Four Emerging Technology Areas Impacting Industry 4.0: Advanced Computing, Artificial intelligence, Big Data & Materials Science.
There’s more AI news out there than anyone can possibly keep up with. But you can stay tolerably up to date on the most interesting developments with this column, which collects AI and machine learning advancements from around the world and explains why they might be important to tech, startups or civilization.
To begin on a lighthearted note: The ways researchers find to apply machine learning to the arts are always interesting — though not always practical. A team from the University of Washington wanted to see if a computer vision system could learn to tell what is being played on a piano just from an overhead view of the keys and the player’s hands.
Audeo, the system trained by Eli Shlizerman, Kun Su and Xiulong Liu, watches video of piano playing and first extracts a piano-roll-like simple sequence of key presses. Then it adds expression in the form of length and strength of the presses, and lastly polishes it up for input into a MIDI synthesizer for output. The results are a little loose but definitely recognizable.
But Horizon Europe also includes new elements that reflect increasing attention to open science, equality, interdisciplinary research and practical applications. Here, Nature takes a look at some of the major changes.
As Horizon Europe issues its first call for grants, Nature reviews some big changes — from open science to goal-oriented ‘missions’.
Dr. Mae Jemison, the first Black woman to travel into space, talks to aspiring scientist, speaker and activist Taylor Richardson about the history of Black women in STEM and the importance of inspiring the next generation.
If we can take just a fraction of the time that’s spent gaming, and make it useful for science, then that’s practically a limitless resource.
The idea of citizen science isn’t a new one. Amateur scientists have been making important discoveries as far back as Ug the Neolithic hunter and her ‘wheel’, while even Newton, Franklin, and Darwin were self-funded for part of their careers, and Herschel discovered Uranus while employed as a musician. It’s only from the late 20th century that it’s crystallised into what we know today, with the North American Butterfly Association using its members to count the popular winged insects since 1975. Zooniverse has users classify images to identify stellar wind bubbles, track coronal mass ejections, and determine the shape of galaxies. Then there’s Folding@Home and other cloud computing projects—they count too.
ARIA’s launch comes hot on the heels of the European Innovation Council’s new fund, which stands at $12 billion. The EIC was set up by the European Commission, the EU’s executive arm, to try to help start-ups across Europe to scale up and compete with rivals in the U.S. and Asia, which have spawned several tech giants with market caps that run well into hundreds of billions of dollars.
The Advanced Research and Invention Agency (ARIA) will fund “high-risk, high-reward” scientific research in the hope of achieving “groundbreaking” discoveries.
Is the Minister of Science, Technology and Telecommunications for the country of Costa Rica and has served in this role since June 1st, 2020.
Dr. Vega-Castillo was previously Deputy Minister of Science and Technology and also served as Vice President for Research and Outreach in the Instituto Tecnológico de Costa Rica (ITCR) where she promoted the strengthening of research and outreach, and linkages with the national and international sector for increasing the scientific publication and patents.
A novel computer algorithm, or set of rules, that accurately predicts the orbits of planets in the solar system could be adapted to better predict and control the behavior of the plasma that fuels fusion facilities designed to harvest on Earth the fusion energy that powers the sun and stars.
A novel computer algorithm, or set of rules, that accurately predicts the orbits of planets in the solar system could be adapted to better predict and control the behavior of the plasma that fuels fusion facilities designed to harvest on Earth the fusion energy that powers the sun and stars.
The algorithm, devised by a scientist at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL), applies machine learning, the form of artificial intelligence (AI) that learns from experience, to develop the predictions. “Usually in physics, you make observations, create a theory based on those observations, and then use that theory to predict new observations,” said PPPL physicist Hong Qin, author of a paper detailing the concept in Scientific Reports. “What I’m doing is replacing this process with a type of black box that can produce accurate predictions without using a traditional theory or law.”
Qin (pronounced Chin) created a computer program into which he fed data from past observations of the orbits of Mercury, Venus, Earth, Mars, Jupiter, and the dwarf planet Ceres. This program, along with an additional program known as a “serving algorithm,” then made accurate predictions of the orbits of other planets in the solar system without using Newton’s laws of motion and gravitation. “Essentially, I bypassed all the fundamental ingredients of physics. I go directly from data to data,” Qin said. “There is no law of physics in the middle.”
