blueprint


MIT researchers develop neural network that recovers clear information from blurry images

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Researchers at MIT's Computer Science & Artificial Intelligence Laboratory (CSAIL) trained the CNN by scanning thousands of pairs of images (projections) -- one of low quality and the other the source (signal) of the blurry picture. The neural network uses the information to essentially reverse engineer the blurring effects by learning the pixel patterns and what created them. Another part of the CNN, called a "variational autocoder," analyzes the output and evaluates how well the network matched the signal. It then creates a "blueprint" to tell the AI how to go from a projection to all possible matching sources. When given a fresh image, the CNN examines the pixel patterns and uses the blueprint to find every possible signal that could have created the blurring.


MLBP Joining the IBM Data Science Community!

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We're excited to announce that the Machine Learning Blueprint is joining the IBM DataScience Community! We've always strived to source high quality content across the web and put deep thought into our curations. However, continually delivering this every week is not easy, so after two years of publishing, we decided to take a pause. Through our work with the IBM Community we identified an opportunity to join forces to grow a community of machine learning practitioners, we were thrilled at the prospect. Their mission was clear: provide a place for data scientists to interact with other experts, share support and insights and start dialogue around relevant topics.


Canopy provides a blueprint for privacy-focused content recommendations

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With the advent of cloud computing, e-commerce, and social media, it's difficult to keep tabs on who has access to our data, and harder still to know how much care they're taking with it -- barely a day goes by without some form of data-breach, lapse, or privacy scandal coming to the fore. But what constitutes "data-misuse" is covered by a broad gamut of scenarios that reach beyond poor security hygiene. Online tracking and profiling is rife -- it turns out there is a heap of money to be made from knowing where you are, what you do, and what you like. It all comes down to personalization: selling things, be it products, playlists, or a political ideology, based on who you are. The Facebook and Cambridge Analytical, which highlighted how social networks armed with vast banks of personal data could be leveraged to profile voters and micro-target with personalized political ads, was something of a watershed moment in terms of elevating the issue of data-privacy and abuse into the public consciousness.


Which flavor of data professional are you?

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The data universe is expanding rapidly -- it's time we started recognizing just how big this field is and that working in one part of it doesn't automatically require us to be experts of all of it. Instead of expecting data people to be able to do all of it, let's start asking one another, "Which kind are you?" Most importantly, it's time we asked ourselves that same question. Working in one part of the data universe doesn't automatically require us to be experts of all of it. Disclaimer: I made these caricatures to help you get a mental map started, but we all know that real life doesn't always color neatly within the lines.


How ants, bees, and fruit flies can be the next big buzz in artificial intelligence

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And on Nov. 2, 2018, NASA's Voyager 2 spacecraft crossed into the vastness of interstellar space, following Voyager 1, which made the leap six years earlier. Since their launch in 1977, the two probes have traveled more than 11 billion miles across the solar system, lasting much longer than scientists anticipated. Powered by plutonium and drawing 400 watts of power each to run their electronics and heat, the probes still snap photos and send them back to NASA. After 42 years, though, only six of Voyager 2's 10 instruments still work, and NASA scientists expect the probe will go dark in 2025, well before it leaves our Solar system. But what if Voyager 2 needed only a couple of watts of power?


How ants, bees, and fruit flies can be the next big buzz in artificial intelligence

#artificialintelligence

And on Nov. 2, 2018, NASA's Voyager 2 spacecraft crossed into the vastness of interstellar space, following Voyager 1, which made the leap six years earlier. Since their launch in 1977, the two probes have traveled more than 11 billion miles across the solar system, lasting much longer than scientists anticipated. Powered by plutonium and drawing 400 watts of power each to run their electronics and heat, the probes still snap photos and send them back to NASA. After 42 years, though, only six of Voyager 2's 10 instruments still work, and NASA scientists expect the probe will go dark in 2025, well before it leaves our Solar system. But what if Voyager 2 needed only a couple of watts of power?


Cities Are Trying--Again--to Plan for Autonomous Vehicles

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On the one hand, autonomous vehicles offer an excellent opportunity to rethink how American cities operate, down to each lane line, crosswalk, and curb. Two years ago, the National Association of City Transportation Officials, representing 81 North American cities, published its first planning guide to self-driving vehicles, highlighting the possibilities. If everyone moves around on electric-powered transit and robotaxis, no one needs to own a car. No one needs to park a car. So that first version outlined an elegant--albeit fanciful--vision of the cities of the future.


Automated Machine Learning: Just How Much?

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There is currently a lot of talk about automated machine learning. There is also a high level of skepticism. I am here with data scientists Paolo Tamagnini, Simon Schmid and Christian Dietz, to ask a few questions on this topic from their point of view and I found this concept of guided automation quite interesting as well, since it is directly involved in the practice of automated machine learning. Rosaria Silipo: What is automated machine learning? Christian Dietz: Automated machine learning is about building a system, process or application able to automatically create, train and test machine learning models with as little human input as possible.


Can Your Organization Avoid AI Failures? - DATAVERSITY

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Artificial intelligence (AI) initiatives come as businesses enthusiasm for the technology grows, and AI initiatives go as the excitement fades in the face of false starts and little proof of value. It doesn't have to be that way. Early AI adopters are actually ramping up their AI investments and launching more projects -- and getting positive returns for their work, too. Multiple organizations have gained a financial return from their AI investments. At the same time, some industries are lagging.


Making machine learning in science an everyday reality - SynBioBeta

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A few months into my postdoc, an Excel spreadsheet dealt me quite a blow. As I was preparing to perform some statistical analyses, I made a horrifying discovery: some of my sample metadata had been incorrectly merged into a single Excel spreadsheet. The metadata had to be fixed, and all of the preliminary analyses I had done had to be repeated. Sadly, even after fixing my metadata, the dataset was unsalvageable. Not enough samples had been collected and categorical metadata were missing for some samples -- there were no statistical tests I could do to identify any meaningful patterns.