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Industry 4.0 and Its Discontents: Four Important Challenges

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When one considers the changing nature of security threats, from employees connecting personal devices to company networks to brute force attacks from hackers, the situation is further complicated, and thus the sophistication in risk identification and neutralisation has to change with it. Such an assessment should examine accessibility to systems, for example, possible threats from internal sources, from disgruntled employees to internal human error, and external sources including hackers. Once such an activity is conducted, designing an action plan for responding to cyber security threats, including backing up data, locking off operations so attackers cannot reach them and using strong shielding software, is necessary to provide additional protection. A key issue will be latency, that is the delay between two machines communicating, as machine to machine communication (M2M) requires instantaneous connection, latency, even a split second of delay, can cause untold damage to a process, slowing the entire system down and causing targets to be missed, resulting in loss of value.


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. 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.


Co-Clustering Can Provide Industrial Data Pattern Discovery

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Data clustering is the classification of data objects into different groups (clusters) such that data objects in one group are similar together and dissimilar from another group. Collaborative information filtering applications such as movie recommender systems co-cluster the accumulated movie rating provided by viewers and the movies they have watched. Using this information, the viewer is recommended other movies by classifying the rating he/she provided to a viewer ratings-movies watched cluster. An entry Cij of the matrix signifies the relation between the data type represented by row i and column j. Co-clustering is the problem of deriving sub-matrices from the larger data matrix by simultaneously clustering rows and columns of the data matrix.


Artificial Intelligence Is Likely to Make a Career in Finance, Medicine or Law a Lot Less Lucrative

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Growing up, there's a good chance you heard the mantra "go to a good school, get a good job, and make lots of money." Perhaps you were encouraged to get a professional degree to land a high paying job like a doctor, dentist, lawyer or something similar. This also seems like great advice, considering a professional degree holder typically earns more than $2 million more in their lifetimes than the average college graduate. Because lawyers tend to pay excellent attention to detail, and are highly versed in logic, a good alternative field would be programming.


Why Marketers Need To Double Down On Artificial Intelligence

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Did you know you can use artificial intelligence to growth hack social media engagement? With social media growing quickly alongside artificial intelligence technology (see below), it's only natural for the two to be mentioned together. Imagine if you would have ignored the power of social media marketing five years ago. Regardless of your industry, company size, or marketing strategy, one thing will never change: there will always be competition.


AI's First Stop: The IoT Edge

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Machine learning and other forms of artificial intelligence will likely infiltrate all levels of the IT infrastructure stack, but some architectures will take to it more readily than others. Indeed, with increased data democratization, even long-standing centralized business intelligence platforms are starting to cede ground to smaller, more targeted approaches to data analysis, such as SQL query, predictive data modeling and auto-generated discovery visualization. With NXP's technology, Greengrass can support functions like real-time data gathering and simultaneous management, analysis and storage. Cole has been covering the high-tech media and computing industries for more than 20 years, having served as editor of TV Technology, Video Technology News, Internet News and Multimedia Weekly.