A deep-learning model identifies a powerful new drug that can kill some antibiotic-resistant bacteria.
Category: robotics/AI – Page 1,749
A Computer Breakthrough Helps Solve a Complex Math Problem 1 Million Times Faster
Reservoir computing, a machine learning algorithm that mimics the workings of the human brain, is revolutionizing how scientists tackle the most complex data processing challenges, and now, researchers have discovered a new technique that can make it up to a million times faster on specific tasks while using far fewer computing resources with less data input.
With the next-generation technique, the researchers were able to solve a complex computing problem in less than a second on a desktop computer — and these overly complex problems, such as forecasting the evolution of dynamic systems like weather that change over time, are exactly why reservoir computing was developed in the early 2000s.
These systems can be extremely difficult to predict, with the “butterfly effect” being a well-known example. The concept, which is closely associated with the work of mathematician and meteorologist Edward Lorenz, essentially describes how a butterfly fluttering its wings can influence the weather weeks later. Reservoir computing is well-suited for learning such dynamic systems and can provide accurate projections of how they will behave in the future; however, the larger and more complex the system, more computing resources, a network of artificial neurons, and more time are required to obtain accurate forecasts.
Sure, AI Could Run the World — Except for Its Fundamental Limits
Who has a different opinion here?
Science writer Charles Q. Choi identifies a number of limitations, including a, perhaps, surprising one: AIs are very bad at math.
AI tradeoffs: Balancing powerful models and potential biases
As developers unlock new AI tools, the risk for perpetuating harmful biases becomes increasingly high — especially on the heels of a year like 2020, which reimagined many of our social and cultural norms upon which AI algorithms have long been trained.
A handful of foundational models are emerging that rely upon a magnitude of training data that makes them inherently powerful, but it’s not without risk of harmful biases — and we need to collectively acknowledge that fact.
Recognition in itself is easy. Understanding is much harder, as is mitigation against future risks. Which is to say that we must first take steps to ensure that we understand the roots of these biases in an effort to better understand the risks involved with developing AI models.
Tesla AI Day 2021 Review — Part 2: Training Data. How Does a Car Learn?
The challenge of getting high-quality real-world data.
Tesla is combining manual labeling, auto labeling, and simulation to create real-world datasets for fully self-driving cars.
Remote assessment of health by robots from anywhere in the world
Intelligent sensing and tele-presence robotic technology, enabling health practitioners to remotely assess a person’s physical and cognitive health from anywhere in the world, is being pioneered in research co-led at the University of Strathclyde.
The technology could aid cost-effective diagnosis, more regular monitoring and health assessments alongside assistance, especially for people living with conditions such as Alzheimer’s disease and other cognitive impairments.
The system was demonstrated for the first time to the UK Government Minister, Iain Stewart during a visit to the construction site of the National Robotarium, hosted at Heriot-Watt University, which is co-leading the research with Strathclyde.
Turkey’s Baykar rolls out its vertical take-off, landing drone
Leading defense company Baykar has unveiled for the first time its newly designed drone that can hover, take off and land vertically at Turkey’s largest technology and aviation event, Teknofest.
The flight tests of the vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) are due to be completed soon. Mass production and delivery phases are expected to start in 2022.
The new UAV does not need a landing track and can take off from several different places, including naval or mobile platforms, said Burak Özbek, an air vehicle design engineer at Baykar, which is already known worldwide for its landmark Bayraktar TB2 and Akıncı drones.
Artificial Intelligence Accurately Predicts RNA Structures, Too
Researchers recently showed that a computer could “learn” from many examples of protein folding to predict the 3D structure of proteins with great speed and precision. Now a recent study in the journal Science shows that a computer also can predict the 3D shapes of RNA molecules [1]. This includes the mRNA that codes for proteins and the non-coding RNA that performs a range of cellular functions.
This work marks an important basic science advance. RNA therapeutics—from COVID-19 vaccines to cancer drugs—have already benefited millions of people and will help many more in the future. Now, the ability to predict RNA shapes quickly and accurately on a computer will help to accelerate understanding these critical molecules and expand their healthcare uses.
Like proteins, the shapes of single-stranded RNA molecules are important for their ability to function properly inside cells. Yet far less is known about these RNA structures and the rules that determine their precise shapes. The RNA elements (bases) can form internal hydrogen-bonded pairs, but the number of possible combinations of pairings is almost astronomical for any RNA molecule with more than a few dozen bases.
Northrop Grumman to launch new satellite-servicing robot aimed at commercial and government market
WASHINGTON — Northrop Grumman today has two Mission Extension Vehicles in orbit providing station-keeping services for two Intelsat geostationary satellites that were running low on fuel.
The company meanwhile is preparing to launch a new servicing vehicle equipped with a robotic arm that will install propulsion jet packs on dying satellites.