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Category: information science – Page 146

New AI Model Counters Bias In Data With A DEI Lens

AI has exploded onto the scene in recent years, bringing both promise and peril. Systems like ChatGPT and Stable Diffusion showcase the tremendous potential of AI to enhance productivity and creativity. Yet they also reveal a dark reality: the algorithms often reflect the same systemic prejudices and societal biases present in their training data.

While the corporate world has quickly capitalized on integrating generative AI systems, many experts urge caution, considering the critical flaws in how AI represents diversity. Whether it’s text generators reinforcing stereotypes or facial recognition exhibiting racial bias, the ethical challenges cannot be ignored.

From generating text that furthers stereotypes to producing discriminatory facial recognition results, biased AI poses ethical and social challenges.

AI-ready architecture doubles power with FeFETs

Hussam Amrouch has developed an AI-ready architecture that is twice as powerful as comparable in-memory computing approaches. As reported in the journal Nature Communications (“First demonstration of in-memory computing crossbar using multi-level Cell FeFET”), the professor at the Technical University of Munich (TUM) applies a new computational paradigm using special circuits known as ferroelectric field effect transistors (FeFETs). Within a few years, this could prove useful for generative AI, deep learning algorithms and robotic applications.

  • The new architecture enables both data storage and calculations to be carried out on the same transistors, boosting efficiency and reducing heat.
  • The chip performs at 885 TOPS/W, significantly outperforming current CMOS chips which operate in the range of 10–20 TOPS/W, making it ideal for applications like real-time drone calculations, generative AI, and deep learning algorithms.

    • Atom Computing is the first to announce a 1,000+ qubit quantum computer

    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.

    Computing with atoms

    Atom Computing, as its name implies, has chosen neutral atoms as its qubit of choice (there are other companies that are working with ions). These systems rely on a set of lasers that create a series of locations that are energetically favorable for atoms. Left on their own, atoms will tend to fall into these locations and stay there until a stray gas atom bumps into them and knocks them out.

    Eureka: With GPT-4 overseeing training, robots can learn much faster

    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.

    Finding flows of a Navier–Stokes fluid through quantum computing

    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.

    Artificial intelligence predicts the future of artificial intelligence research

    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 (ML), the number of 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.

    Redefining the Fabric of Reality: The Growing Evidence for a Simulated Universe

    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.

    Do we live in a computer simulation like in The Matrix? Proposed new law of physics backs up the idea

    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 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.

    NVIDIA’s latest AI model helps robots perform pen spinning tricks as well as humans

    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.

    Accelerating waves shed light on major problems in physics

    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 must experience an acceleration. So far, this has not been accounted for.

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