Buckle up, because we’re entering the era of thinking machines that make humans look like chattering chimps! But don’t worry about polishing your resume to impress our future robot overlords just yet. The experts are wildly divided on when superintelligent AI will actually arrive. It’s like we’re staring at an AI time machine without knowing if it will teleport us to 2 years from now or 2 decades into the future!
Intelligence and superintelligence.
Discover the captivating odyssey of Artificial Intelligence (AI) as it ventures into the enigmatic realm of superintelligence. Uncover the essence of AI and its transformative impact on our lives, while delving into the concept of superintelligence and the profound implications it holds for humanity.
Carlos Bravo-Prieto1,2,3, Ryan LaRose4, M. Cerezo1,5, Yigit Subasi6, Lukasz Cincio1, and Patrick J. Coles1
1Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87,545, USA. 2 Barcelona Supercomputing Center, Barcelona, Spain. 3 Institut de Ciències del Cosmos, Universitat de Barcelona, Barcelona, Spain. 4 Department of Computational Mathematics, Science, and Engineering & Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48,823, USA. 5 Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA 6 Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87,545, USA
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The contrast between the James Webb Space Telescope.
The James Webb Space Telescope (JWST or Webb) is an orbiting infrared observatory that will complement and extend the discoveries of the Hubble Space Telescope. It covers longer wavelengths of light, with greatly improved sensitivity, allowing it to see inside dust clouds where stars and planetary systems are forming today as well as looking further back in time to observe the first galaxies that formed in the early universe.
The James Webb and Hubble space telescopes have combined forces to image a cluster of galaxies 4.3 billion light-years away in one of the most colorful pictures of the universe ever taken.
A new article published in Opto-Electronic Science reviews the fundamentals and applications of optically trapped optical nanoparticles. Optical nanoparticles are one of the key elements of photonics. They not only allow optical imaging of a plethora of systems (from cells to microelectronics), but also behave as highly sensitive remote sensors.
The success of optical tweezers in isolating and manipulating individual optical nanoparticles has been recently demonstrated. This has opened the door to high-resolution, single-particle scanning and sensing.
The most relevant results in the quickly growing fields of optical trapping of individual optical nanoparticles are summarized by this article. According to different materials and their optical properties, the optical nanoparticles are classified into five families: plasmonic nanoparticles, lanthanide-doped nanoparticles, polymeric nanoparticles, semiconductor nanoparticles, and nanodiamonds. For each case, the main advances and applications have been described.
This paper characterizes two distinct philosophies underlying previous work on how Bayesian computations are linked to neural data, highlighting how different theories may be motivated by different tacit assumptions and thereby explain different data.
Japan’s Nippon Telegraph and Telephone Corporation (NTT) is applying its Deep Anomaly Surveillance (DeAnoS) artificial intelligence tool, originally designed for telecom networks, to predict anomalies in nuclear fusion reactors.
DeAnoS is like a detective, trying to understand which part of the equation is making things weird.
Atomic fusion reactors are at the forefront of scientific innovation, harnessing the enormous energy released by atomic nuclei fusion. This process, which is similar to the Sun’s power source, involves the union of two light atomic nuclei, which results in the development of a heavier nucleus and the release of a massive quantity of energy.
DeepMind’s new machine learning algorithm takes less than a minute to make its forecasts and can run on a desktop. But it won’t replace traditional forecasts anytime soon.