Can we study AI the same way we study lab rats? Researchers at DeepMind and Harvard University seem to think so. They built an AI-powered virtual rat that can carry out multiple complex tasks. Then, they used neuroscience techniques to understand how its artificial “brain” controls its movements.
Today’s most advanced AI is powered by artificial neural networks —machine learning algorithms made up of layers of interconnected components called “neurons” that are loosely inspired by the structure of the brain. While they operate in very different ways, a growing number of researchers believe drawing parallels between the two could both improve our understanding of neuroscience and make smarter AI.
The main idea of artificial neural networks (ANN) is to build up representations for complicated functions using compositions of relatively simple functions called layers.
A deep neural network is one that has many layers, or many functions composed together.
Although layers are typically simple functions(e.g. relu(Wx + b)) in general they could be any differentiable functions.
Quantitative biologists David McCandlish and Juannan Zhou at Cold Spring Harbor Laboratory have developed an algorithm with predictive power, giving scientists the ability to see how specific genetic mutations can combine to make critical proteins change over the course of a species’ evolution.
Described in Nature Communications, the algorithm called “minimum epistasis interpolation” results in a visualization of how a protein could evolve to either become highly effective or not effective at all. They compared the functionality of thousands of versions of the protein, finding patterns in how mutations cause the protein to evolve from one functional form to another.
Robots could soon assist humans in a variety of fields, including in manufacturing and industrial settings. A robotic system that can automatically assemble customized products may be particularly desirable for manufacturers, as it could significantly decrease the time and effort necessary to produce a variety of products.
To work most effectively, such a robot should integrate an assembly planner, a component that plans the sequence of movements and actions that a robot should perform to manufacture a specific product. Developing an assembly planner that can rapidly plan the sequences of movements necessary to produce different customized products, however, has so far proved to be highly challenging.
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.
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.
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.
