The human brain has long been considered the world’s most advanced neural network. But AI researchers might have it beat in just one neuron. property= description.
Category: robotics/AI – Page 1,379
What can heal can also be used to destroy?
MegaSyn is built to generate drug candidates with the lowest toxicity for patients. That got Urbina thinking. He retrained the model using data to drive the software toward generating lethal compounds, like nerve gas, and flipped the code so that it ranked its output from high-to-low toxicity. In effect, the software was told to come up with the most deadly stuff possible.
He ran the model and left it overnight to create new molecules.
It was quite impressive and scary at the same time, because in our list of the top 100, we were able to find some molecules that are VX analogues
Steadicopter, a leader in the Rotary Unmanned Aerial Systems (RUAS) industry, and Smart Shooter, a world-class designer, developer, and manufacturer of innovative fire control systems that significantly increase the accuracy and lethality of small arms, have unveiled the Golden Eagle — the first-ever unmanned helicopter with precise hit capabilities. The two companies will present the solution at the ISDEF exhibition in Tel Aviv.
Based on the combat-proven Black Eagle 50E platform, the Golden Eagle incorporates AI-based technology and Smart Shooter’s SMASH Dragon system. The AI-based technology enables superior situational awareness and autonomous multi-target classification and tracking. The SMASH Dragon, a remotely-operated robotic weaponry payload, locks on the target, tracks it and ensures precise target hit. SMASH Dragon integrates a unique stabilization concept with proprietary target acquisition, tracking algorithms and sophisticated computer vision capabilities that allow accurate hitting of static and moving targets while mounted onto the Golden Eagle.
“Using artificial intelligence, the new system provides a field combat solution for the modern battlefield. Forces on the ground can now send a helicopter for autonomous intelligence gathering into the relevant area and, having identified and classified the targets, send in another helicopter with precise attack capabilities.”
Dr. Abraham Mazor, VP Marketing & Business Development at Smart Shooter: “Using AI, computer vision and advanced algorithms, Smart Shooter’s SMASH technology enhances every mission effectiveness through the ability to accurately engage and hit ground, aerial, and naval, either static or moving targets during both day and night operations. Our SMASH Dragon lightweight robotic weaponry payload can be deployed on different unmanned aerial platforms, and we are honored to collaborate with Steadicopter and jointly offer the Golden Eagle RUAS.”
Rather than fret about how many jobs future technologies will destroy, we should focus on how to shape them so that they complement the workforce of tomorrow.
As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can learn by themselves continually in a self-motivated and self-initiated manner rather than being retrained offline periodically on the initiation of human engineers and accommodate or adapt to unexpected or novel circumstances. As the real-world is an open environment that is full of unknowns or novelties, detecting novelties, characterizing them, accommodating or adapting to them, and gathering ground-truth training data and incrementally learning the unknowns/novelties are critical to making the AI agent more and more knowledgeable and powerful over time.
The maker of the lethal drone claims that it can identify targets using artificial intelligence.
Over the past few decades, computer scientists have developed increasingly advanced techniques to train and operate robots. Collectively, these methods could facilitate the integration of robotic systems in an increasingly wide range of real-world settings.
Researchers at Carnegie Mellon University have recently created a new system that allows users to control a robotic hand and arm remotely, simply by demonstrating the movements they want it to replicate in front of a camera. This system, introduced in a paper pre-published on arXiv, could open exciting possibilities for the teleoperation and remote training of robots completing missions in both everyday settings and environments that are inaccessible to humans.
“Prior works in this area rely either on gloves, motion markers or a calibrated multi-camera setup,” Deepak Pathak, one of the researchers who developed the new system, told TechXplore. “Instead, our system works using a single uncalibrated camera. Since no calibration is needed, the user can be standing anywhere and still successfully teleoperate the robot.”
Quantum computers are getting bigger, but there are still few practical ways to take advantage of their extra computing power. To get over this hurdle, researchers are designing algorithms to ease the transition from classical to quantum computers. In a new study in Nature, researchers unveil an algorithm that reduces the statistical errors, or noise, produced by quantum bits, or qubits, in crunching chemistry equations.
Developed by Columbia chemistry professor David Reichman and postdoc Joonho Lee with researchers at Google Quantum AI, the algorithm uses up to 16 qubits on Sycamore, Google’s 53-qubit computer, to calculate ground state energy, the lowest energy state of a molecule. “These are the largest quantum chemistry calculations that have ever been done on a real quantum device,” Reichman said.
The ability to accurately calculate ground state energy, will enable chemists to develop new materials, said Lee, who is also a visiting researcher at Google Quantum AI. The algorithm could be used to design materials to speed up nitrogen fixation for farming and hydrolysis for making clean energy, among other sustainability goals, he said.
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