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Machine Learning


AI Helps Create The Largest 3D Map Of The Universe

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According to the article by Thomas Macaulay, scientists at University of Hawaii's Mānoa Institute for Astronomy (IfA) created the largest 3D map in the world of the universe. The article states, "They trained an algorithm to identify celestial objects in the survey by feeding it spectroscopic measurements that provide definitive object classifications and distances." "Utilizing a state-of-the-art optimization algorithm, we leveraged the spectroscopic training set of almost 4 million light sources to teach the neural network to predict source types and galaxy distances, while at the same time correcting for light extinction by dust in the Milky Way," said lead study author Robert Beck. It is interesting how the technology used to map out the stars is similar to the technology used by Opsani. Opsani also uses a neural network to modify certain settings for your application that can affect performance. We then monitor the performance of the application.


Artificial Intelligence and Society

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When Isaac Asimov wrote about robots and some higher level computer intelligence beings being some integral parts of society, He was considered science fiction visionary. We have came really far away from steam engine to first computers to making machines which can beat humans in his own games and we are moving towards an extreme future with machines and tiny electrons controlling our fate, maybe. Whenever someone talks about Artificial Intelligence the first thought is of robots and machines as depicted in cinema, whether we like it or not cinema is the foremost the mirror of human and machine relationship and depicts it as complex nature, whether it is the terminator series or chappie or some what dystopian future based Pixar's wall-e. And wall-E being the best example of how robots behave based on bias and who makes them and how they learn. Machine Learning bias is a main problem as we are making new and new machine learning models day by day.


10 RNN Open Source Projects You Must Try Your Hands On

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Recurrent Neural Networks (RNNs) are neural networks that recall each and every information through time. In the past few years, this neural network has gained much traction and has been utilised in several applications. The applications include speech recognition, machine translation, video tagging, text summarization, prediction and more. Here, we have listed the top 10 open-source projects on Recurrent Neural Networks (RNNs), in no particular order, that one must try their hands on. About: This project is about Human Activity Recognition (HAR) using TensorFlow on smartphone sensors dataset and an LSTM RNN.


Activists Turn Facial Recognition Tools Against the Police

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Mr. Howell was offended by Mr. Wheeler's characterization of his project but relieved he could keep working on it. "There's a lot of excessive force here in Portland," he said in a phone interview. "Knowing who the officers are seems like a baseline." Mr. Howell, 42, is a lifelong protester and self-taught coder; in graduate school, he started working with neural net technology, an artificial intelligence that learns to make decisions from data it is fed, such as images. He said that the police had tear-gassed him during a midday protest in June, and that he had begun researching how to build a facial recognition product that could defeat officers' attempts to shield their identity.


Overfitting and Resampling Techniques in Machine Learning

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When a model – instead of learning generalizable features – approximates the patients in the training set too closely, it is said to be „overfitted" to the training set. This means that, while the model may demonstrate high performance when making predictions on the patients it was trained on, its performance on new patients will be far poorer because the model has not in fact extracted generalizable rules for prediction. Instead, it has learnt the characteristics of the training set patients by heart. In this situation, the model demonstrates minimal bias (erroneous assumptions) and high variance (sensitivity to small fluctuations). Overfitting can be diagnosed by comparing training error with out-of-sample error (OSE) – if training set error is much lower than OSE, a model is said to overfit.


You can help a Mars Rover's AI learn to tell rocks from dirt – TechCrunch

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Mars Rover Curiosity has been on the Red Planet for going on eight years, but its journey is nowhere near finished -- and it's still getting upgrades. You can help it out by spending a few minutes labeling raw data to feed to its terrain-scanning AI. Curiosity doesn't navigate on its own; there's a whole team of people on Earth who analyze the imagery coming back from Mars and plot a path forward for the mobile science laboratory. In order to do so, however, they need to examine the imagery carefully to understand exactly where rocks, soil, sand and other features are. This is exactly the type of task that machine learning systems are good at: You give them a lot of images with the salient features on them labeled clearly, and they learn to find similar features in unlabeled images.


5 Reasons Why We Need Explainable Artificial Intelligence

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This might be the first time you hear about Explainable Artificial Intelligence, but it is certainly something you should have an opinion about. Explainable AI (XAI) refers to the techniques and methods to build AI applications that humans can understand "why" they make particular decisions. In other words, if we can get explanations from an AI system about its inner logic, this system is considered as an XAI system. Explainability is a new property that started to gain popularity in the AI community, and we will talk about why that happened in recent years. Let's dive into the technical roots of the problem, first.


Is Artificial Intelligence Closer to Common Sense?

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Artificial intelligence researchers have not been successful in giving intelligent agents the common-sense knowledge they need to reason about the world. Without this knowledge, it is impossible for intelligent agents to truly interact with the world. Traditionally, there have been two unsuccessful approaches to getting computers to reason about the world--symbolic logic and deep learning. A new project, called COMET, tries to bring these two approaches together. Although it has not yet succeeded, it offers the possibility of progress.


Is Artificial Intelligence Closer to Common Sense?

#artificialintelligence

Artificial intelligence researchers have not been successful in giving intelligent agents the common-sense knowledge they need to reason about the world. Without this knowledge, it is impossible for intelligent agents to truly interact with the world. Traditionally, there have been two unsuccessful approaches to getting computers to reason about the world--symbolic logic and deep learning. A new project, called COMET, tries to bring these two approaches together. Although it has not yet succeeded, it offers the possibility of progress.


AI (Artificial Intelligence) Governance: How To Get It Right

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AI (Artificial Intelligence) governance is about evaluating and monitoring algorithms for effectiveness, risk, bias and ROI (Return On Investment). But there is a problem: Often not enough attention is paid to this part of the AI process. "AI projects are rarely coordinated across a company and data science teams are often isolated from application development," said Mike Beckley, who is the CTO of Appian. "And now regulators are starting to ask questions businesses don't know how to answer." Keep in mind that AI introduces unique problems.