by Phillip Lan August 23, 2017 The Central Valley Business Journal
Chances are, when you think of the Central Valley, you think about tomato trucks rumbling down the freeway or shakers knocking down almonds amidst endless rows of trees. You probably don’t think about modern tech companies or millennials working while sipping lattes in open, co-working spaces. But an ever-increasing group of local techies and entrepreneurs are working to change that.
Over the last few decades, the Central Valley’s tech community has been growing — slowly and under the radar. Local commuters working in Bay Area tech companies and tech-related professionals in Central Valley companies now number thousands across the region. Many software developers, often known as coders or hackers, have begun gathering at fun local events such as the Modesto-based Valley Hackathon (https://valleyhackathon.com/), a programming contest where Star Wars-costumed teams furiously write computer code to win thousands in cash and prizes.
Local business leaders who see the economic benefit tech jobs can bring to the region have also jumped in to accelerate the development of a tech community in the Central Valley. Launched several years ago, The Huddle is a co-working space in downtown Stockton which provides entrepreneurs a place to collaborate and create synergies to help each other’s companies grow. Tech startups in Sacramento now have communities like Hacker Lab where they can co-locate with other companies and get access to mentors and resources to improve revenue trajectory. In Fresno, Geekwise Academy has trained thousands of people to develop software over the past few years and its parent company, Bitwise Industries (http://bitwiseindustries.com/), has helped create over 1,000 tech-related jobs in the region. Bitwise now partners with Amazon to train developers on the latest platforms and eventually plans to occupy 2.5 million square feet of commercial space in downtown Fresno.
In Modesto, ValleyWorx, a tech and digital design co-working hub, will begin taking applications from tech companies and entrepreneurs later this month. One of its first tenants will be Bay Valley Tech (http://bayvalleytech.com/), a code academy focused on providing affordable hands-on software skills to working professionals and students preparing to enter the work force. Locating tech students and tech companies in the same building will allow students to more easily find internships and jobs. At the same time, companies based out of ValleyWorx will have access to an ever-expanding talent pool proficient in the latest technologies.
You may be wondering how all of these “geeks” are going to help the rest of our non-tech economy. By creating a thriving tech community in the Central Valley, we will make it a more attractive place for software professionals to settle.
Senior software engineers in California now earn an average salary of $129,000, and some make over $200,000 annually, according to Indeed.com. Not only will ‘hackers’ infuse disposable income into our local economy, their presence will attract Bay Area firms looking for tech talent.
For example, Oportun, a Redwood City-based venture-backed company, set up a software development office in downtown Modesto two years ago and is already outgrowing their space due to rapid hiring. Their executives indicated availability of software talent as a key driver for expanding into the Central Valley.
Other Bay Area companies are also considering expanding to Sacramento, Stockton and Modesto due to the availability of high-tech talent.
In order to continue the momentum and attract even more tech companies to the Central Valley, local businesses, non-profits and government entities are working to do the following:
Launch co-working spaces such as The Huddle and ValleyWorx to facilitate collaboration, mentoring and growth.
Expand the Central Valley’s tech community by training thousands more local residents to become software literate through code academies such as Geekwise and Bay Valley Tech. Learning software development skills will help thousands of workers capitalize on upcoming transformative industries such as ag tech, manufacturing automation and self-driving transportation (which all heavily leverage software).
The creation of a growing tech-enabled workforce will make the Central Valley an attractive investment destination for Bay Area tech companies who are now overlooking this region and expanding out of state to cities such as Austin, Denver, Seattle and Portland.The Central Valley needs more high-paying jobs, local residents need a realistic path into software-related careers to prepare for the changing world and Bay Area tech workers need affordable housing.
According to a recent poll by the Bay Area Council, a staggering 46 percent of millennials (people age 18 to 39) living in the San Francisco Bay Area say they’re now ready to leave one of the nation’s most unaffordable housing markets. Many have already left California, expanding the tech talent pool in other states.
As high paying software job openings continue to outpace the supply of programmers in California, this is the perfect time for Central Valley leaders to come together and create a win-win-win solution. A tech ecosystem generating exciting, well-paying jobs will also encourage local students to participate in junior high, high school and college programming and robotics initiatives.
Jumpstarting a tech economy in our ag-focused Central Valley is undoubtedly a Herculean task requiring a community-wide effort. Here are a few opportunities for business and community leaders who would like to help:
In a corner of Alphabet’s campus, there is a team working on a piece of software that may be the key to self-driving cars. No journalist has ever seen it in action until now. They call it Carcraft, after the popular game World of Warcraft.
The software’s creator, a shaggy-haired, baby-faced young engineer named James Stout, is sitting next to me in the headphones-on quiet of the open-plan office. On the screen is a virtual representation of a roundabout. To human eyes, it is not much to look at: a simple line drawing rendered onto a road-textured background. We see a self-driving Chrysler Pacifica at medium resolution and a simple wireframe box indicating the presence of another vehicle.
Months ago, a self-driving car team encountered a roundabout like this in Texas. The speed and complexity of the situation flummoxed the car, so they decided to build a look-alike strip of physical pavement at a test facility. And what I’m looking at is the third step in the learning process: the digitization of the real-world driving. Here, a single real-world driving maneuver—like one car cutting off the other on a roundabout—can be amplified into thousands of simulated scenarios that probe the edges of the car’s capabilities.
Scenarios like this form the base for the company’s powerful simulation apparatus. “The vast majority of work done—new feature work—is motivated by stuff seen in simulation,” Stout tells me. This is the tool that’s accelerated the development of autonomous vehicles at Waymo, which Alphabet (née Google) spun out of its “moon-shot” research wing, X, in December of 2016.If Waymo can deliver fully autonomous vehicles in the next few years, Carcraft should be remembered as a virtual world that had an outsized role in reshaping the actual world on which it is based.
Originally developed as a way to “play back” scenes that the cars experienced while driving on public roads, Carcraft, and simulation generally, have taken on an ever-larger role within the self-driving program.
At any time, there are now 25,000 virtual self-driving cars making their way through fully modeled versions of Austin, Mountain View, and Phoenix, as well as test-track scenarios. Waymo might simulate driving down a particularly tricky road hundreds of thousands of times in a single day. Collectively, they now drive 8 million miles per day in the virtual world. In 2016, they logged 2.5 billion virtual miles versus a little over 3 million miles by Google’s IRL self-driving cars that run on public roads. And crucially, the virtual miles focus on what Waymo people invariably call “interesting” miles in which they might learn something new. These are not boring highway commuter miles.
The simulations are part of an intricate process that Waymo has developed. They’ve tightly interwoven the millions of miles their cars have traveled on public roads with a “structured testing” program they conduct at a secret base in the Central Valley they call Castle.
Waymo has never unveiled this system before. The miles they drive on regular roads show them areas where they need extra practice. They carve the spaces they need into the earth at Castle, which lets them run thousands of different scenarios in situ. And in both kinds of real-world testing, their cars capture enough data to create full digital recreations at any point in the future. In that virtual space, they can unhitch from the limits of real life and create thousands of variations of any single scenario, and then run a digital car through all of them. As the driving software improves, it’s downloaded back into the physical cars, which can drive more and harder miles, and the loop begins again.
To get to Castle, you drive east from San Francisco Bay and south on 99, the Central Valley highway that runs south to Fresno. Cornfields abut subdevelopments; the horizon disappears behind agricultural haze. It’s 30 degrees hotter than San Francisco and so flat that the grade of this “earthen sea,” as John McPhee called it, can only be measured with lasers. You exit near the small town of Atwater, once the home of the Castle Air Force Base, which used to employ 6,000 people to service the B-52 program. Now, it’s on the northern edge of the small Merced metro area, where unemployment broke 20 percent in the early 2010s, and still rarely dips below 10 percent. Forty percent of the people around here speak Spanish. We cross some railroad tracks and swing onto the 1,621 acres of the old base, which now hosts everything from Merced County Animal Control to the U.S. Penitentiary, Atwater.
The directions in my phone are not pointed to an address, but a set of GPS coordinates. We proceed along a tall opaque green fence until Google Maps tells us to stop. There’s nothing to indicate that there’s even a gate. It just looks like another section of fence, but my Waymo host is confident. And sure enough: A security guard appears and slips out a widening crack in the fence to check our credentials.
The self-driving cars are easy to pick out. They’re studded with sensors. The most prominent are the laser scanners (usually called LIDARs) on the tops of the cars. But the Pacificas also have smaller beer-can-sized LIDARs spinning near their side mirrors. And they have radars at the back which look disturbingly like white Shrek ears.
When a car’s sensors are engaged, even while parked, the spinning LIDARs make an odd sound. It’s somewhere between a whine and a whomp, unpleasant only because it’s so novel that my ears can’t filter it out like the rest of the car noises that I’ve grown up with.
There is one even more special car parked across the street from the main building. All over it, there are X’s of different sizes applied in red duct tape. That’s the Level Four car. The levels are Society of Automotive Engineers designations for the amount of autonomy that the car has. Most of what we hear about on the roads is Level One or Level Two, meant to allow for smart cruise control on highways. But the red-X car is a whole other animal. Not only is it fully autonomous, but it cannot be driven by the humans inside it, so they don’t want to get it mixed up with their other cars.As we pull into the parking lot, there are whiffs of Manhattan Project, of scientific outpost, of tech startup. Inside the main building, a classroom-sized portable, I meet the motive force behind this remarkable place. Her name is Steph Villegas.
Villegas wears a long, fitted white collared shirt, artfully torn jeans, and gray knit sneakers, every bit as fashionable as her pre-Google job at the San Francisco boutique Azalea might suggest. She grew up in the East Bay suburbs on the other side of the hills from Berkeley and was a fine-arts major at University of California, Berkeley before finding her way into the self-driving car program in 2011.
“You were a driver?” I ask.
“Always a driver,” Villegas says.
She spent countless hours going up and down 101 and 280, the highways that lead between San Francisco and Mountain View. Like the rest of the drivers, she came to develop a feel for how the cars performed on the open road. And this came to be seen as an important kind of knowledge within the self-driving program. They developed an intuition about what might be hard for the cars. “Doing some testing on newer software and having a bit of tenure on the team, I began to think about ways that we could potentially challenge the system,” she tells me.
So, Villegas and some engineers began to cook up and stage rare scenarios that might allow them to test new behaviors in a controlled way. They started to commandeer the parking lot across from Shoreline Amphitheater, stationing people at all the entrances to make sure only approved Googlers were there.
“That’s where it started,” she says. “It was me and a few drivers every week. We’d come up with a group of things that we wanted to test, get our supplies in a truck, and drive the truck down to the lot and run the tests.”These became the first structured tests in the self-driving program. It turns out that the hard part is not really the what-if-a-zombie-is-eating-a-person-in-the-road scenarios people dream up, but proceeding confidently and reliably like a human driver within the endless variation of normal traffic.
Villegas started gathering props from wherever she could find them: dummies, cones, fake plants, kids’ toys, skateboards, tricycles, dolls, balls, doodads. All of them went into the prop stash. (Eventually, the props were stored in a tent, and now at Castle, in a whole storage unit.)
They needed a base, a secret base. And that’s what Castle provided. They signed a lease and started to build out their dream fake city. “We made conscious decisions in designing to make residential streets, expressway-style streets, cul-de-sacs, parking lots, things like that,” she says, “so we’d have a representative concentration of features that we could drive around.”
We walk from the main trailer office to her car. She hands me a map as we pull away to travel the site. “Like at Disneyland, so you can follow along,” she says. The map has been meticulously constructed. In one corner, there is a Vegas-style sign that says, “Welcome to Fabulous Castle, California.” The different sections of the campus even have their own naming conventions. In the piece we’re traveling through, each road is named after a famous car (DeLorean, Bullitt) or after a car (e.g., Barbaro) from the original Prius fleet in the early days of the program.
We pass by a cluster of pinkish buildings, the old military dormitories, one of which has been renovated: That’s where the Waymo people sleep when they can’t make it back to the Bay. Other than that, there are no buildings in the testing area. It is truly a city for robotic cars: All that matters is what’s on and directly abutting the asphalt.
We pull up to a large, two-lane roundabout. In the center, there is a circle of white fencing. “This roundabout was specifically installed after we experienced a multilane roundabout in Austin, Texas,” Villegas says. “We initially had a single-lane roundabout and were like, ‘Oh, we’ve got it. We’ve got it covered.’ And then we encountered a multi-lane and were like, ‘Horse of a different color! Thanks, Texas.’ So, we installed this bad boy.”
She drives me back to the main office and we hop into a self-driving van, one of the Chrysler Pacificas. Our “left-seat” driver is Brandon Cain. His “right-seat” co-driver in the passenger seat will track the car’s performance on a laptop using software called XView.And then there are the test assistants, who they call “foxes,” a sobriquet that evolved from the word “faux.” They drive cars, create traffic, act as pedestrians, ride bikes, hold stop signs. They are actors, more or less, whose audience is the car.
The first test we’re gonna do is a “simple pass and cut-in,” but at high speed, which in this context means 45 miles per hour. We set up going straight on a wide road they call Autobahn.
After the fox cuts us off, the Waymo car will brake and the team will check a key data point: our deceleration. They are trying to generate scenarios that cause the car to have to brake hard. How hard? Somewhere between a “rats, not gonna make the light” hard stop and “my armpits started involuntarily sweating and my phone flew onto the floor” really hard stop.
Let me say something ridiculous: This is not my first trip in a self-driving vehicle. In the past, I’ve taken two different autonomous rides: first, in one of the Lexus SUVs, which drove me through the streets of Mountain View, and second, in Google’s cute little Firefly, which bopped around the roof of a Google building. They were both unremarkable rides, which was the point.
But, this is different. These are two fast-moving cars, one of which is supposed to cut us off with a move that will be, to use the Waymo term of art, “spicy.”It’s time to go. Cain gets us moving and with a little chime, the car says, “Autodriving.” The other car approaches and cuts us off like a Porsche driver trying to beat us to an exit. We brake hard and fast and smooth. I’m impressed.
Then they check the deceleration numbers and realize that we had not braked nearly hard enough. We have to do it again. And again. And again. The other car cuts us off at different angles and with different approaches. They call this getting “coverage.”
Two cars merging at high speed, one driving itself (Alexis Madrigal)
We go through three other tests: high-speed merges, encountering a car that’s backing out of a driveway while a third blocks the autonomous vehicle’s view, and smoothly rolling to a stop when pedestrians toss a basketball into our path. Each is impressive in its own way, but that cut-off test is the one that sticks with me.
As we line up for another run, Cain shifts in his seat. “Have you ever seen Pacific Rim?” Cain asks me. You know the Guillermo del Toro movie where the guys get synced up with huge robot suits to battle monsters. “I’m trying to get in sync with the car. We share some thoughts.”
I ask Cain to explain what he actually means by syncing with the car. “I’m trying to adjust to the weight difference of people in the car,” he says. “Being in the car a lot, I can feel what the car is doing—it sounds weird, but—with my butt. I kinda know what it wants to do.”
Far from the haze and heat of Castle, there is Google’s comfy headquarters in Mountain View. I’ve come to visit Waymo’s engineers, who are technically housed inside X, which you may know as Google X, the long-term, high-risk research wing of the company. In 2015, when Google restructured itself into a conglomerate called Alphabet, X dropped the Google from its name (their website is literally X.company). A year after the big restructuring, X/Alphabet decided to “graduate” the autonomous vehicle program into its own company as it had done with several other projects before, and that company is Waymo. Waymo is like Google’s child, once removed, or something.
So, Waymo’s offices are still inside the mother ship, though, like two cliques slowly sorting themselves out, the Waymo people all sit together now, I’m told.
The X/Waymo building is large and airy. There are prototypes of Project Wing’s flying drones hanging around. I catch a bit of the cute little Firefly car the company built. (“There’s something sweet about something you build yourself,” Villegas had said back at Castle. “But they had no A/C, so I don’t miss them.”)
Up from the cafeteria, tucked in a corner of a wing, is the Waymo simulation cluster. Here, everyone seems to have Carcraft and XView on their screens. Polygons on black backgrounds abound. These are the people creating the virtual worlds that Waymo’s cars drive through.
Waiting for me is James Stout, Carcraft’s creator. He’s never gotten to speak publicly about his project and his enthusiasm spills out. Carcraft is his child.
“I was just browsing through job posts and I saw that the self-driving car team was hiring,” he says. “I couldn’t believe that they just had a job posting up.” He got on the team and immediately started building the tool that now powers 8 million virtual miles per day.Back then, they primarily used the tool to see what their cars would have done in tricky situations in which human drivers have taken over control of the car. And they started making scenarios from these moments. “It quickly became clear that this was a really useful thing and we could build a lot out of this,” Stout says. The spatial extent of Carcraft’s capabilities grew to include whole cities, the number of cars grew into a huge virtual fleet.
Stout brings in Elena Kolarov, the head of what they call their “scenario maintenance” team to run the controls. She’s got two screens in front of her. On the right, she has up XView, the screen that shows what the car is “seeing.” The car uses cameras, radar, and laser scanning to identify objects in its field of view—and it represents them in the software as little wireframe shapes, outlines of the real world.
Green lines run out from the shapes to show the possible ways the car anticipates the objects could move. At the bottom, there is an image strip that displays what the regular (i.e., visible-light) cameras on the car captured. Kolarov can also turn on the data returned by the laser scanner (LIDAR), which is displayed in orange and purple points.
We see a playback of a real merge on the roundabout at Castle. Kolarov switches into a simulated version. It looks the same, but it’s no longer a data log but a new situation the car has to solve. The only difference is that at the top of the XView screen it says “Simulation” in big red letters. Stout says that they had to add that in because people were confusing simulation for reality.
“Our cars see the world. They understand the world. And then for anything that is a dynamic actor in the environment—a car, a pedestrian, a cyclist, a motorcycle—our cars understand intent. It’s not enough to just track a thing through a space. You have to understand what it is doing,” Dmitri Dolgov, Waymo’s vice president of engineering, tells me. “This is a key problem in building a capable and safe self-driving car. And that sort of modeling, that sort of understanding of the behaviors of other participants in the world, is very similar to this task of modeling them in simulation.”There is one key difference: In the real world, they have to take in fresh, real-time data about the environment and convert it into an understanding of the scene, which they then navigate. But now, after years of work on the program, they feel confident that they can do that because they’ve run “a bunch of tests that show that we can recognize a wide variety of pedestrians,” Stout says.
So, for most simulations, they skip that object-recognition step. Instead of feeding the car raw data it has to identify as a pedestrian, they simply tell the car: A pedestrian is here.
At the four-way stop, Kolarov is making things harder for the self-driving car. She hits V, a hot key for vehicle, and a new object appears in Carcraft. Then she mouses over to a drop-down menu on the righthand side, which has a bunch of different vehicle types, including my favorite: bird_squirrel.
The different objects can be told to follow the logic Waymo has modeled for them or the Carcraft scenario builder can program them to move in a precise way, in order to test specific behaviors. “There’s a nice spectrum between having control of a scenario and just dropping stuff in and letting them go,” Stout says.Once they have the basic structure of a scenario, they can test all the important variations it contains. So, imagine, for a four-way stop, you might want to test the arrival times of the various cars and pedestrians and bicyclists, how long they stop for, how fast they are moving, and whatever else. They simply put in reasonable ranges for those values and then the software creates and runs all the combinations of those scenarios.
They call it “fuzzing,” and in this case, there are 800 scenarios generated by this four-way stop. It creates a beautiful, lacy chart—and engineers can go in and see how different combinations of variables change the path that the car would decide to take.
Here we see a video that shows exactly such a situation. It’s a complex four-way stop that occurred in real life in Mountain View. As the car went to make a left, a bicycle approached, causing the car to stop in the road. Engineers took that class of problem and reworked the software to yield correctly. What the video shows is the real situation and then the simulation running atop it. As the two situations diverge, you’ll see the simulated car keep driving and then a dashed box appear with the label “shadow_vehicle_pose.” That dashed box shows what happened in real life. To Waymo people, this is the clearest visualization of progress.
Both Stout and the Waymo software lead Dolgov stressed that there were three core facets to simulation. One, they drive a lot more miles than would be possible with a physical fleet—and experience is good. Two, those miles focus on the interesting and still-difficult interactions for the cars rather than boring miles. And three, the development cycles for the software can be much, much faster.“That iteration cycle is tremendously important to us and all the work we’ve done on simulation allows us to shrink it dramatically,” Dolgov told me. “The cycle that would take us weeks in the early days of the program now is on the order of minutes.”
Well, I asked him, what about oil slicks on the road? Or blown tires, weird birds, sinkhole-sized potholes, general craziness. Did they simulate those? Dolgov was sanguine. He said, sure, they could, but “how high do you push the fidelity of the simulator along that axis? Maybe some of those problems you get better value or you get confirmation of your simulator by running a bunch of tests in the physical world.” (See: Castle.)
The power of the virtual worlds of Carcraft is not that they are a beautiful, perfect, photorealistic renderings of the real world. The power is that they mirror the real world in the ways that are significant to the self-driving car and allow it to get billions more miles than physical testing would allow. For the driving software running the simulation, it is not like making decisions out there in the real world. It is the same as making decisions out there in the real world.
Waymo drove 635,868 autonomous miles from December 2015 to November 2016. In all those miles, they only disengaged 124 times, for an average of about once every 5,000 miles, or 0.20 disengagements per 1,000 miles. The previous year, they drove 424,331 autonomous miles and had 272 disengagements, for an average of once every 890 miles, or 0.80 disengagements per 1,000 miles.While everyone takes pains to note that these are not exactly apples-to-apples numbers, let’s be real here: These are the best comparisons we’ve got and in California, at least, everybody else drove about 20,000 miles. Combined.
The tack that Waymo has taken is not surprising to outside experts. “Right now, you can almost measure the sophistication of an autonomy team—a drone team, a car team—by how seriously they take simulation,” said Chris Dixon, a venture capitalist at Andreessen Horowitz who led the firm’s investment in the simulation company Improbable. “And Waymo is at the very top, the most sophisticated.”
I asked Allstate Insurance’s head of innovation, Sunil Chintakindi, about Waymo’s program. “Without a robust simulation infrastructure, there is no way you can build [higher levels of autonomy into vehicles].” he said. “And I would not engage in conversation with anyone who thinks otherwise.”
Other self-driving car researchers are also pursuing similar paths. Huei Peng is the director of Mcity, the University of Michigan’s autonomous- and connected- vehicle lab. Peng said that any system that works for self driving cars will be “a combination of more than 99 percent simulation plus some carefully designed structured testing plus some on-road testing.”
He and a graduate student proposed a system for interweaving road miles with simulation to rapidly accelerate testing. It’s not unlike what Waymo has executed. “So what we are arguing is just cut off the boring part of driving and focus on the interesting part,” Peng said. “And that can let you accelerate hundreds of times: A thousand miles becomes a million miles.”
What is surprising is the scale, organization, and intensity of Waymo’s project. I described the structured testing that Google had done to Peng, including the 20,000 scenarios that had made it into simulation from the structured testing team at Castle. But he misheard me and began to say, “Those 2,000 scenarios are impressive,”—when I cut in and corrected him—“It was 20,000 scenarios.” He paused. “20,000,” he said, thinking it over. “That’s impressive.”And in reality, those 20,000 scenarios only represent a fraction of the total scenarios that Waymo has tested. They’re just what’s been created from structured tests. They have even more scenarios than that derived from public driving and imagination.
“They are doing really well,” Peng said. “They are far ahead of everyone else in terms of Level Four,” using the jargon shorthand for full autonomy in a car.
But Peng also presented the position of the traditional automakers. He said that they are trying to do something fundamentally different. Instead of aiming for the full autonomy moon shot, they are trying to add driver-assistance technologies, “make a little money,” and then step forward toward full autonomy. It’s not fair to compare Waymo, which has the resources and corporate freedom to put a $70,000 laser range finder on top of a car, with an automaker like Chevy that might see $40,000 as its price ceiling for mass-market adoption.
“GM, Ford, Toyota, and others are saying ‘Let me reduce the number of crashes and fatalities and increase safety for the mass market.’ Their target is totally different,” Peng said. “We need to think about the millions of vehicles, not just a few thousand.”And even just within the race for full autonomy, Waymo now has more challengers than it used to, Tesla in particular. Chris Gerdes is the director of the Center for Automotive Research at Stanford. Eighteen months ago, he told my colleague Adrienne LaFrance that Waymo “has much greater insight into the depth of the problems and how close we are [to solving them] than anyone else.” When I asked him last week if he still thought that was true, he said that “a lot has changed.”
“Auto manufacturers such as Ford and GM have deployed their own vehicles and built on-road data sets,” he said. “Tesla has now amassed an extraordinary amount of data from Autopilot deployment, learning how the system operates in exactly the conditions its customers experience. Their ability to test algorithms on board in a silent mode and their rapidly expanding base of vehicles combine to form an amazing testbed.”
In the realm of simulation, Gerdes said that he had seen multiple competitors with substantial programs. “I am sure there is quite a range of simulation capabilities but I have seen a number of things that look solid,” he said. “Waymo no longer looks so unique in this respect. They certainly jumped out to an early lead but there are now a lot of groups looking at similar approaches. So it is now more of a question of who can do this best.”
This is not a low-stakes demonstration of a neural network’s “brain-like” capacities. This is making a massive leap forward in artificial intelligence, even for a company inside Alphabet, which has been aggressive in adopting AI. This is not Google Photos, where a mistake doesn’t mean much. This is a system that will live and interact in the human world completely autonomously. It will understand our rules, communicate its desires, be legible to our eyes and minds.Waymo seems like it has driving as a technical skill—the speed and direction parts of it—down. It is driving as a human social activity that they’re working on now. What is it to drive “normally,” not just “legally”? And how does one teach an artificial intelligence what that means?
It turns out that building this kind of artificial intelligence does not simply require endless data and engineering prowess. Those are necessary, but not sufficient. Instead, building this AI requires humans to sync with the cars, understanding the world as they do. As much as anyone can, the drivers out at Castle know what it is to be one of these cars, to see and make decisions like them. Maybe that goes both ways, too: The deeper humans understand the cars, the deeper the cars understand humans.
A memory of a roundabout in Austin becomes a piece of Castle becomes a self-driving car data log becomes a Carcraft scenario becomes a web of simulations becomes new software that finally heads back out on a physical self-driving car to that roundabout in Texas.
Even within the polygon abstraction of the simulation the AI uses to know the world, there are traces of human dreams, fragments of recollections, feelings of drivers. And these components are not mistakes or a human stain to be scrubbed off, but necessary pieces of the system that could revolutionize transportation, cities, and damn near everything else.
Jul 31, 2017 by Jody Meacham Silicon Valley Business Journal.
Merced County is in the process of developing a 2,000-acre site encompassing the former Castle Air Force Base, which it hopes will become the center for testing, development and manufacturing of automotive technology, including for many of the self-driving cars being developed in Silicon Valley.
Adam Wasserman, managing partner of Scottsdale, Arizona-based GLDPartners, which consults with international companies on optimizing their supply chains, said the project expects to announce its first tenant — likely linked to Silicon Valley’s R&D efforts on autonomous driving R&D — by early fall.
Google is already using a site adjacent to Merced County’s planned Mid-California AutoTech Testing, Development and Production Campus for its self-driving car testing. (photo courtesy of Google Inc).
Google is already using a 91-acre site for its own autonomous car testing program adjacent to the planned Mid-California AutoTech Testing, Development and Production Campus, county officials said.
At full build-out, the development plan calls for 8 million square feet of industrial space employing about 9,300 people.
“It just puts us on that technology map that everybody in Silicon Valley is enjoying,” said Daron McDaniel, chair of the county’s board of supervisors.
Merced County hired GLDPartners after several failed attempts to commercialize the Castle property, which it took ownership of in 2006 following the air base’s 1995 closure.
The county’s median family income was about $43,000 in 2016, about 80 percent of the national median, and about a quarter of its 262,000 residents live below the poverty line, according to census figures.
Before settling on auto technology, the company researched several other business sectors including food production, medical products, commercial space systems, industrial machines and specialty chemicals based on how they might fit in those sectors’ supply chains.
“The project takes advantage of the dire lack of testing facilities anywhere in the country, much less in California, where much of the research that is shaping the global auto industry is now taking place,” Wasserman wrote in an email.
The site works because of the concentration of international auto tech research in Silicon Valley, the proximity of Bay Area universities and 13-year-old UC Merced, which is forecast to double its enrollment to 14,000 students within three years and already has solar energy and drone facilities at Castle.
That is coupled with transportation infrastructure including an airfield capable of handling the largest cargo planes and two major railroads connected to ports in Stockton and Oakland so that the site can handle manufacturing as well as testing.
The county is securing $200 million to connect the site to State Route 99 by a road to be called the Atwater-Merced Expressway.
“We strongly believe — and it’s obviously been evidenced by Google and the work they do onsite with their autonomous vehicle program — we’re going to be incredibly competitive in the auto tech sector,” said Mark Hendrickson, the county’s economic development director.
Part of the site was originally pitched by the county to California high-speed rail officials for the system’s heavy maintenance facility, which is to be located in the San Joaquin Valley, but McDaniel said there has been no indication when they would make a decision.
“If high-speed rail wants us they need to pull the trigger right away,” he said.
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