Lifeboat Foundation

How many qubits do we have to have in a quantum computer and accessble to a wide market to trully have something scfi worthy?

Today, a startup called Atom Computing announced that it has been doing internal testing of a 1,180 qubit quantum computer and will be making it available to customers next year. The system represents a major step forward for the company, which had only built one prior system based on neutral atom qubits—a system that operated using only 100 qubits.

The error rate for individual qubit operations is high enough that it won’t be possible to run an algorithm that relies on the full qubit count without it failing due to an error. But it does back up the company’s claims that its technology can scale rapidly and provides a testbed for work on quantum error correction. And, for smaller algorithms, the company says it’ll simply run multiple instances in parallel to boost the chance of returning the right answer.

Continue reading “Atom Computing is the first to announce a 1,000+ qubit quantum computer” »

On Friday, researchers from Nvidia, UPenn, Caltech, and the University of Texas at Austin announced Eureka, an algorithm that uses OpenAI’s GPT-4 language model for designing training goals (called “reward functions”) to enhance robot dexterity. The work aims to bridge the gap between high-level reasoning and low-level motor control, allowing robots to learn complex tasks rapidly using massively parallel simulations that run through trials simultaneously. According to the team, Eureka outperforms human-written reward functions by a substantial margin.

“Leveraging state-of-the-art GPU-accelerated simulation in Nvidia Isaac Gym,” writes Nvidia on its demonstration page, “Eureka is able to quickly evaluate the quality of a large batch of reward candidates, enabling scalable search in the reward function space.

face_with_colon_three This looks awesome :3.

There is great interest in using quantum computers to efficiently simulate a quantum system’s dynamics as existing classical computers cannot do this. Little attention, however, has been given to quantum simulation of a classical nonlinear continuum system such as a viscous fluid even though this too is hard for classical computers. Such fluids obey the Navier–Stokes nonlinear partial differential equations, whose solution is essential to the aerospace industry, weather forecasting, plasma magneto-hydrodynamics, and astrophysics. Here we present a quantum algorithm for solving the Navier–Stokes equations. We test the algorithm by using it to find the steady-state inviscid, compressible flow through a convergent-divergent nozzle when a shockwave is (is not) present.

It has become nearly impossible for human researchers to keep track of the overwhelming abundance of scientific publications in the field of artificial intelligence and to stay up-to-date with advances.

Scientists in an international team led by Mario Krenn from the Max-Planck Institute for the Science of Light have now developed an AI algorithm that not only assists researchers in orienting themselves systematically but also predictively guides them in the direction in which their own research field is likely to evolve. The work was published in Nature Machine Intelligence.

In the field of artificial intelligence (AI) and machine learning (ML), the number of scientific publications is growing exponentially and approximately doubling every 23 months. For human researchers, it is nearly impossible to keep up with progress and maintain a comprehensive overview.

New research on information entropy may offer evidence for the theory that our universe is a sophisticated simulation, with deep implications for various fields, from biology to cosmology.

The simulated universe theory implies that our universe, with all its galaxies, planets and life forms, is a meticulously programmed computer simulation. In this scenario, the physical laws governing our reality are simply algorithms. The experiences we have are generated by the computational processes of an immensely advanced system.

While inherently speculative, the simulated universe theory has gained attention from scientists and philosophers due to its intriguing implications. The idea has made its mark in popular culture, across movies, TV shows, and books – including the 1999 film The Matrix.

The simulated universe theory implies that our universe, with all its galaxies, planets and life forms, is a meticulously programmed computer simulation. In this scenario, the physical laws governing our reality are simply algorithms. The experiences we have are generated by the computational processes of an immensely advanced system.

While inherently speculative, the simulated universe theory has gained attention from scientists and philosophers due to its intriguing implications. The idea has made its mark in popular culture, across movies, TV shows and books—including the 1999 film “The Matrix.”

The earliest records of the concept that reality is an illusion are from ancient Greece. There, the question “What is the nature of our reality?” posed by Plato (427 BC) and others, gave birth to idealism. Idealist ancient thinkers such as Plato considered mind and spirit as the abiding reality. Matter, they argued, was just a manifestation or illusion.

Eureka has also taught quadruped, dexterous hands, cobot arms and other robots to open drawers, use scissors, catch balls and nearly 30 different tasks. According to NVIDIA Research, the AI agent’s trial and error-based reward programs are 80 percent more effective than those written by human experts. This shift meant the robots’ performance also improved by over 50 percent. Eureka also self-evaluates based on training results, instructing changes in reward functions as it sees fit.

NVIDIA Research has published a library of its Eureka algorithms, encouraging others to try them out on NVIDIA Isaac Gym, the organization’s “physics simulation reference application for reinforcement learning research.”

The idea of robots teaching robots is seeing increased interest and success. A May 2023 paper published in the Transactions on Machine Learning Research journal presented a new system called SKILL (Shared Knowledge Lifelong Learning), which allowed AI systems to learn 102 different skills, including diagnosing diseases from chest X-rays and identifying species of flowers. The AIs shared their knowledge — acting as teachers in a way — with each other over a communication network and were able to master each of the 102 skills. Researchers at schools like MIT and the University of Bristol have also had success, specifically in using AI to teach robots how to manipulate objects.

Whenever light interacts with matter, light appears to slow down. This is not a new observation and standard wave mechanics can describe most of these daily phenomena.

For example, when light is incident on an interface, the standard wave equation is satisfied on both sides. To analytically solve such a problem, one would first find what the wave looks like at either side of the interface, and then employ electromagnetic boundary conditions to link the two sides together. This is called a piecewise continuous solution.

However, at the boundary, the incident light must experience an acceleration. So far, this has not been accounted for.

Two types of technologies could change the privacy afforded in encrypted messages, and changes to this space could impact all of us.

On October 9, I moderated a panel on encryption, privacy policy, and human rights at the United Nations’s annual Internet Governance Forum. I shared the stage with some fabulous panelists including Roger Dingledine, the director of the Tor Project; Sharon Polsky, the president of the Privacy and Access Council of Canada; and Rand Hammoud, a campaigner at Access Now, a human rights advocacy organization. All strongly believe in and champion the protection of encryption.

I want to tell you about one thing that came up in our conversation: efforts to, in some way, monitor encrypted messages.

Policy proposals have been popping up around the world (like in Australia, India, and, most recently, the UK) that call for tech companies to build in ways to gain information about encrypted messages, including through back-door access. There have also been efforts to increase moderation and safety on encrypted messaging apps, like Signal and Telegram, to try to prevent the spread of abusive content, like child sexual abuse material, criminal networking, and drug trafficking.

Continue reading “IGF 2023 WS #356 Encryption’s Critical Role in Safeguarding Human Rights” »