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Google AI Helps you to bring your ideas through visual communication

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Google went big on art this week. The company launched a platform to help people who are terrible at art communicate visually. It also published research about teaching art to another terrible stick-figure drawer: a neural network. On Tuesday, the company announcedAutoDraw, a web-based service aimed at users who lack drawing talent. Essentially, the program allows you to use your finger (or mouse if you’re on a computer) to sketch out basic images like apples and zebras. Then, it analyzes your pathetical drawing and suggests a professionally-drawn version of the same thing. You then click on the nice drawing you wanted, and it replaces yours with the better one. It’s like autocorrect, but for drawing. Nooka Jones, the team lead at Google’s creative lab, says that AutoDraw is about helping people express themselves. “A lot of people are fairly bad at drawing, but it shouldn’t limit them from being able to communicate visually,” he says. “What if we could help people sketch out their id…

The Ethics of AI Artificial Intelligence

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In this industry, it's a tired old cliche to say that we're building the future. But that's true now more than at any time since the Industrial Revolution. The proliferation of personal computers, laptops, and cell phones has changed our lives, but by replacing or augmenting systems that were already in place. Email supplanted the post office; online shopping replaced the local department store; digital cameras and photo sharing sites such as Flickr pushed out film and bulky, hard-to-share photo albums. AI presents the possibility of changes that are fundamentally more radical: changes in how we work, how we interact with each other, how we police and govern ourselves.
Fear of a mythical "evil AI" derived from reading too much sci-fi won't help. But we do need to ensure that AI works for us rather than against us; we need to think ethically about the systems that we're building. Microsoft's CEO, Satya Nadella, writes: The debate should be about the v…

Simple Introduction To Linear Regression

After knowing the relationship between two variables we may be interested in estimating (predicting) the value of one variable given the value of another. The variable predicted on the basis of other variables is called the “dependent” or the ‘explained’ variable and the other the ‘independent’ or the ‘predicting’ variable. The prediction is based on average relationship derived statistically by regression analysis. The equation, linear or otherwise, is called the regression equation or the explaining equation.

For example, if we know that advertising and sales are correlated we may find out expected amount of sales for a given advertising expenditure or the required amount of expenditure for attaining a given amount of sales.

The relationship between two variables can be considered between, say, rainfall and agricultural production, price of an input and the overall cost of product, consumer expenditure and disposable income. Thus, regression analysis reveals average relationship be…

Inside the Washington Post’s popularity prediction experiment

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In the distributed age, news organizations are likely to see their stories shared more widely, potentially reaching thousands of readers in a short amount of time. At the Washington Post, we asked ourselves if it was possible to predict which stories will become popular. For the Post newsroom, this would be an invaluable tool, allowing editors to more efficiently allocate resources to support a better reading experience and richer story package, adding photos, videos, links to related content, and more, in order to more deeply engage the new and occasional readers clicking through to a popular story. Here’s a behind-the-scenes look at how we approached article popularity prediction. Data science application: Article popularity prediction There has not been much formal work in article popularity prediction in the news domain, which made this an open challenge. For our first approach to this task, Washington Post data scientists identified the most-viewed articles on five randomly sele…

A Complete Report On Data Scientist Salary

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Executive Summary O’Reilly Data Science Salary Survey, we’ve analyzed input from 983 respondents working in the data space, across a variety of industries— representing 45 countries and 45 US states. Through the results of our 64-question survey, we’ve explored which tools data scientists, analysts, and engineers use, which tasks they engage in, and of course—how much they make. Key findings include: Python and Spark are among the tools that contribute most to salary.Among those who code, the highest earners are the ones who code the most.SQL, Excel, R and Python are the most commonly used tools.Those who attend more meetings, earn more.Women make less than men, for doing the same thing.Country and US state GDP serves as a decent proxy for geographic salary variation (not as a directestimate, but as an additional input for a model).The most salient division between tool and tasks usage is between those who mostly use Excel, SQL, and a small number of closed source tools—and those who …