Lifeboat Foundation

A recent paper tries to quantify the ability of neural networks to generalized abstract concepts.

What if you could travel to the country of your choice in just 1 click? If that was possible, your train of thought would be, Let’s go to Switzerland, no Iceland…you know what, let’s go everywhere. Teleportation is a common part of science fiction characters but is it achievable?

The pandemic has been hard on us and forced us to step out only when it is absolutely necessary. But you know what, Teleportation can be the perfect thing for you. And Earth is not the limit, you can put on a suit and some oxygen cylinder and you can just teleport to the moon…Elon Musk, you there?😃

But as far as we know, everyone told us while watching science fiction, this is not possible but you know what they are not entirely correct.

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.

Two scientists as different as could be — one a bookish astrophysicist who formerly served as NASA’s chief scientist, the other a charismatic mathematician who moonlights as a painter — have teamed up to unlock the secrets of dark matter.

From his Washington, DC office at NASA headquarters, Dr. Jim Green admitted that although he retired as NASA’s top scientist in January, he was already back as a consultant. He told Futurism the story of meeting up with his friend, Yeshiva University mathematician Ed Belbruno, when the latter invited the former to speak at the University of Augsburg in Germany.

Over lunch, they got to talking about the Pioneer Anomaly, the astrophysics-speak term for the bizarre slowing down effect witnessed by Pioneers 10 and 11. One thing led to another, and the pair soon found themselves with a long shot concept for an “Interstellar Probe” mission that they say could gather unprecedented data about dark matter and its place in the cosmos.

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.

Researchers have proposed a novel principle for a unique kind of computer that would use analog technology in place of digital or quantum components.

The unique device would be able to carry out complex computations extremely quickly—possibly, even faster than today’s supercomputers and at vastly less cost than any existing quantum computers.

The principle uses time delay to overcome the barriers in optimization problems (choosing the best option from a large number of possibilities), such as Google searches—which aim to find the optimal results matching the search request.

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.

The human brain holds the secret to our unique personalities. But did you know that it can also form the basis of highly efficient computing devices? Researchers from Nagoya University, Japan, recently showed how to do this, through graphene-diamond junctions that mimic some of the human brain’s functions.

But, why would scientists try to emulate the human brain? Today, existing computer architectures are subjected to complex data, limiting their processing speed. The human brain, on the other hand, can process highly complex data, such as images, with high efficiency. Scientists have, therefore, tried to build “neuromorphic” architectures that mimic the neural network in the brain.

A phenomenon essential for memory and learning is “synaptic plasticity,” the ability of synapses (neuronal links) to adapt in response to an increased or decreased activity. Scientists have tried to recreate a similar effect using transistors and “memristors” (electronic memory devices whose resistance can be stored). Recently developed light-controlled memristors, or “photomemristors,” can both detect light and provide non-volatile memory, similar to human visual perception and memory. These excellent properties have opened the door to a whole new world of materials that can act as artificial optoelectronic synapses!