Data Science for Science

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