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Preparing the Network for AI and Machine Learning - insideBIGDATA

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Other organizations can leverage business data to drive data-informed project management, allowing business leaders to more accurately determine how long certain operations may take and will cost. The fundamentals of these technologies are rooted in data-driven algorithms that enable machines to develop learned responses or predictive capabilities. As a result, with AI and machine learning comes data--big data--that requires resources to be allocated, not only specialists like programmers, but additional on-premises resources such as storage, server CPUs, networking bandwidth, and cloud-hosted storage services. As businesses look to develop their digital transformation strategies and create unique competitive advantage, AI and machine learning are increasingly considered the keys to unlocking the value of an organization's accumulated data.


MIT aims to pry open 'black box' of machine learning systems

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The conference was a joint effort between the Massachusetts Technology Leadership Council and MIT to bring industry and academic experts together to discuss advances in artificial intelligence (AI). The computer science and artificial intelligence laboratory, aka CSAIL, at MIT wants to shed light on the black box of today's machine learning systems with a new initiative, SystemsThatLearn@CSAIL. In its quest to shed light on machine learning's black box, SystemsThatLearn@CSAIL had to break down some academic barriers. The program joins the research teams that develop algorithms at MIT with the research teams that develop the large-scale systems the algorithms run on.


AI in HR: Artificial intelligence to bring out the best in people

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Its main AI and HR analytics product is Cornerstone Insights, what CTO Mark Goldin called "machine learning in a box." The dispassionate analysis that AI brought to Expedia's recruiting practices can also be applied to performance management, which Holger Mueller, vice president and principal analyst at Constellation Research, considers talent management's core function -- and the part that's most broken. "The applications of AI basically are analytics applications, where the software is using history and algorithms and data to be smarter and smarter over time," Bersin explained. HR is a good target for AI because many HR practices are "handcrafted," cultural in nature and could be better at handling data, according to Josh Bersin, principal and founder of consulting firm Bersin by Deloitte.


Moore's Law may be out of steam, but the power of artificial intelligence is accelerating

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A paper from Google's researchers says they simultaneously used as many as 800 of the powerful and expensive graphics processors that have been crucial to the recent uptick in the power of machine learning (see "10 Breakthrough Technologies 2013: Deep Learning"). Feeding data into deep learning software to train it for a particular task is much more resource intensive than running the system afterwards, but that still takes significant oomph. Intel has slowed the pace at which it introduces generations of new chips with smaller, denser transistors (see "Moore's Law Is Dead. It also motivates the startups--and giants such as Google--creating new chips customized to power machine learning (see "Google Reveals a Powerful New AI Chip and Supercomputer").


Moore's Law may be out of steam, but the power of artificial intelligence is accelerating

#artificialintelligence

A paper from Google's researchers says they simultaneously used as many as 800 of the powerful and expensive graphics processors that have been crucial to the recent uptick in the power of machine learning (see "10 Breakthrough Technologies 2013: Deep Learning"). Feeding data into deep learning software to train it for a particular task is much more resource intensive than running the system afterwards, but that still takes significant oomph. Intel has slowed the pace at which it introduces generations of new chips with smaller, denser transistors (see "Moore's Law Is Dead. It also motivates the startups--and giants such as Google--creating new chips customized to power machine learning (see "Google Reveals a Powerful New AI Chip and Supercomputer").


The Case for a New "Final Frontier" in Data Analytics

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There is no shortage of attention lately on the "Internet of Things". As a case in point, see the "Developing Innovation and Growing the Internet of Things Act" or "DIGIT Act", i.e., S. 2607, a bill introduced in the Senate on March 1, 2016 and amended on September 28, 2016, "to ensure appropriate spectrum planning and inter-agency coordination to support the Internet of Things" – A companion bill, H.R. 5117, was introduced in the House of Representatives on April 28, 2016. However, since there is no "internet" dedicated to "things", it is fair to state that the Internet of Things does not exist as such. We are left with a definitional vacuum, but it is hammering the obvious to acknowledge that there is no dearth of attempts around the world to fill the gap. Perhaps as a helpful shortcut, we could view the expression as a metaphor that captures the arrival of almost anything and everything, until now out of scope, into the communications space.


Artificial intelligence and HR: partnering now for better business tomorrow

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Human resources departments rarely, if ever, are thought of as cutting edge when it comes to the use of technology. A closer look, however, shows the implementation of new technologies, including solutions powered by Artificial Intelligence (AI), in almost every aspect of the talent function. According to a recent Towers Watson HR Service Delivery and Technology Survey, HR professionals are overhauling structure to improve quality and efficiency with 33% of the group spending significantly more on technology in the last year. HR's investment in new technology has also spurred the creation of new data sources. Data around employee productivity, wellness, manager effectiveness, and a host of other activities is quickly dwarfing the traditional data set that HR has traditionally been using.


What Artificial Intelligence Means for Markets & Investing

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Artificial intelligence and machine learning is suddenly all the rage, and for good reason. It is the future of this, and every other industry. If you've been paying attention to the evolution of technology over the past 2.6 million years, you knew it was coming. Wherever the bulk of the effort has been shouldered by human beings, we have always sought to replace us with technology that could do the job better, faster, more efficiently and, since the invention of capital, cheaper. It began with the most basic, brute force physical tasks and has progressively involved more nuanced, cognitive processes.


[slides] #Monitoring with #AI @CloudExpo @Dynatrace #ML #IoT #DL #DigitalTransformation

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Today we can collect lots and lots of performance data. We build beautiful dashboards and even have fancy query languages to access and transform the data. Still performance data is a secret language only a couple of people understand. The more business becomes digital the more stakeholders are interested in this data including how it relates to business. Some of these people have never used a monitoring tool before.


Global Bigdata Conference

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No longer was it an esoteric discipline commanded by the few, the proud, the data scientists. Now it was, in theory, everyone's business. Machine learning's power and promise, and all that surrounded and supported it, moved more firmly into the enterprise development mainstream. GET A 15% DISCOUNT through Jan.15, 2017: Use code 8TIISZ4Z. Cut to the key news in technology trends and IT breakthroughs with the InfoWorld Daily newsletter, our summary of the top tech happenings.