Lifeboat Foundation

(Inside Science) — It took years of painstaking work for Carlos Souza and his colleagues to map out every road in the Brazilian Amazon biome. Official maps of the 4.2 million-square-kilometer region only show roads built by federal and local governments. But by carefully tracing lines on satellite images, the researchers concluded in 2016 that the true length of all the roads combined was nearly 13 times higher.

“When we don’t have a good understanding of how much roadless areas we have on the landscape, we probably will misguide any conservation plans for that territory,” said Souza, a geographer at a Brazil-based environmental nonprofit organization called Imazon.

Now, Imazon researchers have built an artificial intelligence algorithm to find such roads automatically. Currently, the algorithm is reaching about 70% accuracy, which rises to 87%-90% with some additional automated processing, said Souza. Analysts then confirm potential roads by examining the satellite images. Souza presented the research last month at a virtual meeting of the American Geophysical Union.

O,.o.

It is no secret that Apple is working on the development of electric vehicle technology for almost 5–6 years now. Codenamed as Project Titan, the project contains many ex-employees of renowned automobile brands such as Tesla, Land Rover, and Aston Martin. Recently, there were rumours of Apple linking up with TSMC (Taiwan Semiconductor Manufacturing Company) for producing self-driving chips for their planned vehicles.

It was unclear until now whether Apple will be manufacturing the vehicles on their own, or will they act as a software provider for existing automobile brands. Now, however, there are reports that the tech company is in early talks with the Hyundai Motor Group, among others.

Continue reading “Apple in Talks with Hyundai Motor Group for Electric Vehicle Tie-Up” »

Despite the inherent challenges that voice-interaction may create, researchers at the Penn State College of Information Sciences and Technology recently found that deaf and hard-of-hearing users regularly use smart assistants like Amazon’s Alexa and Apple’s Siri in homes, workplaces and mobile devices.

The work highlights a clear need for more inclusive design, and presents an opportunity for deaf and hard-of-hearing users to have a more active role in the research and development of new systems, according to Johnna Blair, an IST doctoral student and member of the research team.

“As smart assistants become more common, are preloaded on every smartphone, and continue to provide benefits to the user beyond just the ease of voice activation, it’s important to understand how deaf and hard-of-hearing users have made smart assistants work for them and the realistic challenges they continue to face,” said Blair.

Over the past few years, computer scientists have developed increasingly advanced and sophisticated artificial intelligence (AI) tools, which can tackle a wide variety of tasks. This includes generative adversarial networks (GANs), machine-learning models that can learn to generate new data, including text, audio files or images. Some of these models can also be tailored for creative purposes, for instance, to create unique drawings, songs or poems.

Researchers at Tongji University in Shanghai in China and the University of Delaware in the US have recently created a GAN-based model that can generate abstract artworks inspired by Chinese calligraphy. The term Chinese calligraphy refers to the artistic form in which Chinese characters were traditionally written.

“In 2019, we collaborated with a restaurant based in Shanghai to showcase some AI technologies for better customer engagement and experience,” Professor Harry Jiannan Wang, one of the researchers who carried out the study, told TechXplore. “We then had the idea to use AI technologies to generate personalized abstract art based on the dishes customers order and present the artwork to entertain customers while they wait for their meals to be served.”

Circa 2019

MIT’s Biomimetics Robotics department took a whole herd of its new ‘mini cheetah’ robots out for a group demonstration on campus recently – and the result is an adorable, impressive display of the current state of robotic technology in action.

The school’s students are seen coordinating the actions of 9 of the dog-sized robots running through a range of activities, including coordinated movements, doing flips, springing in slow motion from under piles of fall leaves, and even playing soccer.

Continue reading “Watch MIT’s ‘mini cheetah’ robots frolic, fall, flip – and play soccer together” »

Lightmatter bets that optical computing can solve AI’s efficiency problem.

Elon Musk’s Neuralink has a straightforward outlook on artificial intelligence: “If you can’t beat em, join em.” The company means that quite literally — it’s building a device that aims to connect our brains with electronics, which would enable us, in theory, to control computers with our thoughts.

But how? What material would companies like Neuralink use to connect electronics with human tissue?

One potential solution was recently revealed at the American Chemical Society’s Fall 2020 Virtual Meeting & Expo. A team of researchers from the University of Delaware presented a new biocompatible polymer coating that could help devices better fuse with the brain.

Mercedes’ new AI screen is extremely wide! Check out how it could up the stakes against Tesla.

Deep learning has come a long way since the days when it could only recognize handwritten characters on checks and envelopes. Today, deep neural networks have become a key component of many computer vision applications, from photo and video editors to medical software and self-driving cars.

Roughly fashioned after the structure of the brain, neural networks have come closer to seeing the world as humans do. But they still have a long way to go, and they make mistakes in situations where humans would never err.

These situations, generally known as adversarial examples, change the behavior of an AI model in befuddling ways. Adversarial machine learning is one of the greatest challenges of current artificial intelligence systems. They can lead to machine learning models failing in unpredictable ways or becoming vulnerable to cyberattacks.