China may be gearing up to pull ahead of the U.S. in the race to overhaul road delivery with fleets of self-driving long-haul trucks. A number of companies are developing automation technologies that promise to lower costs, reduce accidents, and improve overall efficiency for the trucking industry by allowing drivers to make longer trips that include periods of rest. In Europe and the U.S., Volvo, Daimler, Uber, and others are testing trucks capable of driving themselves under expert supervision. But several Chinese-based companies are working on automated trucks, and lenient regulations as well as a desire to overhaul the country's chaotic trucking industry may smooth the way for the technology's introduction. This could provide a handy edge in the race to develop a lucrative new way of hauling goods.

Google's new slew of AI tools includes a kind of game called "Quick, Draw!" that can guess what you're doodling. It's impressive just how good the tool is--though some of its misses would be obvious to the human eye. "This is a game built with machine learning," says a tutorial site. "You draw, and a neural network tries to guess what you're drawing. But the more you play with it, the more it will learn. It's just one example of how you can use machine learning in fun ways."

Facebook's Connectivity Lab today released its high-resolution population maps for Malawi, South Africa, Ghana, Haiti and Sri Lanka, with the promise to make more datasets available over the coming months. The population maps are a joint effort between the Facebook Connectivity Lab, Columbia University and the World Bank, though Facebook is interested in the project as part of its effort to launch wireless communication services in rural regions around the globe. Facebook and friends used software to identify buildings in commercially available satellite images, and then estimated population using census data and a few other surveys and programs. Convolutional neural networks powered a model capable of identifying individual buildings in images from across the world. "There has been a lot of work recently on neural networks that can recognize individual buildings with very high accuracy, but these models are finely tuned on the local characteristics of the region where they are trained," the Connectivity Lab's Tobias Tiecke writes. "We found that these models do not perform well at a global scale with realistic amounts of training data.

What started with Mandarin Chinese is expanding to English; French; German; Japanese; Korean; Portuguese and Turkish, as Google has increased the languages its Neural Machine Translation (NMT) handle. "These represent the native languages of around one-third of the world's population, covering more than 35 percent of all Google Translate queries," according to The Keyword blog. The promise here is that because NMT uses the context of the entire sentence, rather than translating individual words on their own, the results will be more accurate, especially as time goes on, thanks to machine learning. For a comparison of the two methods, check out the GIF embedded below. Google says that the ultimate goal is to have all 103 languages in Translate using machine learning.

A picture may be worth a thousand words, but sound is just as important to how we experience the world as how we see it -- that's why a team at Disney Research is working on a computer vision system that can not only recognize what an image is, but how it sounds, too. In an initial study presented at the European Conference on Computer Vision, the group's system successfully managed to pair appropriate audio with images of doors closing, glasses clinking and vehicles driving down the road. Audio association might be easy for humans, but teaching a computer to do it is actually pretty challenging. Disney researchers trained AI to recognize the sound of images by feeding it a collection of videos demonstrating an object making a specific sound, but background noise, narration or sound made from other objects could easily confuse the system. If the system was fed samples with most of the uncorrelated sounds filtered out, however, it did a pretty good job of suggesting the right sound for each image.

WhatsApp has temporarily suspended giving parent company Facebook information about users in Europe for ad targeting, responding to concerns there over privacy, a source close to the matter said Tuesday. Conversations with officials in Europe over the past few months resulted in the social network deciding to only tapping into WhatsApp user data there for purposes such as fighting spam, according to the source. The break was described as an effort to give regulators time to share privacy concerns and for Facebook to consider ways to address them. German data protection authorities in September cited privacy concerns when they blocked Facebook from collecting subscriber data from WhatsApp there. "It has to be (the users') decision whether they want to connect their account with Facebook," Hamburg's Commissioner for Data Protection and Freedom of Information Johannes Caspar said at the time.

Recent observations of galaxies at $z \gtrsim 7$, along with the low value of the electron scattering optical depth measured by the Planck mission, make galaxies plausible as dominant sources of ionizing photons during the epoch of reionization. However, scenarios of galaxy-driven reionization hinge on the assumption that the average escape fraction of ionizing photons is significantly higher for galaxies in the reionization epoch than in the local Universe. The NIRSpec instrument on the James Webb Space Telescope (JWST) will enable spectroscopic observations of large samples of reionization-epoch galaxies. While the leakage of ionizing photons will not be directly measurable from these spectra, the leakage is predicted to have an indirect effect on the spectral slope and the strength of nebular emission lines in the rest-frame ultraviolet and optical. Here, we apply a machine learning technique known as lasso regression on mock JWST/NIRSpec observations of simulated $z 7$ galaxies in order to obtain a model that can predict the escape fraction from JWST/NIRSpec data.

In 2011, in an article in the Wall Street Journal, Marc Andreessen proclaimed that software is eating the world. He argued that a slew of technological innovations, including advanced microprocessors and high-speed connectivity, will revolutionize traditional business and that every company should become a software company. Many pundits have subsequently claimed that even traditional businesses will need to rethink their business models. Andreessen even went so far as to say that the entire retail vertical will eventually die due to the scalability of companies like Amazon. In short, if you aren't thinking about how you will disrupt your industry, you can bet your competitors are already doing so.

Sundar Pichai is huddling with five Google staffers in a room next to his office that's known--appropriately enough--as "Sundar's Huddle." The employees are members of the Google Photos team, and they're here this morning to update Pichai on something they've been working on for months. The group has barely begun its presentation when Pichai starts peppering them with questions, opinions, and advice. For half an hour, the discussion careens from subject to subject: the power of artificial intelligence, the value of integrating Google Photos with other products such as Google Drive, the importance of creating an emotional bond with the users of an app. After the team shows Pichai a rough cut of a promotional video, his feedback is unguarded and heartfelt: "That's awesome!" Google's bearded, 44-year-old CEO is, unmistakably, in his element. "Nothing makes me happier than a product review in which I can sit with the team and they're showing me something they're building," Pichai had told me a few days earlier. "Being able to react to it and think through, 'When users get this, what will their feedback be?' I'm always on a quest to do that better and do more of it."

Tagged In Issaquah, Washington King County Library System Due Date Packt As part of Google's slew of artificial intelligence announcements today, the company is releasing a number of AI web experiments powered by its cloud services that anyone can go and play with. One -- called Quick, Draw! -- gives you a prompt to draw an image of a written word or phrase in under 20 seconds with your mouse cursor in such a way that a neural network can identify it. It's both a hilarious and fascinating exercise with broader implications for how AI can self-learn over time in key AI… Tagged In Facebook Artificial Intelligence Machine Learning Artificial Neural Network Optical Character Recognition Tagged In Artificial Intelligence Application Programming Interface Open Source Machine Learning Bird Google believes the key to growing its cloud computing business is artificial intelligence. As part of Google's slew of artificial intelligence announcements today, the company is releasing a number of AI web experiments powered by its cloud services that anyone can go and play with. One -- called Quick, Draw! -- gives you a prompt to draw an image of a written word or phrase in under 20 seconds with your mouse cursor in such a way that a neural network can identify it.