Toggle light / dark theme

A neural autoencoder to enhance sensory neuroprostheses

New technologies have the potential to greatly simplify the lives of humans, including those of blind individuals. One of the most promising types of tools designed to assist the blind are visual prostheses.

Visual prostheses are that can be implanted in the brain. These devices could help to restore vision in people affected by different types of blindness. Despite their huge potential, most existing visual prostheses achieved unimpressive results, as the vision they can produce is extremely rudimentary.

A team of researchers a University of California, Santa Barbara recently developed a that could significantly enhance the performance of visual prostheses, as well as other sensory neuroprostheses (i.e., devices aimed at restoring lost sensory functions or augmenting human abilities). The model they developed, presented in a paper pre-published on arXiv, is based on the use of a neural autoencoder, a brain-inspired architecture that can discover specific patterns in data and create representations of them.

Quantum Artificial Intelligence | My PhD at MIT

Algorithms, Shor’s Quantum Factoring Algorithm for breaking RSA Security, and the Future of Quantum Computing.

▬ In this video ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
I talk about my PhD research at MIT in Quantum Artificial Intelligence. I also explain the basic concepts of quantum computers, and why they are superior to conventional computers for specific tasks. Prof. Peter Shor, the inventor of Shor’s algorithm and one of the founding fathers of Quantum Computing, kindly agreed to participate in this video.

▬ Follow me ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
LinkedIn: https://www.linkedin.com/in/samuel-bosch/
Instagram: https://www.instagram.com/samuel.bosch/

▬ Credits ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
Some of the animations were taken from “Quanta Magazine” (Quantum Computers, Explained With Quantum Physics): https://www.youtube.com/channel/UCTpmmkp1E4nmZqWPS-dl5bg.

Other animations are from “Josh’s Channel” (How Quantum Computers Work): https://www.youtube.com/channel/UCnNEI3UdreSoQ6XUNcKoUeg.

The quantum circuit animations are from “Kurzgesagt – In a Nutshell” (Quantum Computers Explained – Limits of Human Technology): https://www.youtube.com/channel/UCsXVk37bltHxD1rDPwtNM8Q

Anton van den Hengel’s journey from intellectual property law to computer vision pioneer

The world’s most-cited researcher in visual question-answering, Anton van den Hengel, is also Amazon’s director of applied science. Learn how his journey to computer vision started with law—and how his work is supporting Amazon’s business through the development and application of state-of-the-art computer vision and scalable machine learning.

#ComputerVision #CVPR2022


Amazon’s director of applied science in Adelaide, Australia, believes the economic value of computer vision has “gone through the roof”.

MIT researchers have built a new LEGO-like AI chip

With a more sustainable world goal, MIT researchers have succeeded in developing a new LEGO-like AI chip. Imagine a world where cellphones, smartwatches, and other wearable technologies don’t have to be put away or discarded for a new model. Instead, they could be upgraded with the newest sensors and processors that would snap into a device’s internal chip – similar to how LEGO bricks can be incorporated into an existing structure. Such reconfigurable chips might keep devices current while lowering electronic waste. This is really important because green computing is the key to a sustainable future.

MIT engineers have developed a stackable, reprogrammable LEGO-like AI chip. The chip’s layers communicate thanks optically to alternating layers of sensing and processing components, as well as light-emitting diodes (LEDs). Other modular chip designs use conventional wiring to transmit signals between layers. Such intricate connections are difficult, if not impossible, to cut and rewire, making stackable configurations nonreconfigurable.

Rather than relying on physical wires, the MIT design uses light to transfer data across the AI chip. As a result, the chip’s layers may be swapped out or added upon, for example, to include extra sensors or more powerful processors.