big data



Sberbank creates algorithm to do data scientists' job

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It seems that even data scientists are not immune to the corrosive impact of artificial intelligence on the jobs market. Russia's Sberbank claims to have created an algorithm - Auto ML (machine learning) - that "acts like a data scientist", creating its own models that then solve application tasks.


Seven of the Coolest–and Best-Paying–Jobs in Emerging Tech

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In the Digital Age, we often contemplate which professions today won't exist five or 10 years from now. But, fortunately, innovation is creating new ones to replace them, with many of these positions commanding salaries well into six-figure territory. Even better, a great deal of jobs in emerging tech qualify as "very cool"; they're all about pursuing intriguing discoveries while using the latest versions of IT tools. To find out which seven stand out as the very best, consider this list of the "Hottest Emerging Tech Jobs in 2019" from FitSmallBusiness.com. They include R&D test engineers for self-driving cars, machine learning experts and blockchain developers.


Seven of the Coolest–and Best-Paying–Jobs in Emerging Tech

#artificialintelligence

In the Digital Age, we often contemplate which professions today won't exist five or 10 years from now. But, fortunately, innovation is creating new ones to replace them, with many of these positions commanding salaries well into six-figure territory. Even better, a great deal of jobs in emerging tech qualify as "very cool"; they're all about pursuing intriguing discoveries while using the latest versions of IT tools. To find out which seven stand out as the very best, consider this list of the "Hottest Emerging Tech Jobs in 2019" from FitSmallBusiness.com. They include R&D test engineers for self-driving cars, machine learning experts and blockchain developers.


A Guide to Solving Social Problems with Machine Learning

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You sit down to watch a movie and ask Netflix for help. Zoolander 2?") The Netflix recommendation algorithm predicts what movie you'd like by mining data on millions of previous movie-watchers using sophisticated machine learning tools. And then the next day you go to work and every one of your agencies will make hiring decisions with little idea of which candidates would be good workers; community college students will be largely left to their own devices to decide which courses are too hard or too easy for them; and your social service system will implement a reactive rather than preventive approach to homelessness because they don't believe it's possible to forecast which families will wind up on the streets. You'd love to move your city's use of predictive analytics into the 21st century, or at least into the 20th century. You just hired a pair of 24-year-old computer programmers to run your data science team. But should they be the ones to decide which problems are amenable to these tools? Or to decide what success looks like?


Defense Department Releases Artificial Intelligence Strategy Inside Government Contracts

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On February 12, 2019 the Department of Defense released a summary and supplementary fact sheet of its artificial intelligence strategy ("AI Strategy"). The AI Strategy has been a couple of years in the making as the Trump administration has scrutinized the relative investments and advancements in artificial intelligence by the United States, its allies and partners, and potential strategic competitors such as China and Russia. The animating concern was articulated in the Trump administration's National Defense Strategy ("NDS"): strategic competitors such as China and Russia has made investments in technological modernization, including artificial intelligence, and conventional military capability that is eroding U.S. military advantage and changing how we think about conventional deterrence. As the NDS states, "[t]he reemergence of long-term strategic competition, rapid dispersion of technologies" such as "advanced computing, "big data" analytics, artificial intelligence" and others will be necessary to "ensure we will be able to fight and win the wars of the future." The AI Strategy offers that "[t]he United States, together with its allies and partners, must adopt AI to maintain its strategic position, prevail on future battlefields, and safeguard [a free and open international] order. We will also seek to develop and use AI technologies in ways that advance security, peace, and stability in the long run. We will lead in the responsible use and development of AI by articulating our vision and guiding principles for using AI in a lawful and ethical manner."


Top 5 Python IDEs For Data Science

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The truth is that you can code in almost any software, from prompt command to Windows notepad, but you may also want a proper programming environment which combines coding facility with a debugging environment. So why would or do you choose a traditional IDE instead of, for example, a notepad? The answer would be practicality. For instance, imagine that you are coding in any text editor like Windows notepad. When your code is ready, you'll need to run it.


Most popular programming language frameworks and tools for machine learning

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If you're wondering which of the growing suite of programming language libraries and tools are a good choice for implementing machine-learning models then help is at hand. More than 1,300 people mainly working in the tech, finance and healthcare revealed which machine-learning technologies they use at their firms, in a new O'Reilly survey. The list is a mix of software frameworks and libraries for data science favorite Python, big data platforms, and cloud-based services that handle each stage of the machine-learning pipeline. Most firms are still at the evaluation stage when it comes to using machine learning, or AI as the report refers to it, and the most common tools being implemented were those for'model visualization' and'automated model search and hyperparameter tuning'. Unsurprisingly, the most common form of ML being used was supervised learning, where a machine-learning model is trained using large amounts of labelled data.


American AI Initiative Opens Up Data - Connected World

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There is no question the United States is on a mission to preserve its role as a global leader in AI (artificial intelligence) adoption and innovation. Perhaps even more noteworthy is what this latest initiative has in common with past data-related initiatives? If you watched the 2019 State of the Union address a couple of weeks ago, you heard President Trump say he's eager to work with Congress to invest in "cutting edge industries of the future." He referred to this investment in cutting-edge industries as a necessity, not an option. Candidly, I was eagerly awaiting more commentary and was hoping he would elaborate.


SAS wants to spread its footprint

ZDNet

When BI emerged back in the 1990s, SAS was already a mature player that catered to a narrow elite of data miners and quants. You may have used Business Objects or MicroStrategy to generate sales by region dashboards, but when it came to crunching deep analytic models, SAS had few rivals. For SAS, the overriding theme in recent years has been broadening its footprint in a landscape where AI (mostly machine learning) is reshaping BI and driving the need to treat modeling, not as a one-off but as a lifecycle that integrates with operational systems both on-premises and in the public cloud. Both developments are making the landscape where SAS competes increasingly crowded, from the self-service visualization tools of business analysts, and the data science collaboration tools that are coming from an expanding array of venture-backed startups. Then there's open source, the elephant in the room, which SAS no longer treats as a mortal threat but a heterogenous system; SAS's contribution is applying process and governance that picks up where open source leaves off.