Software upgrades could help resolve the issue.
A collaboration of researchers from the U.S. and Japan has demonstrated that a laser attack could be used to blind autonomous cars and delete pedestrians from their view, endangering those in its path, according to a press release.
Microbial molecules from soil, seawater and human bodies are among the planet’s least understood proteins.
The future of work is here.
As industries begin to see humans working closely with robots, there’s a need to ensure that the relationship is effective, smooth and beneficial to humans. Robot trustworthiness and humans’ willingness to trust robot behavior are vital to this working relationship. However, capturing human trust levels can be difficult due to subjectivity, a challenge researchers in the Wm Michael Barnes ‘64 Department of Industrial and Systems Engineering at Texas A&M University aim to solve.
Dr. Ranjana Mehta, associate professor and director of the NeuroErgonomics Lab, said her lab’s human-autonomy trust research stemmed from a series of projects on human-robot interactions in safety-critical work domains.
ARTIFICIAL intelligence has discovered a new life-changing drug and human trials are already underway.
The biotech company behind the breakthrough has dosed its first patient with an AI-developed treatment for ALS patients.
Alice Zhang, 33, is the founder of Verge Genomics and a former neuroscience doctoral student at University of California.
Discover agile mobile robots for research efforts across a wide range of industries — from construction to manufacturing, energy & utilities, oil & gas, and more.
Life on Earth would not exist as we know it, if not for the protein molecules that enable critical processes from photosynthesis and enzymatic degradation to sight and our immune system. And like most facets of the natural world, humanity has only just begun to discover the multitudes of protein types that actually exist. But rather scour the most inhospitable parts of the planet in search of novel microorganisms that might have a new flavor of organic molecule, Meta researchers have developed a first-of-its-kind metagenomic database, the ESM Metagenomic Atlas, that could accelerate existing protein-folding AI performance by 60x.
Metagenomics is just coincidentally named. It is a relatively new, but very real, scientific discipline that studies “the structure and function of entire nucleotide sequences isolated and analyzed from all the organisms (typically microbes) in a bulk sample.” Often used to identify the bacterial communities living on our skin or in the soil, these techniques are similar in function to gas chromatography, wherein you’re trying to identify what’s present in a given sample system.
Continue reading “Meta’s newest AI determines proper protein folds 60 times faster” »
He and his co-authors took four well-established computer vision models, which are commonly used for applications like autonomous driving, and trained them to tackle this problem.
Simulating blobs
To train these models, they created a vast dataset of synthetic video clips that captured the blobs’ random and unpredictable nature.
Recent technological advancements are opening new and exciting opportunities for communicating with others and visiting places remotely. These advancements include telepresence robots, moving robotic systems that allow users to virtually navigate remote environments and interact with people in these environments.
Researchers at Hanyang University and Duksung Women’s University in South Korea have recently developed a promising telepresence system based on a humanoid robot, a head mounted display, a motion transporter, a voice transporter, and a vision transporter system.
This system, introduced in a paper published in the International Journal of Social Robotics, allows users to take full-body ownership of a humanoid robot’s body, thus accessing remote environments and interacting with both humans and objects in these environments as if they were physically there.
Of course running a state of the art machine learning model, with billions of parameters, is not exactly easy when memory is measured in kilobytes. But with some creative thinking and a hybrid approach that leverages the power of the cloud and blends it with the advantages of tinyML, it may just be possible. A team of researchers at MIT has shown how this may be possible with their method called Netcast that relies on heavily-resourced cloud computers to rapidly retrieve model weights from memory, then transmit them nearly instantaneously to the tinyML hardware via a fiber optic network. Once those weights are transferred, an optical device called a broadband “Mach-Zehnder” modulator combines them with sensor data to perform lightning-fast calculations locally.
The team’s solution makes use of a cloud computer with a large amount of memory to retain the weights of a full neural network in RAM. Those weights are streamed to the connected device as they are needed through an optical pipe with enough bandwidth to transfer an entire full feature-length movie in a single millisecond. This is one of the biggest limiting factors that prevents tinyML devices from executing large models, but it is not the only factor. Processing power is also at a premium on these devices, so the researchers also proposed a solution to this problem in the form of a shoe box-sized receiver that performs super-fast analog computations by encoding input data onto the transmitted weights.
This scheme makes it possible to perform trillions of multiplications per second on a device that is resourced like a desktop computer from the early 1990s. In the process, on-device machine learning that ensures privacy, minimizes latency, and that is highly energy efficient is made possible. Netcast was test out on image classification and digit recognition tasks with over 50 miles separating the tinyML device and cloud resources. After only a small amount of calibration work, average accuracy rates exceeding 98% were observed. Results of this quality are sufficiently good for use in commercial products.
A network of autonomous cable cars transports people and goods around a city in this concept developed by transport design studio Half Company.
Halfgrid is a proposal for a city-wide transport system that uses artificial intelligence (AI) to move suspended capsules to their designated location.
It is billed as “a fully autonomous transit system for people and goods, running on a separate layer above the ground and based on the smallest unit possible — an individual person-sized pod”.
