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

Posted on February 14, 2018 by Android

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.

Image Credit: hfzimages / Shutterstock.com

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Posted in Human Robots

#432009 How Swarm Intelligence Is Making Simple ...

Posted on February 9, 2018 by Android

As a group, simple creatures following simple rules can display a surprising amount of complexity, efficiency, and even creativity. Known as swarm intelligence, this trait is found throughout nature, but researchers have recently begun using it to transform various fields such as robotics, data mining, medicine, and blockchains.

Ants, for example, can only perform a limited range of functions, but an ant colony can build bridges, create superhighways of food and information, wage war, and enslave other ant species—all of which are beyond the comprehension of any single ant. Likewise, schools of fish, flocks of birds, beehives, and other species exhibit behavior indicative of planning by a higher intelligence that doesn’t actually exist.

It happens by a process called stigmergy. Simply put, a small change by a group member causes other members to behave differently, leading to a new pattern of behavior.

When an ant finds a food source, it marks the path with pheromones. This attracts other ants to that path, leads them to the food source, and prompts them to mark the same path with more pheromones. Over time, the most efficient route will become the superhighway, as the faster and easier a path is, the more ants will reach the food and the more pheromones will be on the path. Thus, it looks as if a more intelligent being chose the best path, but it emerged from the tiny, simple changes made by individuals.

So what does this mean for humans? Well, a lot. In the past few decades, researchers have developed numerous algorithms and metaheuristics, such as ant colony optimization and particle swarm optimization, and they are rapidly being adopted.

Swarm Robotics
A swarm of robots would work on the same principles as an ant colony: each member has a simple set of rules to follow, leading to self-organization and self-sufficiency.

For example, researchers at Georgia Robotics and InTelligent Systems (GRITS) created a small swarm of simple robots that can spell and play piano. The robots cannot communicate, but based solely on the position of surrounding robots, they are able to use their specially-created algorithm to determine the optimal path to complete their task.

This is also immensely useful for drone swarms.

Last February, Ehang, an aviation company out of China, created a swarm of a thousand drones that not only lit the sky with colorful, intricate displays, but demonstrated the ability to improvise and troubleshoot errors entirely autonomously.

Further, just recently, the University of Cambridge and Koc University unveiled their idea for what they call the Energy Neutral Internet of Drones. Amazingly, this drone swarm would take initiative to share information or energy with other drones that did not receive a communication or are running low on energy.

Militaries all of the world are utilizing this as well.

Last year, the US Department of Defense announced it had successfully tested a swarm of miniature drones that could carry out complex missions cheaper and more efficiently. They claimed, “The micro-drones demonstrated advanced swarm behaviors such as collective decision-making, adaptive formation flying, and self-healing.”

Some experts estimate at least 30 nations are actively developing drone swarms—and even submersible drones—for military missions, including intelligence gathering, missile defense, precision missile strikes, and enhanced communication.

NASA also plans on deploying swarms of tiny spacecraft for space exploration, and the medical community is looking into using swarms of nanobots for precision delivery of drugs, microsurgery, targeting toxins, and biological sensors.

What If Humans Are the Ants?
The strength of any blockchain comes from the size and diversity of the community supporting it. Cryptocurrencies like Bitcoin, Ethereum, and Litecoin are driven by the people using, investing in, and, most importantly, mining them so their blockchains can function. Without an active community, or swarm, their blockchains wither away.

When viewed from a great height, a blockchain performs eerily like an ant colony in that it will naturally find the most efficient way to move vast amounts of information.

Miners compete with each other to perform the complex calculations necessary to add another block, for which the winner is rewarded with the blockchain’s native currency and agreed-upon fees. Of course, the miner with the more powerful computers is more likely to win the reward, thereby empowering the winner’s ability to mine and receive even more rewards. Over time, fewer and fewer miners are going to exist, as the winners are able to more efficiently shoulder more of the workload, in much the same way that ants build superhighways.

Further, a company called Unanimous AI has developed algorithms that allow humans to collectively make predictions. So far, the AI algorithms and their human participants have made some astoundingly accurate predictions, such as the first four winning horses of the Kentucky Derby, the Oscar winners, the Stanley Cup winners, and others. The more people involved in the swarm, the greater their predictive power will be.

To be clear, this is not a prediction based on group consensus. Rather, the swarm of humans uses software to input their opinions in real time, thus making micro-changes to the rest of the swarm and the inputs of other members.

Studies show that swarm intelligence consistently outperforms individuals and crowds working without the algorithms. While this is only the tip of the iceberg, some have suggested swarm intelligence can revolutionize how doctors diagnose a patient or how products are marketed to consumers. It might even be an essential step in truly creating AI.

While swarm intelligence is an essential part of many species’ success, it’s only a matter of time before humans harness its effectiveness as well.

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Posted in Human Robots

#431957 Is Conversation with Humans the Best ...

Posted on January 26, 2018 by Android

Robots are evolving in fascinating ways. Cloud computing, big data and the Internet of Things have all helped open new doors for artificial intelligence. Robots are also learning from much simpler mediums, such as human speech. Researchers Hit Roadblocks With AI Development Some experts believe that engaging in conversations with humans is going to play …

The post Is Conversation with Humans the Best Machine Learning Model for Robots? appeared first on TFOT. Continue reading →

Posted in Human Robots

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

Posted on January 18, 2018 by Android

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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Posted in Human Robots

#431907 The Future of Cancer Treatment Is ...

Posted on January 11, 2018 by Android

In an interview at Singularity University’s Exponential Medicine in San Diego, Richard Wender, chief cancer control officer at the American Cancer Society, discussed how technology has changed cancer care and treatment in recent years.
Just a few years ago, microscopes were the primary tool used in cancer diagnoses, but we’ve come a long way since.
“We still look at a microscope, we still look at what organ the cancer started in,” Wender said. “But increasingly we’re looking at the molecular signature. It’s not just the genomics, and it’s not just the genes. It’s also the cellular environment around that cancer. We’re now targeting our therapies to the mutations that are found in that particular cancer.”
Cancer treatments in the past have been largely reactionary, but they don’t need to be. Most cancer is genetic, which means that treatment can be preventative. This is one reason why newer cancer treatment techniques are searching for actionable targets in the specific gene before the cancer develops.

When asked how artificial intelligence and machine learning technologies are reshaping clinical trials, Wender acknowledged that how clinical trials have been run in the past won’t work moving forward.
“Our traditional ways of learning about cancer were by finding a particular cancer type and conducting a long clinical trial that took a number of years enrolling patients from around the country. That is not how we’re going to learn to treat individual patients in the future.”
Instead, Wender emphasized the need for gathering as much data as possible, and from as many individual patients as possible. This data should encompass clinical, pathological, and molecular data and should be gathered from a patient all the way through their final outcome. “Literally every person becomes a clinical trial of one,” Wender said.
For the best cancer treatment and diagnostics, Wender says the answer is to make the process collaborative by pulling in resources from organizations and companies that are both established and emerging.
It’s no surprise to hear that the best solutions come from pairing together uncommon partners to innovate.
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Posted in Human Robots