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You’re on the PRO Robots channel, and today we’re bringing you some high-tech news. Robots from Boston Dynamics will get advanced artificial intelligence, neural networks will be able to translate the language of all animals, incredibly fast nanorobots will travel inside the human body, a robot-surgeon will perform an operation on the ISS. See these and other technology news in one video right now!

0:00 Intro.
0:28 Robots from Boston Dynamics get advanced artificial intelligence.
1:52 AI will never be intelligent.
2:50 Earth Species Project hopes to develop a neural network that can decipher animal language.
3:16 Species Project decides to go around and create an algorithm.
4:07 A gadget to control your smart home with your mind.
5:04 Nanobots.
5:19 The world’s fastest bowel robot.
6:10 Robots will join the U.S. space forces.
6:47 Surgical robot to be tested on ISS
7:37 GITAI News.
7:59 The first launch in NASA’s Artemis lunar mission.
8:34 Super Heavy rocket successfully passes first static firing test.
8:57 Gigafactory in Canada.
9:22 Baidu says its Jidu robot car autopilot will be a generation ahead of Tesla’s autopilot.
10:02 A system that can calculate the optimal end design and calculate the best trajectory for grabbing objects of any shape.
10:25 A drone to search for gold and jewelry.
11:22 Engineers have trained a drone with 12 rotary screws to manipulate objects.
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10 Seconds to the Future | Mutation | Free Documentary

2077 — 10 Seconds to the Future — Mutation | Science Documentary.

2077 — 10 Seconds to the Future | Global Estrangement: https://youtu.be/CTOduDIkcdM

We are at the starting line of an exponential technological change. In the coming decades we will experience the dematerialization of technology. Computers will abandon desks to be installed in eyes, in walls and in everything that surrounds us. Chips will be integrated in virtually everything around us, transmitting vital information. The quality of life and the average life expectancy will increase astoundingly, and aging will be delayed. We will have the capacity to choose genes for our children and to create new forms of life. In 2007, a smartphone had more power than the computers NASA used to take man to the moon in 1969. In 2077 it’s likely that we will control the objects around us through our thought. The opinion that the revolution under way is the biggest and fastest ever is unanimous, with the interception of genetics, nanotechnology and artificial intelligence. The consequences are many and cross-cutting, with great impact on our health. However, the rise of the machine raises unprecedented challenges, even the possibility of the extinction of Humankind itself.
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Enjoy stories about nature, wildlife, culture, people, history and more to come.

Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology Images

Background: Breast cancer is one of the most common cancers and the leading cause of death from cancer among women worldwide. The genetic predisposition to breast cancer may be associated with a mutation in particular genes such as gene BRCA1/2. Patients who carry a germline pathogenic mutation in BRCA1/2 genes have a significantly increased risk of developing breast cancer and might benefit from targeted therapy. However, genetic testing is time consuming and costly. This study aims to predict the risk of gBRCA mutation by using the whole-slide pathology features of breast cancer H&E stains and the patients’ gBRCA mutation status.

Methods: In this study, we trained a deep convolutional neural network (CNN) of ResNet on whole-slide images (WSIs) to predict the gBRCA mutation in breast cancer. Since the dimensions are too large for slide-based training, we divided WSI into smaller tiles with the original resolution. The tile-based classification was then combined by adding the positive classification result to generate the combined slide-based accuracy. Models were trained based on the annotated tumor location and gBRCA mutation status labeled by a designated breast cancer pathologist. Four models were trained on tiles cropped at 5×, 10×, 20×, and 40× magnification, assuming that low magnification and high magnification may provide different levels of information for classification.

Results: A trained model was validated through an external dataset that contains 17 mutants and 47 wilds. In the external validation dataset, AUCs (95% CI) of DL models that used 40×, 20×, 10×, and 5× magnification tiles among all cases were 0.766 (0.763–0.769), 0.763 (0.758–0.769), 0.750 (0.738–0.761), and 0.551 (0.526–0.575), respectively, while the corresponding magnification slides among all cases were 0.774 (0.642–0.905), 0.804 (0.676–0.931), 0.828 (0.691–0.966), and 0.635 (0.471–0.798), respectively. The study also identified the influence of histological grade to the accuracy of the prediction.

A low-cost, viable solution for self-driving cars to spot hacked GPS

A lot of hurdles remain before the emerging technology of self-driving personal and commercial vehicles is common, but transportation researchers at The University of Alabama developed a promising, inexpensive system to overcome one challenge: GPS hacking that can send a self-driving vehicle to the wrong destination.

Initial research shows a vehicle can use already installed sensors to detect traveling the wrong route when passengers are unaware of the change, thwarting an attempt to spoof the GPS signal to the vehicle, according to findings outlined in recently published papers in the IEEE Transactions on Intelligent Transportation Systems and Transportation Research Record: Journal of the Transportation Research Board.

Relying on software code and in-vehicle sensors already part of the self-driving system would be cheaper for consumer and to deny the hacked directions used to steer cargo or people away from their intended destination, said Dr. Mizanur Rahman, assistant professor of civil, construction and and affiliate researcher with the Alabama Transportation Institute.

An architect asked AI to design skyscrapers of the future. This is what it proposed

Manas Bhatia has a bold vision of the future — one where residential skyscrapers covered in trees, plants and algae act as “air purification towers.” In a series of detailed images, the New Delhi-based architect and computational designer has brought the idea to life. His imagined buildings are depicted rising high above a futuristic metropolis, their curved forms inspired by shapes found in nature.

But the pictures were not entirely of his own imagination.

For his conceptual project, “AI x Future Cities,” Bhatia turned to an artificial intelligence imaging tool, Midjourney, that generates elaborate pictures based on written prompts.

A New Delhi-based architect’s bold vision of the future is not entirely of his own imagination.

Startup Behind AI Image Generator Stable Diffusion Is In Talks To Raise At A Valuation Up To $1 Billion

With the image generator Stable Diffusion, you can conjure within seconds a potrait of Beyoncé as if painted by Vincent van Gogh, a cyberpunk cityscape in the style of 18th century Japanese artist Hokusai and a complex alien world straight out of science fiction. Released to the public just two weeks ago, it’s become one of several popular AI-powered text-to-image generators, including DALL-E 2, that have taken the internet by storm.

Now, the company behind Stable Diffusion is in discussions to raise $100 million from investors, according to three people with knowledge of the matter.

Stability AI’s open source text-to-image generator was released to the general public in late August. It has already accumulated massive community goodwill — and controversy over how it’s been used by individuals on websites like 4chan.

Collaborative machine learning that preserves privacy

Training a machine-learning model to effectively perform a task, such as image classification, involves showing the model thousands, millions, or even billions of example images. Gathering such enormous datasets can be especially challenging when privacy is a concern, such as with medical images. Researchers from MIT and the MIT-born startup DynamoFL have now taken one popular solution to this problem, known as federated learning, and made it faster and more accurate.

Federated learning is a collaborative method for training a machine-learning model that keeps sensitive user data private. Hundreds or thousands of users each train their own model using their own data on their own device. Then users transfer their models to a central server, which combines them to come up with a better model that it sends back to all users.

A collection of hospitals located around the world, for example, could use this method to train a machine-learning model that identifies brain tumors in medical images, while keeping patient data secure on their local servers.