Each robot "is very greedy and wants to do what they want to do." If they all work together, both robots can achieve their goal, but they can't communicate with the other robots in any meaningful way. But the AMOLF team wants to get away from that -- they wanted to stay as simple as possible, Overvelde says, in order to make the behaviors simple as well, because, "the more complex the behavior, it's hard to tell in the end what it's going to do." Abstract: One of the main challenges in robotics is the development of systems that can adapt to their environment and achieve autonomous behavior. By letting each unit adapt its behavior independently using a basic Monte Carlo scheme, the assembled system is able to learn and maintain optimal behavior in a dynamic environment as long as its memory is representative of the current environment, even when incurring damage. As a result, such a distributed learning approach can be easily scaled to larger assemblies, blurring the boundaries between materials and robots, paving the way for a new class of modular "robotic matter" that can autonomously learn to thrive in dynamic or unfamiliar situations, for example, encountered by soft robots or self-assembled (micro)robots in various environments spanning from the medical realm to space explorations.

The number of connected devices collecting data is continually expanding. This requires more storage and computational capacity and more Artificial Intelligence (AI) to be brought at the Edge: Eurotech combines rugged embedded and Edge computers, computational power and IoT components to enable Edge AI. By bringing these high-performance computing capacity to the Edge, Eurotech enables Artificial Intelligence (AI) applications directly on field devices. They are able to process data autonomously and perform Machine Learning (ML) in the field and apply Deep learning (DL) models and algorithms for advanced autonomous applications, such as Autonomous Driving. The virtually unlimited capacity of the Cloud can be integrated with sophisticated and high-performance Edge Computers in the field, enabling the "Intelligent Edge".

I have the habit of indexing many of the interesting tools and resources I find online. I will admit that sometimes I exaggerate, but the idea with this is to keep track of as many practically useful resources as I can to be able to come back to it as needed.

In 1953, a Harvard psychologist thought he discovered pleasure – accidentally – within the cranium of a rat. With an electrode inserted into a specific area of its brain, the rat was allowed to pulse the implant by pulling a lever. It kept returning for more: insatiably, incessantly, lever-pulling. In fact, the rat didn't seem to want to do anything else. Seemingly, the reward centre of the brain had been located.

The symptoms and side effects of Covid-19 are scattered across a diagnostic spectrum. Some patients are asymptomatic or experience a mild immune response, while others report significant long-term illnesses, lasting complications, or suffer fatal outcomes. Three researchers from the Georgia Institute of Technology and one from Emory University are trying to help clinicians sort through these factors and spectrum of patient outcomes by equipping healthcare professionals with a new "decision prioritization tool." The team's new artificial intelligence-based tool helps clinicians understand and better predict which adverse effects their Covid-19 patients could experience, based on comorbidities and current side effects -; and, in turn, also helps suggest specific Food and Drug Administration-approved (FDA) drugs that could help treat the disease and improve patient health outcomes. The researcher's latest findings are the focus of a new study published October 21 in Scientific Reports. The team's new methodology, or tool, is called MOATAI-VIR (Mode Of Action proteins & Targeted therapeutic discovery driven by Artificial Intelligence for VIRuses.

Chatbots have a checkered past of often not delivering the performance their providers have promised. This is especially true in the IT service management (ITSM) and multilingual NLP spaces, where service desks found support teams deluged with complaints -- yes, about the support chatbots. Just getting English language nuance right and how enterprises communicate often require chatbots to be custom programmed with constraint and logic workflows supported with natural language processing (NLP) and machine learning. If that sounds like a science project, it is, and IT users are the test subjects. Because of their complexity, chatbots were contributing to already overflowing trouble-ticket queues.

Children inevitably adapt to the culture in which they were raised. Parents or guardians shape the lens through which they view the world, largely through the examples they set. Many parents experience humored horror when a child picks up on an inappropriate word, likely from an overheard adult conversation, and begins to employ that expression in their everyday speech. It does not matter whether the parent is intentionally or unintentionally crafting the lens for the child -- they will still pick up on the parents' viewpoints and habits. We are witnessing this same progression in the tech world.

Artificial intelligence (AI) has great potential for forensic psychiatry but can also bring moral hazard, said Richard Cockerill, MD, assistant professor of psychiatry at Northwestern University Feinberg School of Medicine in Chicago, Saturday at the American Academy of Psychiatry and the Law annual meeting. He defined AI as computer algorithms that can be used in specific tasks. There are two types of AI, Cockerill explained. The first type, "machine learning," involves having a computer use algorithms to perform tasks that were previously only done by humans. The second type, "deep learning," is when the computer -- using what it has learned previously -- trains itself to improve algorithms on its own, with little or no human supervision.

Autonomous self-driving cars have now become a trend in the world. There are big automakers and technology companies involved in the automation industry, but it is the start-ups that are making their place in the industry with new inventions. In a populous field of more than 300 start-ups in self-driving cars, let's take a closer look at the top autonomous self-driving car companies that have achieved unicorn status. Velodyne Lidar provides smart, powerful lidar solutions for autonomous vehicles, driver assistance, delivery solutions, robotics, navigation, mapping, and more. Headquartered in San Jose, Calif, Velodyne is known worldwide for its portfolio of breakthrough lidar sensor technologies.

It was the latest twist in a saga that has disrupted typical car industry practices and drawn the attention of safety advocates and regulators, who fear the consequences of Tesla foisting the largely untested software on the public. Full Self-Driving is an expanded iteration of the software that Tesla calls Autopilot, which can navigate highways, summon and park cars, and conduct other maneuvers with an attentive driver behind the wheel. Full Self-Driving brings those capabilities to city streets, allowing the software to navigate Tesla cars through local roads and residential areas. Users must pay attention at all times, and the software -- despite its name -- is not considered autonomous by industry or regulatory definitions.