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The 6 top machine learning trends

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The complexity, as well as the number of active servers to manage, has increased significantly, resulting in a much larger amount of collected data to sort through and track. Despite the increase in instrumentation capabilities and the amount of collected data, enterprises barely use significantly larger data sets to improve availability and performance process effectiveness with root cause analysis and incident prediction. This field studies how to design algorithms that can learn by observing data, discovering new insights in data, developing systems that can automatically adapt and customize themselves, and designing systems where it is too complicated and costly to implement all possible circumstances (such as search engines and self-driving cars). Many organizations are finding that machine learning allows them to better analyze large amounts of data, gain valuable insights, reduce incident investigation time, determine which alerts are correlated, and what causes event storms – and even prevent incidents from happening in the first place.


How 970 million voicebots shipped by 2020 will change sales forever

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Stephen Hawking famously said, "The development of full artificial intelligence could spell the end of the human race." Before that happens, however, the emergence of AI-driven sales technology will almost certainly spell the end of selling as we know it now -- and could spell the end of the line for businesses that don't appreciate the power in the next wave of technology. Imagine a digital sales assistant that responds to verbal commands. Those commands could be simple -- "call the next most qualified lead in my queue," for example. Or they could be complex -- "provide an analysis of which up-sell offers have worked best for prospects like the one I'm working on."


Samsung Galaxy 8 Event: Analyst Quick Take

Forbes

Infinity Display and design: Ten years ago, designers did mockups of what phones would look in the distant future as one slab of glass. The S8 is the closest phone yet that represents that vision. The 5.8" and 6.2" are not only large display, but have incredibly-high resolutions of 2960x1440 (Quad HD) with 570 and 529 PPI (pixels per inch). The Infinity Display is stunning and I'm looking forward to seeing how this performs with the GearVR where PPI is paramount to the experience. DeX docking: There have been many attempts at smartphone-to-PC modularity and each one gets better.


Exploring the Intersection of Machine Learning and Analytics Sisense

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When I was a young boy I saw the classic movie "2001 A Space Odyssey" with HAL, the voice interactive computer system that bordered on AI, and that sparked in me, a lifelong interest and career in IT. Today we are seeing devices that are starting to provide the beginnings of that same functionality like the Amazon Echo, Dot or Google Home. It's one thing to sit in your living room and call out to the air "Alexa, how old is Matt Damon" or "Alexa, play the Logical Song by Supertramp", it's another when your 6 year old is having a conversation with Alexa and orders a bunch of things from Amazon and it is quite another when you are trying to find ways to use it in the office to make your company more productive. The difference between Alexa and HAL is pretty dramatic, but at the core of them both, and AI in general, is Machine Learning. As Guy Levy-Yurista, Sisense Head of Product, described in this recent blog post "Sisense employs machine learning as a core element of its In-Chip data processing algorithms….We call it query recycling – breaking queries into smaller blocks that are later reassembled to answer future queries: if user A asks a completely new question such as'what was our average deal size last year?' and user B later asks'what is our year-over-year growth in sales?',


How Artificial Intelligence is Changing Online Retail Forever

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Another AI technique, called online learning, can be of use here, where sites are able to analyze every click through an online inventory in real time to understand customer preferences and create a personalized shopping experience. Already we're seeing multiple, superior avenues for product discovery enabled by AI: You'll be able to take pictures of items you like, search visually online and get personal recommendations based on an AI-generated model. Inspired by principles of survival of the fittest, EAs generate a population of candidate solutions -- in this case, configurations for online content -- then measure their performance and move on to building new candidates based on the more successful candidates already measured. Interestingly, as far as consumers are concerned, a lot of these AI breakthroughs will lead to one central concept: adaptive, in-the-moment personalization -- AI that can intuit what a shopper's style is and adapt its recommendations as she or he shops; AI that can evolve a website to specific consumer needs; and AI that can understand user concerns and answer complicated questions.


Gadget : Machine learning will go big and small in 2017

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The machine learning sector is really beginning to take form in South Africa with various start-ups taking off and entering the international scene. At DataProphet, we specialise in the application of machine learning algorithms to provide actionable solutions for a variety of industries. Fortunately, South African companies – not generally known for their customer care – are starting to wake up to the possibilities of efficient customer relationship management (CRM) through bespoke products, targeted marketing and improved customer service. Many of the very best machine learning products are open-source and open-data which allow for the establishment of social-good machine learning applications that many may not have even considered yet.


Exploring the Intersection of Machine Learning and Analytics - DZone Big Data

#artificialintelligence

When I was a young boy I saw the classic movie "2001 A Space Odyssey" with HAL, the voice interactive computer system that bordered on AI, and that sparked in me, a lifelong interest and career in IT. Today we are seeing devices that are starting to provide the beginnings of that same functionality like the Amazon Echo, Dot, or Google Home. It's one thing to sit in your living room and call out to the air "Alexa, how old is Matt Damon" or "Alexa, play'The Logical Song' by Supertramp", it's another when your 6 year old is having a conversation with Alexa and orders a bunch of things from Amazon and it is quite another when you are trying to find ways to use it in the office to make your company more productive. The difference between Alexa and HAL is pretty dramatic, but at the core of them both, and AI in general, is Machine Learning. As Guy Levy-Yurista, Sisense Head of Product, described in this recent blog post: "Sisense employs machine learning as a core element of its In-Chip data processing algorithms….We call it query recycling – breaking queries into smaller blocks that are later reassembled to answer future queries: if user A asks a completely new question such as'what was our average deal size last year?' and user B later asks'what is our year-over-year growth in sales?',


Are You Ready for Artificial Intelligence Leadership? Centurysoft Blog

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With your human boss you always have to ensure that you are in the boss's good books. When you are working under the direction of artificial intelligence you could be working under an interactive conversational assistant who is most capable of providing all of the leadership you need to rely on to be a successful and valued employee. A truly good leader should be there to assist their staff and when artificial intelligence is integrated into the management role of a business it is an intelligent virtual assistant. Nick Gordon is a senior writer at Centurysoft Blog, where he covers topics such as Digital Media, Data Analytics, Chatbots, Artificial Intelligence and Business Intelligence.


Artificial Intelligence, IBM, NVIDIA Driving Changes to Credit Cards, Health Care, Physical Security

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More important to me is how this will change our lives. I spent some time last week talking to IBM about how its partnership with NVIDIA and its advancements with Watson and OpenPOWER will be changing the world around us. We spoke about a number of artificial intelligence trends and several stood out for me. Artificial Intelligence and Credit Card Security Every year, financial institutions write of billions in losses due to credit card fraud, and a great deal of focus has been placed on stopping this steady drip, drip, drip of illegal cost. Currently, systems are advanced enough to do four fraud checks at the time of the transaction, but they simply aren't enough to stop the flood of people cloning, stealing and skimming credit cards to steal money.


10 Steps to Train an Effective Chatbot and its Machine Learning Models

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While the answer depends greatly on the problem being solved and the data powering the solution, in this blog we offer a common methodology for training the machine learning (ML) models powering your chat bot solution. For example, when training the Watson Business Coach application, we interviewed sellers, partners, and clients to collect questions and utterances like: "show me a Watson demo in Healthcare, "how can I use cognitive to improve customer service", "how is cognitive different from analytics", etc. Once training is complete, run the test set against the trained classifier and collect performance metrics such as accuracy, precision, and recall. For example, when training the Watson Business Coach application, we interviewed sellers, partners, and clients to collect questions and utterances like: "show me a Watson demo in Healthcare, "how can I use cognitive to improve customer service", "how is cognitive different from analytics", etc.