Lifeboat Foundation

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!

Multifunctional and diverse artificial neural systems can incorporate multimodal plasticity, memory and supervised learning functions to assist neuromorphic computation. In a new report, Jinran Yu and a research team in nanoenergy, nanoscience and materials science in China and the US., presented a bioinspired mechano-photonic artificial synapse with synergistic mechanical and optical plasticity. The team used an optoelectronic transistor made of graphene/molybdenum disulphide (MoS₂) heterostructure and an integrated triboelectric nanogenerator to compose the artificial synapse. They controlled the charge transfer/exchange in the heterostructure with triboelectric potential and modulated the optoelectronic synapse behaviors readily, including postsynaptic photocurrents, photosensitivity and photoconductivity. The mechano-photonic artificial synapse is a promising implementation to mimic the complex biological nervous system and promote the development of interactive artificial intelligence. The work is now published on Science Advances.

Brain-inspired neural networks.

The human brain can integrate cognition, learning and memory tasks via auditory, visual, olfactory and somatosensory interactions. This process is difficult to be mimicked using conventional von Neumann architectures that require additional sophisticated functions. Brain-inspired neural networks are made of various synaptic devices to transmit information and process using the synaptic weight. Emerging photonic synapse combine the optical and electric neuromorphic modulation and computation to offer a favorable option with high bandwidth, fast speed and low cross-talk to significantly reduce power consumption. Biomechanical motions including touch, eye blinking and arm waving are other ubiquitous triggers or interactive signals to operate electronics during artificial synapse plasticization. In this work, Yu et al. presented a mechano-photonic artificial synapse with synergistic mechanical and optical plasticity.

The human brain, fed on just the calorie input of a modest diet, easily outperforms state-of-the-art supercomputers powered by full-scale station energy inputs. The difference stems from the multiple states of brain processes versus the two binary states of digital processors, as well as the ability to store information without power consumption—non-volatile memory. These inefficiencies in today’s conventional computers have prompted great interest in developing synthetic synapses for use in computers that can mimic the way the brain works. Now, researchers at King’s College London, UK, report in ACS Nano Letters an array of nanorod devices that mimic the brain more closely than ever before. The devices may find applications in artificial neural networks.

Efforts to emulate biological synapses have revolved around types of memristors with different resistance states that act like memory. However, unlike the brain the devices reported so far have all needed a reverse polarity electrical voltage to reset them to the initial state. “In the brain a change in the chemical environment changes the output,” explains Anatoly Zayats, a professor at King’s College London who led the team behind the recent results. The King’s College London researchers have now been able to demonstrate this brain-like behavior in their synaptic synapses as well.

Zayats and team build an array of gold nanorods topped with a polymer junction (poly-L-histidine, PLH) to a metal contact. Either light or an electrical voltage can excite plasmons—collective oscillations of electrons. The plasmons release hot electrons into the PLH, gradually changing the chemistry of the polymer, and hence changing it to have different levels of conductivity or light emissivity. How the polymer changes depends on whether oxygen or hydrogen surrounds it. A chemically inert nitrogen chemical environment will preserve the state without any energy input required so that it acts as non-volatile memory.