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Category: robotics/AI – Page 1,089

A neuromorphic bionic eye with filter-free color vision using hemispherical perovskite nanowire array retina Communications

Cameras for machine vision and robotics are essentially bionic devices mimicking human eyes. These applications require advanced color imaging systems to possess a number of attributes such as high resolution, large FoV, compact design, light-weight and low energy consumption, etc1. Conventional imaging systems based on CCD/CMOS image sensors suffer from relatively low FoV, bulkiness, high complexity, and power consumption issues, especially with mechanically tunable optics. Recently, spherical bionic eyes with curved image sensor retinas have triggered enormous research interest1,2,3,4,5,6,7. This type of devices possess several appealing features such as simplified lens design, low image aberration, wide FoV, and appearance similar to that of the biological eyes rendering them suitable for humanoid robots8,9,10,11,12,13. However, the existing spherical bionic eyes with curved retinas typically only have fixed lens and can only acquire mono color images. Fixed lenses cannot image objects with varying distances. On the other hand, conventional color imaging function of CCD/CMOS image sensors are achieved by using color filter arrays, which add complexity to the device fabrication and cause optical loss14,15,16,17,18,19. Typical absorptive organic dye filters suffer from poor UV and high-temperature stabilities, and plasmonic color filters suffer from low transmission20,21,22. And it is even more challenging to fabricate color filter arrays on hemispherical geometry where most traditional microelectronic fabrication methods are not applicable.

Herein, we demonstrate a novel bionic eye design that possesses adaptive optics and a hemispherical nanowire array retina with filter-free color imaging and neuromorphic preprocessing abilities. The primary optical sensing function of the artificial retina is realized by using a hemispherical all-inorganic CsPbI3 nanowire array that can produce photocurrent without external bias leading to a self-powered working mode. Intriguingly, an electrolyte-assisted color-dependent bidirectional synaptic photo-response is discovered in a well-engineered hybrid nanostructure. Inspired by the vertical alignment of a color-sensitive cone cell and following neurons, the device structure vertically integrates a SnO2/NiO double-shell nanotube filled with ionic liquid in the core on top of a CsPbI3/NiO core-shell nanowire. It is found that the positive surrounding gate effect of NiO due to photo hole injection can be partially or fully balanced by electrolyte under shorter (blue) or longer (green and red) wavelength illuminations, respectively. Thus, the device can yield either positive or negative photocurrent under shorter or longer wavelength illumination, respectively. The carriers can be accumulated in SnO2/NiO structure, giving rise to the bidirectional synaptic photo-response. This color-sensitive bidirectional photo-response instills a unique filter-free color imaging function to the retina. The synaptic behavior-based neuromorphic preprocessing ability, along with the self-powered feature, effectively reduce the energy consumption of the system23,24,25,26,27,28. Moreover, the color selectivity of each pixel can be tuned by a small external bias to detect more accurate color information. We demonstrate that the device can reconstruct color images with high fidelity for convolutional neural network (CNN) classifications. In addition, our bionic eye integrates adaptive optics in the device, by integrating an artificial crystalline lens and an electronic iris based on liquid crystals. The artificial crystalline lens can switch focal length to detect objects from different distances, and the electronic iris can control the amount of light reaching the retina which enhances the dynamic range. Both of the optical components can be easily tuned by the electric field, which are fast, compact, and much more energy efficient compared to the conventional mechanically controlled optics reported hitherto. (Supplementary Table 1 compares our system with some commercial zoom lenses.) The combination of all these unique features makes the bionic eye structurally and functionally equivalent to its biological counterpart.

Stable Diffusion can visualize human thoughts from MRI data

Researchers show how Stable Diffusion can read minds. The method reconstructs images from fMRI scans with amazing accuracy.

Researchers have been using AI models to decode information from the human brain for years. At their core, most methods involve using pre-recorded fMRI images as input to a generative AI model for text or images.

In early 2018, for example, a group of researchers from Japan demonstrated how a neural network reconstructed images from fMRI recordings. In 2019, a group reconstructed images from monkey neurons, and Meta’s research group, led by Jean-Remi King, has published new work that derives text from fMRI data, for example.

Microsoft Designer Is The Very Worst Example Of AI

Microsoft is determined to thrust “AI” into all of its products at the moment and Microsoft Designer is no exception. This supposedly AI-driven service — currently in preview — is meant to create stunning social media posts, flyers etc. from your written prompts alone. Sadly, it’s about as intelligent as a Big Mac.

This is sort-of fine for a two-for one drinks offer:

This is, at best, conceptual:

Microsoft Designer has a very similar interface and set of features as Adobe Express, which I’ve used regularly to create social media posts, posters and other materials other the past couple of years.

Cormify leverages machine learning and 3D printing to make the best mouse for you

A lightweight, customized mouse delivering maximum comfort and peak performance that fits snugly into your palm and your palm alone.

In this day and age, where we spend hours hunched over a computer, there is a case for everything being ergonomic.

Into this niche steps Formify, a team based out of Toronto with the belief that individualized design should be accessible to everyone.

Berkeley researcher deploys robots and AI to increase pace of research by 100 times

No human intervention is required.

A research team led by Yan Zeng, a scientist at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab), has built a new material research laboratory where robots do the work and artificial intelligence (AI) can make routine decisions. This allows work to be conducted around the clock, thereby accelerating the pace of research.

Research facilities and instrumentation have come a long way over the years, but the nature of research remains the same. At the center of each experiment is a human doing the measurements, making sense of data, and deciding the next steps to be taken. At the A-Lab set up at Berkeley, the researchers led by Zeng want to break the current pace of research by using robotics and AI.

Using intelligent neuroprostheses to treat motor disorders

Scientists have long studied neurostimulation to treat paralysis and sensory deficits caused by strokes and spinal cord injuries, which in Canada affect some 380,000 people across the country.

A new study published in the journal Cell Reports Medicine demonstrates the possibility of autonomously optimizing the stimulation parameters of prostheses implanted in the brains of animals, without .

The work was done at Université de Montréal by neuroscience professors Marco Bonizzato, Numa Dancause and Marina Martinez, in collaboration with mathematics professor and Mila researcher Guillaume Lajoie.

Organoid Intelligence: Computing on the Brain

In parallel to recent developments in machine learning like GPT-4, a group of scientists has recently proposed the use of neural tissue itself, carefully grown to recreate the structures of the animal brain, as a computational substrate. After all, if AI is inspired by neurological systems, what better medium to do computing than an actual neurological system? Gathering developments from the fields of computer science, electrical engineering, neurobiology, electrophysiology, and pharmacology, the authors propose a new research initiative they call “organoid intelligence.”

OI is a collective effort to promote the use of brain organoids —tiny spherical masses of brain tissue grown from stem cells—for computation, drug research and as a model to study at a small scale how a complete brain may function. In other words, organoids provide an opportunity to better understand the brain, and OI aims to use that knowledge to develop neurobiological computational systems that learn from less data and with less energy than silicon hardware.

The development of organoids has been made possible by two bioengineering breakthroughs: induced pluripotent stem cells and 3D cell culturing techniques.

Artificial intelligence and machine learning are powering efforts to bioengineer new enzymes, expedite drug development and improve access to radiotherapy

Orion in March announced it has set out on a four-year project to build a cutting-edge ecosystem for pharmaceutical research in Finland.

Consisting of companies, universities and research institutes, the ecosystem will utilise artificial intelligence and machine learning in order to reduce the time required for studying and developing pharmaceutical products.

“Utilising data with the help of artificial intelligence is a competitive advantage for developing new innovative medicines because it expedites development and significantly increases the probability of success,” toldOuti Vaarala, director of innovative medicines at Orion.