Millennium Project envisions a world wherein portable intelligent devices combined with integrated systems for lifelong learning contribute to better well-being. In this vision, “continuous evaluation of individual learning processes designed to prevent people from growing unstable and/or becoming mentally ill, along with programs aimed at eliminating prejudice and hate, could bring about a more beautiful, loving world.”

There is exciting potential for technology to be leveraged to contribute to human happiness at a massive scale. Yet, technology shouldn’t consume every aspect of our lives, since a life worth living is often about balance. Sometimes, even if just for a few moments, what would make us feel happier is we disconnected from technology to begin with.

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#432880 Google’s Duplex Raises the Question: ...

By now, you’ve probably seen Google’s new Duplex software, which promises to call people on your behalf to book appointments for haircuts and the like. As yet, it only exists in demo form, but already it seems like Google has made a big stride towards capturing a market that plenty of companies have had their eye on for quite some time. This software is impressive, but it raises questions.

Many of you will be familiar with the stilted, robotic conversations you can have with early chatbots that are, essentially, glorified menus. Instead of pressing 1 to confirm or 2 to re-enter, some of these bots would allow for simple commands like “Yes” or “No,” replacing the buttons with limited ability to recognize a few words. Using them was often a far more frustrating experience than attempting to use a menu—there are few things more irritating than a robot saying, “Sorry, your response was not recognized.”

Google Duplex scheduling a hair salon appointment:

Google Duplex calling a restaurant:

Even getting the response recognized is hard enough. After all, there are countless different nuances and accents to baffle voice recognition software, and endless turns of phrase that amount to saying the same thing that can confound natural language processing (NLP), especially if you like your phrasing quirky.

You may think that standard customer-service type conversations all travel the same route, using similar words and phrasing. But when there are over 80,000 ways to order coffee, and making a mistake is frowned upon, even simple tasks require high accuracy over a huge dataset.

Advances in audio processing, neural networks, and NLP, as well as raw computing power, have meant that basic recognition of what someone is trying to say is less of an issue. Soundhound’s virtual assistant prides itself on being able to process complicated requests (perhaps needlessly complicated).

The deeper issue, as with all attempts to develop conversational machines, is one of understanding context. There are so many ways a conversation can go that attempting to construct a conversation two or three layers deep quickly runs into problems. Multiply the thousands of things people might say by the thousands they might say next, and the combinatorics of the challenge runs away from most chatbots, leaving them as either glorified menus, gimmicks, or rather bizarre to talk to.

Yet Google, who surely remembers from Glass the risk of premature debuts for technology, especially the kind that ask you to rethink how you interact with or trust in software, must have faith in Duplex to show it on the world stage. We know that startups like Semantic Machines and x.ai have received serious funding to perform very similar functions, using natural-language conversations to perform computing tasks, schedule meetings, book hotels, or purchase items.

It’s no great leap to imagine Google will soon do the same, bringing us closer to a world of onboard computing, where Lens labels the world around us and their assistant arranges it for us (all the while gathering more and more data it can convert into personalized ads). The early demos showed some clever tricks for keeping the conversation within a fairly narrow realm where the AI should be comfortable and competent, and the blog post that accompanied the release shows just how much effort has gone into the technology.

Yet given the privacy and ethics funk the tech industry finds itself in, and people’s general unease about AI, the main reaction to Duplex’s impressive demo was concern. The voice sounded too natural, bringing to mind Lyrebird and their warnings of deepfakes. You might trust “Do the Right Thing” Google with this technology, but it could usher in an era when automated robo-callers are far more convincing.

A more human-like voice may sound like a perfectly innocuous improvement, but the fact that the assistant interjects naturalistic “umm” and “mm-hm” responses to more perfectly mimic a human rubbed a lot of people the wrong way. This wasn’t just a voice assistant trying to sound less grinding and robotic; it was actively trying to deceive people into thinking they were talking to a human.

Google is running the risk of trying to get to conversational AI by going straight through the uncanny valley.

“Google’s experiments do appear to have been designed to deceive,” said Dr. Thomas King of the Oxford Internet Institute’s Digital Ethics Lab, according to Techcrunch. “Their main hypothesis was ‘can you distinguish this from a real person?’ In this case it’s unclear why their hypothesis was about deception and not the user experience… there should be some kind of mechanism there to let people know what it is they are speaking to.”

From Google’s perspective, being able to say “90 percent of callers can’t tell the difference between this and a human personal assistant” is an excellent marketing ploy, even though statistics about how many interactions are successful might be more relevant.

In fact, Duplex runs contrary to pretty much every major recommendation about ethics for the use of robotics or artificial intelligence, not to mention certain eavesdropping laws. Transparency is key to holding machines (and the people who design them) accountable, especially when it comes to decision-making.

Then there are the more subtle social issues. One prominent effect social media has had is to allow people to silo themselves; in echo chambers of like-minded individuals, it’s hard to see how other opinions exist. Technology exacerbates this by removing the evolutionary cues that go along with face-to-face interaction. Confronted with a pair of human eyes, people are more generous. Confronted with a Twitter avatar or a Facebook interface, people hurl abuse and criticism they’d never dream of using in a public setting.

Now that we can use technology to interact with ever fewer people, will it change us? Is it fair to offload the burden of dealing with a robot onto the poor human at the other end of the line, who might have to deal with dozens of such calls a day? Google has said that if the AI is in trouble, it will put you through to a human, which might help save receptionists from the hell of trying to explain a concept to dozens of dumbfounded AI assistants all day. But there’s always the risk that failures will be blamed on the person and not the machine.

As AI advances, could we end up treating the dwindling number of people in these “customer-facing” roles as the buggiest part of a fully automatic service? Will people start accusing each other of being robots on the phone, as well as on Twitter?

Google has provided plenty of reassurances about how the system will be used. They have said they will ensure that the system is identified, and it’s hardly difficult to resolve this problem; a slight change in the script from their demo would do it. For now, consumers will likely appreciate moves that make it clear whether the “intelligent agents” that make major decisions for us, that we interact with daily, and that hide behind social media avatars or phone numbers are real or artificial.

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#432878 Chinese Port Goes Full Robot With ...

By the end of 2018, something will be very different about the harbor area in the northern Chinese city of Caofeidian. If you were to visit, the whirring cranes and tractors driving containers to and fro would be the only things in sight.

Caofeidian is set to become the world’s first fully autonomous harbor by the end of the year. The US-Chinese startup TuSimple, a specialist in developing self-driving trucks, will replace human-driven terminal tractor-trucks with 20 self-driving models. A separate company handles crane automation, and a central control system will coordinate the movements of both.

According to Robert Brown, Director of Public Affairs at TuSimple, the project could quickly transform into a much wider trend. “The potential for automating systems in harbors and ports is staggering when considering the number of deep-water and inland ports around the world. At the same time, the closed, controlled nature of a port environment makes it a perfect proving ground for autonomous truck technology,” he said.

Going Global
The autonomous cranes and trucks have a big task ahead of them. Caofeidian currently processes around 300,000 TEU containers a year. Even if you were dealing with Lego bricks, that number of units would get you a decent-sized cathedral or a 22-foot-long aircraft carrier. For any maritime fans—or people who enjoy the moving of heavy objects—TEU stands for twenty-foot equivalent unit. It is the industry standard for containers. A TEU equals an 8-foot (2.43 meter) wide, 8.5-foot (2.59 meter) high, and 20-foot (6.06 meter) long container.

While impressive, the Caofeidian number pales in comparison with the biggest global ports like Shanghai, Singapore, Busan, or Rotterdam. For example, 2017 saw more than 40 million TEU moved through Shanghai port facilities.

Self-driving container vehicles have been trialled elsewhere, including in Yangshan, close to Shanghai, and Rotterdam. Qingdao New Qianwan Container Terminal in China recently laid claim to being the first fully automated terminal in Asia.

The potential for efficiencies has many ports interested in automation. Qingdao said its systems allow the terminal to operate in complete darkness and have reduced labor costs by 70 percent while increasing efficiency by 30 percent. In some cases, the number of workers needed to unload a cargo ship has gone from 60 to 9.

TuSimple says it is in negotiations with several other ports and also sees potential in related logistics-heavy fields.

Stable Testing Ground
For autonomous vehicles, ports seem like a perfect testing ground. They are restricted, confined areas with few to no pedestrians where operating speeds are limited. The predictability makes it unlike, say, city driving.

Robert Brown describes it as an ideal setting for the first adaptation of TuSimple’s technology. The company, which, amongst others, is backed by chipmaker Nvidia, have been retrofitting existing vehicles from Shaanxi Automobile Group with sensors and technology.

At the same time, it is running open road tests in Arizona and China of its Class 8 Level 4 autonomous trucks.

The Camera Approach
Dozens of autonomous truck startups are reported to have launched in China over the past two years. In other countries the situation is much the same, as the race for the future of goods transportation heats up. Startup companies like Embark, Einride, Starsky Robotics, and Drive.ai are just a few of the names in the space. They are facing competition from the likes of Tesla, Daimler, VW, Uber’s Otto subsidiary, and in March, Waymo announced it too was getting into the truck race.

Compared to many of its competitors, TuSimple’s autonomous driving system is based on a different approach. Instead of laser-based radar (LIDAR), TuSimple primarily uses cameras to gather data about its surroundings. Currently, the company uses ten cameras, including forward-facing, backward-facing, and wide-lens. Together, they produce the 360-degree “God View” of the vehicle’s surroundings, which is interpreted by the onboard autonomous driving systems.

Each camera gathers information at 30 frames a second. Millimeter wave radar is used as a secondary sensor. In total, the vehicles generate what Robert Brown describes with a laugh as “almost too much” data about its surroundings and is accurate beyond 300 meters in locating and identifying objects. This includes objects that have given LIDAR problems, such as black vehicles.

Another advantage is price. Companies often loathe revealing exact amounts, but Tesla has gone as far as to say that the ‘expected’ price of its autonomous truck will be from $150,0000 and upwards. While unconfirmed, TuSimple’s retrofitted, camera-based solution is thought to cost around $20,000.

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#432031 Why the Rise of Self-Driving Vehicles ...

It’s been a long time coming. For years Waymo (formerly known as Google Chauffeur) has been diligently developing, driving, testing and refining its fleets of various models of self-driving cars. Now Waymo is going big. The company recently placed an order for several thousand new Chrysler Pacifica minivans and next year plans to launch driverless taxis in a number of US cities.

This deal raises one of the biggest unanswered questions about autonomous vehicles: if fleets of driverless taxis make it cheap and easy for regular people to get around, what’s going to happen to car ownership?

One popular line of thought goes as follows: as autonomous ride-hailing services become ubiquitous, people will no longer need to buy their own cars. This notion has a certain logical appeal. It makes sense to assume that as driverless taxis become widely available, most of us will eagerly sell the family car and use on-demand taxis to get to work, run errands, or pick up the kids. After all, vehicle ownership is pricey and most cars spend the vast majority of their lives parked.

Even experts believe commercial availability of autonomous vehicles will cause car sales to drop.

Market research firm KPMG estimates that by 2030, midsize car sales in the US will decline from today’s 5.4 million units sold each year to nearly half that number, a measly 2.1 million units. Another market research firm, ReThinkX, offers an even more pessimistic estimate (or optimistic, depending on your opinion of cars), predicting that autonomous vehicles will reduce consumer demand for new vehicles by a whopping 70 percent.

The reality is that the impending death of private vehicle sales is greatly exaggerated. Despite the fact that autonomous taxis will be a beneficial and widely-embraced form of urban transportation, we will witness the opposite. Most people will still prefer to own their own autonomous vehicle. In fact, the total number of units of autonomous vehicles sold each year is going to increase rather than decrease.

When people predict the demise of car ownership, they are overlooking the reality that the new autonomous automotive industry is not going to be just a re-hash of today’s car industry with driverless vehicles. Instead, the automotive industry of the future will be selling what could be considered an entirely new product: a wide variety of intelligent, self-guiding transportation robots. When cars become a widely used type of transportation robot, they will be cheap, ubiquitous, and versatile.

Several unique characteristics of autonomous vehicles will ensure that people will continue to buy their own cars.

1. Cost: Thanks to simpler electric engines and lighter auto bodies, autonomous vehicles will be cheaper to buy and maintain than today’s human-driven vehicles. Some estimates bring the price to $10K per vehicle, a stark contrast with today’s average of $30K per vehicle.

2. Personal belongings: Consumers will be able to do much more in their driverless vehicles, including work, play, and rest. This means they will want to keep more personal items in their cars.

3. Frequent upgrades: The average (human-driven) car today is owned for 10 years. As driverless cars become software-driven devices, their price/performance ratio will track to Moore’s law. Their rapid improvement will increase the appeal and frequency of new vehicle purchases.

4. Instant accessibility: In a dense urban setting, a driverless taxi is able to show up within minutes of being summoned. But not so in rural areas, where people live miles apart. For many, delay and “loss of control” over their own mobility will increase the appeal of owning their own vehicle.

5. Diversity of form and function: Autonomous vehicles will be available in a wide variety of sizes and shapes. Consumers will drive demand for custom-made, purpose-built autonomous vehicles whose form is adapted for a particular function.

Let’s explore each of these characteristics in more detail.

Autonomous vehicles will cost less for several reasons. For one, they will be powered by electric engines, which are cheaper to construct and maintain than gasoline-powered engines. Removing human drivers will also save consumers money. Autonomous vehicles will be much less likely to have accidents, hence they can be built out of lightweight, lower-cost materials and will be cheaper to insure. With the human interface no longer needed, autonomous vehicles won’t be burdened by the manufacturing costs of a complex dashboard, steering wheel, and foot pedals.

While hop-on, hop-off autonomous taxi-based mobility services may be ideal for some of the urban population, several sizeable customer segments will still want to own their own cars.

These include people who live in sparsely-populated rural areas who can’t afford to wait extended periods of time for a taxi to appear. Families with children will prefer to own their own driverless cars to house their childrens’ car seats and favorite toys and sippy cups. Another loyal car-buying segment will be die-hard gadget-hounds who will eagerly buy a sexy upgraded model every year or so, unable to resist the siren song of AI that is three times as safe, or a ride that is twice as smooth.

Finally, consider the allure of robotic diversity.

Commuters will invest in a home office on wheels, a sleek, traveling workspace resembling the first-class suite on an airplane. On the high end of the market, city-dwellers and country-dwellers alike will special-order custom-made autonomous vehicles whose shape and on-board gadgetry is adapted for a particular function or hobby. Privately-owned small businesses will buy their own autonomous delivery robot that could range in size from a knee-high, last-mile delivery pod, to a giant, long-haul shipping device.

As autonomous vehicles near commercial viability, Waymo’s procurement deal with Fiat Chrysler is just the beginning.

The exact value of this future automotive industry has yet to be defined, but research from Intel’s internal autonomous vehicle division estimates this new so-called “passenger economy” could be worth nearly $7 trillion a year. To position themselves to capture a chunk of this potential revenue, companies whose businesses used to lie in previously disparate fields such as robotics, software, ships, and entertainment (to name but a few) have begun to form a bewildering web of what they hope will be symbiotic partnerships. Car hailing and chip companies are collaborating with car rental companies, who in turn are befriending giant software firms, who are launching joint projects with all sizes of hardware companies, and so on.

Last year, car companies sold an estimated 80 million new cars worldwide. Over the course of nearly a century, car companies and their partners, global chains of suppliers and service providers, have become masters at mass-producing and maintaining sturdy and cost-effective human-driven vehicles. As autonomous vehicle technology becomes ready for mainstream use, traditional automotive companies are being forced to grapple with the painful realization that they must compete in a new playing field.

The challenge for traditional car-makers won’t be that people no longer want to own cars. Instead, the challenge will be learning to compete in a new and larger transportation industry where consumers will choose their product according to the appeal of its customized body and the quality of its intelligent software.

Melba Kurman and Hod Lipson are the authors of Driverless: Intelligent Cars and the Road Ahead and Fabricated: the New World of 3D Printing.

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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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