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Category: robotics/AI – Page 1,003

Shining a light on neuromorphic computing

AI, machine learning, and ChatGPT may be relatively new buzzwords in the public domain, but developing a computer that functions like the human brain and nervous system—both hardware and software combined—has been a decades-long challenge. Engineers at the University of Pittsburgh are today exploring how optical “memristors” may be a key to developing neuromorphic computing.

Resistors with memory, or memristors, have already demonstrated their versatility in electronics, with applications as computational circuit elements in and compact memory elements in high-density data storage. Their unique design has paved the way for in-memory computing and captured significant interest from scientists and engineers alike.

A new review article published in Nature Photonics, titled “Integrated Optical Memristors,” sheds light on the evolution of this technology—and the work that still needs to be done for it to reach its full potential.

Ben Goertzel — 2021 Reflection and Update on SNET, Ecosystem and Path to AGI

Dr. Ben Goertzel shares his thoughts on where we are at the end of 2021, beginning of 2022 — how progress toward AGI looks in retrospect, and looking into the future — updates on the ecosystem…

And the importance of the SingularityNET Community 🥰
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SingularityNET is a decentralized marketplace for artificial intelligence. We aim to create the world’s global brain with a full-stack AI solution powered by a decentralized protocol.

We gathered the leading minds in machine learning and blockchain to democratize access to AI technology. Now anyone can take advantage of a global network of AI algorithms, services, and agents.

Website: https://singularitynet.io.
Forum: https://community.singularitynet.io.
Telegram: https://t.me/singularitynet.
Twitter: https://twitter.com/singularity_net.
Facebook: https://facebook.com/singularitynet.io.
Instagram: https://instagram.com/singularitynet.io.
Github: https://github.com/singnet.
Linkedin: https://www.linkedin.com/company/singularitynet

The case for why our Universe may be a giant neural network

For example, scientists have recently emphasized that the physical organization of the Universe mirrors the structure of a brain. Theoretical physicist Sabine Hossenfelder — renowned for her skepticism — wrote a bold article for Time Magazine in August of 2022 titled “Maybe the Universe Thinks. Hear Me Out,” which describes the similarities. Like our nervous system, the Universe has a highly interconnected, hierarchical organization. The estimated 200 billion detectable galaxies aren’t distributed randomly, but lumped together by gravity into clusters that form even larger clusters, which are connected to one another by “galactic filaments,” or long thin threads of galaxies. When one zooms out to envision the cosmos as a whole, the “cosmic web” formed by these clusters and filaments looks strikingly similar to the “connectome,” a term that refers to the complete wiring diagram of the brain, which is formed by neurons and their synaptic connections. Neurons in the brain also form clusters, which are grouped into larger clusters, and are connected by filaments called axons, which transmit electrical signals across the cognitive system.

Hossenfelder explains that this resemblance between the cosmic web and the connectome is not superficial, citing a rigorous study by a physicist and a neuroscientist that analyzed the features common to both, and based on the shared mathematical properties, concluded that the two structures are “remarkably similar.” Due to these uncanny similarities, Hossenfelder speculates as to whether the Universe itself could be thinking.

DeepMind AI creates algorithms that sort data faster than those built by people

Computer scientists have, for decades, been optimizing how computers sort data to shave off crucial milliseconds in returning search results or alphabetizing contact lists. Now DeepMind, based in London, has vastly improved sorting speeds by applying the technology behind AlphaZero — its artificial-intelligence system for playing the board games chess, Go and shogi — to a game of building sorting algorithms. “This is an exciting result,” said Emma Brunskill, a computer scientist at Stanford University, California.

The system, AlphaDev, is described in a paper in Nature1, and has invented faster algorithms that are already part of two standard C++ coding libraries, so are being used trillions of times per day by programmers around the world.

AI researcher, Stanford professor Andrew Ng: AI poses ‘no extinction risk’ for humans

Unlike many of his peers in the artificial intelligence community, Andrew Ng isn’t convinced about the dangers of AI.

In a video posted to Twitter this week, Ng, a Stanford University professor and founder of several Silicon Valley AI startups, expressed doubt about the doomsday predictions of other executives and experts in the field.

Technology For Technology’s Sake Is The Downfall Of The CIO

A unique use case for AI is around enhanced transaction monitoring to help combat financial fraud. Traditional rule-based approaches to anti-money laundering (AML) use static thresholds that only capture one element of a transaction, meaning they deliver a high rate of false positives. Not only is this hugely inefficient, but it can also be very demotivating for staff. With AI, multiple factors can be reviewed simultaneously to extract a risk score and develop an intelligent understanding of what risky behavior looks like. A feedback loop based on advanced analytics means that the more data is collected, the more intelligent the solution becomes. Pinpointing financial crime becomes more efficient and employees also benefit from more free time to focus efforts on other areas of importance like strategy and business development.

Thanks to its ever-increasing applications to evolving business challenges, regulators and financial institutions can no longer turn a blind eye to the potential of AI, with the power to revolutionize the financial system. It presents unique opportunities to reduce the capacity for human error, costing highly regulated industries billions each year.

What’s clear is that some technologies will, over time, become too difficult to ignore. As we saw with the adoption of the cloud, failure to embrace innovative technologies means organizations will get left behind. The cloud was once a pipedream, but now it’s a crucial part of all business operations today. Businesses implemented (or are in the process of implementing) huge digital transformation projects to migrate business processes to the cloud. Similarly, new organizations will kickstart their businesses in the cloud. This is a lesson that technologists must remain alert and continue to keep their finger on the pulse when it comes to incorporating fresh solutions.