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Category: robotics/AI – Page 1,017

Turbocharged Python: AI Accelerates Computing Speed by Thousands of Times

Their development Scalene, an open-source tool for dramatically speeding up the programming language Python, circumvents hardware issues limiting computer processing speeds.

A team of computer scientists at the University of Massachusetts Amherst, led by Emery Berger, recently unveiled a prize-winning Python profiler called Scalene. Programs written with Python are notoriously slow—up to 60,000 times slower than code written in other programming languages—and Scalene works to efficiently identify exactly where Python is lagging, allowing programmers to troubleshoot and streamline their code for higher performance.

There are many different programming languages—C++, Fortran, and Java are some of the more well-known ones—but, in recent years, one language has become nearly ubiquitous: Python.

The art of the prompt: How to get the best out of generative AI

Generative AI models can crank out anything from poetry and prose to images and code at your command. But to coax your desired output from these AI tools, you need to craft the right input — AKA, the prompt.

Prompts are what guide the AI model’s output and influence its tone, style and quality. And good prompts are what elicit brilliant text and stunning images.

“Writing good prompts is the key to unlocking the power and potential of generative AI,” said Jennifer Marsman, principal engineer in Microsoft’s Office of the Chief Technology Officer.

An energy-efficient object detection system for UAVs based on edge computing

Unmanned aerial vehicles (UAVs), commonly known as drones, are already used in countless settings to tackle real-world problems. These flying robotic systems can, among other things, help to monitor natural environments, detect fires or other environmental hazards, monitor cities and find survivors of natural disasters.

To tackle all of these missions effectively, UAVs should be able to reliably detect targets and objects of interest in their surroundings. Computer scientists have thus been trying to devise new computational techniques that could enable these capabilities, using deep learning or other approaches.

Researchers at Yunnan University and the Chinese Academy of Sciences recently introduced a new object-detection system based on edge computing. Their proposed system, introduced in the IEEE Internet of Things Journal, could provide UAVs with the ability to spot relevant objects and targets in their surroundings without significantly increasing their power-consumption.

US Army Brags About Plans to Mount Rifle on Robot Dog

Id wonder, and Doubt, if it could handle recoil. Weapons on Dog bots and Mini Uav s i would of liked to see would use electric centrifuge weapons, recoilless weapons, but development on has stalled also.

The brain geniuses at the Pentagon have decided that a good use of the taxpayer dollar is to attach rifles onto robot dogs, because why the hell not, right?

As Military.com reports, a spokesperson for the US Army said that the branch is considering arming remote-controlled robot dogs with state-of-the-art rifles as part of its plan to “explore the realm of the possible” in the future of combat.

The vision, as you’ve probably gathered, is pretty simple: to mount a rifle onto a robotic dog for domestic tasks across the military — and send it out into an unspecified battlefield.

AI Tools for Graphic Designers in 2023

What is an AI Graphic Design Tool?

Artificial intelligence (AI) models human intelligence processes in computers and computer-controlled robots. This enables computer systems to undertake arduous jobs, allowing people to concentrate on more vital matters.

As a result, the need for AI integrations in the workplace has grown over time. In fact, researchers project that the global AI software industry will be worth $791.5 billion by 2025.

NYU Researchers Developed a New Artificial Intelligence Technique to Change a Person’s Apparent Age in Images while Maintaining their Unique Identifying Features

AI systems are increasingly being employed to accurately estimate and modify the ages of individuals using image analysis. Building models that are robust to aging variations requires a lot of data and high-quality longitudinal datasets, which are datasets containing images of a large number of individuals collected over several years.

Numerous AI models have been designed to perform such tasks; however, many encounter challenges when effectively manipulating the age attribute while preserving the individual’s facial identity. These systems face the typical challenge of assembling a large set of training data consisting of images that show individual people over many years.

The researchers at NYU Tandon School of Engineering have developed a new artificial intelligence technique to change a person’s apparent age in images while ensuring the preservation of the individual’s unique biometric identity.

Brain-inspired learning algorithm realizes metaplasticity in artificial and spiking neural networks

Catastrophic forgetting, an innate issue with backpropagation learning algorithms, is a challenging problem in artificial and spiking neural network (ANN and SNN) research.

The brain has somewhat solved this problem using multiscale plasticity. Under global regulation through specific pathways, neuromodulators are dispersed to target , where both synaptic and neuronal plasticity are modulated by neuromodulators locally. Specifically, neuromodulators modify the capacity and property of neuronal and . This modification is known as metaplasticity.

Researchers led by Prof. Xu Bo from the Institute of Automation of the Chinese Academy of Sciences and their collaborators have proposed a novel brain-inspired learning method (NACA) based on neural modulation dependent plasticity, which can help mitigate catastrophic forgetting in ANN and SNN. The study was published in Science Advances on Aug. 25.

DeepMind’s ChatGPT-Like Brain for Robots Lets Them Learn From the Internet

Examples the team gives include choosing an object to use as a hammer when there’s no hammer available (the robot chooses a rock) and picking the best drink for a tired person (the robot chooses an energy drink).

“RT-2 shows improved generalization capabilities and semantic and visual understanding beyond the robotic data it was exposed to,” the researchers wrote in a Google blog post. “This includes interpreting new commands and responding to user commands by performing rudimentary reasoning, such as reasoning about object categories or high-level descriptions.”

The dream of general-purpose robots that can help humans with whatever may come up—whether in a home, a commercial setting, or an industrial setting—won’t be achievable until robots can learn on the go. What seems like the most basic instinct to us is, for robots, a complex combination of understanding context, being able to reason through it, and taking actions to solve problems that weren’t anticipated to pop up. Programming them to react appropriately to a variety of unplanned scenarios is impossible, so they need to be able to generalize and learn from experience, just like humans do.

AI predicts chemicals’ smells from their structures

To explore the association between a chemical’s structure and its odour, Wiltschko and his team at Osmo designed a type of artificial intelligence (AI) system called a neural network that can assign one or more of 55 descriptive words, such as fishy or winey, to an odorant. The team directed the AI to describe the aroma of roughly 5,000 odorants. The AI also analysed each odorant’s chemical structure to determine the relationship between structure and aroma.

The system identified around 250 correlations between specific patterns in a chemical’s structure with a particular smell. The researchers combined these correlations into a principal odour map (POM) that the AI could consult when asked to predict a new molecule’s scent.

To test the POM against human noses, the researchers trained 15 volunteers to associate specific smells with the same set of descriptive words used by the AI. Next, the authors collected hundreds of odorants that don’t exist in nature but are familiar enough for people to describe. They asked the human volunteers to describe 323 of them and asked the AI to predict each new molecule’s scent on the basis of its chemical structure. The AI’s guess tended to be very close to the average response given by the humans — often closer than any individual’s guess.

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