US regulators have devised a set of new rules specifically targeting automated driving system (ADS) vehicles that do not feature human controls.
Physicists have discovered a new way to coat soft robots in materials that allow them to move and function in a more purposeful way. The research, led by the UK’s University of Bath, is described today in Science Advances.
Authors of the study believe their breakthrough modeling on ‘active matter’ could mark a turning point in the design of robots. With further development of the concept, it may be possible to determine the shape, movement and behavior of a soft solid not by its natural elasticity but by human-controlled activity on its surface.
The surface of an ordinary soft material always shrinks into a sphere. Think of the way water beads into droplets: the beading occurs because the surface of liquids and other soft material naturally contracts into the smallest surface area possible—i.e. a sphere. But active matter can be designed to work against this tendency. An example of this in action would be a rubber ball that’s wrapped in a layer of nano-robots, where the robots are programmed to work in unison to distort the ball into a new, pre-determined shape (say, a star).
A new approach to producing realistic expressions of pain on robotic patients could help to reduce error and bias during physical examination.
A team led by researchers at Imperial College London has developed a way to generate more accurate expressions of pain on the face of medical training robots during physical examination of painful areas.
Findings, published today in Scientific Reports, suggest this could help teach trainee doctors to use clues hidden in patient facial expressions to minimize the force necessary for physical examinations.
Algorithms that use the brain’s communication signal can now work on analog neuromorphic chips, which closely mimic our energy-efficient brains.
A Stanford University-led research team has set a new Guinness World Record for the fastest DNA sequencing technique using AI computing to accelerate workflow speed.
The research, led by Dr Euan Ashley, professor of medicine, genetics and biomedical data science at Stanford School of Medicine, in collaboration with Nvidia, Oxford Nanopore Technologies, Google, Baylor College of Medicine, and the University of California, achieved sequencing in just five hours and two minutes.
The study, published in The New England Journal of Medicine, involved speeding up every step of genome sequencing workflow by relying on new technology. This included using nanopore sequencing on Oxford Nanopore’s PromethION Flow Cells to generate more than 100 gigabases of data per hour, and Nvidia GPUs on Google Cloud to speed up the base calling and variant calling processes.
A recent paper tries to quantify the ability of neural networks to generalized abstract concepts.
Artificial intelligence is making its significance in most of the major sectors these days. The top AI startups are making a buzz in the market that has the potential to revolutionize the world. There are thousands of AI startups available today and this blog will share some of the most promising AI Startups that are making waves in the AI technology field.
Before jumping into sharing about these startups, let’s understand what area of field AI is contributing to. Mentioned below are some of the major fields where AI is contributing and bringing a change.
Researchers at the Max Planck Institute for Intelligent Systems in Stuttgart have designed and fabricated an untethered microrobot that can slip along either a flat or curved surface in a liquid when exposed to ultrasound waves. Its propulsion force is two to three orders of magnitude stronger than the propulsion force of natural microorganisms such as bacteria or algae. Additionally, it can transport cargo while swimming. The acoustically propelled robot hence has significant potential to revolutionize the future minimally invasive treatment of patients.
Stuttgart—Researchers at the Max Planck Institute for Intelligent Systems (MPI-IS) in Stuttgart developed a bullet-shaped, synthetic miniature robot with a diameter of 25 micrometers, which is acoustically propelled forward—a speeding bullet, in the truest sense of the word. Less than the diameter of a human hair in size, never before has such an actuated microrobot reached this speed. Its smart design is so efficient it even outperforms the swimming capabilities of natural microorganisms.
The scientists designed the 3D-printed polymer microrobot with a spherical cavity and a small tube-like nozzle towards the bottom (see figure 1). Surrounded by liquid such as water, the cavity traps a spherical air bubble. Once the robot is exposed to acoustic waves of around 330 kHz, the air bubble pulsates, pushing the liquid inside the tube towards the back end of the microrobot. The liquid’s movement then propels the bullet forward quite vigorously at up to 90 body lengths per second. That is a thrust force two to three orders of magnitude stronger than those of natural microorganisms such as algae or bacteria. Both are among the most efficient microswimmers in nature, optimized by evolution.
From chatbots that answer tax questions to algorithms that drive autonomous vehicles and dish out medical diagnoses, artificial intelligence undergirds many aspects of daily life. Creating smarter, more accurate systems requires a hybrid human-machine approach, according to researchers at the University of California, Irvine. In a study published this month in Proceedings of the National Academy of Sciences, they present a new mathematical model that can improve performance by combining human and algorithmic predictions and confidence scores.
“Humans and machine algorithms have complementary strengths and weaknesses. Each uses different sources of information and strategies to make predictions and decisions,” said co-author Mark Steyvers, UCI professor of cognitive sciences. “We show through empirical demonstrations as well as theoretical analyses that humans can improve the predictions of AI even when human accuracy is somewhat below [that of] the AI—and vice versa. And this accuracy is higher than combining predictions from two individuals or two AI algorithms.”
To test the framework, researchers conducted an image classification experiment in which human participants and computer algorithms worked separately to correctly identify distorted pictures of animals and everyday items—chairs, bottles, bicycles, trucks. The human participants ranked their confidence in the accuracy of each image identification as low, medium or high, while the machine classifier generated a continuous score. The results showed large differences in confidence between humans and AI algorithms across images.
Scientists are getting better at making neurone-like junctions for computers that mimic the human brain’s random information processing, storage and recall. Fei Zhuge of the Chinese Academy of Sciences and colleagues reviewed the latest developments in the design of these “memristors” for the journal Science and Technology of Advanced Materials.
Computers apply artificial intelligence programs to recall previously learned information and make predictions. These programs are extremely energy-and time-intensive: typically, vast volumes of data must be transferred between separate memory and processing units. To solve this, researchers have been developing computer hardware that allows for more random and simultaneous information transfer and storage, much like the human brain.
Electronic circuits in these “neuromorphic” computers include memristors that resemble the synaptic junctions between neurones. Energy flows through a material from one electrode to another, much like a neurone firing a signal across the synapse to the next neurone. Scientists are now finding ways to better tune this intermediate material so the information flow is more stable and reliable.
