Stephen Wolfram is a cult figure in programming and mathematics. He is the brains behind Wolfram Alpha, a website that tries to answer questions by using algorithms to sift through a massive database of information. He is also responsible for Mathematica, a computer system used by scientists the world over.
Last week, Wolfram launched a new venture: the Wolfram Physics Project, an ambitious attempt to develop a new physics of our Universe.
The new physics, he declares, is computational. The guiding idea is that everything can be boiled down to the application of simple rules to fundamental building blocks.
A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning.
Researchers fed the robot nearly 5,000 complete songs — from Beethoven to the Beatles to Lady Gaga to Miles Davis — and more than 2 million motifs, riffs and licks of music. Aside from giving the machine a seed, or the first four measures to use as a starting point, no humans are involved in either the composition or the performance of the music.
The first two compositions are roughly 30 seconds in length. The robot, named Shimon, can be seen and heard playing them here and here.
Using machine learning three groups, including researchers at IBM and DeepMind, have simulated atoms and small molecules more accurately than existing quantum chemistry methods. In separate papers on the arXiv preprint server the teams each use neural networks to represent wave functions of electrons that surround the molecules’ atoms. This wave function is the mathematical solution of the Schrödinger equation, which describes the probabilities of where electrons can be found around molecules. It offers the tantalising hope of ‘solving chemistry’ altogether, simulating reactions with complete accuracy. Normally that goal would require impractically large amounts of computing power. The new studies now offer a compromise of relatively high accuracy at a reasonable amount of processing power.
Each group only simulates simple systems, with ethene among the most complex, and they all emphasise that the approaches are at their very earliest stages. ‘If we’re able to understand how materials work at the most fundamental, atomic level, we could better design everything from photovoltaics to drug molecules,’ says James Spencer from DeepMind in London, UK. ‘While this work doesn’t achieve that quite yet, we think it’s a step in that direction.’
An artificial neural network can reveal patterns in huge amounts of gene expression data, and discover groups of disease-related genes. This has been shown by a new study led by researchers at Linköping University, published in Nature Communications. The scientists hope that the method can eventually be applied within precision medicine and individualised treatment.
It’s common when using social media that the platform suggests people whom you may want to add as friends. The suggestion is based on you and the other person having common contacts, which indicates that you may know each other. In a similar manner, scientists are creating maps of biological networks based on how different proteins or genes interact with each other. The researchers behind a new study have used artificial intelligence, AI, to investigate whether it is possible to discover biological networks using deep learning, in which entities known as “artificial neural networks” are trained by experimental data. Since artificial neural networks are excellent at learning how to find patterns in enormous amounts of complex data, they are used in applications such as image recognition. However, this machine learning method has until now seldom been used in biological research.
“We have for the first time used deep learning to find disease-related genes. This is a very powerful method in the analysis of huge amounts of biological information, or ‘big data’,” says Sanjiv Dwivedi, postdoc in the Department of Physics, Chemistry and Biology (IFM) at Linköping University.
Upper row Associate American Corner librarian Donna Lyn G. Labangon, Space Apps global leader Dr. Paula S. Bontempi, former DICT Usec. Monchito B. Ibrahim, Animo Labs executive director Mr. Federico C. Gonzalez, DOST-PCIEERD deputy executive director Engr. Raul C. Sabularse, PLDT Enterprise Core Business Solutions vice president and head Joseph Ian G. Gendrano, lead organizer Michael Lance M. Domagas, and Animo Labs program manager Junnell E. Guia. Lower row Dominic Vincent D. Ligot, Frances Claire Tayco, Mark Toledo, and Jansen Dumaliang Lopez of Aedes project.
MANILA, Philippines — A dengue case forecasting system using space data made by Philippine developers won the 2019 National Aeronautics and Space Administration’s International Space Apps Challenge. Over 29,000 participating globally in 71 countries, this solution made it as one of the six winners in the best use of data, the solution that best makes space data accessible, or leverages it to a unique application.
Dengue fever is a viral, infectious tropical disease spread primarily by Aedes aegypti female mosquitoes. With 271,480 cases resulting in 1,107 deaths reported from January 1 to August 31, 2019 by the World Health Organization, Dominic Vincent D. Ligot, Mark Toledo, Frances Claire Tayco, and Jansen Dumaliang Lopez from CirroLytix developed a forecasting model of dengue cases using climate and digital data, and pinpointing possible hotspots from satellite data.
Sentinel-2 Copernicus and Landsat 8 satellite data used to reveal potential dengue hotspots.
Correlating information from Sentinel-2 Copernicus and Landsat 8 satellites, climate data from the Philippine Atmospheric, Geophysical and Astronomical Services Administration of the Department of Science and Technology (DOST-PAGASA) and trends from Google search engines, potential dengue hotspots will be shown in a web interface.
Using satellite spectral bands like green, red, and near-infrared (NIR), indices like Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and Normalized Difference Vegetation Index (NDVI) are calculated in identifying areas with green vegetation while Normalized Difference Water Index (NDWI) identifies areas with water. Combining these indices reveal potential areas of stagnant water capable of being breeding grounds for mosquitoes, extracted as coordinates through a free and open-source cross-platform desktop geographic information system QGIS.
Winners visit the Philippine Earth Data Resource and Observation (PEDRO) Center at the DOST-Advanced Science and Technology Institute in Diliman, Quezon City with Dr. Joel Joseph S. Marciano, Jr.
“AEDES aims to improve public health response against dengue fever in the Philippines by pinpointing possible hotspots using Earth observations,” Dr. Argyro Kavvada of NASA Earth Science and Booz Allen Hamilton explained.
The DOST-Philippine Council for Industry, Energy and Emerging Technology Research and Development (DOST-PCIEERD) deputy executive director Engr. Raul C. Sabularse said that the winning solution “benefits the community especially those countries suffering from malaria and dengue, just like the Philippines. I think it has a global impact. This is the new science to know the potential areas where dengue might occur. It is a good app.”
“It is very relevant to the Philippines and other countries which usually having problems with dengue. The team was able to show that it’s not really difficult to have all the data you need and integrate all of them and make them accessible to everyone for them to be able to use it. It’s a working model,” according to Monchito B. Ibrahim, industry development committee chairman of the Analytics Association of the Philippines and former undersecretary of the Department of Information and Communications Technology.
Biological oceanographer Dr. Paula S. Bontempi, acting deputy director of the Earth Science Mission, NASA’s Science Mission Directorate and the current leader of the Space Apps global organizing team
The leader of the Space Apps global organizing team Dr. Paula S. Bontempi, acting deputy director of the Earth Science Mission, NASA’s Science Mission Directorate remembers the pitch of the winning team when she led the hackathon in Manila. “They were terrific. Well deserved!” she said.
“I am very happy we landed in the winning circle. This would be a big help particularly in addressing our health-related problems. One of the Sustainable Development Goals (SDGs) is on Good Health and Well Being and the problem they are trying to address is analysis related to dengue,“ said Science and Technology secretary Fortunato T. de la Peña. Rex Lor from the United Nations Development Programme (UNDP) in the Philippines explained that the winning solution showcases the “pivotal role of cutting-edge digital technologies in the creation of strategies for sustainable development in the face of evolving development issues.”
U.S Public Affairs counselor Philip W. Roskamp and PLDT Enterprise Core Business Solutions vice president and head Joseph Ian G. Gendrano congratulates the next group of Pinoy winners.
Sec. de la Peña is also very happy on this second time victory for the Philippines on the global competition of NASA. The first winning solution ISDApp uses “data analysis, particularly NASA data, to be able to help our fishermen make decisions on when is the best time to catch fish.” It is currently being incubated by Animo Labs, the technology business incubator and Fab Lab of De La Salle University in partnership with DOST-PCIEERD. Project AEDES will be incubated by Animo Labs too.
University president Br. Raymundo B. Suplido FSC hopes that NASA Space Apps would “encourage our young Filipino researchers and scientists to create ideas and startups based on space science and technology, and pave the way for the promotion and awareness of the programs of our own Philippine space agency.”
Philippine vice president Leni Robredo recognized Space Apps as a platform “where some of our country’s brightest minds can collaborate in finding and creating solutions to our most pressing problems, not just in space, but more importantly here on Earth.”
“Space Apps is a community of scientists and engineers, artists and hackers coming together to address key issues here on Earth. At the heart of Space Apps are data that come to us from spacecraft flying around Earth and are looking at our world,” explained by Dr. Thomas Zurbuchen, NASA associate administrator for science.
“Personally, I’m more interested in supporting the startups that are coming out of the Space Apps Challenge,” according to DOST-PCIEERD executive director Dr. Enrico C. Paringit.
In the Philippines, Space Apps is a NASA-led initiative organized in collaboration with De La Salle University, Animo Labs, DOST-PCIEERD, PLDT InnoLab, American Corner Manila, U.S. Embassy, software developer Michael Lance M. Domagas, and celebrates the Design Week Philippines with the Design Center of the Philippines of the Department of Trade and Industry. It is globally organized by Booz Allen Hamilton, Mindgrub, and SecondMuse.
Space Apps is a NASA incubator innovation program. The next hackathon will be on October 2–4, 2020.
#SpaceApps #SpaceAppsPH
Filipino developers gather together to address real-world problems on Earth and space using NASA’s free and open source data.
Here, in this article, I will try to give you an idea of how a genetic algorithm works and we will implement the genetic algorithm for function optimization. So, let’s start.
Last March, Chinese researchers announced an ingenious and potentially devastating attack against one of America’s most prized technological assets—a Tesla electric car.
The team, from the security lab of the Chinese tech giant Tencent, demonstrated several ways to fool the AI algorithms on Tesla’s car. By subtly altering the data fed to the car’s sensors, the researchers were able to bamboozle and bewilder the artificial intelligence that runs the vehicle.
Researchers from Carnegie Mellon and the University of Pittsburgh today published research showing how they’d solved a frustrating problem for people who use a brain-computer interface (BCI) to control prosthetic devices with their thoughts.
While the research itself is interesting – they created an algorithm that keeps the devices from constantly needing to be re-calibrated to handle the human brain’s fluctuating neuronal activity – the real takeaway here is how close we are to a universal BCI.
BCIs have been around for decades in one form or another, but they’re costly to maintain and difficult to keep working properly. Currently they only make sense for narrow use – specifically, in the case of those who’ve lost limbs. Because they’re already used to using their brain to control an appendage, it’s easier for scientists and researchers to harness those brainwaves to control prosthetic devices.
The research team, which also included Rodriguez’w PhD students Zou Geng and Kevin Peters, increased and decreased the distances between the mirrors at different speeds and noted how light transmitted through the cavity was affected. They saw that the direction in which the mirrors moved influenced how much light got through the cavity, finding that “the transmission of light through the cavity is non-linear.” This behavior of light, called hysteresis, is present in the phase transitions of boiling water or magnetic materials.
The scientists also increased the speed with which the oil-filled cavity opened and closed, observing that under such conditions the hysteresis was not always present. This allowed them to extrapolate a universal law. “The equations that describe how light behaves in our oil-filled cavity are similar to those describing collections of atoms, superconductors and even high energy physics,” elaborated Rodriguez, adding: “Therefore, the universal behavior we discovered is likely to be observed in such systems as well.”
For the last couple of years, Artificial Intelligence (AI) has been changing many fields and increasing efficiency by using improved datasets. One of those areas where AI has accelerated evolution is the robo-advisory, which is a field having extensive financial big data to analyze.
Robo-advisors are the systems that use algorithms to automatically perform investment decisions or tasks which are mostly done by human advisors. “Robo advisors are a potential solution to the complexities of financial decision making,” said Jill E. Fisch, a law professor at the University of Pennsylvania at a conference of Pension Research Council.
In the main scheme, robo-advisors are merging customers’ information such as their financial goals, risk tolerances, timeframes, with the right asset allocation that qualifies customer’s needs. While making this merge, they use many algorithms including machine learning models to create the best fit for the customer. In the process of timeframe, they take lots of actions as well such as rebalancing the portfolio or performing tax-loss harvesting. This automatically increases efficiency while taking decisions at the right time for the portfolio.
