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"AI Clinician" Makes Treatment Plans for Patients With Sepsis

IEEE Spectrum Robotics Channel

Most experiments with artificial intelligence in medicine thus far have worked on the diagnostic side. AI systems have used computer vision to examine images like X-rays or pathology slides, and they have combed through data in electronic medical records to spot subtle patterns that humans can miss. Just last week, IEEE Spectrum reported on hospitals that are trying out AI systems that identify patients with the first signs of sepsis, a life-threatening condition where the body responds to infection with widespread inflammation, which can lead to organ failure. Sepsis is the third leading cause of death worldwide, and the primary cause of death in hospitals. But the technology that goes by the name AI Clinician, described today in a paper in Nature Medicine, doesn't diagnose--it makes decisions.


AI is changing the customer experience KDR Recruitment

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There is no question about it, artificial intelligence is changing how customers interact with brands and develop through the buyer journey. For many customers interacting with AI has become the norm, whether they realise it or not. As artificial intelligence continues to develop businesses are implementing it more across their digital marketing platforms. Chatbots are massively changing the way the consumer communicates with a business. Chatbots are becoming the norm on social media for many businesses and can help with a major part of the customer journey.


How Artificial Intelligence May Make A Dent In The Technology Productivity Crisis

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So far, the impact of information technology on overall productivity has been a mixed bag, and even disappointing. IT has been reshaping workplaces in a big way since the 1980s, yet, there appears to be little to show for all this progress -- many argue that technology may even inhibit productivity growth. There are many reasons why the proliferation of technology doesn't automatically translate to productivity growth. For one, "technological disruption is, well, disruptive," Harvard's Jeffrey Frankel observed in a recent World Economic Forum report. "It demands that people learn new skills, adapt to new systems, and change their behavior. While a new iteration of computer software or hardware may offer more capacity, efficiency, or performance, those advantages are at least partly offset by the time users have to spend learning to use it. And glitches often bedevil the transition."


Is This Creepy New AI Assistant Too Lifelike?

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Some people already talk to Amazon's virtual assistant Alexa like she's a real person, setting her up for jokes, and having conversations that go way beyond the basic commands like "Alexa, play'Hurt' by Nine Inch Nails." And that's how people are treating a disembodied voice. But what if you could see her -- and what if she looked disturbingly human? Magic Leap, an augmented-reality startup, introduced the next evolution of the virtual assistant at their conference earlier this month. Mica performs many of the same functions as Alexa or Apple's Siri, but when users wear Magic Leap's augmented reality glasses, they can also see her incredibly life-like avatar.


How ML Can Help With Your BI Insights

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Companies in all industries must stay up to date with the latest tech to survive in this digital world. This is especially true in the case of machine learning (ML), which has the potential to transform the way businesses process and use their data. While ML has a number of useful applications in the business world, applying it to business intelligence (BI) insights can help you optimize your processes and make even better decisions. Thirteen members of Forbes Technology Council shared some creative ways to combine business intelligence with machine learning to produce the best results for your company. One of the most unique ways to combine business intelligence and machine learning is the identification of fraud indicators.


How Artificial Intelligence May Make A Dent In The Technology Productivity Crisis

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So far, the impact of information technology on overall productivity has been a mixed bag, and even disappointing. IT has been reshaping workplaces in a big way since the 1980s, yet, there appears to be little to show for all this progress -- many argue that technology may even inhibit productivity growth. There are many reasons why the proliferation of technology doesn't automatically translate to productivity growth. For one, "technological disruption is, well, disruptive," Harvard's Jeffrey Frankel observed in a recent World Economic Forum report. "It demands that people learn new skills, adapt to new systems, and change their behavior. While a new iteration of computer software or hardware may offer more capacity, efficiency, or performance, those advantages are at least partly offset by the time users have to spend learning to use it. And glitches often bedevil the transition."


The Practical Uses of AI for Procurement

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One of the discussions at eWorld recently came from Julien Nadaud, Chief Product Officer at Determine; he talked about the practical implications of AI in procurement and contract management, putting its use into real contexts. It was an interesting session, attracting a full room of delegates, in which he squarely layed out the real use cases for AI we can expect to see and how they are impacting related tasks. He's written quite a bit on that subject by the way – some of which can be found here. Procurement is becoming more'intelligent' -- this we know. AI learns continuously (with machine learning at its core) using big data coupled with historical knowledge of successful outcomes.


3 Ways to Enhance the Customer Experience Using AI and Machine Learning

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Digital transformation is atop the list of every marketing leader's initiatives. While there's a lot of hype around AI and machine learning, there seems to be less understanding of what it does and how it can help move the needle on business-critical goals. I'd like to demystify it and show you how you can use it to better understand your customers and how doing so can have a significant impact on your bottom line. In a time where acquiring a new customer can be 25 times more expensive than retaining an existing customer, leveraging AI to enhance your customer experience is something you'll want to include while preparing your digital transformation roadmap. Historically, marketers have created segments that group customers of a kind together and the whole group receives a similar experience.


Where artificial intelligence could take agriculture

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Typically, when AI is brought up around farmers, the conversation turns to how many brood cows they covered this year for breeding. In this article, AI refers to artificial intelligence. The ability to capture data on the farm has never been more readily available than it is today. Many questions about how to use and implement data are daunting and prevent producers from moving beyond the comfort of basic yield monitors and autosteer. To make the leap into data management less daunting, original equipment manufacturers (OEMs) and farm management information system groups have shifted their attention toward taking some of the burden out of making data-based decisions by using machine learning algorithms.


Get Smart: from Theory, to Practice, to the Future of A.I.

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This piece accompanies a dedicated series from Ben around intelligence, A.I, and data-driven design and development in retail – all of which you can find in our 7th Edition. Similarly, you will find references to other'features', which denote to the other editorial pieces in our 7th Edition Report.] Just as WhichPLM did for both of our previous special editorial examinations (covering 3D in 2015, and the Internet of Things in 2016) the last exclusive feature in our 7th Edition acts as the final piece of the puzzle, collecting guidance, food for thought, and practical recommendations for retailers and brands who may be looking to lay the long-term groundwork for their own A.I. initiatives, or to embark on a particular, more pressing project. The clearest question for prospective customers of A.I. solutions: are these viable products, with clear return on investment potential? Broadly speaking, the answer is yes. While general intelligence – a single machine to run everything, with mental capacities far in excess of our own, across essentially all of human endeavour – remains a pipe dream, more focused applications of narrow, specialised A.I. are limited only by customers' ability to find the right technology partner and to gain access to their own information and broader market data in sufficient volume to deliver results.