Data Science for Science

Artificial Intelligence is now helping to predict the eventual onset of Alzheimer's disease in healthy people. The research conducted by IBM Research and Pfizer uses short standardised speech tests with better results than current methods.  Alzheimer's is a disease of the brain. Those afflicted by it, typically people over 60 years of age, slowly lose their ability to think, ability to recall from memory and eventually the ability to carry out simple tasks. Prediction of the onset of the disease can lead to the development of a simple, straightforward and accessible metric to help clinicians assess the risk of Alzheimer’s disease in an individual, leading to earlier intervention. There is no effective cure or prevention of this disease but the best way to delay onset and slow progression is to intervene early which may be possible one day.  AI techniques were used to train models using short language samples from the Framingham Heart Study . Sample were selected based on age-gr

Archaeologists, most exemplified in popular culture by the character of Indiana Jones, are more often seen as people who do a lot of manual field work: exploring hitherto unknown areas of land to find objects or patterns that would give some clues to the ancient past. They are now catching up with the rest of the sciences in utilising the amazing technology of AI. Uncovering long-abandoned ancient settlements in Madagascar, detecting nearly indiscernible bumps of earthen mounds left behind by prehistoric North American cultures, mapping Bronze Age river systems in the Indus Valley, and many more such activities are seeing the involvement of AI. Such activities called Landscape Archaeology are examples of areas where AI is helping scientists hunt for new archaeological digs as well as understand ancient cultures at large scale and great pace. So what data is used by AI to help with Landscape Archaeology? The near-ubiquitous availability of satellite data and other kinds of aerial ima

Aloha! Today we have an entry in Geology. Earthquakes are notoriously difficult to predict. Now researchers have found a way, using AI techniques, to predict the occurrence of "slow" earthquakes. Slow earthquakes can last for a period of weeks or even months but because of their low intensity nature do not cause much damage. They may not even be felt by us. Continuous seismic waves carry the potential signature of an upcoming  slow slip  failure. These seismic waves can be analysed to find patterns and from the regularity in these patterns predictions can be made as to when an event could occur and its intensity. Credit: Earthquake Research Institute, University of Tokyo The researchers created a list of characteristics that precede the appearance of earthquakes. Among them, the researchers noted an exponential increase in seismic energy prior to rupture, as if more and more tiny seismic waves were being emitted from the seismic zone. These crackles were noticeable up to thre

Namaskaram! Today we have an application of AI in Astronomy. The Universe is vast. So mind-boggling is its vastness that astronomers need the use of technology to find and classify astronomical objects. The Subaru Telescope, located in Hawaii, USA, had taken numerous images of galaxies from Earth's vantage point. Thanks to its high sensitivity, as many as 560,000 galaxies have been detected in the images. It would be extremely difficult to visually process this large number of galaxies one by one with human eyes for morphological (shape) classification. The AI technique enabled astronomers of the NAOJ (National Astronomical Observatory of Japan) to process these galaxies without human intervention. Deep Learning based image classification techniques have been used to classify images based on their pixel-data. You might have heard of the "dog" and "cat" classifier. Well, it turns out Deep Learning is also good at distinguishing galaxies "with spiral patterns

Hello, hello! This one's going to sound like it's from a sci-fi movie, if you haven't already heard about it. It's about reconstructing images by reading signals from the human brain! And it is non-invasive! The study of signals in the human brain (brain waves) using fMRI (functional Magnetic Resonance Imaging or EEG (Electroencephalogram) has been around for some time now. For the first time, image reconstruction from brain waves using A.I techniques is producing decent results.  The brain-computer interface (BCI) developed by MIPT (Moscow Institute of Physics and Technology) and Neurobotics relies on artificial neural networks and electroencephalography, or EEG, a technique for recording brain waves via electrodes placed noninvasively on the scalp. By analysing brain activity, the system reconstructs the images seen by a person undergoing EEG in real time. Feel free to skip this bit if you don't need the technicals. The BCI Operation Algorithm laid out in this res

Courtesy NASA/JPL-Caltech Hello, Today we have an entry in the field of Astronomy. It is specifically about planetary systems around other stars and their orbits. Astronomers detect planets around other stars and collect data. There are enough data points to confirm that they have indeed detected a planet but not enough to determine the exact orbit of that planet around its host star. Throw in multiple planets around a single host star and you have a planetary system for which you do not know the stable orbital configuration. Knowing the stable orbital configuration allows astronomers to predict planetary positions and movements which would in turn be useful for making observations (such as atmospheric composition) and help bolster or weaken theories of exoplanets. How can ML help? Previously, orbital configurations would have to be simulated over many billions of orbits using brute-force techniques in order to find stable configurations. These would take many hours even on modern supe

  The world-famous electric vehicle manufacturing company Tesla has recently filed a patent. The patent is about a “Neural Network System with Hardware Adaptability”. It has come about by Tesla’s acquisition of a company called DeepScale, which is an AI startup specialising in Neural Network solutions. A Neural Network is a type of AI model which is inspired by the widely-accepted biological model of the brain. It is composed of artificial neurons connected to each other with a certain structure. Each neuron is a simple construct and models a simple computation. Multiple neurons connected in a structure forming a neural network can model very complex computations which can be used to extract meaningful information from data. Such capabilities can be harnessed for a wide-variety of applications: such as self-driving cars, the ones Tesla produces.  Neural Networks are incredibly efficient in finding patterns and providing control outputs once they have been trained and their paramete