Lifeboat Foundation

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” »

As Shor looked for applications for his quantum period-finding algorithm, he rediscovered a previously known but obscure mathematical theorem: For every number, there exists a periodic function whose periods are related to the number’s prime factors. So if there’s a number you want to factor, you can compute the corresponding function and then solve the problem using period finding — “exactly what quantum computers are so good at,” Regev said.

On a classical computer, this would be an agonizingly slow way to factor a large number — slower even than trying every possible factor. But Shor’s method speeds up the process exponentially, making period finding an ideal way to construct a fast quantum factoring algorithm.

Shor’s algorithm was one of a few key early results that transformed quantum computing from an obscure subfield of theoretical computer science to the juggernaut it is today. But putting the algorithm into practice is a daunting task, because quantum computers are notoriously susceptible to errors: In addition to the qubits required to perform their computations, they need many others doing extra work to keep them from failing. A recent paper by Ekerå and the Google researcher Craig Gidney estimates that using Shor’s algorithm to factor a security-standard 2,048-bit number (about 600 digits long) would require a quantum computer with 20 million qubits. Today’s state-of-the-art machines have at most a few hundred.

Jailbroken large language models (LLMs) and generative AI chatbots — the kind any hacker can access on the open Web — are capable of providing in-depth, accurate instructions for carrying out large-scale acts of destruction, including bio-weapons attacks.

An alarming new study from RAND, the US nonprofit think tank, offers a canary in the coal mine for how bad actors might weaponize this technology in the (possibly near) future.

In an experiment, experts asked an uncensored LLM to plot out theoretical biological weapons attacks against large populations. The AI algorithm was detailed in its response and more than forthcoming in its advice on how to cause the most damage possible, and acquire relevant chemicals without raising suspicion.