Over more than a decade, self-driving vehicles have logged millions of miles on roadways across the globe. Despite all that driving, researchers say, the machines are still unable to replicate the sophisticated problem-solving and spontaneity human drivers employ each time they get behind the wheel. In their ambitious attempt to create an autonomous car service, companies like Waymo run their software through millions of potential scenarios, create three-dimensional maps using lasers, and outfit their vehicles powerful sensors like LIDAR that can cost more than the cars they guide. The goal is to prepare the vehicle for anything it might encounter before it touches the road, creating a system of rules that predetermine behavior. Now, an upstart British company called Wayve claims to have created a self-driving car using technology that almost sounds Stone Age compared to the competition.
The new cabling will provide a backup power circuit for the station's Canadian-made robot arm and expand wireless communications. The battery work involves re-installing two old batteries. One of the six new lithium-ion batteries doesn't work, and so the outdated pair made of nickel hydrogen need to go back into the slot.
At George Mason University in Virginia, a fleet of several dozen autonomous robots deliver food to students on campus. At George Mason University in Virginia, a fleet of several dozen autonomous robots deliver food to students on campus. George Mason University looks like any other big college campus with its tall buildings, student housing, and manicured green lawns – except for the robots. This Northern Virginia university recently set up several dozen meal delivery robots from Starship Technologies to make it easier for students to access food. Multiple colleges across the country have deployed delivery robots – including University of the Pacific in Stockton, Calif., and Northern Arizona University – but George Mason University is the first college in the United States to incorporate robots into its student dining plan.
After little more than a week, Google backtracked on creating its Advanced Technology External Advisory Council, or ATEAC--a committee meant to give the company guidance on how to ethically develop new technologies such as AI. The inclusion of the Heritage Foundation's president, Kay Coles James, on the council caused an outcry over her anti-environmentalist, anti-LGBTQ, and anti-immigrant views, and led nearly 2,500 Google employees to sign a petition for her removal. Instead, the internet giant simply decided to shut down the whole thing. How did things go so wrong? And can Google put them right?
Google's attempt to wrest more cloud computing dollars from market leaders Amazon and Microsoft got a new boss late last year. Next week, Thomas Kurian is expected to lay out his vision for the business at the company's cloud computing conference, building on his predecessor's strategy of emphasizing Google's strength in artificial intelligence. That strategy is complicated by controversies over how Google and its clients use the powerful technology. After employee protests over a Pentagon contract in which Google trained algorithms to interpret drone imagery, the cloud unit now subjects its--and its customers'--AI projects to ethical reviews. They have caused Google to turn away some business.
If you look under the hood of the internet, you'll find lots of gears churning along that make it all possible. For example, take a company like AT&T. They have to intimately understand what internet data are going where so that they can better accommodate different levels of usage. But it isn't practical to precisely monitor every packet of data, because companies simply don't have unlimited amounts of storage space. Because of this, tech companies use special algorithms to roughly estimate the amount of traffic heading to different IP addresses.
What goes into making plants taste good? For scientists in MIT's Media Lab, it takes a combination of botany, machine-learning algorithms, and some good old-fashioned chemistry. Using all of the above, researchers in the Media Lab's Open Agriculture Initiative report that they have created basil plants that are likely more delicious than any you have ever tasted. No genetic modification is involved: The researchers used computer algorithms to determine the optimal growing conditions to maximize the concentration of flavorful molecules known as volatile compounds. But that is just the beginning for the new field of "cyber agriculture," says Caleb Harper, a principal research scientist in MIT's Media Lab and director of the OpenAg group.
If you haven't seen the latest Boston Dynamics video, released last week, it shows an upgraded version of the company's Handle robot moving boxes in a warehouse. Handle is a mobile manipulator that integrates both legs and wheels, and the new version features a swinging "tail" that serves as a counterweight and allows the robot to balance and move in a dynamic fashion--just as you'd expect from the company that created such nimble machines as Atlas, Spot, and BigDog. Boston Dynamics, which SoftBank bought from Google in 2017, is showing off Handle toiling in a warehouse for a reason: The company is officially entering the logistics market, with plans to offer robots for material-handling applications. As part of that strategy, it is announcing today the acquisition of Kinema Systems, a startup based in Menlo Park, Calif., that develops vision sensors and deep-learning software to enable industrial robot arms to locate and move boxes. Boston Dynamics founder and CEO Marc Raibert says the two Handle robots seen in the video aren't moving as fast as they could, and one of the factors limiting their performance is their vision systems.
Since 2017, AI researchers have been using AI neural networks to help design better and faster AI neural networks. Applying AI in pursuit of better AI has, to date, been a largely academic pursuit--mainly because this approach requires tens of thousands of GPU hours. If that's what it takes, it's likely quicker and simpler to design real-world AI applications with the fallible guidance of educated guesswork. Next month, however, a team of MIT researchers will be presenting a so-called "neural architecture search" algorithm that can speed up the AI-optimized AI design process by 240 times or more. That would put faster and more accurate AI within practical reach for a broad class of image recognition algorithms and other related applications.