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Intel Innovation Delivers Shared Ecosystem Value! Developer First, Power of Partnership and Scaling AI Everywhere

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This was the milestone moment achieved during the first ever Intel InnovatiON event #IntelOn where Intel celebrated 50 years of heritage since the launch of its inaugural commercial processor, the 4004. Fast forward onto the latest launch of the all-new 12th Gen Intel Core processors built on Intel 7 process technology and we find the most significant shift in x86 architecture in over a decade. And what an incredible trajectory lies ahead for the next year, let alone the next 50! And this is an innovation journey that will be followed at every step across this brand new Intel On series, designed to afford the tools, technology and talent to enable developers, architects and students alike to learn, experiment, innovate and grow across clouds, sectors, open source communities, start-ups … and much more. As an Intel Influencer it was a pleasure to immerse in this combination of Tech Insights sessions, Ecosystem Tech Showcases, and Demo Experiences hands-on – and these are my key reflections.


How AI is speeding up the digital transformation in enterprise

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The operating environment of enterprises is rapidly and fundamentally being altered. Powered by a smarter, more demanding customer spoilt for choices, and doting employees who expect a consumerised experience to deliver value for their organisation, enterprises who tend to delay the adoption to newer and cutting-edge business demands are bound to be left behind and relegated to irrelevance. While some leaders are already moving ahead, many others are still studying the strategic justification for moving beyond BI reporting systems to implement Artificial Intelligence. While the benefits can be profound, the commitment is significant too. In a fast-evolving business environment, strategic objectives need to be paired with the ability to make more frequent, more responsive, and more accurate business decisions.


digitalmarketing_2021-12-06_16-03-43.xlsx

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The graph represents a network of 3,452 Twitter users whose tweets in the requested range contained "digitalmarketing", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 07 December 2021 at 00:17 UTC. The requested start date was Monday, 06 December 2021 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 3-day, 3-hour, 48-minute period from Thursday, 02 December 2021 at 14:05 UTC to Sunday, 05 December 2021 at 17:53 UTC.


Top 5 reasons low codes can boost artificial intelligence - Techiexpert.com

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Artificial Intelligence is a high technology niche of computer science that requires hard work, vast technical knowledge, and experience. But Low code has made it much easier for non-professional developers to integrate Artificial Intelligence easily with their latest and newest technologies. Low code AI has changed the particular mindset of AI product development by providing simple, easy to use and intuitive solutions. The emergence of the low code AI development platform offers a readymade building block for building AI solutions, in which users can use the Graphical User Interface (GUI) and configuration to build apps in a few hours. The time-saving and cost-saving parameters are indisputable; along with that, it offers enterprises flexibility by hassle-free customization of applications. Low code Artificial Intelligence allows non-AI developers to create AI apps from predefined components.


Artificial intelligence drives next-generation street sign

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Smartphones and GPS have made paper maps virtually obsolete and put the power of navigation in our pockets. But now, engineers are working on a high-tech update for another directional tool that could revolutionize how we find our way around. The first street signs date back hundreds of years. They help you figure out where you are and where you're going. But what if they could be updated throughout the day, hour by hour to keep you informed about what's happening around you? "This is a fully-functioning street sign that allows you to essentially market, advertise and communicate out to the public," Michael Ottoman said, showing off a hi-tech version of the old street sign.


Misconceptions of Procurement Analytics: Data strategy is a one-time initiative

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Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Why it is crucial to think about Data Strategy and Governance as a lifecycle, rather than a "set it and forget it" approach It's free, we don't spam, and we never share your email address.


Council Post: Why Simple Machine Learning Models Are Key To Driving Business Decisions

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This article was co-written with my colleague and fellow YEC member, Nirman Dave, CEO at Obviously AI. Back in March of this year, MIT Sloan Management Review made a sobering discovery: The majority of data science projects in businesses are deemed failures. A staggering proportion of companies are failing to obtain meaningful ROI from their data science projects. A failure rate of 85% was reported by a Gartner Inc. analyst back in 2017, 87% was reported by VentureBeat in 2019 and 85.4% was reported by Forbes in 2020. Despite the breakthroughs in data science and machine learning (ML), despite the development of several data management softwares and despite hundreds of articles and videos online, why is it that production-ready ML models are just not hitting the mark?


How AI Can Solve Prior Authorization - Insurance Thought Leadership

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Physicians spend nearly two full business days per week on prior authorization requests as part of an antiquated, manual process. Prior authorization is the "single highest cost for the healthcare industry" in the U.S., totaling some $767 million a year, according to the CAQH index. Physicians spend nearly two full business days per week on prior authorization requests, and payers devote thousands of manhours reviewing and approving them in an antiquated, manual process involving phone calls and faxes. The arduous task often delays necessary treatment and sometimes results in treatment abandonment -- patients just get tired of waiting, so they give up -- both of which hurt patient outcomes and ultimately raise costs in the long run. Prior authorization has been identified as one of the biggest opportunities for applying artificial intelligence (AI) to help lower the administrative burden and cost.


Artificial Intelligence at American Express - Two Current Use Cases

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Ryan Owen holds an MBA from the University of South Carolina, and has rich experience in financial services, having worked with Liberty Mutual, Sun Life, and other financial firms. Ryan writes and edits AI industry trends and use-cases for Emerj's editorial and client content. American Express began as a freight forwarding company in the mid-19th century. Expanding over time to include financial products and travel services, American Express today reports some 114 million cards in force and $1.2 trillion in billed business worldwide. American Express trades on the NYSE with a market cap that exceeds $136 billion, as of November 2021.


6 Metrics to Evaluate your Classification Algorithms

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Building a classification algorithm is always a fun project to do when you are getting into Data Science and Machine Learning. Along with Regression, Classification problems are the most common ones that businesses jump right into when they start experimenting with predictive modelling. But, evaluating a classification algorithm may get confusing, really fast. As soon as you develop a logistic regression or a classification decision tree and output your first ever probability spitted from a model, you immediately think: how should I use this outcome? First and foremost, when it comes to evaluating your classification algorithms there is a big choice that you must do: do you want to use a metric that is already tied to a threshold of your "probability" outcome?