Artificial intelligence (AI)

    Biggest Pain in Machine Learning? Dirty Spreadsheet Data


    If you imagine the life of a machine learning researcher, you might think it’s quite glamorous. You’ll program self-driving cars, work for the biggest names in tech, and your software could even lead to the downfall of humanity. So cool! But, as a new survey of data scientists and machine learners shows, those expectations need adjusting, because the biggest challenge in these professions is something quite mundane: cleaning dirty data.

    This comes from a survey conducted by data science community Kaggle (which was acquired by Google earlier this year). Some 16,700 of the site’s 1.3 million members responded to the questionnaire, and when asked about the biggest barriers faced at work, the most common answer was “dirty data,” followed by a lack of talent in the field.

    But what exactly is dirty data, and why is it such a problem?

    Al Chen is an Excel aficionado. Watch as he shows you how to clean up raw data for processing in Excel. This is also a great resource for data visualization projects.

    It’s axiomatic to say that data is the new oil of the digital economy, but this is especially true in fields like machine learning. Contemporary AI systems generally learn by example, so if you show one a lot of pictures of a cat, over time it’ll start to recognize characteristics that constitute ‘cattyness’. This is why companies like Google and Amazon have been able to build such effective image and speech recognition platforms: they have a ton of data from users.

    There’s the joke that 80 percent of data science is cleaning the data and 20 percent is complaining about cleaning the data, In reality, it really varies. But data cleaning is a much higher proportion of data science than an outsider would expect. Actually, training models is typically a relatively small proportion (less than 10 percent) of what a machine learner or data scientist does.

    — Anthony Goldbloom, Kaggle founder and CEO

    But AI systems are still computer programs, which means they’re prone to flipping out if you press the wrong button at the wrong time. This inflexibility includes the data they can learn from. Think of these programs like fussy infants who refuse to eat unless their bananas are mashed just so. But instead of prepping bananas, workers in the field have to comb through datasets with hundreds of thousands of entries, tracking down missing values and remove any formatting errors. Making aeroplane noises while they do so is optional.

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    Davos 2016 - The State of Artificial Intelligence



    http://www.weforum.org/ How close are technologies to simulating or overtaking human intelligence and what are the implications for industry and society?

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    PICTIONARY w/ ARTIFICIAL INTELLIGENCE - Quick, Draw!



    Today we're playing Quick, Draw! A game in which you have 20 seconds to draw a picture, and the AI tries to guess what you're drawing. It's so simple, yet my ...

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    Artificial Intelligence Path Finding Robot

    Artificial Intelligence Autonomous Navigation Robot Self navigating robot vehicle using ultrasonic ranging sonar and arduino uno.

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    Research at NVIDIA: AI Reconstructs Photos with Realistic Results



    Researchers from NVIDIA, led by Guilin Liu, introduced a state-of-the-art deep learning method that can edit images or reconstruct a corrupted image, one that ...

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    A* in Action - Artificial Intelligence for Robotics



    This video is part of an online course, Intro to Artificial Intelligence. Check out the course here: https://www.udacity.com/course/cs271.

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    Artificial Intelligence: A Very Short Introduction (Very Short Introductions)

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    The applications of Artificial Intelligence lie all around us; in our homes, schools and offices, in our cinemas, in art galleries and - not least - on the Internet. The results of Artificial Intelligence have been invaluable to biologists, psychologists, and linguists in helping to understand the processes of memory, learning, and language from a fresh angle.

    As a concept, Artificial Intelligence has fuelled and sharpened the philosophical debates concerning the nature of the mind, intelligence, and the uniqueness of human beings. In this Very Short Introduction , Margaret A. Boden reviews the philosophical and technological challenges raised by Artificial Intelligence, considering whether programs could ever be really intelligent, creative or even conscious, and shows how the pursuit of Artificial Intelligence has helped us to appreciate
    how human and animal minds are possible.

    ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.

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    Darknet



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    From the million-copy bestselling author of CyberStorm comes a frighteningly realistic new thriller, an adrenaline-fueled mystery with a noir/tech edge that will delight fans of Baldacci, Child, and Cussler.


    A GLOBAL CORPORATION HIDES A DARK SECRET…

    A TERRIFYING NEW ADVANCE IN TECHNOLOGY…

    WILL BE REVEALED.


    Follow one man’s journey through a shadowy underworld that threatens the security of humanity.

    In a world where corporations have the same rights as human beings…
    Profit is everything and psychopathic executives call the shots…
    Anything is possible.

    FROM THE BACK COVER - DARKNET

    Jake O'Connell leaves a life of crime and swears he'll never return, but his new life as a stock broker in New York is ripped away when his childhood friend Sean Womack is murdered.

    Thousands of miles away in Hong Kong, scientist Jin Huang finds a list of wealthy dead people in a massive banking conspiracy. Problem is, some of the people don't stay dead. As Jin begins her investigation, she's petrified to discover her own name on the growing list of dead-but-alive.

    On the run, Jake O'Connell and Jin Huang race across continents to uncover a dark secret spreading like a cancer into the world. Why was Sean killed, and how is the list of wealthy dead connected? Are some of them really coming back to life? But all this becomes irrelevant when Jake's family is attacked...


    About the Author

    Translated into 18 languages, published in 23 countries, with multiple TV and movie contracts including 20th Century Fox developing his second novel, CyberStorm, for a major film release, Matthew Mather's books are worldwide bestsellers. He began his career at the McGill Center for Intelligent Machines researching artificial intelligence before starting high-tech ventures in everything from computational nanotechnology to electronic health records to weather prediction systems. He now works as a full-time author of speculative fiction.

    Darknet has ranked as a top best seller in Amazon’s techno thriller, conspiracy, and science fiction categories, and if you enjoy A.G. Riddle, Hugh Howey, and Michael Crichton, then you'll love this new thriller based in the world of bitcoin and cryptocurrency the New York Times called "the inspiration for Utopian dreams".

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