Lifeboat Foundation

By default, every quantum computer is going to be a hybrid that combines quantum and classical compute. Microsoft estimates that a quantum computer that will be able to help solve some of the world’s most pressing questions will require at least a million stable qubits. It’ll take massive classical compute power — which is really only available in the cloud — to control a machine like this and handle the error correction algorithms needed to keep it stable. Indeed, Microsoft estimates that to achieve the necessary fault tolerance, a quantum computer will need to be integrated with a peta-scale compute platform that can manage between 10 to 100 terabits per second of data moving between the quantum and classical machine. At the American Physical Society March Meeting in Las Vegas, Microsoft today is showing off some of the work it has been doing on enabling this and launching what it calls the “Integrated Hybrid” feature in Azure Quantum.

“With this Integrated Hybrid feature, you can start to use — within your quantum applications — classical code right alongside quantum code,” Krysta Svore, Microsoft’s VP of Advanced Quantum Development, told me. “It’s mixing that classical and quantum code together that unlocks new types, new styles of quantum algorithms, prototypes, sub routines, if you will, where you can control what you do to qubits based on classical information. This is a first in the industry.”

Robots are all around us, from drones filming videos in the sky to serving food in restaurants and diffusing bombs in emergencies. Slowly but surely, robots are improving the quality of human life by augmenting our abilities, freeing up time, and enhancing our personal safety and well-being. While existing robots are becoming more proficient with simple tasks, handling more complex requests will require more development in both mobility and intelligence.

Columbia Engineering and Toyota Research Institute computer scientists are delving into psychology, physics, and geometry to create algorithms so that robots can adapt to their surroundings and learn how to do things independently. This work is vital to enabling robots to address new challenges stemming from an aging society and provide better support, especially for seniors and people with disabilities.

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Artificial intelligence can create images based on text prompts, but scientists unveiled a gallery of pictures the technology produces by reading brain activity. The new AI-powered algorithm reconstructed around 1,000 images, including a teddy bear and an airplane, from these brain scans with 80 percent accuracy.

HRL Laboratories, LLC, has published the first demonstration of universal control of encoded spin qubits. This newly emerging approach to quantum computation uses a novel silicon-based qubit device architecture, fabricated in HRL’s Malibu cleanroom, to trap single electrons in quantum dots. Spins of three such single electrons host energy-degenerate qubit states, which are controlled by nearest-neighbor contact interactions that partially swap spin states with those of their neighbors.

Posted online ahead of publication in the journal Nature, the HRL experiment demonstrated universal control of their encoded qubits, which means the qubits can be used successfully for any kind of quantum computational algorithm implementation. The encoded silicon/silicon germanium quantum dot qubits use three electron spins and a control scheme whereby voltages applied to metal gates partially swap the directions of those electron-spins without ever aligning them in any particular direction. The demonstration involved applying thousands of these precisely calibrated voltage pulses in strict relation to one another over the course of a few millionths of a second. The article is entitled “Universal logic with encoded spin qubits in silicon.”

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As the use of machine learning (ML) algorithms continues to grow, computer scientists worldwide are constantly trying to identify and address ways in which these algorithms could be used maliciously or inappropriately. Due to their advanced data analysis capabilities, in fact, ML approaches have the potential to enable third parties to access private data or carry out cyberattacks quickly and effectively.

Morteza Varasteh, a researcher at the University of Essex in the U.K., has recently identified new type of inference attack that could potentially compromise confidential user data and share it with other parties. This attack, which is detailed in a paper pre-published on arXiv, exploits vertical federated learning (VFL), a distributed ML scenario in which two different parties possess different information about the same individuals (clients).

“This work is based on my previous collaboration with a colleague at Nokia Bell Labs, where we introduced an approach for extracting private user information in a data center, referred to as the passive party (e.g., an insurance company),” Varasteh told Tech Xplore. “The passive party collaborates with another data center, referred to as the active party (e.g., a bank), to build an ML algorithm (e.g., a credit approval algorithm for the bank).”

In recent years, the field of artificial intelligence has made tremendous strides, but what happens when #AI systems become #selfaware? In this video, we’ll explore the concept of AI self-awareness, its #scary implications for society, and what it means for the #future of AI.

AI self-awareness is the ability of an #artificialintelligence system to recognize its own existence and understand the consequences of its actions. While there are different levels of self-awareness that an AI system could potentially exhibit, it generally involves the system being able to recognize and respond to changes in its own state.
One way that researchers are exploring AI self-awareness is by using neural networks and other machine learning algorithms. For example, researchers have created AI systems that can recognize and respond to their own errors, which is an important first step in developing higher-order self-awareness.

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Quantum bits inspired by Schrödinger’s cat could allow quantum computers to make fewer mistakes and more efficiently crack algorithms used for encryption.

By Karmela Padavic-Callaghan

X-AR uses wireless signals and computer vision to enable users to perceive things that are invisible to the human eye (i.e., to deliver non-line-of-sight perception). It combines new antenna designs, wireless signal processing algorithms, and AI-based fusion of different sensors.

This design introduces three main innovations:

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The field equations of Einstein’s General Relativity theory say that faster-than-light (FTL) travel is possible, so a handful of researchers are working to see whether a Star Trek-style warp drive, or perhaps a kind of artificial wormhole, could be created through our technology.

But even if shown feasible tomorrow, it’s possible that designs for an FTL system could be as far ahead of a functional starship as Leonardo da Vinci’s 16th century drawings of flying machines were ahead of the Wright Flyer of 1903. But this need not be a showstopper against human interstellar flight in the next century or two. Short of FTL travel, there are technologies in the works that could enable human expeditions to planets orbiting some of the nearest stars.

Certainly, feasibility of such missions will depend on geopolitical-economic factors. But it also will depend on the distance to nearest Earth-like exoplanet. Located roughly 4.37 light years away, Alpha Centauri is the Sun’s closest neighbor; thus science fiction, including Star Trek, has envisioned it as humanity’s first interstellar destination.