Tag Archives: NVIDIA

#435474 Watch China’s New Hybrid AI Chip Power ...

Posted on August 7, 2019 by Android

When I lived in Beijing back in the 90s, a man walking his bike was nothing to look at. But today, I did a serious double-take at a video of a bike walking his man.

No kidding.

The bike itself looks overloaded but otherwise completely normal. Underneath its simplicity, however, is a hybrid computer chip that combines brain-inspired circuits with machine learning processes into a computing behemoth. Thanks to its smart chip, the bike self-balances as it gingerly rolls down a paved track before smoothly gaining speed into a jogging pace while navigating dexterously around obstacles. It can even respond to simple voice commands such as “speed up,” “left,” or “straight.”

Far from a circus trick, the bike is a real-world demo of the AI community’s latest attempt at fashioning specialized hardware to keep up with the challenges of machine learning algorithms. The Tianjic (天机*) chip isn’t just your standard neuromorphic chip. Rather, it has the architecture of a brain-like chip, but can also run deep learning algorithms—a match made in heaven that basically mashes together neuro-inspired hardware and software.

The study shows that China is readily nipping at the heels of Google, Facebook, NVIDIA, and other tech behemoths investing in developing new AI chip designs—hell, with billions in government investment it may have already had a head start. A sweeping AI plan from 2017 looks to catch up with the US on AI technology and application by 2020. By 2030, China’s aiming to be the global leader—and a champion for building general AI that matches humans in intellectual competence.

The country’s ambition is reflected in the team’s parting words.

“Our study is expected to stimulate AGI [artificial general intelligence] development by paving the way to more generalized hardware platforms,” said the authors, led by Dr. Luping Shi at Tsinghua University.

A Hardware Conundrum
Shi’s autonomous bike isn’t the first robotic two-wheeler. Back in 2015, the famed research nonprofit SRI International in Menlo Park, California teamed up with Yamaha to engineer MOTOBOT, a humanoid robot capable of driving a motorcycle. Powered by state-of-the-art robotic hardware and machine learning, MOTOBOT eventually raced MotoGPTM world champion Valentino Rossi in a nail-biting match-off.

However, the technological core of MOTOBOT and Shi’s bike vastly differ, and that difference reflects two pathways towards more powerful AI. One, exemplified by MOTOBOT, is software—developing brain-like algorithms with increasingly efficient architecture, efficacy, and speed. That sounds great, but deep neural nets demand so many computational resources that general-purpose chips can’t keep up.

As Shi told China Science Daily: “CPUs and other chips are driven by miniaturization technologies based on physics. Transistors might shrink to nanoscale-level in 10, 20 years. But what then?” As more transistors are squeezed onto these chips, efficient cooling becomes a limiting factor in computational speed. Tax them too much, and they melt.

For AI processes to continue, we need better hardware. An increasingly popular idea is to build neuromorphic chips, which resemble the brain from the ground up. IBM’s TrueNorth, for example, contains a massively parallel architecture nothing like the traditional Von Neumann structure of classic CPUs and GPUs. Similar to biological brains, TrueNorth’s memory is stored within “synapses” between physical “neurons” etched onto the chip, which dramatically cuts down on energy consumption.

But even these chips are limited. Because computation is tethered to hardware architecture, most chips resemble just one specific type of brain-inspired network called spiking neural networks (SNNs). Without doubt, neuromorphic chips are highly efficient setups with dynamics similar to biological networks. They also don’t play nicely with deep learning and other software-based AI.

Brain-AI Hybrid Core
Shi’s new Tianjic chip brought the two incompatibilities together onto a single piece of brainy hardware.

First was to bridge the deep learning and SNN divide. The two have very different computation philosophies and memory organizations, the team said. The biggest difference, however, is that artificial neural networks transform multidimensional data—image pixels, for example—into a single, continuous, multi-bit 0 and 1 stream. In contrast, neurons in SNNs activate using something called “binary spikes” that code for specific activation events in time.

Confused? Yeah, it’s hard to wrap my head around it too. That’s because SNNs act very similarly to our neural networks and nothing like computers. A particular neuron needs to generate an electrical signal (a “spike”) large enough to transfer down to the next one; little blips in signals don’t count. The way they transmit data also heavily depends on how they’re connected, or the network topology. The takeaway: SNNs work pretty differently than deep learning.

Shi’s team first recreated this firing quirk in the language of computers—0s and 1s—so that the coding mechanism would become compatible with deep learning algorithms. They then carefully aligned the step-by-step building blocks of the two models, which allowed them to tease out similarities into a common ground to further build on. “On the basis of this unified abstraction, we built a cross-paradigm neuron scheme,” they said.

In general, the design allowed both computational approaches to share the synapses, where neurons connect and store data, and the dendrites, the outgoing branches of the neurons. In contrast, the neuron body, where signals integrate, was left reconfigurable for each type of computation, as were the input branches. Each building block was combined into a single unified functional core (FCore), which acts like a deep learning/SNN converter depending on its specific setup. Translation: the chip can do both types of previously incompatible computation.

The Chip
Using nanoscale fabrication, the team arranged 156 FCores, containing roughly 40,000 neurons and 10 million synapses, onto a chip less than a fifth of an inch in length and width. Initial tests showcased the chip’s versatility, in that it can run both SNNs and deep learning algorithms such as the popular convolutional neural network (CNNs) often used in machine vision.

Compared to IBM TrueNorth, the density of Tianjic’s cores increased by 20 percent, speeding up performance ten times and increasing bandwidth at least 100-fold, the team said. When pitted against GPUs, the current hardware darling of machine learning, the chip increased processing throughput up to 100 times, while using just a sliver (1/10,000) of energy.

Although these stats are great, real-life performance is even better as a demo. Here’s where the authors gave their Tianjic brain a body. The team combined one chip with multiple specialized networks to process vision, balance, voice commands, and decision-making in real time. Object detection and target tracking, for example, relied on a deep neural net CNN, whereas voice commands and balance data were recognized using an SNN. The inputs were then integrated inside a neural state machine, which churned out decisions to downstream output modules—for example, controlling the handle bar to turn left.

Thanks to the chip’s brain-like architecture and bilingual ability, Tianjic “allowed all of the neural network models to operate in parallel and realized seamless communication across the models,” the team said. The result is an autonomous bike that rolls after its human, balances across speed bumps, avoids crashing into roadblocks, and answers to voice commands.

General AI?
“It’s a wonderful demonstration and quite impressive,” said the editorial team at Nature, which published the study on its cover last week.

However, they cautioned, when comparing Tianjic with state-of-the-art chips designed for a single problem toe-to-toe on that particular problem, Tianjic falls behind. But building these jack-of-all-trades hybrid chips is definitely worth the effort. Compared to today’s limited AI, what people really want is artificial general intelligence, which will require new architectures that aren’t designed to solve one particular problem.

Until people start to explore, innovate, and play around with different designs, it’s not clear how we can further progress in the pursuit of general AI. A self-driving bike might not be much to look at, but its hybrid brain is a pretty neat place to start.

*The name, in Chinese, means “heavenly machine,” “unknowable mystery of nature,” or “confidentiality.” Go figure.

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Posted in Human Robots

#435110 5 Coming Breakthroughs in Energy and ...

Posted on May 18, 2019 by Android

The energy and transportation industries are being aggressively disrupted by converging exponential technologies.

In just five days, the sun provides Earth with an energy supply exceeding all proven reserves of oil, coal, and natural gas. Capturing just 1 part in 8,000 of this available solar energy would allow us to meet 100 percent of our energy needs.

As we leverage renewable energy supplied by the sun, wind, geothermal sources, and eventually fusion, we are rapidly heading towards a future where 100 percent of our energy needs will be met by clean tech in just 30 years.

During the past 40 years, solar prices have dropped 250-fold. And as these costs plummet, solar panel capacity continues to grow exponentially.

On the heels of energy abundance, we are additionally witnessing a new transportation revolution, which sets the stage for a future of seamlessly efficient travel at lower economic and environmental costs.

Top 5 Transportation Breakthroughs (2019-2024)
Entrepreneur and inventor Ramez Naam is my go-to expert on all things energy and environment. Currently serving as the Energy Co-Chair at Singularity University, Naam is the award-winning author of five books, including the Nexus series of science fiction novels. Having spent 13 years at Microsoft, his software has touched the lives of over a billion people. Naam holds over 20 patents, including several shared with co-inventor Bill Gates.

In the next five years, he forecasts five respective transportation and energy trends, each poised to disrupt major players and birth entirely new business models.

Let’s dive in.

Autonomous cars drive 1 billion miles on US roads. Then 10 billion

Alphabet’s Waymo alone has already reached 10 million miles driven in the US. The 600 Waymo vehicles on public roads drive a total of 25,000 miles each day, and computer simulations provide an additional 25,000 virtual cars driving constantly. Since its launch in December, the Waymo One service has transported over 1,000 pre-vetted riders in the Phoenix area.

With more training miles, the accuracy of these cars continues to improve. Since last year, GM Cruise has improved its disengagement rate by 321 percent since last year, trailing close behind with only one human intervention per 5,025 miles self-driven.

Autonomous taxis as a service in top 20 US metro areas

Along with its first quarterly earnings released last week, Lyft recently announced that it would expand its Waymo partnership with the upcoming deployment of 10 autonomous vehicles in the Phoenix area. While individuals previously had to partake in Waymo’s “early rider program” prior to trying Waymo One, the Lyft partnership will allow anyone to ride in a self-driving vehicle without a prior NDA.

Strategic partnerships will grow increasingly essential between automakers, self-driving tech companies, and rideshare services. Ford is currently working with Volkswagen, and Nvidia now collaborates with Daimler (Mercedes) and Toyota. Just last week, GM Cruise raised another $1.15 billion at a $19 billion valuation as the company aims to launch a ride-hailing service this year.

“They’re going to come to the Bay Area, Los Angeles, Houston, other cities with relatively good weather,” notes Naam. “In every major city within five years in the US and in some other parts of the world, you’re going to see the ability to hail an autonomous vehicle as a ride.”

Cambrian explosion of vehicle formats

Naam explains, “If you look today at the average ridership of a taxi, a Lyft, or an Uber, it’s about 1.1 passengers plus the driver. So, why do you need a large four-seater vehicle for that?”

Small electric, autonomous pods that seat as few as two people will begin to emerge, satisfying the majority of ride-hailing demands we see today. At the same time, larger communal vehicles will appear, such as Uber Express, that will undercut even the cheapest of transportation methods—buses, trams, and the like. Finally, last-mile scooter transit (or simply short-distance walks) might connect you to communal pick-up locations.

By 2024, an unimaginably diverse range of vehicles will arise to meet every possible need, regardless of distance or destination.

Drone delivery for lightweight packages in at least one US city

Wing, the Alphabet drone delivery startup, recently became the first company to gain approval from the Federal Aviation Administration (FAA) to make deliveries in the US. Having secured approval to deliver to 100 homes in Canberra, Australia, Wing additionally plans to begin delivering goods from local businesses in the suburbs of Virginia.

The current state of drone delivery is best suited for lightweight, urgent-demand payloads like pharmaceuticals, thumb drives, or connectors. And as Amazon continues to decrease its Prime delivery times—now as speedy as a one-day turnaround in many cities—the use of drones will become essential.

Robotic factories drive onshoring of US factories… but without new jobs

The supply chain will continue to shorten and become more agile with the re-onshoring of manufacturing jobs in the US and other countries. Naam reasons that new management and software jobs will drive this shift, as these roles develop the necessary robotics to manufacture goods. Equally as important, these robotic factories will provide a more humane setting than many of the current manufacturing practices overseas.

Top 5 Energy Breakthroughs (2019-2024)

First “1 cent per kWh” deals for solar and wind signed

Ten years ago, the lowest price of solar and wind power fell between 10 to 12 cents per kilowatt hour (kWh), over twice the price of wholesale power from coal or natural gas.

Today, the gap between solar/wind power and fossil fuel-generated electricity is nearly negligible in many parts of the world. In G20 countries, fossil fuel electricity costs between 5 to 17 cents per kWh, while the average cost per kWh of solar power in the US stands at under 10 cents.

Spanish firm Solarpack Corp Technological recently won a bid in Chile for a 120 MW solar power plant supplying energy at 2.91 cents per kWh. This deal will result in an estimated 25 percent drop in energy costs for Chilean businesses by 2021.

Naam indicates, “We will see the first unsubsidized 1.0 cent solar deals in places like Chile, Mexico, the Southwest US, the Middle East, and North Africa, and we’ll see similar prices for wind in places like Mexico, Brazil, and the US Great Plains.”

Solar and wind will reach >15 percent of US electricity, and begin to drive all growth

Just over eight percent of energy in the US comes from solar and wind sources. In total, 17 percent of American energy is derived from renewable sources, while a whopping 63 percent is sourced from fossil fuels, and 17 percent from nuclear.

Last year in the U.K., twice as much energy was generated from wind than from coal. For over a week in May, the U.K. went completely coal-free, using wind and solar to supply 35 percent and 21 percent of power, respectively. While fossil fuels remain the primary electricity source, this week-long experiment highlights the disruptive potential of solar and wind power that major countries like the U.K. are beginning to emphasize.

“Solar and wind are still a relatively small part of the worldwide power mix, only about six percent. Within five years, it’s going to be 15 percent in the US and more than close to that worldwide,” Naam predicts. “We are nearing the point where we are not building any new fossil fuel power plants.”

It will be cheaper to build new solar/wind/batteries than to run on existing coal

Last October, Northern Indiana utility company NIPSCO announced its transition from a 65 percent coal-powered state to projected coal-free status by 2028. Importantly, this decision was made purely on the basis of financials, with an estimated $4 billion in cost savings for customers. The company has already begun several initiatives in solar, wind, and batteries.

NextEra, the largest power generator in the US, has taken on a similar goal, making a deal last year to purchase roughly seven million solar panels from JinkoSolar over four years. Leading power generators across the globe have vocalized a similar economic case for renewable energy.

ICE car sales have now peaked. All car sales growth will be electric

While electric vehicles (EV) have historically been more expensive for consumers than internal combustion engine-powered (ICE) cars, EVs are cheaper to operate and maintain. The yearly cost of operating an EV in the US is about $485, less than half the $1,117 cost of operating a gas-powered vehicle.

And as battery prices continue to shrink, the upfront costs of EVs will decline until a long-term payoff calculation is no longer required to determine which type of car is the better investment. EVs will become the obvious choice.

Many experts including Naam believe that ICE-powered vehicles peaked worldwide in 2018 and will begin to decline over the next five years, as has already been demonstrated in the past five months. At the same time, EVs are expected to quadruple their market share to 1.6 percent this year.

New storage technologies will displace Li-ion batteries for tomorrow’s most demanding applications

Lithium ion batteries have dominated the battery market for decades, but Naam anticipates new storage technologies will take hold for different contexts. Flow batteries, which can collect and store solar and wind power at large scales, will supply city grids. Already, California’s Independent System Operator, the nonprofit that maintains the majority of the state’s power grid, recently installed a flow battery system in San Diego.

Solid-state batteries, which consist of entirely solid electrolytes, will supply mobile devices in cars. A growing body of competitors, including Toyota, BMW, Honda, Hyundai, and Nissan, are already working on developing solid-state battery technology. These types of batteries offer up to six times faster charging periods, three times the energy density, and eight years of added lifespan, compared to lithium ion batteries.

Final Thoughts
Major advancements in transportation and energy technologies will continue to converge over the next five years. A case in point, Tesla’s recent announcement of its “robotaxi” fleet exemplifies the growing trend towards joint priority of sustainability and autonomy.

On the connectivity front, 5G and next-generation mobile networks will continue to enable the growth of autonomous fleets, many of which will soon run on renewable energy sources. This growth demands important partnerships between energy storage manufacturers, automakers, self-driving tech companies, and ridesharing services.

In the eco-realm, increasingly obvious economic calculi will catalyze consumer adoption of autonomous electric vehicles. In just five years, Naam predicts that self-driving rideshare services will be cheaper than owning a private vehicle for urban residents. And by the same token, plummeting renewable energy costs will make these fuels far more attractive than fossil fuel-derived electricity.

As universally optimized AI systems cut down on traffic, aggregate time spent in vehicles will decimate, while hours in your (or not your) car will be applied to any number of activities as autonomous systems steer the way. All the while, sharing an electric vehicle will cut down not only on your carbon footprint but on the exorbitant costs swallowed by your previous SUV. How will you spend this extra time and money? What new natural resources will fuel your everyday life?

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#434637 AI Is Rapidly Augmenting Healthcare and ...

Posted on February 15, 2019 by Android

When it comes to the future of healthcare, perhaps the only technology more powerful than CRISPR is artificial intelligence.

Over the past five years, healthcare AI startups around the globe raised over $4.3 billion across 576 deals, topping all other industries in AI deal activity.

During this same period, the FDA has given 70 AI healthcare tools and devices ‘fast-tracked approval’ because of their ability to save both lives and money.

The pace of AI-augmented healthcare innovation is only accelerating.

In Part 3 of this blog series on longevity and vitality, I cover the different ways in which AI is augmenting our healthcare system, enabling us to live longer and healthier lives.

In this blog, I’ll expand on:

Machine learning and drug design
Artificial intelligence and big data in medicine
Healthcare, AI & China

Let’s dive in.

Machine Learning in Drug Design
What if AI systems, specifically neural networks, could predict the design of novel molecules (i.e. medicines) capable of targeting and curing any disease?

Imagine leveraging cutting-edge artificial intelligence to accomplish with 50 people what the pharmaceutical industry can barely do with an army of 5,000.

And what if these molecules, accurately engineered by AIs, always worked? Such a feat would revolutionize our $1.3 trillion global pharmaceutical industry, which currently holds a dismal record of 1 in 10 target drugs ever reaching human trials.

It’s no wonder that drug development is massively expensive and slow. It takes over 10 years to bring a new drug to market, with costs ranging from $2.5 billion to $12 billion.

This inefficient, slow-to-innovate, and risk-averse industry is a sitting duck for disruption in the years ahead.

One of the hottest startups in digital drug discovery today is Insilico Medicine. Leveraging AI in its end-to-end drug discovery pipeline, Insilico Medicine aims to extend healthy longevity through drug discovery and aging research.

Their comprehensive drug discovery engine uses millions of samples and multiple data types to discover signatures of disease, identify the most promising protein targets, and generate perfect molecules for these targets. These molecules either already exist or can be generated de novo with the desired set of parameters.

In late 2018, Insilico’s CEO Dr. Alex Zhavoronkov announced the groundbreaking result of generating novel molecules for a challenging protein target with an unprecedented hit rate in under 46 days. This included both synthesis of the molecules and experimental validation in a biological test system—an impressive feat made possible by converging exponential technologies.

Underpinning Insilico’s drug discovery pipeline is a novel machine learning technique called Generative Adversarial Networks (GANs), used in combination with deep reinforcement learning.

Generating novel molecular structures for diseases both with and without known targets, Insilico is now pursuing drug discovery in aging, cancer, fibrosis, Parkinson’s disease, Alzheimer’s disease, ALS, diabetes, and many others. Once rolled out, the implications will be profound.

Dr. Zhavoronkov’s ultimate goal is to develop a fully-automated Health-as-a-Service (HaaS) and Longevity-as-a-Service (LaaS) engine.

Once plugged into the services of companies from Alibaba to Alphabet, such an engine would enable personalized solutions for online users, helping them prevent diseases and maintain optimal health.

Insilico, alongside other companies tackling AI-powered drug discovery, truly represents the application of the 6 D’s. What was once a prohibitively expensive and human-intensive process is now rapidly becoming digitized, dematerialized, demonetized and, perhaps most importantly, democratized.

Companies like Insilico can now do with a fraction of the cost and personnel what the pharmaceutical industry can barely accomplish with thousands of employees and a hefty bill to foot.

As I discussed in my blog on ‘The Next Hundred-Billion-Dollar Opportunity,’ Google’s DeepMind has now turned its neural networks to healthcare, entering the digitized drug discovery arena.

In 2017, DeepMind achieved a phenomenal feat by matching the fidelity of medical experts in correctly diagnosing over 50 eye disorders.

And just a year later, DeepMind announced a new deep learning tool called AlphaFold. By predicting the elusive ways in which various proteins fold on the basis of their amino acid sequences, AlphaFold may soon have a tremendous impact in aiding drug discovery and fighting some of today’s most intractable diseases.

Artificial Intelligence and Data Crunching
AI is especially powerful in analyzing massive quantities of data to uncover patterns and insights that can save lives. Take WAVE, for instance. Every year, over 400,000 patients die prematurely in US hospitals as a result of heart attack or respiratory failure.

Yet these patients don’t die without leaving plenty of clues. Given information overload, however, human physicians and nurses alone have no way of processing and analyzing all necessary data in time to save these patients’ lives.

Enter WAVE, an algorithm that can process enough data to offer a six-hour early warning of patient deterioration.

Just last year, the FDA approved WAVE as an AI-based predictive patient surveillance system to predict and thereby prevent sudden death.

Another highly valuable yet difficult-to-parse mountain of medical data comprises the 2.5 million medical papers published each year.

For some time, it has become physically impossible for a human physician to read—let alone remember—all of the relevant published data.

To counter this compounding conundrum, Johnson & Johnson is teaching IBM Watson to read and understand scientific papers that detail clinical trial outcomes.

Enriching Watson’s data sources, Apple is also partnering with IBM to provide access to health data from mobile apps.

One such Watson system contains 40 million documents, ingesting an average of 27,000 new documents per day, and providing insights for thousands of users.

After only one year, Watson’s successful diagnosis rate of lung cancer has reached 90 percent, compared to the 50 percent success rate of human doctors.

But what about the vast amount of unstructured medical patient data that populates today’s ancient medical system? This includes medical notes, prescriptions, audio interview transcripts, and pathology and radiology reports.

In late 2018, Amazon announced a new HIPAA-eligible machine learning service that digests and parses unstructured data into categories, such as patient diagnoses, treatments, dosages, symptoms and signs.

Taha Kass-Hout, Amazon’s senior leader in health care and artificial intelligence, told the Wall Street Journal that internal tests demonstrated that the software even performs as well as or better than other published efforts.

On the heels of this announcement, Amazon confirmed it was teaming up with the Fred Hutchinson Cancer Research Center to evaluate “millions of clinical notes to extract and index medical conditions.”

Having already driven extraordinary algorithmic success rates in other fields, data is the healthcare industry’s goldmine for future innovation.

Healthcare, AI & China
In 2017, the Chinese government published its ambitious national plan to become a global leader in AI research by 2030, with healthcare listed as one of four core research areas during the first wave of the plan.

Just a year earlier, China began centralizing healthcare data, tackling a major roadblock to developing longevity and healthcare technologies (particularly AI systems): scattered, dispersed, and unlabeled patient data.

Backed by the Chinese government, China’s largest tech companies—particularly Tencent—have now made strong entrances into healthcare.

Just recently, Tencent participated in a $154 million megaround for China-based healthcare AI unicorn iCarbonX.

Hoping to develop a complete digital representation of your biological self, iCarbonX has acquired numerous US personalized medicine startups.

Considering Tencent’s own Miying healthcare AI platform—aimed at assisting healthcare institutions in AI-driven cancer diagnostics—Tencent is quickly expanding into the drug discovery space, participating in two multimillion-dollar, US-based AI drug discovery deals just this year.

China’s biggest, second-order move into the healthtech space comes through Tencent’s WeChat. In the course of a mere few years, already 60 percent of the 38,000 medical institutions registered on WeChat allow patients to digitally book appointments through Tencent’s mobile platform. At the same time, 2,000 Chinese hospitals accept WeChat payments.

Tencent has additionally partnered with the U.K.’s Babylon Health, a virtual healthcare assistant startup whose app now allows Chinese WeChat users to message their symptoms and receive immediate medical feedback.

Similarly, Alibaba’s healthtech focus started in 2016 when it released its cloud-based AI medical platform, ET Medical Brain, to augment healthcare processes through everything from diagnostics to intelligent scheduling.

Conclusion
As Nvidia CEO Jensen Huang has stated, “Software ate the world, but AI is going to eat software.” Extrapolating this statement to a more immediate implication, AI will first eat healthcare, resulting in dramatic acceleration of longevity research and an amplification of the human healthspan.

Next week, I’ll continue to explore this concept of AI systems in healthcare.

Particularly, I’ll expand on how we’re acquiring and using the data for these doctor-augmenting AI systems: from ubiquitous biosensors, to the mobile healthcare revolution, and finally, to the transformative power of the health nucleus.

As AI and other exponential technologies increase our healthspan by 30 to 40 years, how will you leverage these same exponential technologies to take on your moonshots and live out your massively transformative purpose?

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#434518 NVIDIA Launches New Robotics Lab in ...

Posted on January 23, 2019 by Android

Dieter Fox tells us about his plans for NVIDIA's new robotics lab Continue reading →

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#432878 Chinese Port Goes Full Robot With ...

Posted on May 18, 2018 by Android

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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