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Global Bigdata Conference

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Among the tasks you can train a computer to perform is scanning the skies over the U.S. for the alarming number of surveillance and spy aircraft. The news web site BuzzFeed did just that, reporting this week that it employed a machine-learning algorithm to first recognize known spy planes, and then combine that model with a large set of flight-tracking data from a commercial web site. The AI project mapped thousands of surveillance flights operated by federal agencies over a four-month period, including a military contractor tracking terrorists in Africa that is also flying surveillance aircraft over U.S. cities, BuzzFeed reported. Flightradar24 gathers data from a network of ground-based receivers supplemented by Federal Aviation Administration receivers. The ground radars sweep up a flight data transmitted by aircraft transponders, including unique identifiers for each plane.


The Hitchhiker's Guide to Machine Learning in Python

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Machine learning is undoubtedly on the rise, slowly climbing into'buzzword' territory. This is in large part due to misuse and simple misunderstanding of the topics that come with the term. Take a quick glance at the chart below and you'll see this illustrated quite clearly thanks to Google Trends' analysis of interest in the term over the last few years. However, the goal of this article is not to simply reflect on the popularity of machine learning. It is rather to explain and implement relevant machine learning algorithms in a clear and concise way.


Want a Diagnosis Tomorrow, Not Next Year? Turn to AI

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Inside a red-bricked building on the north side of Washington DC, internist Shantanu Nundy rushes from one examining room to the next, trying to see all 30 patients on his schedule. Most days, five of them will need to follow up with some kind of specialist. And odds are they never will. Year-long waits, hundred-mile drives, and huge out of pocket costs mean 90 percent of America's most needy citizens can't follow through on a specialist referral from their primary care doc. But Nundy's patients are different.


Getting a Grip on Artificial Intelligence

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Artificial intelligence means many different things to many different people. One thing that is certain is that it is coming and with it, it brings both opportunities and threats. Understanding both is essential, because already, artificial intelligence is working its way into many aspects of our lives, from search engines and personal assistants, to algorithms monitoring and controlling everything from energy consumption to traffic. Simply put, artificial intelligence is a computer capable of exhibiting intelligence. This is done through processes such as learning and problem solving.


Machine Learning in a Nutshell

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Remember the recent Elon Musk and Mark Zuckerberg clash on the future of Artificial Intelligence? So, my colleague and I were discussing the topic and after a while she said she doesn't understand machine learning & Artificial Intelligence fully. Are you one of those, who understand the basics of AI, the robots and more; yet when it comes to deep and in depth technical understanding, it suddenly becomes confusing? If yes, worry not for you have landed at the right place. We'll try to understand Machine Learning like a beginner.


Artificial intelligence: here's what you need to know to understand how machines learn

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From Jeopardy winners and Go masters to infamous advertising-related racial profiling, it would seem we have entered an era in which artificial intelligence developments are rapidly accelerating. But a fully sentient being whose electronic "brain" can fully engage in complex cognitive tasks using fair moral judgement remains, for now, beyond our capabilities. Unfortunately, current developments are generating a general fear of what artificial intelligence could become in the future. Its representation in recent pop culture shows how cautious – and pessimistic – we are about the technology. The problem with fear is that it can be crippling and, at times, promote ignorance.


Managing algorithmic risks--safeguarding the use of complex algorithms and machine learning

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The rise of advanced data analytics and cognitive technologies has led to an explosion in the use of algorithms across a range of purposes, industries, and business functions. Decisions that have a profound impact on individuals are being influenced by these algorithms--including what information individuals are exposed to, what jobs they're offered, whether their loan applications are approved, what medical treatment their doctors recommend, and even their treatment in the judicial system. What's more, dramatically increasing complexity is fundamentally turning algorithms into inscrutable black boxes of decision making. An aura of objectivity and infallibility may be ascribed to algorithms. But these black boxes are vulnerable to risks, such as accidental or intentional biases, errors, and frauds--raising the question of how to "trust" algorithmic systems.


Getting a Grip on Artificial Intelligence

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August 12, 2017 Wishful Thinking Artificial intelligence means many different things to many different people. One thing that is certain is that it is coming and with it, it brings both opportunities and threats. Understanding both is essential, because already, artificial intelligence is working its way into many aspects of our lives, from search engines and personal assistants, to algorithms monitoring and controlling everything from energy consumption to traffic. Simply put, artificial intelligence is a computer capable of exhibiting intelligence. This is done through processes such as learning and problem solving.


Are You Joining The Machine Learning Revolution?

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Have you noticed that the better you know someone, the easier it is to communicate with them? When we are particularly close, this can border on the telepathic as we start to anticipate what the other person is going to say and finish their sentences. Unconsciously, our brains are collecting, processing, storing, and recalling a huge range of verbal and nonverbal signals, then translating this learning and familiarity into actions. Of course, we're a long way from understanding – let alone replicating – the infinite complexities of the human brain. But in the simplest of terms, this is how machines can learn to interact with people.