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#432293 An Innovator’s City Guide to Shanghai

Posted on March 14, 2018 by Android

Shanghai is a city full of life. With its population of 24 million, Shanghai embraces vibrant growth, fosters rising diversity, and attracts visionaries, innovators, and adventurers. Fintech, artificial intelligence, and e-commerce are booming. Now is a great time to explore this multicultural, inspirational city as it experiences quick growth and ever greater influence.

Meet Your Guide

Qingsong (Dora) Ke
Singularity University Chapter: Shanghai Chapter
Profession: Associate Director for Asia Pacific, IE Business School and IE University; Mentor, Techstars Startup Weekend; Mentor, Startupbootcamp; China President, Her Century

Your City Guide to Shanghai, China
Top three industries in the city: Automotive, Retail, and Finance

1. Coworking Space: Mixpace

With 10 convenient locations in the Shanghai downtown area, Mixpace offers affordable prices and various office and event spaces to both foreign and local entrepreneurs and startups.

2. Makerspace: XinCheJian

The first hackerspace and a non-profit in China, Xinchejian was founded to support projects in physical computing, open source hardware, and the Internet of Things. It hosts regular events and talks to facilitate development of hackerspaces in China.

3. Local meetups/ networks: FinTech Connector

FinTech Connector is a community connecting local fintech entrepreneurs and start-ups with global professionals, thought leaders, and investors for the purpose of disrupting financial services with cutting-edge technology.

4. Best coffee shop with free WiFi: Seesaw

Clean and modern décor, convenient locations, a quiet environment, and high-quality coffee make Seesaw one of the most popular coffee shops in Shanghai.

5. The startup neighborhood: Knowledge & Innovation Community (KIC)

Located near 10 prestigious universities and over 100 scientific research institutions, KIC attempts to integrate Silicon Valley’s innovative spirit with the artistic culture of the Left Bank in Paris.

6. Well-known investor or venture capitalist: Nanpeng (Neil) Shen

Global executive partner at Sequoia Capital, founding and managing partner at Sequoia China, and founder of Ctrip.com and Home Inn, Neil Shen was named Best Venture Capitalist by Forbes China in 2010–2013 and ranked as the best Chinese investor among Global Best Investors by Forbes in 2012–2016.

7. Best way to get around: Metro

Shanghai’s 17 well-connected metro lines covering every corner of the city at affordable prices are the best way to get around.

8. Local must-have dish and where to get it: Mini Soupy Bun (steamed dumplings, xiaolongbao) at Din Tai Fung in Shanghai.

Named one of the top ten restaurants in the world by the New York Times, Din Tai Fung makes the best xiaolongbao, a delicious soup with stuffed dumplings.

9. City’s best-kept secret: Barber Shop

This underground bar gets its name from the barber shop it’s hidden behind. Visitors must discover how to unlock the door leading to Barber Shop’s sophisticated cocktails and engaging music. (No website for this underground location, but the address is 615 Yongjia Road).

10. Touristy must-do: Enjoy the nightlife and the skyline at the Bund

On the east side of the Bund are the most modern skyscrapers, including Shanghai Tower, Shanghai World Financial Centre, and Jin Mao Tower. The west side of the Bund features 26 buildings of diverse architectural styles, including Gothic, Baroque, Romanesque, and others; this area is known for its exotic buildings.

11. Local volunteering opportunity: Shanghai Volunteer

Shanghai Volunteer is a platform to connect volunteers with possible opportunities in various fields, including education, elderly care, city culture, and environment.

12. Local University with great resources: Shanghai Jiao Tong University

Established in 1896, Shanghai Jiao Tong University is the second-oldest university in China and one of the country’s most prestigious. It boasts notable alumni in government and politics, science, engineering, business, and sports, and it regularly collaborates with government and the private sector.

This article is for informational purposes only. All opinions in this post are the author’s alone and not those of Singularity University. Neither this article nor any of the listed information therein is an official endorsement by Singularity University.

Image Credits: Qinsong (Dora) Ke

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Posted in Human Robots

#431995 The 10 Grand Challenges Facing Robotics ...

Posted on February 7, 2018 by Android

Robotics research has been making great strides in recent years, but there are still many hurdles to the machines becoming a ubiquitous presence in our lives. The journal Science Robotics has now identified 10 grand challenges the field will have to grapple with to make that a reality.

Editors conducted an online survey on unsolved challenges in robotics and assembled an expert panel of roboticists to shortlist the 30 most important topics, which were then grouped into 10 grand challenges that could have major impact in the next 5 to 10 years. Here’s what they came up with.

1. New Materials and Fabrication Schemes
Roboticists are beginning to move beyond motors, gears, and sensors by experimenting with things like artificial muscles, soft robotics, and new fabrication methods that combine multiple functions in one material. But most of these advances have been “one-off” demonstrations, which are not easy to combine.

Multi-functional materials merging things like sensing, movement, energy harvesting, or energy storage could allow more efficient robot designs. But combining these various properties in a single machine will require new approaches that blend micro-scale and large-scale fabrication techniques. Another promising direction is materials that can change over time to adapt or heal, but this requires much more research.

2. Bioinspired and Bio-Hybrid Robots
Nature has already solved many of the problems roboticists are trying to tackle, so many are turning to biology for inspiration or even incorporating living systems into their robots. But there are still major bottlenecks in reproducing the mechanical performance of muscle and the ability of biological systems to power themselves.

There has been great progress in artificial muscles, but their robustness, efficiency, and energy and power density need to be improved. Embedding living cells into robots can overcome challenges of powering small robots, as well as exploit biological features like self-healing and embedded sensing, though how to integrate these components is still a major challenge. And while a growing “robo-zoo” is helping tease out nature’s secrets, more work needs to be done on how animals transition between capabilities like flying and swimming to build multimodal platforms.

3. Power and Energy
Energy storage is a major bottleneck for mobile robotics. Rising demand from drones, electric vehicles, and renewable energy is driving progress in battery technology, but the fundamental challenges have remained largely unchanged for years.

That means that in parallel to battery development, there need to be efforts to minimize robots’ power utilization and give them access to new sources of energy. Enabling them to harvest energy from their environment and transmitting power to them wirelessly are two promising approaches worthy of investigation.

4. Robot Swarms
Swarms of simple robots that assemble into different configurations to tackle various tasks can be a cheaper, more flexible alternative to large, task-specific robots. Smaller, cheaper, more powerful hardware that lets simple robots sense their environment and communicate is combining with AI that can model the kind of behavior seen in nature’s flocks.

But there needs to be more work on the most efficient forms of control at different scales—small swarms can be controlled centrally, but larger ones need to be more decentralized. They also need to be made robust and adaptable to the changing conditions of the real world and resilient to deliberate or accidental damage. There also needs to be more work on swarms of non-homogeneous robots with complementary capabilities.

5. Navigation and Exploration
A key use case for robots is exploring places where humans cannot go, such as the deep sea, space, or disaster zones. That means they need to become adept at exploring and navigating unmapped, often highly disordered and hostile environments.

The major challenges include creating systems that can adapt, learn, and recover from navigation failures and are able to make and recognize new discoveries. This will require high levels of autonomy that allow the robots to monitor and reconfigure themselves while being able to build a picture of the world from multiple data sources of varying reliability and accuracy.

6. AI for Robotics
Deep learning has revolutionized machines’ ability to recognize patterns, but that needs to be combined with model-based reasoning to create adaptable robots that can learn on the fly.

Key to this will be creating AI that’s aware of its own limitations and can learn how to learn new things. It will also be important to create systems that are able to learn quickly from limited data rather than the millions of examples used in deep learning. Further advances in our understanding of human intelligence will be essential to solving these problems.

7. Brain-Computer Interfaces
BCIs will enable seamless control of advanced robotic prosthetics but could also prove a faster, more natural way to communicate instructions to robots or simply help them understand human mental states.

Most current approaches to measuring brain activity are expensive and cumbersome, though, so work on compact, low-power, and wireless devices will be important. They also tend to involve extended training, calibration, and adaptation due to the imprecise nature of reading brain activity. And it remains to be seen if they will outperform simpler techniques like eye tracking or reading muscle signals.

8. Social Interaction
If robots are to enter human environments, they will need to learn to deal with humans. But this will be difficult, as we have very few concrete models of human behavior and we are prone to underestimate the complexity of what comes naturally to us.

Social robots will need to be able to perceive minute social cues like facial expression or intonation, understand the cultural and social context they are operating in, and model the mental states of people they interact with to tailor their dealings with them, both in the short term and as they develop long-standing relationships with them.

9. Medical Robotics
Medicine is one of the areas where robots could have significant impact in the near future. Devices that augment a surgeon’s capabilities are already in regular use, but the challenge will be to increase the autonomy of these systems in such a high-stakes environment.

Autonomous robot assistants will need to be able to recognize human anatomy in a variety of contexts and be able to use situational awareness and spoken commands to understand what’s required of them. In surgery, autonomous robots could perform the routine steps of a procedure, giving way to the surgeon for more complicated patient-specific bits.

Micro-robots that operate inside the human body also hold promise, but there are still many roadblocks to their adoption, including effective delivery systems, tracking and control methods, and crucially, finding therapies where they improve on current approaches.

10. Robot Ethics and Security
As the preceding challenges are overcome and robots are increasingly integrated into our lives, this progress will create new ethical conundrums. Most importantly, we may become over-reliant on robots.

That could lead to humans losing certain skills and capabilities, making us unable to take the reins in the case of failures. We may end up delegating tasks that should, for ethical reasons, have some human supervision, and allow people to pass the buck to autonomous systems in the case of failure. It could also reduce self-determination, as human behaviors change to accommodate the routines and restrictions required for robots and AI to work effectively.

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#431928 How Fast Is AI Progressing? Stanford’s ...

Posted on January 19, 2018 by Android

When? This is probably the question that futurists, AI experts, and even people with a keen interest in technology dread the most. It has proved famously difficult to predict when new developments in AI will take place. The scientists at the Dartmouth Summer Research Project on Artificial Intelligence in 1956 thought that perhaps two months would be enough to make “significant advances” in a whole range of complex problems, including computers that can understand language, improve themselves, and even understand abstract concepts.
Sixty years later, and these problems are not yet solved. The AI Index, from Stanford, is an attempt to measure how much progress has been made in artificial intelligence.
The index adopts a unique approach, and tries to aggregate data across many regimes. It contains Volume of Activity metrics, which measure things like venture capital investment, attendance at academic conferences, published papers, and so on. The results are what you might expect: tenfold increases in academic activity since 1996, an explosive growth in startups focused around AI, and corresponding venture capital investment. The issue with this metric is that it measures AI hype as much as AI progress. The two might be correlated, but then again, they may not.
The index also scrapes data from the popular coding website Github, which hosts more source code than anyone in the world. They can track the amount of AI-related software people are creating, as well as the interest levels in popular machine learning packages like Tensorflow and Keras. The index also keeps track of the sentiment of news articles that mention AI: surprisingly, given concerns about the apocalypse and an employment crisis, those considered “positive” outweigh the “negative” by three to one.
But again, this could all just be a measure of AI enthusiasm in general.
No one would dispute the fact that we’re in an age of considerable AI hype, but the progress of AI is littered by booms and busts in hype, growth spurts that alternate with AI winters. So the AI Index attempts to track the progress of algorithms against a series of tasks. How well does computer vision perform at the Large Scale Visual Recognition challenge? (Superhuman at annotating images since 2015, but they still can’t answer questions about images very well, combining natural language processing and image recognition). Speech recognition on phone calls is almost at parity.
In other narrow fields, AIs are still catching up to humans. Translation might be good enough that you can usually get the gist of what’s being said, but still scores poorly on the BLEU metric for translation accuracy. The AI index even keeps track of how well the programs can do on the SAT test, so if you took it, you can compare your score to an AI’s.
Measuring the performance of state-of-the-art AI systems on narrow tasks is useful and fairly easy to do. You can define a metric that’s simple to calculate, or devise a competition with a scoring system, and compare new software with old in a standardized way. Academics can always debate about the best method of assessing translation or natural language understanding. The Loebner prize, a simplified question-and-answer Turing Test, recently adopted Winograd Schema type questions, which rely on contextual understanding. AI has more difficulty with these.
Where the assessment really becomes difficult, though, is in trying to map these narrow-task performances onto general intelligence. This is hard because of a lack of understanding of our own intelligence. Computers are superhuman at chess, and now even a more complex game like Go. The braver predictors who came up with timelines thought AlphaGo’s success was faster than expected, but does this necessarily mean we’re closer to general intelligence than they thought?
Here is where it’s harder to track progress.
We can note the specialized performance of algorithms on tasks previously reserved for humans—for example, the index cites a Nature paper that shows AI can now predict skin cancer with more accuracy than dermatologists. We could even try to track one specific approach to general AI; for example, how many regions of the brain have been successfully simulated by a computer? Alternatively, we could simply keep track of the number of professions and professional tasks that can now be performed to an acceptable standard by AI.

“We are running a race, but we don’t know how to get to the endpoint, or how far we have to go.”

Progress in AI over the next few years is far more likely to resemble a gradual rising tide—as more and more tasks can be turned into algorithms and accomplished by software—rather than the tsunami of a sudden intelligence explosion or general intelligence breakthrough. Perhaps measuring the ability of an AI system to learn and adapt to the work routines of humans in office-based tasks could be possible.
The AI index doesn’t attempt to offer a timeline for general intelligence, as this is still too nebulous and confused a concept.
Michael Woodridge, head of Computer Science at the University of Oxford, notes, “The main reason general AI is not captured in the report is that neither I nor anyone else would know how to measure progress.” He is concerned about another AI winter, and overhyped “charlatans and snake-oil salesmen” exaggerating the progress that has been made.
A key concern that all the experts bring up is the ethics of artificial intelligence.
Of course, you don’t need general intelligence to have an impact on society; algorithms are already transforming our lives and the world around us. After all, why are Amazon, Google, and Facebook worth any money? The experts agree on the need for an index to measure the benefits of AI, the interactions between humans and AIs, and our ability to program values, ethics, and oversight into these systems.
Barbra Grosz of Harvard champions this view, saying, “It is important to take on the challenge of identifying success measures for AI systems by their impact on people’s lives.”
For those concerned about the AI employment apocalypse, tracking the use of AI in the fields considered most vulnerable (say, self-driving cars replacing taxi drivers) would be a good idea. Society’s flexibility for adapting to AI trends should be measured, too; are we providing people with enough educational opportunities to retrain? How about teaching them to work alongside the algorithms, treating them as tools rather than replacements? The experts also note that the data suffers from being US-centric.
We are running a race, but we don’t know how to get to the endpoint, or how far we have to go. We are judging by the scenery, and how far we’ve run already. For this reason, measuring progress is a daunting task that starts with defining progress. But the AI index, as an annual collection of relevant information, is a good start.
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Posted in Human Robots

#431873 Why the World Is Still Getting ...

Posted on January 5, 2018 by Android

If you read or watch the news, you’ll likely think the world is falling to pieces. Trends like terrorism, climate change, and a growing population straining the planet’s finite resources can easily lead you to think our world is in crisis.
But there’s another story, a story the news doesn’t often report. This story is backed by data, and it says we’re actually living in the most peaceful, abundant time in history, and things are likely to continue getting better.
The News vs. the Data
The reality that’s often clouded by a constant stream of bad news is we’re actually seeing a massive drop in poverty, fewer deaths from violent crime and preventable diseases. On top of that, we’re the most educated populace to ever walk the planet.
“Violence has been in decline for thousands of years, and today we may be living in the most peaceful era in the existence of our species.” –Steven Pinker
In the last hundred years, we’ve seen the average human life expectancy nearly double, the global GDP per capita rise exponentially, and childhood mortality drop 10-fold.

That’s pretty good progress! Maybe the world isn’t all gloom and doom.If you’re still not convinced the world is getting better, check out the charts in this article from Vox and on Peter Diamandis’ website for a lot more data.
Abundance for All Is Possible
So now that you know the world isn’t so bad after all, here’s another thing to think about: it can get much better, very soon.
In their book Abundance: The Future Is Better Than You Think, Steven Kotler and Peter Diamandis suggest it may be possible for us to meet and even exceed the basic needs of all the people living on the planet today.
“In the hands of smart and driven innovators, science and technology take things which were once scarce and make them abundant and accessible to all.”
This means making sure every single person in the world has adequate food, water and shelter, as well as a good education, access to healthcare, and personal freedom.
This might seem unimaginable, especially if you tend to think the world is only getting worse. But given how much progress we’ve already made in the last few hundred years, coupled with the recent explosion of information sharing and new, powerful technologies, abundance for all is not as out of reach as you might believe.
Throughout history, we’ve seen that in the hands of smart and driven innovators, science and technology take things which were once scarce and make them abundant and accessible to all.
Napoleon III
In Abundance, Diamandis and Kotler tell the story of how aluminum went from being one of the rarest metals on the planet to being one of the most abundant…
In the 1800s, aluminum was more valuable than silver and gold because it was rarer. So when Napoleon III entertained the King of Siam, the king and his guests were honored by being given aluminum utensils, while the rest of the dinner party ate with gold.
But aluminum is not really rare.
In fact, aluminum is the third most abundant element in the Earth’s crust, making up 8.3% of the weight of our planet. But it wasn’t until chemists Charles Martin Hall and Paul Héroult discovered how to use electrolysis to cheaply separate aluminum from surrounding materials that the element became suddenly abundant.
The problems keeping us from achieving a world where everyone’s basic needs are met may seem like resource problems — when in reality, many are accessibility problems.
The Engine Driving Us Toward Abundance: Exponential Technology
History is full of examples like the aluminum story. The most powerful one of the last few decades is information technology. Think about all the things that computers and the internet made abundant that were previously far less accessible because of cost or availability … Here are just a few examples:

Easy access to the world’s information
Ability to share information freely with anyone and everyone
Free/cheap long-distance communication
Buying and selling goods/services regardless of location

Less than two decades ago, when someone reached a certain level of economic stability, they could spend somewhere around $10K on stereos, cameras, entertainment systems, etc — today, we have all that equipment in the palm of our hand.
Now, there is a new generation of technologies heavily dependant on information technology and, therefore, similarly riding the wave of exponential growth. When put to the right use, emerging technologies like artificial intelligence, robotics, digital manufacturing, nano-materials and digital biology make it possible for us to drastically raise the standard of living for every person on the planet.

These are just some of the innovations which are unlocking currently scarce resources:

IBM’s Watson Health is being trained and used in medical facilities like the Cleveland Clinic to help doctors diagnose disease. In the future, it’s likely we’ll trust AI just as much, if not more than humans to diagnose disease, allowing people all over the world to have access to great diagnostic tools regardless of whether there is a well-trained doctor near them.

Solar power is now cheaper than fossil fuels in some parts of the world, and with advances in new materials and storage, the cost may decrease further. This could eventually lead to nearly-free, clean energy for people across the world.

Google’s GMNT network can now translate languages as well as a human, unlocking the ability for people to communicate globally as we never have before.

Self-driving cars are already on the roads of several American cities and will be coming to a road near you in the next couple years. Considering the average American spends nearly two hours driving every day, not having to drive would free up an increasingly scarce resource: time.

The Change-Makers
Today’s innovators can create enormous change because they have these incredible tools—which would have once been available only to big organizations—at their fingertips. And, as a result of our hyper-connected world, there is an unprecedented ability for people across the planet to work together to create solutions to some of our most pressing problems today.
“In today’s hyperlinked world, solving problems anywhere, solves problems everywhere.” –Peter Diamandis and Steven Kotler, Abundance
According to Diamandis and Kotler, there are three groups of people accelerating positive change.

DIY InnovatorsIn the 1970s and 1980s, the Homebrew Computer Club was a meeting place of “do-it-yourself” computer enthusiasts who shared ideas and spare parts. By the 1990s and 2000s, that little club became known as an inception point for the personal computer industry — dozens of companies, including Apple Computer, can directly trace their origins back to Homebrew. Since then, we’ve seen the rise of the social entrepreneur, the Maker Movement and the DIY Bio movement, which have similar ambitions to democratize social reform, manufacturing, and biology, the way Homebrew democratized computers. These are the people who look for new opportunities and aren’t afraid to take risks to create something new that will change the status-quo.
Techno-PhilanthropistsUnlike the robber barons of the 19th and early 20th centuries, today’s “techno-philanthropists” are not just giving away some of their wealth for a new museum, they are using their wealth to solve global problems and investing in social entrepreneurs aiming to do the same. The Bill and Melinda Gates Foundation has given away at least $28 billion, with a strong focus on ending diseases like polio, malaria, and measles for good. Jeff Skoll, after cashing out of eBay with $2 billion in 1998, went on to create the Skoll Foundation, which funds social entrepreneurs across the world. And last year, Mark Zuckerberg and Priscilla Chan pledged to give away 99% of their $46 billion in Facebook stock during their lifetimes.
The Rising BillionCisco estimates that by 2020, there will be 4.1 billion people connected to the internet, up from 3 billion in 2015. This number might even be higher, given the efforts of companies like Facebook, Google, Virgin Group, and SpaceX to bring internet access to the world. That’s a billion new people in the next several years who will be connected to the global conversation, looking to learn, create and better their own lives and communities.In his book, Fortune at the Bottom of the Pyramid, C.K. Pahalad writes that finding co-creative ways to serve this rising market can help lift people out of poverty while creating viable businesses for inventive companies.

The Path to Abundance
Eager to create change, innovators armed with powerful technologies can accomplish incredible feats. Kotler and Diamandis imagine that the path to abundance occurs in three tiers:

Basic Needs (food, water, shelter)
Tools of Growth (energy, education, access to information)
Ideal Health and Freedom

Of course, progress doesn’t always happen in a straight, logical way, but having a framework to visualize the needs is helpful.
Many people don’t believe it’s possible to end the persistent global problems we’re facing. However, looking at history, we can see many examples where technological tools have unlocked resources that previously seemed scarce.
Technological solutions are not always the answer, and we need social change and policy solutions as much as we need technology solutions. But we have seen time and time again, that powerful tools in the hands of innovative, driven change-makers can make the seemingly impossible happen.

You can download the full “Path to Abundance” infographic here. It was created under a CC BY-NC-ND license. If you share, please attribute to Singularity University.
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#431828 This Self-Driving AI Is Learning to ...

Posted on December 19, 2017 by Android

I don’t have to open the doors of AImotive’s white 2015 Prius to see that it’s not your average car. This particular Prius has been christened El Capitan, the name written below the rear doors, and two small cameras are mounted on top of the car. Bundles of wire snake out from them, as well as from the two additional cameras on the car’s hood and trunk.
Inside is where things really get interesting, though. The trunk holds a computer the size of a microwave, and a large monitor covers the passenger glove compartment and dashboard. The center console has three switches labeled “Allowed,” “Error,” and “Active.”
Budapest-based AImotive is working to provide scalable self-driving technology alongside big players like Waymo and Uber in the autonomous vehicle world. On a highway test ride with CEO Laszlo Kishonti near the company’s office in Mountain View, California, I got a glimpse of just how complex that world is.
Camera-Based Feedback System
AImotive’s approach to autonomous driving is a little different from that of some of the best-known systems. For starters, they’re using cameras, not lidar, as primary sensors. “The traffic system is visual and the cost of cameras is low,” Kishonti said. “A lidar can recognize when there are people near the car, but a camera can differentiate between, say, an elderly person and a child. Lidar’s resolution isn’t high enough to recognize the subtle differences of urban driving.”
Image Credit: AImotive
The company’s aiDrive software uses data from the camera sensors to feed information to its algorithms for hierarchical decision-making, grouped under four concurrent activities: recognition, location, motion, and control.
Kishonti pointed out that lidar has already gotten more cost-efficient, and will only continue to do so.
“Ten years ago, lidar was best because there wasn’t enough processing power to do all the calculations by AI. But the cost of running AI is decreasing,” he said. “In our approach, computer vision and AI processing are key, and for safety, we’ll have fallback sensors like radar or lidar.”
aiDrive currently runs on Nvidia chips, which Kishonti noted were originally designed for graphics, and are not terribly efficient given how power-hungry they are. “We’re planning to substitute lower-cost, lower-energy chips in the next six months,” he said.
Testing in Virtual Reality
Waymo recently announced its fleet has now driven four million miles autonomously. That’s a lot of miles, and hard to compete with. But AImotive isn’t trying to compete, at least not by logging more real-life test miles. Instead, the company is doing 90 percent of its testing in virtual reality. “This is what truly differentiates us from competitors,” Kishonti said.
He outlined the three main benefits of VR testing: it can simulate scenarios too dangerous for the real world (such as hitting something), too costly (not every company has Waymo’s funds to run hundreds of cars on real roads), or too time-consuming (like waiting for rain, snow, or other weather conditions to occur naturally and repeatedly).
“Real-world traffic testing is very skewed towards the boring miles,” he said. “What we want to do is test all the cases that are hard to solve.”
On a screen that looked not unlike multiple games of Mario Kart, he showed me the simulator. Cartoon cars cruised down winding streets, outfitted with all the real-world surroundings: people, trees, signs, other cars. As I watched, a furry kangaroo suddenly hopped across one screen. “Volvo had an issue in Australia,” Kishonti explained. “A kangaroo’s movement is different than other animals since it hops instead of running.” Talk about cases that are hard to solve.
AImotive is currently testing around 1,000 simulated scenarios every night, with a steadily-rising curve of successful tests. These scenarios are broken down into features, and the car’s behavior around those features fed into a neural network. As the algorithms learn more features, the level of complexity the vehicles can handle goes up.
On the Road
After Kishonti and his colleagues filled me in on the details of their product, it was time to test it out. A safety driver sat in the driver’s seat, a computer operator in the passenger seat, and Kishonti and I in back. The driver maintained full control of the car until we merged onto the highway. Then he flicked the “Allowed” switch, his copilot pressed the “Active” switch, and he took his hands off the wheel.
What happened next, you ask?
A few things. El Capitan was going exactly the speed limit—65 miles per hour—which meant all the other cars were passing us. When a car merged in front of us or cut us off, El Cap braked accordingly (if a little abruptly). The monitor displayed the feed from each of the car’s cameras, plus multiple data fields and a simulation where a blue line marked the center of the lane, measured by the cameras tracking the lane markings on either side.
I noticed El Cap wobbling out of our lane a bit, but it wasn’t until two things happened in a row that I felt a little nervous: first we went under a bridge, then a truck pulled up next to us, both bridge and truck casting a complete shadow over our car. At that point El Cap lost it, and we swerved haphazardly to the right, narrowly missing the truck’s rear wheels. The safety driver grabbed the steering wheel and took back control of the car.
What happened, Kishonti explained, was that the shadows made it hard for the car’s cameras to see the lane markings. This was a new scenario the algorithm hadn’t previously encountered. If we’d only gone under a bridge or only been next to the truck for a second, El Cap may not have had so much trouble, but the two events happening in a row really threw the car for a loop—almost literally.
“This is a new scenario we’ll add to our testing,” Kishonti said. He added that another way for the algorithm to handle this type of scenario, rather than basing its speed and positioning on the lane markings, is to mimic nearby cars. “The human eye would see that other cars are still moving at the same speed, even if it can’t see details of the road,” he said.
After another brief—and thankfully uneventful—hands-off cruise down the highway, the safety driver took over, exited the highway, and drove us back to the office.
Driving into the Future
I climbed out of the car feeling amazed not only that self-driving cars are possible, but that driving is possible at all. I squint when driving into a tunnel, swerve to avoid hitting a stray squirrel, and brake gradually at stop signs—all without consciously thinking to do so. On top of learning to steer, brake, and accelerate, self-driving software has to incorporate our brains’ and bodies’ unconscious (but crucial) reactions, like our pupils dilating to let in more light so we can see in a tunnel.
Despite all the progress of machine learning, artificial intelligence, and computing power, I have a wholly renewed appreciation for the thing that’s been in charge of driving up till now: the human brain.
Kishonti seemed to feel similarly. “I don’t think autonomous vehicles in the near future will be better than the best drivers,” he said. “But they’ll be better than the average driver. What we want to achieve is safe, good-quality driving for everyone, with scalability.”
AImotive is currently working with American tech firms and with car and truck manufacturers in Europe, China, and Japan.
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Posted in Human Robots