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Category: robotics/AI – Page 1384

The DeepMind team has made probably the most ambitious attempt yet to deploy AI to calculate electron density, the end result of DFT calculations. “It’s sort of the ideal problem for machine learning: you know the answer, but not the formula you want to apply,” says Aron Cohen, a theoretical chemist who has long worked on DFT and who is now at DeepMind.

A team led by scientists at the London-based artificial-intelligence company DeepMind has developed a machine-learning model that suggests a molecule’s characteristics by predicting the distribution of electrons within it. The approach, described in the 10 December issue of Science1, can calculate the properties of some molecules more accurately than existing techniques.

“To make it as accurate as they have done is a feat,” says Anatole von Lilienfeld, a materials scientist at the University of Vienna.

The paper is “a solid piece of work”, says Katarzyna Pernal, a computational chemist at Lodz University of Technology in Poland. But she adds that the machine-learning model has a long way to go before it can be useful for computational chemists.

Continue reading “DeepMind AI tackles one of chemistry’s most valuable techniques” | >

I wonder how many iterations of “kinematic reproduction” would result in sentience.

Artificial Intelligence has made a landmark achievement by creating robots that can reproduce. US scientists who created the first living robots claim they can now reproduce on their own. Scientists now claim the discovery is a new form of biological reproduction that was not known to science yet. Experts say the parent robot and its babies, called Xenobots, are entirely biological.

#Xenobots. #LivingRobots. #ArtificialIntelligence.

Continue reading “Meet Xenobots 3.0 — World’s First Living Robots, Made of Frog Cells, That Can Reproduce” | >

According to a newly released survey from nonprofit technical organization IEEE, about one in five respondents say AI and machine learning (21%), cloud computing (20%), and 5G (17%) will be the most important technologies next year. The study examines the most important technologies in 2022, the industries expected to be most impacted by technology in the year ahead, and anticipated technology trends through the next decade.

What industries are expected to be most impacted by technology in the year ahead? Technology leaders surveyed cited manufacturing (25%), financial services (19%), health care (16%), and energy (13%) as industries poised for major disruption.

Regarding the key technology trends to expect through the next decade, an overwhelming majority (95%) agree — including 66% who strongly agree — that AI will drive the majority of innovation across nearly every industry sector in the next one to five years. Furthermore, 81% agree that, in the next five years, one-quarter of what they do will be enhanced by robots, and 77% agree that, in the same timeframe, robots will be deployed across their organization to enhance nearly every business function, from sales and human resources to marketing and IT. A majority of respondents agree (78%) that in the next 10 years, half or more of what they do will be enhanced by robots.

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What’s needed instead is something more like the engineering that goes into a race car, where the initial design is as perfect as the engineers know how to make it upfront, but every few laps during a race, they fine-tune it further for the specific conditions on the track that day, Venne said. His inspiration for a solution that is less labor-intensive than car racing also comes from the world of automobiles — specifically self-driving cars.

In addition to knowing the basic rules of the road, a self-driving car needs to be able to adapt to the unexpected, such as swerving to avoid hitting the squirrel crossing the road ahead, Venne said. “It occurred to me that if we’re doing this with cars, we should be able to do the same with the technology that drives the mechanical side of the building.”

BrainBox AI focuses primarily on controlling the heating, ventilation, and air conditioning (HVAC) systems within a building, which accounts for the majority of the energy consumption in most buildings, Venne said. A next-level goal is to get multiple neighboring buildings in a city working in tandem to produce better results, like helping utilities balance the consumption of electricity during periods of peak demand. A pilot project based on that concept won a Tech for Our Planet challenge at the recently concluded COP26 United Nations conference on controlling climate change.

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From self-healing robots to reconfigurable electronic circuits, the applications of liquid metal are only limited by the imaginations of the scientists working with them. Let’s take a look at some of the latest revolutions, discoveries, and innovations in this material.

2D morphing metal

In 2017, scientists at the University of Sussex and Swansea University invented a way to morph liquid metal into 2D shapes using an electrical charge. Though still in the early stages of development, this team’s research could open up new possibilities in soft robotics, smart electronics, computer graphics, and flexible displays.

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IBM has announced a new type of Simulation Software which is meant to train Artificial Intelligence Robots interact with the real environment in a rapid and cost effective manner. This type of AI Model Training is potentially going to be the future of training going forward.
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TIMESTAMPS:
00:00 A playground for Robots.
02:10 How these Virtual Worlds are made.
05:25 How Simulations improve Artificial Intelligence.
09:06 Last Words.
–
#ai #simulation #elonmusk

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A new “common-sense” approach to computer vision enables artificial intelligence that interprets scenes more accurately than other systems do.

Computer vision systems sometimes make inferences about a scene that fly in the face of common sense. For example, if a robot were processing a scene of a dinner table, it might completely ignore a bowl that is visible to any human observer, estimate that a plate is floating above the table, or misperceive a fork to be penetrating a bowl rather than leaning against it.

Move that computer vision system to a self-driving car and the stakes become much higher — for example, such systems have failed to detect emergency vehicles and pedestrians crossing the street.

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A maze is a popular device among psychologists to assess the learning capacity of mice or rats. But how about robots? Can they learn to successfully navigate the twists and turns of a labyrinth? Now, researchers at the Eindhoven University of Technology (TU/e) in the Netherlands and the Max Planck Institute for Polymer Research in Mainz, Germany, have proven they can. Their robot bases its decisions on the very system humans use to think and act: the brain. The study, which was published in Science Advances, paves the way to exciting new applications of neuromorphic devices in health and beyond.

Machine learning and neural networks have become all the rage in recent years, and quite understandably so, considering their many successes in image recognition, medical diagnosis, e-commerce and many other fields. Still though, this software-based approach to machine intelligence has its drawbacks, not least because it consumes so.

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The accelerated growth in ecommerce and online marketplaces has led to a surge in fraudulent behavior online perpetrated by bots and bad actors alike. A strategic and effective approach to online fraud detection will be needed in order to tackle increasingly sophisticated threats to online retailers.

These market shifts come at a time of significant regulatory change. Across the globe, new legislation is coming into force that alters the balance of responsibility in fraud prevention between users, brands, and the platforms that promote them digitally. For example, the EU Digital Services Act and US Shop Safe Act will require online platforms to take greater responsibility for the content on their websites, a responsibility that was traditionally the domain of brands and users to monitor and report.

Can AI find what’s hiding in your data? In the search for security vulnerabilities, behavioral analytics software provider Pasabi has seen a sharp rise in interest in its AI analytics platform for online fraud detection, with a number of key wins including the online reviews platform, Trustpilot. Pasabi maintains its AI models based on anonymised sets of data collected from multiple sources.

Using bespoke models and algorithms, as well as some open source and commercial technology such as TensorFlow and Neo4j, Pasabi’s platform is proving itself to be advantageous in the detection of patterns in both text and visual data. Customer data is provided to Pasabi by its customers for the purposes of analysis to identify a range of illegal activities — - illegal content, scams, and counterfeits, for example — - upon which the customer can then act.

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Continue reading “Why AI is the future of fraud detection” | >

The system could help physicians select the least risky treatments in urgent situations, such as treating sepsis.

Sepsis claims the lives of nearly 270,000 people in the U.S. each year. The unpredictable medical condition can progress rapidly, leading to a swift drop in blood pressure, tissue damage, multiple organ failure, and death.

Prompt interventions by medical professionals save lives, but some sepsis treatments can also contribute to a patient’s deterioration, so choosing the optimal therapy can be a difficult task. For instance, in the early hours of severe sepsis, administering too much fluid intravenously can increase a patient’s risk of death.

To help clinicians avoid remedies that may potentially contribute to a patient’s death, researchers at MIT and elsewhere have developed a machine-learning model that could be used to identify treatments that pose a higher risk than other options. Their model can also warn doctors when a septic patient is approaching a medical dead end — the point when the patient will most likely die no matter what treatment is used — so that they can intervene before it is too late.

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Continue reading “Machine-learning system flags remedies that might do more harm than good” | >