STEM


Teaching computers to plan for the future

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As humans, we've gotten pretty good at shaping the world around us. We can choose the molecular design of our fruits and vegetables, travel faster and further and stave off life threatening diseases with personalized medical care. However, what continues to elude our molding grasp is the airy notion of "time" – how to see further than our present moment, and ultimately how to make the most of it. As it turns out, robots might be the ones who can answer this question. Computer scientists from the University of Bonn in Germany wrote this week that they were able to design a software that could predict a sequence of events up to five minutes in the future with accuracy between 15 and 40 percent.


The Essentials of Data Science and Machine Learning

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This webinar covers a high-level introduction to data science and machine learning and their potential in a data-driven organization. Learn key trends and concepts, proven use cases and an overview of leading technologies.


A computer program that learns to "imagine" the world shows how AI can think more like us

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Machines will need to get a lot better at making sense of the world on their own if they are ever going to become truly intelligent. DeepMind, the AI-focused subsidiary of Alphabet, has taken a step in that direction by making a computer program that builds a mental picture of the world all by itself. You might say that it learns to imagine the world around it. The system, which uses what DeepMind's researchers call a generative query network (GQN), looks at a scene from several angles and can then describe what it would look like from another angle. This might seem trivial, but it requires a relatively sophisticated ability to learn about the physical world.


Why Social Science Needs Evolutionary Theory - Facts So Romantic

Nautilus

My high school biology teacher, Mr. Whittington, put a framed picture of a primate ancestor in the front of his classroom--a place of reverence. In a deeply religious and conservative community in rural America, this was a radical act. Evolution, among the most well-supported scientific theories in human history, was then, and still is, deliberately censored from biological science education. But Whittington taught evolution unapologetically, as "the single best idea anybody ever had," as the philosopher Dan Dennett described it. Whittington saw me looking at the primate in wonder one day and said, "Cristine, look at its hands.


Top 7 Data Science&Machine Learning GitHub Repositories in 2018

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Not only can you follow the work happening in different domains, but you can also collaborate on multiple open source projects. All tech companies, from Google to Facebook, upload their open source project codes on GitHub so the wider coding / ML community can benefit from it. But, if you are too busy, or find following GitHub difficult, we bring you a summary of top repositories month on month. You can keep yourself updated with the latest breakthroughs and even replicate the code on your own machine! This month's list includes some awesome libraries.


A computer program that learns to "imagine" the world shows how AI can think more like us

#artificialintelligence

Machines will need to get a lot better at making sense of the world on their own if they are ever going to become truly intelligent. DeepMind, the AI-focused subsidiary of Alphabet, has taken a step in that direction by making a computer program that builds a mental picture of the world all by itself. You might say that it learns to imagine the world around it. The system, which uses what DeepMind's researchers call a generative query network (GQN), looks at a scene from several angles and can then describe what it would look like from another angle. This might seem trivial, but it requires a relatively sophisticated ability to learn about the physical world.


Graph-based machine learning: Part 2 – Insight Data

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In my previous post, we discussed the foundation of community detection using modularity optimization. One major constraint however, is that your graph needs to fit in memory. This quickly turns problematic as your number of nodes surpass billions, and the number of edges becomes trillions. Thankfully we can leverage distributed computation systems in order to solve this limitation. To do this we first need to define the state of a node so that it contains all the information needed during computation; this will serve as a basic structure to pass around between the machines of our distributed cluster.


Searching for New Physics through Transparent Machine Learning

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Abstract: Significant advances have been made in developing powerful machine learning algorithms such as boosted decision trees and, more recently, convolutional neural networks. The transparent use of these machine learning methods in the search for new physics involving tops and missing transverse energy with ATLAS is presented. Future improvements to the sensitivity to soft tops (pT 200 GeV) plus missing transverse energy are explored by using boosted decision trees and convolutional neural networks. Additionally, improvements to Phase-I and Phase-II calorimeter trigger efficiency for soft top production using convolutional neutral network are discussed.


Life lessons from artificial intelligence: What Microsoft's AI chief wants computer science grads to know about the future

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In addition to awarding Bachelors, Masters and Ph.D. degrees, the Allen School recognized two 2018 Alumni Impact Award recipients, Yaw Anokwa and Eileen Bjorkman.


Data Science vs Machine Learning - Exploring the two paradigms Byte Academy Top Coding School for Python, Blockchain & Fintech

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Data Science is the coveted new career around the block but not many can define the exact role of a data scientist. Being a relatively new field of work with people signing up for the role from different backgrounds, data science as a discipline requires a very broad skill set. Data mining, data analysis, machine learning, business analysis, data visualization, A/B testing are some of the skills a data scientist should have. Machine learning is a large discipline in itself, with companies like Facebook relying on machine learning algorithms to sift through user behaviour patterns on a daily basis. Machine learning also involves a lot of data analysis, A/B testing and data visualization.