Trust the exponential. flat looking backwards, vertical looking forwards.
Physicists have purportedly created the first-ever wormhole, a kind of tunnel theorized in 1935 by Albert Einstein and Nathan Rosen that leads from one place to another by passing into an extra dimension of space.
The wormhole emerged like a hologram out of quantum bits of information, or “qubits,” stored in tiny superconducting circuits. By manipulating the qubits, the physicists then sent information through the wormhole, they reported today in the journal Nature.
The team, led by Maria Spiropulu of the California Institute of Technology, implemented the novel “wormhole teleportation protocol” using Google’s quantum computer, a device called Sycamore housed at Google Quantum AI in Santa Barbara, California. With this first-of-its-kind “quantum gravity experiment on a chip,” as Spiropulu described it, she and her team beat a competing group of physicists who aim to do wormhole teleportation with IBM and Quantinuum’s quantum computers.”
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Google is shutting down Duplex on the Web, its AI-powered set of services that navigated sites to simplify the process of ordering food, purchasing movie tickets and more. According to a note on a Google support page, Google on the Web and any automation features enabled by it will no longer be supported as of this month.
“As we continue to improve the Duplex experience, we’re responding to the feedback we’ve heard from users and developers about how to make it even better,” a Google spokesperson told TechCrunch via email, adding that Duplex on the Web partners have been notified to help them prepare for the shutdown. “By the end of this year, we’ll turn down Duplex on the Web and fully focus on making AI advancements to the Duplex voice technology that helps people most every day.”
Google introduced Duplex on the Web, an outgrowth of its call-automating Duplex technology, during its 2019 Google I/O developer conference. To start, it was focused on a couple of narrow use cases, including opening a movie theater chain’s website to fill out all of the necessary information on a user’s behalf — pausing to prompt for choices like seats. But Duplex on the Web later expanded to passwords, helping users automatically change passwords exposed in a data breach, as well as assist with checkout for e-commerce retailers, flight check-in for airline sites and automatic discount finding.
It’s probably not a secret to those doing a lot of focused work in the space, but when it comes to generative AI, it’s quickly becoming apparent that how a user interfaces with generative models and systems is at least as important as the underlying training and inference technology. The latest, and I think best example, comes via OpenAI’s ChatGPT, which launched as a free research preview for anyone to try this week.
In case you haven’t seen the buzz around ChatGPT yet, it’s basically an implementation of their new GPT-3.5 natural language generation technology, but implemented in such a way that you just chat with it in a web browser as if you were slacking with a colleague or interacting with a customer support agent on a website.
OpenAI has already made waves with its DALL-E image generation technology, and its GPT series has drawn attention with each successive release (and occasional existential dread on the part of writers). But the latest chat-style iteration has seemingly broadened its appeal and audience, in some ways moving the conversation from “wow, undergrads are going to use this to submit bad but workable term papers” to “wow, this could actually help me debug code that I intend to put in production.”
The UK government has announced that artificial intelligence algorithms that come up with new technologies will not be able to patent their inventions.
The Intellectual Property Office said on Tuesday that it also plans to tweak existing laws to make it easier for people and institutions to use AI, machine learning and data mining software in order to rapidly advance research and innovation without requiring extensive permissions from copyright owners.
‘It will tell you what’s going to happen in future,’ says University of Chicago professor. ‘It’s not magical, there are limitations… but it works really well’
New AI crime prediction tech is reminiscent of the 2002 sci-fi film Minority report, based on the 1956 short story by Philip K. Dick
An artificial intelligence algorithm that can predict crimes a week in advance with a 90 per cent accuracy has been demonstrated for the first time.
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.
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.
