Breakthroughs, discoveries, and DIY tips sent every weekday. One of the major barriers they haven't overcome is the ability to "feel" sensations like a human. Although researchers have tried various sensors to give robots a rudimentary sense of touch, these systems are often costly, inaccurate, and limited to detecting only one type of sensation at a time. But that may be about to change. Researchers from the University of Cambridge and University College London have developed a new type of responsive "synthetic skin."

Researchers often use simulations when designing new algorithms, since testing ideas in the real world can be both costly and risky. But since it's impossible to capture every detail of a complex system in a simulation, they typically collect a small amount of real data that they replay while simulating the components they want to study. Known as trace-driven simulation (the small pieces of real data are called traces), this method sometimes results in biased outcomes. This means researchers might unknowingly choose an algorithm that is not the best one they evaluated, and which will perform worse on real data than the simulation predicted that it should. MIT researchers have developed a new method that eliminates this source of bias in trace-driven simulation.

Researchers at the University of Edinburgh invented electronic skin that can allow robots to feel just like humans.

… has created a distinctive machine-learning approach to track the way in which the photoactive yellow protein (PYP) undergoes changes in its …

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. As part of Microsoft's research into ways to use machine learning and AI to improve security defenses, the company has released an open source attack toolkit to let researchers create simulated network environments and see how they fare against attacks. Microsoft 365 Defender Research released CyberBattleSim, which creates a network simulation and models how threat actors can move laterally through the network looking for weak points. When building the attack simulation, enterprise defenders and researchers create various nodes on the network and indicate which services are running, which vulnerabilities are present, and what type of security controls are in place. Automated agents, representing threat actors, are deployed in the attack simulation to randomly execute actions as they try to take over the nodes. "The simulated attacker's goal is to take ownership of some portion of the network by exploiting these planted vulnerabilities.

As part of Microsoft's research into ways to use machine learning and AI to improve security defenses, the company has released an open source attack toolkit to let researchers create simulated network environments and see how they fare against attacks. Microsoft 365 Defender Research released CyberBattleSim, which creates a network simulation and models how threat actors can move laterally through the network looking for weak points. When building the attack simulation, enterprise defenders and researchers create various nodes on the network and indicate which services are running, which vulnerabilities are present, and what type of security controls are in place. Automated agents, representing threat actors, are deployed in the attack simulation to randomly execute actions as they try to take over the nodes. "The simulated attacker's goal is to take ownership of some portion of the network by exploiting these planted vulnerabilities. While the simulated attacker moves through the network, a defender agent watches the network activity to detect the presence of the attacker and contain the attack," the Microsoft 365 Defender Research Team wrote in a post discussing the project.

Researchers at the University of Washington have made an important breakthrough in robotic suction, a simple concept that's been difficult to master. The scientists took inspiration from the Northern clingfish, a species common in the Pacific Northwest known for clinging to the underside of slipper oceanic rocks. The fish has a suction cup on its belly, which uses small hairlike structures to create a powerful connection to the slipperiest surfaces. The Nothern clingfish (pictured above) is famous for being able to attach to the slipperiest and most uneven surfaces, something which made it a main focus of researchers. A half-pound fish can create suction strong enough to lift a rock twelve times its bodyweight, and the connection is so powerful it remains in tact even after the fish has died.

A team of researchers who have built an artificially-intelligent writer say they are withholding the technology as it might be used for "malicious" purposes. OpenAI, based in San Francisco, is a research institute backed by Silicon Valley luminaries including Elon Musk and Peter Thiel. It shared some new research on using machine learning to create a system capable of producing natural language, but in doing so the team expressed concern the tool could be used to mass-produce convincing fake news. Which, to put it another way, is of course also an admission that what its system puts out there is unreliable, made-up rubbish. Still, when it works well, the results are impressively realistic in tone - which is why I've shared a sample of it below.

Tracking sharks and dolphins in order to study their habits is tough. Doing so requires researchers to attach some sort of sensor or robot to the animal, but it has to be able to stay on underwater and withstand fast swimming speeds as well as twists, turns and bends. So far, that's been hard to accomplish. But researchers at Beihang University, Harvard University and Boston College have developed a robot that hang on to slick skin underwater and withstand high speeds and sharp movements. They did so by modeling it after an animal that does those things naturally -- the remora.

Researchers have created a'Bot Dylan' computer that is capable of writing its own folk music. The system uses artificial intelligence to compose new tunes after it was trained using 23,000 pieces of Irish folk music. This allowed the machine to learn the patterns and structures that make for a catchy tune before it created its own pieces of music that we showcased at a concert in London this week. It marks a significant step forward for the capabilities of artificial intelligence. The new computerised composer can compose new tunes after being trained using 23,000 pieces of Irish folk music.