Large Scale Machine Learning for Payment Fraud Prevention Recorded at:

How advanced #machinelearning algorithms are applied at @PayPal for #fraud prevention. 

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  • Venkatesh Ramanathan is a senior data scientist at PayPal where he is working on building state-of-the-art tools for payment fraud detection.
  • Venkatesh has worked on various problems in the areas of Anti-Spam, Phishing Detection, and Face Recognition.
  • Data Science is an emerging field that allows businesses to effectively mine historical data and better understand consumer behavior.
  • This type of scientific data management approach is critical for any business to successfully launch its products and better serve its existing markets.

Venkatesh Ramanathan presents how advanced machine learning algorithms such as Deep Learning and Gradient Boosting are applied at PayPal for fraud prevention.
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Data in, intelligence out: Machine learning pipelines demystified

How machine learning pipelines work: Data in, intelligence out #AI #ML #datascience

  • It’s tempting to think of machine learning as a magic black box.
  • If you’re in the business of deriving actionable insights from data through machine learning, it helps for the process not to be a black box.
  • The more you know what’s inside the box, the better you’ll understand every step of the process for how data can be transformed into predictions, and the more powerful your predictions can be.
  • There’s also a pipeline for data as it flows through machine learning solutions.
  • Mastering how that pipeline comes together is a powerful way to know machine learning itself from the inside out.

Data plus algorithms equals machine learning, but how does that all unfold? Let’s lift the lid on the way those pieces fit together, beginning to end
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The dawn of the augmented writing era – Textio Word Nerd

Product genius @jensenharris @textio writes how machine learning is fundamental to writing.

  • It has the potential to be a fundamental breakthrough in human communication.Surprisingly, because of the slow pace of innovation in writing software, augmented writing is really only the third disruption in nearly half a century of computer-assisted writing.Let’s take a brief trip down memory lane.A brief history of writing softwareThe text mode eraThough there was once something sold called a “word processing machine” (basically a glorified typewriter with a small screen so that you didn’t have to use as much gunky Liquid Paper to correct mistakes), the true beginning of mass market word processing started with the advent of IBM’s personal computer and the subsequent PC revolution.The IBM Personal Computer.
  • The idea was that you could see on the screen exactly what a document would look like when it was printed.Microsoft had a purpose-built applications team founded with the mission to build great WYSIWYG word processing software, and they were quick to market with a little product called Microsoft Word that took full advantage of this new technology.Microsoft Word let everyone create garish documents with just the click of a button!For the first time, the printed page was right there on the screen!
  • Once people had Word, they never ever wanted to go back.WordPerfect didn’t have WYSIWYG in their blood… the team and software hadn’t been purpose-built for the graphical era.
  • Even Quip, the biggest writing exit of the last few years, is fully of this collaborative era.The missing linkSo we’ve seen writing software evolve from text mode through WYSIWYG editing to today’s focus on collaborative editing.What does all the software from all of these eras have in common?It doesn’t make your writing better.There are features galore to decorate text… to make it red and bold, or to encapsulate it in fancy bulleted lists or surround it with ornate borders.
  • There are features to quickly get back to earlier revisions of your document.But these products miss the highest, most important potential of writing software — the capability to make the human a more successful writer.The augmented writing eraAnd so now, we stand at the precipice of the next era of writing software — the era of augmented writing.Each era in writing software was fueled by a disruptive technology that changed people’s expectations of what writing software was capable ofThe rise of machine intelligenceAugmented writing builds on an incredibly disruptive technology: machine intelligence.The core tech now exists to be able to quantitatively predict with a high degree of accuracy whether a document or email you’re writing will get the outcome you want.This predictive power is paired with a new kind of writing user interface which x-rays your document in real-time.

Humans have long imagined language superpowers. Imagine if you could know — in advance — exactly how other people would react to your words. This is augmented writing, and it’s here already.
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Data in, intelligence out: Machine learning pipelines demystified

Master how to construct a  #machinelearning pipeline

  • It’s tempting to think of machine learning as a magic black box.
  • If you’re in the business of deriving actionable insights from data through machine learning, it helps for the process not to be a black box.
  • The more you know what’s inside the box, the better you’ll understand every step of the process for how data can be transformed into predictions, and the more powerful your predictions can be.
  • There’s also a pipeline for data as it flows through machine learning solutions.
  • Mastering how that pipeline comes together is a powerful way to know machine learning itself from the inside out.

Data plus algorithms equals machine learning, but how does that all unfold? Let’s lift the lid on the way those pieces fit together, beginning to end
Continue reading “Data in, intelligence out: Machine learning pipelines demystified”

Five years ago, artificial intelligence was struggling to identify cats. Now it’s trying to tackle 5000 species — Quartz

Five years ago, #AI was struggling to identify cats. Now it’s trying to tackle 5000 species

  • Google’s neural network, software which uses statistics to approximate how the brain learns, taught itself to detect the shapes of cats and humans with more than 70% accuracy.
  • “Over the last five years it’s been pretty incredible, the progress of deep [neural] nets,” says Grant Van Horn, lead competition organizer and graduate student at California Institute of Technology.
  • Van Horn says this latest Google competition differs from ImageNet, which forces algorithms to identify a wide variety of objects like cars and houses and boats, because iNat requires AI to examine the “nitty-gritty details” that separate one species from another.
  • On a scale from general image recognition (ImageNet) to specific (facial recognition,where most faces generally look the same and only slight variations matter), iNat lies somewhere in the middle, Van Horn says.
  • Van Horn, who has specialized in building AI that distinguishes differences between birds, said that the iNat competition illustrates how AI is beginning to help people learn about the world around them, rather than just help them organize their photos, for instance.

In 2012, Google made a breakthrough: It trained its AI to recognize cats in YouTube videos. Google’s neural network, software which uses statistics to approximate how the brain learns, taught itself to detect the shapes of cats and humans with more than 70% accuracy.  It was a 70% improvement over any other machine learning at the time. Five years later,…
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Artificial Intelligence Is Changing How We Shop Online

How #AI is changing the way we do our shopping online:

  • These deep learning algorithms have been used in the autonomous driving industry for quite some time, and only now is it beginning to branch out into other industries, such as online shopping.
  • One company that offers machine learning solutions for e-commerce businesses and others is Adobe Marketing Cloud as they recognize the need to make use of AI as early as possible.
  • Andrew Zhai is an engineer working on the visual search side of things at Pinterest, and he said, “For shopping specifically, improvements to online discovery means new ways to find products you’re interested in but may not have the words for.
  • Etsy is also keen to jump onboard with deep learning technology, and just last fall purchased Blackbird Technologies to integrate the firm image recognition and natural language processing into its search function.
  • Some of Adobe’s marketing tools also use deep learning techniques and are used to predict their customer’s shopping behaviors and patterns.

There’s no doubt about it that our future is one that involves artificial intelligence (AI) in a big way. While some companies are faster than others at
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Biblio’s Blood – Chapter 23 – Matthew S. Lawrence

Chapter 23

#AI #BigData #MachineLearning #SciFi #SEO #SerialNovel #F4F #MTVHottest #books

  • Frank paused, there were more clicks and whirs. “
  • Carlton was nodding as Frank spoke. “
  • You are commenting using your Facebook account.
  • Carlton was finding the conversation more and more disturbing, but, Frank was absolutely right.
  • Ms. Skirt did not write this code.”

When Carlton finally got home he and Frank were both bursting to tell the other about the day’s events. Carlton had to call a time-out to get the conversation into some sort of order, but Frank already knew most of what Carlton had to say because Biblio had told him. It was Carlton’s turn to…
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