Lifeboat Foundation

An adaptable new device can transform into all the key electric components needed for artificial-intelligence hardware, for potential use in robotics and autonomous systems, a new study finds.

Brain-inspired or “neuromorphic” computer hardware aims to mimic the human brain’s exceptional ability to adaptively learn from experience and rapidly process information in an extraordinarily energy-efficient manner. These features of the brain are due in large part to its plastic nature —its ability to evolve its structure and function over time through activity such as neuron formation or “neurogenesis.”

“We hypothesized if we could mimic these neurogenesis behaviors in electrical hardware, we could make machines that learn throughout their life-spans,” says study senior author Shriram Ramanathan, an electrical engineer and materials scientist at Purdue University, in West Lafayette, Ind.

For the first time, artificial intelligence has been integrated into a MEMS device.

In the new study, Spagnolo and his colleagues instead developed a quantum memristor that relies on a stream of photons existing in superpositions where each single photon can travel down two separate paths laser-written onto glass. One of the channels in this single-qubit integrated photonic circuit is used to measure the flow of these photons, and this data, through a complex electronic feedback scheme, controls the transmissions on the other path, resulting in the device behaving like a memristor.

Normally, memristive behavior and quantum effects are not expected to coexist, Spagnolo notes. Memristors are devices that essentially work by measuring the data flowing within them, but quantum effects are infamously fragile when it comes to any outside interference such as measurements. The researchers note they overcame this apparent contradiction by engineering interactions within their device to be strong enough to enable memristivity but weak enough to preserve quantum behavior.

Using computer simulations, the researchers suggest quantum memristors could lead to an exponential growth in performance in a machine-learning approach known as reservoir computing that excels at learning quickly. “Potentially, quantum reservoir computing may have a quantum advantage over classical reservoir computing,” Spagnolo says.

Materials that learn to change their shape in response to an external stimulus are a step closer to reality, thanks to a prototype system produced by engineers at UCLA.

Living entities constantly learn, adapting their behaviors to the environment so that they can thrive regardless of their surroundings. Inanimate materials typically don’t learn, except in science fiction movies. Now a team led by Jonathan Hopkins of the University of California, Los Angeles (UCLA), has demonstrated a so-called architected material that is capable of learning [1]. The material, which is made up of a network of beam-like components, learns to adapt its structure in response to a stimulus so that it can take on a specific shape. The team says that the material could act as a model system for future “intelligent” manufacturing.

The material developed by Hopkins and colleagues is a so-called mechanical neural network (MNN). If produced on a commercial scale, scientists think that these intelligent materials could revolutionize manufacturing in fields from building construction to fashion design. For example, an aircraft wing made from a MNN could learn to morph its shape in response to a change in wind conditions to maintain the aircraft’s flying efficiency; a house made from a MNN could adjust its structure to maintain the building’s integrity during an earthquake; and a shirt weaved from a MNN could alter its pattern so that it fits a person of any size.

AI programs that combine sensing and action and world models might achieve fish-level intelligence, says the NeurIPS 2022 keynote speaker.

The experiments were done to prove the effectiveness of 70mm rockets.

BAE Systems has tested its latest drone hunting missiles machine by conducting ground-to-air test firings, according to a press release by the company published on Tuesday.

Rockets fired from a containerized weapon system.

Continue reading “BAE Systems’ new drone-hunting missiles can take down unmanned aerial systems” »

ChatGPT is remarkable. It’s a new AI model from OpenAI that’s designed to chat in a conversational manner. It’s also a liar. Stuck for ideas on what to talk to a machine about, I decided to interview ChatGPT about the ethics of AI. Would it have the level of self-awareness to be honest about its own dangers? Would it even be willing to answer questions on how it behaves?

Yes, it would. And while ChatGPT started off by being commendably upfront about the ethics of what it does, it eventually descended into telling outright lies. It even issued a non-apology for doing so.

An interview with the cutting-edge chatbot, ChatGPT, ends in a little white lie.

Continue reading “I Interviewed An AI About The Ethics Of AI” »

Disney Research Studios’ newest neural network, FRAN, can shave hours off of VFX artists’ workloads to create convincingly re-aged actors.

Will human biases prevent us from enjoying computer-generated creative works?