The Canadian whistleblower at the centre of an international scandal that allegedly helped the Trump campaign capitalize politically from private Facebook information got his start in politics with the Liberal Party of Canada. But several senior Liberal officials from that time, about a decade ago, insist they have almost no recollection of then-teenager Christopher Wylie - if any at all.
A self-driving vehicle made by Uber has struck and killed a pedestrian. It’s the first such incident and will certainly be scrutinized like no other autonomous vehicle interaction in the past. But on the face of it it’s hard to understand how, short of a total system failure, this could happen when the entire car has essentially been designed around preventing exactly this situation from occurring.
Something unexpectedly entering the vehicle’s path is pretty much the first emergency event that autonomous car engineers look at. The situation could be many things — a stopped car, a deer, a pedestrian — and the systems are one and all designed to detect them as early as possible, identify them, and take appropriate action. That could be slowing, stopping, swerving, anything.
Uber’s vehicles are equipped with several different imaging systems which work both ordinary duty (monitoring nearby cars, signs, and lane markings) and extraordinary duty like that just described. No less than four different ones should have picked up the victim in this case.
Top-mounted lidar. The bucket-shaped item on top of these cars is a lidar, or light detection and ranging, system that produces a 3D image of the car’s surroundings multiple times per second. Using infrared laser pulses that bounce off objects and return to the sensor, lidar can detect static and moving objects in considerable detail, day or night.
Heavy snow and fog can obscure a lidar’s lasers, and its accuracy decreases with range, but for anything from a few feet to a few hundred feet, it’s an invaluable imaging tool and one that is found on practically every self-driving car.
The lidar unit, if operating correctly, should have been able to make out the person in question, if they were not totally obscured, while they were still more than a hundred feet away, and passed on their presence to the “brain” that collates the imagery.
Front-mounted radar. Radar, like lidar, sends out a signal and waits for it to bounce back, but it uses radio waves instead of light. This makes it more resistant to interference, since radio can pass through snow and fog, but also lowers its resolution and changes its range profile.
Depending on the radar unit Uber employed — likely multiple in both front and back to provide 360 degrees of coverage — the range could differ considerably. If it’s meant to complement the lidar, chances are it overlaps considerably, but is built more to identify other cars and larger obstacles.
The radar signature of a person is not nearly so recognizable, but it’s very likely they would have at least shown up, confirming what the lidar detected.
Short and long-range optical cameras. Lidar and radar are great for locating shapes, but they’re no good for reading signs, figuring out what color something is, and so on. That’s a job for visible-light cameras with sophisticated computer vision algorithms running in real time on their imagery.
The cameras on the Uber vehicle watch for telltale patterns that indicate braking vehicles (sudden red lights), traffic lights, crossing pedestrians, and so on. Especially on the front end of the car, multiple angles and types of camera would be used, so as to get a complete picture of the scene into which the car is driving.
Detecting people is one of the most commonly attempted computer vision problems, and the algorithms that do it have gotten quite good. “Segmenting” an image, as it’s often called, generally also involves identifying things like signs, trees, sidewalks and more.
That said, it can be hard at night. But that’s an obvious problem, the answer to which is the previous two systems, which work night and day. Even in pitch darkness, a person wearing all black would show up on lidar and radar, warning the car that it should perhaps slow and be ready to see that person in the headlights. That’s probably why a night-vision system isn’t commonly found in self-driving vehicles (I can’t be sure there isn’t one on the Uber car, but it seems unlikely).
Safety driver. It may sound cynical to refer to a person as a system, but the safety drivers in these cars are very much acting in the capacity of an all-purpose failsafe. People are very good at detecting things, even though we don’t have lasers coming out of our eyes. And our reaction times aren’t the best, but if it’s clear that the car isn’t going to respond, or has responded wrongly, a trained safety driver will react correctly.
Worth mentioning is that there is also a central computing unit that takes the input from these sources and creates its own more complete representation of the world around the car. A person may disappear behind a car in front of the system’s sensors, for instance, and no longer be visible for a second or two, but that doesn’t mean they ceased existing. This goes beyond simple object recognition and begins to bring in broader concepts of intelligence such as object permanence, predicting actions, and the like.
It’s also arguably the most advance and closely guarded part of any self-driving car system and so is kept well under wraps.
It isn’t clear what the circumstances were under which this tragedy played out, but the car was certainly equipped with technology that was intended to, and should have, detected the person and caused the car to react appropriately. Furthermore, if one system didn’t work, another should have sufficed — multiple failbacks are only practical in high stakes matters like driving on public roads.
We’ll know more as Uber, local law enforcement, federal authorities, and others investigate the accident.
Over the weekend, the Information Technology Industry Council and 44 other trade associations banded together and published a letter demanding that the Trump administration take “measured” steps to stop China’s unfair trade practices and voiced its opposition to unilateral tariffs that could damage industries as diverse as electronics and agriculture.
As we have been covering on TechCrunch, the Trump administration is readying a comprehensive “all of the above” series of policies to fight China, including tariffs that might reach above $100 billion, visa restrictions on Chinese nationals, and prohibitions on Chinese capital from buying or investing in American companies. The Trump administration is expected to develop a policy here shortly as part of the conclusion of its section 301 trade investigation into China.
The letter warns that tariffs in particular could lead to “a chain reaction of negative consequences for the U.S. economy, provoking retaliation; stifling U.S. agriculture, goods, and services exports; and raising costs for businesses and consumers.”
Interestingly, the letter leaves open the door for tariffs. From the letter:
In particular, it is critically important that the Administration work with like-minded partners to address common concerns with China’s trade and investment policies. Imposition of unilateral tariffs by the Administration would only serve to split the United States from its allies, hinder joint action to effectively address shared challenges, and ensure that foreign companies take the place of markets that American companies, farmers and ranchers must vacate when China retaliates against U.S. tariffs.
Considering the wide variety or organizations that signed onto this letter, it is interesting to note that free trade arguments are not being used here forcefully, but rather that America should only implement trade restrictions with the cooperation of other nations.
The letter from the trade association in many ways mirrors a letter released by House Republicans two weeks ago that similarly urged the administration against imposing unilateral tariffs on aluminum and steel, tariffs that the Trump administration had already announced that it is implementing.
Outside ITI, the signatories of the trade association letter included a spate of tech industry-affiliated associations, including Allied for Startups, CompTIA, the Computer and Communications Industry Association, the Consumer Technology Association, the Developers Alliance, the Internet Association, the Software & Information Industry Association, TechNet, and the Telecommunications Industry Association.
NEW YORK (AP) — A New York City council member launched an investigation Monday into the Kushner Cos.' routine filing of paperwork falsely claiming zero rent-regulated tenants in its buildings, saying that the deception should have been uncovered long ago because the documents are online for all to see.
Last year, Google launched Instant Apps, a way for developers to give users a native app experience that didn’t involve having to install anything. Users would simply click on a link on the search results page and the instant app would load. Today, the company is extending this program to games. Thanks to this, you can now see what playing a level or two of Clash Royale, Final Fantasy XV: A New Empire or Panda Pop is like without having to go through the usual install procedure. Instead, you simply head for the Google Play store, find a game that supports this feature, and hit the “Try now” button.
Google Play product managers Jonathan Karmel and Benjamin Frenkel told me that the team learned a lot from the experience with building Instant Apps. For games, though, the team decided to increase the maximum file size from 2 MB to 10 MB, which isn’t really a surprise, given that a game needs a few more graphical assets than your regular to-do list app. In my experience testing this feature, this still allows the games to load quickly enough, though it doesn’t feel quite as instant as most of the regular instant apps do.
The main idea behind this project, Karmel and Frenkel said, is to drive discovery. To do this, the team is adding a new ‘arcade’ tab in the newly redesigned Google Play Games app to highlight the current crop of Instant games and launching an Instant Gameplay collection in the Google Play Store. The main advantage of these Instant games, though, is that users can try the game without having to install anything. As the team noted, every extra step in the install process offers potential players yet another chance to drop off and move on. Indeed, many users actually install a game and then never open it.
Some casual games already take up less than 10 MB and those developers will be able to opt to make their complete game available as a Play Instant app, too.
For now, this project is still a closed beta, though Google plans to open it up to more developers later this year. Some games that currently support Play Instant include Clash Royale, Words with Friends 2, Bubble Witch 3 Saga and Panda Pop, as well as a few other titles from Playtika, Jam City, MZ, and Hothead.
As Karmel and Frenkel told me, their teams are still working on providing developers with better tooling for building these apps and Google is also working with the likes of Unity and the Cocos2D-x teams to make building instant apps easier. For the most part, though, building an Instant Play game means bringing the file size to under 10 MB and adding a few lines to the app’s manifest. That’s probably easier said than done, though, given that you still want players to have an interesting experience.
Unsurprisingly, some developers currently make better use of that limited file size than others. When you try Final Fantasy XV: A New Empire, all you can do is regularly tap on some kind of blue monster and get some gold until the game informs you how much gold you received. That’s it. Over time, though, I’m sure developers will figure out how to best use this feature.[gallery ids="1608771,1608772,1608770"]
Wylie said Facebook knew in 2015 that it had gathered information, and asked Cambridge Analytica to delete the data, but never followed up to check if it did. Over the weekend, news broke that a data firm known Cambridge Analytica used Facebook to harvest 50 million user profiles illegitimately, which was used to target voters during Trump's 2016 campaign for the US presidency, as well as the Brexit Leave campaign. Cambridge Analytica, a data-analytics company that worked for the Trump campaign in 2016, pulled user information by paying people to take a quiz, and then proceeded to use the information it gathered from the users' friends without their permission, or permission from Facebook.