The company StoryLife developed technology Holographic Conversational Video Experience that allows you to communicate with holograms of deceased relatives.
What we know
A U.S. startup has learned how to create a digital clone of a person before they die. It uses two dozen synchronized cameras to do so. They record answers to questions and then the resulting material is used to train artificial intelligence.
Carnegie Mellon researchers have developed an open-source software that enables more agile movement in legged robots.
Robots can help humans with tasks like aiding disaster recovery efforts or monitoring the environment. In the case of quadrupeds, robots that walk on four legs, their mobility requires many software components to work together seamlessly. Most researchers must spend much of their time developing lower-level infrastructure instead of focusing on high-level behaviors.
Aaron Johnson’s team in the Robomechanics Lab at Carnegie Mellon University’s College of Engineering has experienced these frustrations firsthand. The researchers have often had to rely on simple models for their work because existing software solutions were not open-sourced, did not provide a modular framework, and lacked end-to-end functionality.
Color coding makes aerial maps much more easily understood. Through color, we can tell at a glance where there is a road, forest, desert, city, river or lake.
Working with several universities, the U.S. Department of Energy’s (DOE) Argonne National Laboratory has devised a method for creating color-coded graphs of large volumes of data from X-ray analysis. This new tool uses computational data sorting to find clusters related to physical properties, such as an atomic distortion in a crystal structure. It should greatly accelerate future research on structural changes on the atomic scale induced by varying temperature.
The research team published their findings in the Proceedings of the National Academy of Sciences in an article titled “Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction.”
Cybersecurity researchers have elaborated a novel attack technique that weaponizes programmable logic controllers (PLCs) to gain an initial foothold in engineering workstations and subsequently invade the operational technology (OT) networks.
Dubbed “Evil PLC” attack by industrial security firm Claroty, the issue impacts engineering workstation software from Rockwell Automation, Schneider Electric, GE, B&R, Xinje, OVARRO, and Emerson.
Programmable logic controllers are a crucial component of industrial devices that control manufacturing processes in critical infrastructure sectors. PLCs, besides orchestrating the automation tasks, are also configured to start and stop processes and generate alarms.
Researchers discover a new variant of the SOVA Android banking trojan with upgraded capabilities.
This same feature could also play a role in our ability to tell what is human, and what is not. Several experiments have shown that when humanoid robots exhibit human-like variability in response times or motion patterns, we perceive them as more human-like.
In a study published in Science Robotics1, researchers have observed this same effect when the human and the robot are performing a shared activity. “To evaluate the impact of behavioural variability in the attribution of humanness to a robot, in our experiment the robot was either teleoperated by another human or controlled by a computer”, says Agnieszka Wykowska, senior researcher at the Italian Institute of Technology (IIT) in Genoa, and the coordinator of the study.
The research has also shown that the effect applies even when the variability of the robot’s behaviour does not closely resemble the human one, if it falls in the same range. “Depending on the context and on the function that the robot needs to perform, roboticists can endow their machines with a different degree of humanness by modulating the variability of their behaviors,” Wykowska adds.
The chip is an artificial neuron, but nothing like previous chips built to mimic the brain’s electrical signals. Rather, it adopts and adapts the brain’s other communication channel: chemicals.
Called neurotransmitters, these chemicals are the brain’s “natural language,” said Dr. Benhui Hu at Nanjing Medical University in China. An artificial neuron using a chemical language could, in theory, easily tap into neural circuits—to pilot a mouse’s leg, for example, or build an entirely new family of brain-controlled prosthetics or neural implants.
A new study led by Hu and Dr. Xiaodong Chen at Nanyang Technological University, Singapore, took a lengthy stride towards seamlessly connecting artificial and biological neurons into a semi-living circuit. Powered by dopamine, the setup wasn’t a simple one-way call where one component activated another. Rather, the artificial neuron formed a loop with multiple biological counterparts, pulsing out dopamine while receiving feedback to change its own behavior.
Even the simplest human tasks are unbelievably complex. The way we perceive and interact with the world requires a lifetime of accumulated experience and context. For example, if a person tells you, “I am running out of time,” you don’t immediately worry they are jogging on a street where the space-time continuum ceases to exist. You understand that they’re probably coming up against a deadline. And if they hurriedly walk toward a closed door, you don’t brace for a collision, because you trust this person can open the door, whether by turning a knob or pulling a handle.
The latest language models are amazing. But for general artificial intelligence, I suspect we’re barking up the wrong tree.
It’s odd to try to create artificial intelligence without taking inspiration from the only existing natural intelligences we have: humans and animals. Yet that is the current direction of AI research.
Engineers have created intelligent 3D printers that can quickly detect and correct errors, even in previously unseen designs, or unfamiliar materials like ketchup and mayonnaise, by learning from the experiences of other machines.
The engineers, from the University of Cambridge, developed a machine learning algorithm that can detect and correct a wide variety of different errors in real time, and can be easily added to new or existing machines to enhance their capabilities. 3D printers using the algorithm could also learn how to print new materials by themselves. Details of their low-cost approach are reported in the journal Nature Communications.
3D printing has the potential to revolutionize the production of complex and customized parts, such as aircraft components, personalized medical implants, or even intricate sweets, and could also transform manufacturing supply chains. However, it is also vulnerable to production errors, from small-scale inaccuracies and mechanical weaknesses through to total build failures.
