Tag Archives: cars

#433739 No Safety Driver Here—Volvo’s New ...

Each time there’s a headline about driverless trucking technology, another piece is taken out of the old equation. First, an Uber/Otto truck’s safety driver went hands-off once the truck reached the highway (and said truck successfully delivered its valuable cargo of 50,000 beers). Then, Starsky Robotics announced its trucks would start making autonomous deliveries without a human in the vehicle at all.

Now, Volvo has taken the tech one step further. Its new trucks not only won’t have safety drivers, they won’t even have the option of putting safety drivers behind the wheel, because there is no wheel—and no cab, either.

Vera, as the technology’s been dubbed, was unveiled in September, and consists of a sort of flat-Tesla-like electric car with a standard trailer hookup. The vehicles are connected to a cloud service, which also connects them to each other and to a control center. The control center monitors the trucks’ positioning (they’re designed to locate their position to within centimeters), battery charge, load content, service requirements, and other variables. The driveline and battery pack used in the cars are the same as those Volvo uses in its existing electric trucks.

You won’t see these cruising down an interstate highway, though, or even down a local highway. Vera trucks are designed to be used on short, repetitive routes contained within limited areas—think shipping ports, industrial parks, or logistics hubs. They’re limited to slower speeds than normal cars or trucks, and will be able to operate 24/7. “We will see much higher delivery precision, as well as improved flexibility and productivity,” said Mikael Karlsson, VP of Autonomous Solutions at Volvo Trucks. “Today’s operations are often designed according to standard daytime work hours, but a solution like Vera opens up the possibility of continuous round-the-clock operation and a more optimal flow. This in turn can minimize stock piles and increase overall productivity.”

The trucks are sort of like bigger versions of Amazon’s Kiva robots, which scoot around the aisles of warehouses and fulfillment centers moving pallets between shelves and fetching goods to be shipped.

Pairing trucks like Vera with robots like Kiva makes for a fascinating future landscape of logistics and transport; cargo will be moved from docks to warehouses by a large, flat robot-on-wheels, then distributed throughout that warehouse by smaller, flat robots-on-wheels. To really see the automated process through to the end point, even smaller flat robots-on-wheels will be used to deliver peoples’ goods right to their front doors.

Sounds like a lot of robots and not a lot of humans, right? Anticipating its technology’s implication in the ongoing uproar over technological unemployment, Volvo has already made statements about its intentions to continue to employ humans alongside the driverless trucks. “I foresee that there will be an increased level of automation where it makes sense, such as for repetitive tasks. This in turn will drive prosperity and increase the need for truck drivers in other applications,” said Karlsson.

The end-to-end automation concept has already been put into practice in Caofeidian, a northern Chinese city that houses the world’s first fully autonomous harbor, aiming to be operational by the end of this year. Besides replacing human-driven trucks with autonomous ones (made by Chinese startup TuSimple), the port is using automated cranes and a coordinating central control system.

Besides Uber/Otto, Tesla, or Daimler, which are all working on driverless trucks with a more conventional design (meaning they still have a cab and look like you’d expect a truck to look), Volvo also has competition from a company called Einride. The Swedish startup’s electric, cabless T/Pod looks a lot like Vera, but has some fundamental differences. Rather than being tailored to short distances and high capacity, Einride’s trucks are meant for medium distance and capacity, like moving goods from a distribution center to a series of local stores.

Vera trucks are currently still in the development phase. But since their intended use is quite specific and limited (Karlsson noted “Vera is not intended to be a solution for everyone, everywhere”), the technology could likely be rolled out faster than its more general-use counterparts. Having cabless electric trucks take over short routes in closed environments would be one more baby step along the road to a driverless future—and a testament to the fact that self-driving technology will move into our lives and our jobs incrementally, ostensibly giving us the time we’ll need to adapt and adjust.

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#433725 This Week’s Awesome Stories From ...

The Demise of Rethink Robotics Shows How Hard It Is to Make Machines Truly Smart
Will Knight | MIT Technology Review
“There’s growing interest in using recent advances in AI to make industrial robots a lot smarter and more useful. …But look carefully and you’ll see that these technologies are at a very early stage, and that deploying them commercially could prove extremely challenging. The demise of Rethink doesn’t mean industrial robotics isn’t flourishing, or that AI-driven advances won’t come about. But it shows just how hard doing real innovation in robotics can be.”

The Human Cell Atlas Is Biologists’ Latest Grand Project
Megan Molteni | Wired
“Dubbed the Human Cell Atlas, the project intends to catalog all of the estimated 37 trillion cells that make up a human body. …By decoding the genes active in single cells, pegging different cell types to a specific address in the body, and tracing the molecular circuits between them, participating researchers plan to create a more comprehensive map of human biology than has ever existed before.”

US Will Rewrite Safety Rules to Permit Fully Driverless Cars on Public Roads
Andrew J. Hawkins | The Verge
“Under current US safety rules, a motor vehicle must have traditional controls, like a steering wheel, mirrors, and foot pedals, before it is allowed to operate on public roads. But that could all change under a new plan released on Thursday by the Department of Transportation that’s intended to open the floodgates for fully driverless cars.”

When an AI Goes Full Jack Kerouac
Brian Merchant | The Atlantic
“By the end of the four-day trip, receipts emblazoned with artificially intelligent prose would cover the floor of the car. …it is a hallucinatory, oddly illuminating account of a bot’s life on the interstate; the Electric Kool-Aid Acid Test meets Google Street View, narrated by Siri.”

New Autonomous Farm Wants to Produce Food Without Human Workers
Erin Winick | MIT Technology Review
“As the firm’s cofounder Brandon Alexander puts it: ‘We are a farm and will always be a farm.’ But it’s no ordinary farm. For starters, the company’s 15 human employees share their work space with robots who quietly go about the business of tending rows and rows of leafy greens.”

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Posted in Human Robots

#433689 The Rise of Dataism: A Threat to Freedom ...

What would happen if we made all of our data public—everything from wearables monitoring our biometrics, all the way to smartphones monitoring our location, our social media activity, and even our internet search history?

Would such insights into our lives simply provide companies and politicians with greater power to invade our privacy and manipulate us by using our psychological profiles against us?

A burgeoning new philosophy called dataism doesn’t think so.

In fact, this trending ideology believes that liberating the flow of data is the supreme value of the universe, and that it could be the key to unleashing the greatest scientific revolution in the history of humanity.

What Is Dataism?
First mentioned by David Brooks in his 2013 New York Times article “The Philosophy of Data,” dataism is an ethical system that has been most heavily explored and popularized by renowned historian, Yuval Noah Harari.

In his 2016 book Homo Deus, Harari described dataism as a new form of religion that celebrates the growing importance of big data.

Its core belief centers around the idea that the universe gives greater value and support to systems, individuals, and societies that contribute most heavily and efficiently to data processing. In an interview with Wired, Harari stated, “Humans were special and important because up until now they were the most sophisticated data processing system in the universe, but this is no longer the case.”

Now, big data and machine learning are proving themselves more sophisticated, and dataists believe we should hand over as much information and power to these algorithms as possible, allowing the free flow of data to unlock innovation and progress unlike anything we’ve ever seen before.

Pros: Progress and Personal Growth
When you let data run freely, it’s bound to be mixed and matched in new ways that inevitably spark progress. And as we enter the exponential future where every person is constantly connected and sharing their data, the potential for such collaborative epiphanies becomes even greater.

We can already see important increases in quality of life thanks to companies like Google. With Google Maps on your phone, your position is constantly updating on their servers. This information, combined with everyone else on the planet using a phone with Google Maps, allows your phone to inform you of traffic conditions. Based on the speed and location of nearby phones, Google can reroute you to less congested areas or help you avoid accidents. And since you trust that these algorithms have more data than you, you gladly hand over your power to them, following your GPS’s directions rather than your own.

We can do the same sort of thing with our bodies.

Imagine, for instance, a world where each person has biosensors in their bloodstreams—a not unlikely or distant possibility when considering diabetic people already wear insulin pumps that constantly monitor their blood sugar levels. And let’s assume this data was freely shared to the world.

Now imagine a virus like Zika or the Bird Flu breaks out. Thanks to this technology, the odd change in biodata coming from a particular region flags an artificial intelligence that feeds data to the CDC (Center for Disease Control and Prevention). Recognizing that a pandemic could be possible, AIs begin 3D printing vaccines on-demand, predicting the number of people who may be afflicted. When our personal AIs tell us the locations of the spreading epidemic and to take the vaccine it just delivered by drone to our homes, are we likely to follow its instructions? Almost certainly—and if so, it’s likely millions, if not billions, of lives will have been saved.

But to quickly create such vaccines, we’ll also need to liberate research.

Currently, universities and companies seeking to benefit humankind with medical solutions have to pay extensively to organize clinical trials and to find people who match their needs. But if all our biodata was freely aggregated, perhaps they could simply say “monitor all people living with cancer” to an AI, and thanks to the constant stream of data coming in from the world’s population, a machine learning program may easily be able to detect a pattern and create a cure.

As always in research, the more sample data you have, the higher the chance that such patterns will emerge. If data is flowing freely, then anyone in the world can suddenly decide they have a hunch they want to explore, and without having to spend months and months of time and money hunting down the data, they can simply test their hypothesis.

Whether garage tinkerers, at-home scientists, or PhD students—an abundance of free data allows for science to progress unhindered, each person able to operate without being slowed by lack of data. And any progress they make is immediately liberated, becoming free data shared with anyone else that may find a use for it.

Any individual with a curious passion would have the entire world’s data at their fingertips, empowering every one of us to become an expert in any subject that inspires us. Expertise we can then share back into the data stream—a positive feedback loop spearheading progress for the entirety of humanity’s knowledge.

Such exponential gains represent a dataism utopia.

Unfortunately, our current incentives and economy also show us the tragic failures of this model.

As Harari has pointed out, the rise of datism means that “humanism is now facing an existential challenge and the idea of ‘free will’ is under threat.”

Cons: Manipulation and Extortion
In 2017, The Economist declared that data was the most valuable resource on the planet—even more valuable than oil.

Perhaps this is because data is ‘priceless’: it represents understanding, and understanding represents control. And so, in the world of advertising and politics, having data on your consumers and voters gives you an incredible advantage.

This was evidenced by the Cambridge Analytica scandal, in which it’s believed that Donald Trump and the architects of Brexit leveraged users’ Facebook data to create psychological profiles that enabled them to manipulate the masses.

How powerful are these psychological models?

A team who built a model similar to that used by Cambridge Analytica said their model could understand someone as well as a coworker with access to only 10 Facebook likes. With 70 likes they could know them as well as a friend might, 150 likes to match their parents’ understanding, and at 300 likes they could even come to know someone better than their lovers. With more likes, they could even come to know someone better than that person knows themselves.

Proceeding With Caution
In a capitalist democracy, do we want businesses and politicians to know us better than we know ourselves?

In spite of the remarkable benefits that may result for our species by freely giving away our information, do we run the risk of that data being used to exploit and manipulate the masses towards a future without free will, where our daily lives are puppeteered by those who own our data?

It’s extremely possible.

And it’s for this reason that one of the most important conversations we’ll have as a species centers around data ownership: do we just give ownership of the data back to the users, allowing them to choose who to sell or freely give their data to? Or will that simply deter the entrepreneurial drive and cause all of the free services we use today, like Google Search and Facebook, to begin charging inaccessible prices? How much are we willing to pay for our freedom? And how much do we actually care?

If recent history has taught us anything, it’s that humans are willing to give up more privacy than they like to think. Fifteen years ago, it would have been crazy to suggest we’d all allow ourselves to be tracked by our cars, phones, and daily check-ins to our favorite neighborhood locations; but now most of us see it as a worthwhile trade for optimized commutes and dating. As we continue navigating that fine line between exploitation and innovation into a more technological future, what other trade-offs might we be willing to make?

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#433486 This AI Predicts Obesity ...

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

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

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

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

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

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

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

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

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

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

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

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

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Posted in Human Robots

#433282 The 4 Waves of AI: Who Will Own the ...

Recently, I picked up Kai-Fu Lee’s newest book, AI Superpowers.

Kai-Fu Lee is one of the most plugged-in AI investors on the planet, managing over $2 billion between six funds and over 300 portfolio companies in the US and China.

Drawing from his pioneering work in AI, executive leadership at Microsoft, Apple, and Google (where he served as founding president of Google China), and his founding of VC fund Sinovation Ventures, Lee shares invaluable insights about:

The four factors driving today’s AI ecosystems;
China’s extraordinary inroads in AI implementation;
Where autonomous systems are headed;
How we’ll need to adapt.

With a foothold in both Beijing and Silicon Valley, Lee looks at the power balance between Chinese and US tech behemoths—each turbocharging new applications of deep learning and sweeping up global markets in the process.

In this post, I’ll be discussing Lee’s “Four Waves of AI,” an excellent framework for discussing where AI is today and where it’s going. I’ll also be featuring some of the hottest Chinese tech companies leading the charge, worth watching right now.

I’m super excited that this Tuesday, I’ve scored the opportunity to sit down with Kai-Fu Lee to discuss his book in detail via a webinar.

With Sino-US competition heating up, who will own the future of technology?

Let’s dive in.

The First Wave: Internet AI
In this first stage of AI deployment, we’re dealing primarily with recommendation engines—algorithmic systems that learn from masses of user data to curate online content personalized to each one of us.

Think Amazon’s spot-on product recommendations, or that “Up Next” YouTube video you just have to watch before getting back to work, or Facebook ads that seem to know what you’ll buy before you do.

Powered by the data flowing through our networks, internet AI leverages the fact that users automatically label data as we browse. Clicking versus not clicking; lingering on a web page longer than we did on another; hovering over a Facebook video to see what happens at the end.

These cascades of labeled data build a detailed picture of our personalities, habits, demands, and desires: the perfect recipe for more tailored content to keep us on a given platform.

Currently, Lee estimates that Chinese and American companies stand head-to-head when it comes to deployment of internet AI. But given China’s data advantage, he predicts that Chinese tech giants will have a slight lead (60-40) over their US counterparts in the next five years.

While you’ve most definitely heard of Alibaba and Baidu, you’ve probably never stumbled upon Toutiao.

Starting out as a copycat of America’s wildly popular Buzzfeed, Toutiao reached a valuation of $20 billion by 2017, dwarfing Buzzfeed’s valuation by more than a factor of 10. But with almost 120 million daily active users, Toutiao doesn’t just stop at creating viral content.

Equipped with natural-language processing and computer vision, Toutiao’s AI engines survey a vast network of different sites and contributors, rewriting headlines to optimize for user engagement, and processing each user’s online behavior—clicks, comments, engagement time—to curate individualized news feeds for millions of consumers.

And as users grow more engaged with Toutiao’s content, the company’s algorithms get better and better at recommending content, optimizing headlines, and delivering a truly personalized feed.

It’s this kind of positive feedback loop that fuels today’s AI giants surfing the wave of internet AI.

The Second Wave: Business AI
While internet AI takes advantage of the fact that netizens are constantly labeling data via clicks and other engagement metrics, business AI jumps on the data that traditional companies have already labeled in the past.

Think banks issuing loans and recording repayment rates; hospitals archiving diagnoses, imaging data, and subsequent health outcomes; or courts noting conviction history, recidivism, and flight.

While we humans make predictions based on obvious root causes (strong features), AI algorithms can process thousands of weakly correlated variables (weak features) that may have much more to do with a given outcome than the usual suspects.

By scouting out hidden correlations that escape our linear cause-and-effect logic, business AI leverages labeled data to train algorithms that outperform even the most veteran of experts.

Apply these data-trained AI engines to banking, insurance, and legal sentencing, and you get minimized default rates, optimized premiums, and plummeting recidivism rates.

While Lee confidently places America in the lead (90-10) for business AI, China’s substantial lag in structured industry data could actually work in its favor going forward.

In industries where Chinese startups can leapfrog over legacy systems, China has a major advantage.

Take Chinese app Smart Finance, for instance.

While Americans embraced credit and debit cards in the 1970s, China was still in the throes of its Cultural Revolution, largely missing the bus on this technology.

Fast forward to 2017, and China’s mobile payment spending outnumbered that of Americans’ by a ratio of 50 to 1. Without the competition of deeply entrenched credit cards, mobile payments were an obvious upgrade to China’s cash-heavy economy, embraced by 70 percent of China’s 753 million smartphone users by the end of 2017.

But by leapfrogging over credit cards and into mobile payments, China largely left behind the notion of credit.

And here’s where Smart Finance comes in.

An AI-powered app for microfinance, Smart Finance depends almost exclusively on its algorithms to make millions of microloans. For each potential borrower, the app simply requests access to a portion of the user’s phone data.

On the basis of variables as subtle as your typing speed and battery percentage, Smart Finance can predict with astounding accuracy your likelihood of repaying a $300 loan.

Such deployments of business AI and internet AI are already revolutionizing our industries and individual lifestyles. But still on the horizon lie two even more monumental waves— perception AI and autonomous AI.

The Third Wave: Perception AI
In this wave, AI gets an upgrade with eyes, ears, and myriad other senses, merging the digital world with our physical environments.

As sensors and smart devices proliferate through our homes and cities, we are on the verge of entering a trillion-sensor economy.

Companies like China’s Xiaomi are putting out millions of IoT-connected devices, and teams of researchers have already begun prototyping smart dust—solar cell- and sensor-geared particulates that can store and communicate troves of data anywhere, anytime.

As Kai-Fu explains, perception AI “will bring the convenience and abundance of the online world into our offline reality.” Sensor-enabled hardware devices will turn everything from hospitals to cars to schools into online-merge-offline (OMO) environments.

Imagine walking into a grocery store, scanning your face to pull up your most common purchases, and then picking up a virtual assistant (VA) shopping cart. Having pre-loaded your data, the cart adjusts your usual grocery list with voice input, reminds you to get your spouse’s favorite wine for an upcoming anniversary, and guides you through a personalized store route.

While we haven’t yet leveraged the full potential of perception AI, China and the US are already making incredible strides. Given China’s hardware advantage, Lee predicts China currently has a 60-40 edge over its American tech counterparts.

Now the go-to city for startups building robots, drones, wearable technology, and IoT infrastructure, Shenzhen has turned into a powerhouse for intelligent hardware, as I discussed last week. Turbocharging output of sensors and electronic parts via thousands of factories, Shenzhen’s skilled engineers can prototype and iterate new products at unprecedented scale and speed.

With the added fuel of Chinese government support and a relaxed Chinese attitude toward data privacy, China’s lead may even reach 80-20 in the next five years.

Jumping on this wave are companies like Xiaomi, which aims to turn bathrooms, kitchens, and living rooms into smart OMO environments. Having invested in 220 companies and incubated 29 startups that produce its products, Xiaomi surpassed 85 million intelligent home devices by the end of 2017, making it the world’s largest network of these connected products.

One KFC restaurant in China has even teamed up with Alipay (Alibaba’s mobile payments platform) to pioneer a ‘pay-with-your-face’ feature. Forget cash, cards, and cell phones, and let OMO do the work.

The Fourth Wave: Autonomous AI
But the most monumental—and unpredictable—wave is the fourth and final: autonomous AI.

Integrating all previous waves, autonomous AI gives machines the ability to sense and respond to the world around them, enabling AI to move and act productively.

While today’s machines can outperform us on repetitive tasks in structured and even unstructured environments (think Boston Dynamics’ humanoid Atlas or oncoming autonomous vehicles), machines with the power to see, hear, touch and optimize data will be a whole new ballgame.

Think: swarms of drones that can selectively spray and harvest entire farms with computer vision and remarkable dexterity, heat-resistant drones that can put out forest fires 100X more efficiently, or Level 5 autonomous vehicles that navigate smart roads and traffic systems all on their own.

While autonomous AI will first involve robots that create direct economic value—automating tasks on a one-to-one replacement basis—these intelligent machines will ultimately revamp entire industries from the ground up.

Kai-Fu Lee currently puts America in a commanding lead of 90-10 in autonomous AI, especially when it comes to self-driving vehicles. But Chinese government efforts are quickly ramping up the competition.

Already in China’s Zhejiang province, highway regulators and government officials have plans to build China’s first intelligent superhighway, outfitted with sensors, road-embedded solar panels and wireless communication between cars, roads and drivers.

Aimed at increasing transit efficiency by up to 30 percent while minimizing fatalities, the project may one day allow autonomous electric vehicles to continuously charge as they drive.

A similar government-fueled project involves Beijing’s new neighbor Xiong’an. Projected to take in over $580 billion in infrastructure spending over the next 20 years, Xiong’an New Area could one day become the world’s first city built around autonomous vehicles.

Baidu is already working with Xiong’an’s local government to build out this AI city with an environmental focus. Possibilities include sensor-geared cement, computer vision-enabled traffic lights, intersections with facial recognition, and parking lots-turned parks.

Lastly, Lee predicts China will almost certainly lead the charge in autonomous drones. Already, Shenzhen is home to premier drone maker DJI—a company I’ll be visiting with 24 top executives later this month as part of my annual China Platinum Trip.

Named “the best company I have ever encountered” by Chris Anderson, DJI owns an estimated 50 percent of the North American drone market, supercharged by Shenzhen’s extraordinary maker movement.

While the long-term Sino-US competitive balance in fourth wave AI remains to be seen, one thing is certain: in a matter of decades, we will witness the rise of AI-embedded cityscapes and autonomous machines that can interact with the real world and help solve today’s most pressing grand challenges.

Join Me
Webinar with Dr. Kai-Fu Lee: Dr. Kai-Fu Lee — one of the world’s most respected experts on AI — and I will discuss his latest book AI Superpowers: China, Silicon Valley, and the New World Order. Artificial Intelligence is reshaping the world as we know it. With U.S.-Sino competition heating up, who will own the future of technology? Register here for the free webinar on September 4th, 2018 from 11:00am–12:30pm PST.

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Posted in Human Robots