Scientist


New Smart Robots In The Neighbourhood -- GadgTecs.com

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As a result, when one member is unable to function, another member can take the charge and lead the group. The leader can decide to add new robots to the group and link it with everyone. The way these robots have been created and functioned proves that robots can perform any given task by linking up with one another. This advancement in the creation of robots can solve a lot of problems in the world and one day, they might make the world free of evil.


New Smart Robots In The Neighbourhood -- GadgTecs.com

#artificialintelligence

As a result, when one member is unable to function, another member can take the charge and lead the group. The leader can decide to add new robots to the group and link it with everyone. The way these robots have been created and functioned proves that robots can perform any given task by linking up with one another. This advancement in the creation of robots can solve a lot of problems in the world and one day, they might make the world free of evil.


Forget Police Sketches: Researchers Perfectly Reconstruct Faces by Reading Brainwaves

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Using brain scans and direct neuron recording from macaque monkeys, the team found specialized "face patches" that respond to specific combinations of facial features. In the early 2000s, while recording from epilepsy patients with electrodes implanted into their brains, Quian Quiroga and colleagues found that face cells are particularly picky. In a stroke of luck, Tsao and team blew open the "black box" of facial recognition while working on a different problem: how to describe a face mathematically, with a matrix of numbers. In macaque monkeys with electrodes implanted into their brains, the team recorded from three "face patches"--brain areas that respond especially to faces--while showing the monkeys the computer-generated faces.


Facebook heads to Canada in search of the next big AI advance

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Several leading figures in AI, including LeCun, have studied or taught at Canadian universities. Reinforcement learning builds on deep learning to let machines learn through experimentation. Michael Bowling, a U.S.-born computer scientist who leads a lab at the University of Alberta that has produced cutting-edge poker-playing machines, says the new Facebook lab simply shows that Canada already leads the rest of the world in AI. Indeed, after seeing AI researchers snapped up by big U.S. companies in recent years, Canada may well hope that the environment fostered by new labs, including the one in Montreal, will eventually produce companies that rival the likes of Facebook.


Jeanne Ross The Fatal Flaw of AI Implementation

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Jeanne Ross is principal research scientist for MIT's Center for Information Systems Research. Because, as with enterprise systems, AI inserted into businesses drives value by improving processes through automation. An AI application might allow financial analysts to spend less time extracting data on financial performance, but it adds value only if someone spends more time considering the implications of that performance. Jeanne Ross is principal research scientist for MIT's Center for Information Systems Research.


Google Is Using Machine Learning To Study The Eclipse

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The idea is to gather a rich data set around the first total solar eclipse to cross a large portion of the United States in almost 100 years. Technology has changed exponentially in the last century; this rare cosmic event is the first time many will experience a total eclipse, and it's also an opportunity to experience it with new technology. And in Google's case, that means using their machine learning to study this eclipse and develop new ways to study cosmic events in the future. The initiative is in collaboration with a group of scientists led by University of California, Berkeley's Space Sciences Laboratory, who came up with the idea of crowdsourcing an image archive of next week's total solar eclipse back in 2011.


Scientists made an AI that can read minds

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By reverse-engineering signals sent by the brain, researchers at Carnegie Mellon University have been working on an AI that can read complex thoughts simply by looking at brain scans. Using data collected from a functional magnetic resonance imaging (fMRI) machine, the CMU scientists feed that data into their machine learning algorithms, which then locate the building blocks that the brain uses to create complex thoughts. It's by understanding these triggers that the algorithm can use the brain scans to predict what is being thought about at the time, connecting these thoughts into a coherent sentence. Selecting 239 of these complex sentences and feeding the AI the corresponding brain scans, the algorithm managed to successfully predict the correct thoughts with an astounding 87 percent accuracy.


How AI Is Transforming Drug Creation

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But samples also were sent to a lab where computers using artificial intelligence are changing the way pharmaceutical companies develop drugs. Biological insights driven by machine learning also could help pharmaceutical companies better identify and recruit patients for clinical trials of therapies most likely to work for them, perhaps boosting the chances of those medications' getting approved by regulatory agencies such as the Food and Drug Administration. AI systems trained on various data sources, including preclinical data sets, have helped make "significant performance improvements" by enabling "better selections of which compounds to…make and test" in the lab and by "flagging" whether compounds might have "toxic" effects or "unexpected favorable" ones, he says. German pharmaceutical company Merck KGaA has developed two drugs using computer-vision software, which analyzes images of cells and tissues, and other AI systems capable of drawing insights from public databases of genetic and chemical information, says Joern-Peter Halle, Merck KGaA's head of external innovation.


Scientists use machine learning algorithms to create a real-life Doodle God

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Scientists from the University of Liverpool have taught computers to sift through the infinite possibilities of atoms in search of new materials. The computers use machine-learning to help scientists narrow their focus when combining atoms to create something entirely new. This new research will allow scientists to input previously known materials into a machine learning algorithm so that the computer can then predict what similar atomic pairings will produce. At the University of Liverpool they used the research to predict crystal growth.


Which Machine Learning Algorithm Should I Use?

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This resource is designed primarily for beginner to intermediate data scientists or analysts who are interested in identifying and applying machine learning algorithms to address the problems of their interest. The machine learning algorithm cheat sheet helps you to choose from a variety of machine learning algorithms to find the appropriate algorithm for your specific problems. Since the cheat sheet is designed for beginner data scientists and analysts, we will make some simplified assumptions when talking about the algorithms. The algorithms recommended here result from compiled feedback and tips from several data scientists and machine learning experts and developers.