IT


Zenith unveils 10 artificial intelligence trends for marketers – Zenith

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Using bespoke algorithms, a team of data scientists and strategists from Zenith developed sophisticated machine learning technology that enabled the network to create an'automation loop': data collection, attribution and planning changes across multiple touchpoints – all done automatically. Our 10 trends assess how machine learning and other areas of AI will enhance the consumer experience along the journey to purchase and will create new marketing opportunities for brands. The Passive User Interface continually collects behavioural data from consumers' digital devices and by applying machine learning techniques can provide brands with powerful insights than can be used to customise consumer experiences. Powered by machine learning, chatbots enable automated interaction between consumers and brands via a messaging interface.


How to build a recommendation engine using Apache's Prediction IO Machine Learning Server

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This post will guide you through installing Apache Prediction IO machine learning server. You've got bunch of data and you need to predict something accurately so you can help your business grow its sales, grow customers, grow profits, grow conversion, or whatever the business need is. The very first look at the documentation makes me feel good because it's giving me access to a powerful tech stack for solving machine learning problems. Considering this problem, we'll use a Recommendation Template with Prediction IO Machine Learning server.


Tools for Making Machine Learning Easier and Smoother

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The computer vision, speech recognition, natural language processing, and audio recognition applications being developed using DL techniques need large amounts of computational power to process large amounts of data. There are three types of ML: supervised machine learning, unsupervised machine learning, and reinforcement learning. Another interesting example is Google DeepMind, which used DL techniques in AlphaGo, a computer program developed to play the board game Go. Using one of the world's most popular computer games, the developers of the project are creating a research environment open to artificial intelligence and machine learning researchers around the world.


christophM/rulefit

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The algorithm can be used for predicting an output vector y given an input matrix X. In the first step a tree ensemble is generated with gradient boosting. The trees are then used to form rules, where the paths to each node in each tree form one rule. A rule is a binary decision if an observation is in a given node, which is dependent on the input features that were used in the splits.


AI to drive GDP gains of $15.7 trillion with productivity, personalisation improvements

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Healthcare: Data-driven diagnostic support: Pandemic identification: Imaging diagnostics (radiology, pathology) Automotive: Autonomous fleets for ride sharing; Smart cars/driver assist; Predictive and autonomous maintenance Financial services: Personalised financial planning; Fraud detection and anti-money laundering; Transaction automation Retail: Personalised design and production; Customer insights generation; Inventory and delivery management Technology, communications and entertainment: Media archiving and search; Content creation (marketing, film, music, etc.); Personalized marketing and advertising Manufacturing; Enhanced monitoring and auto-correction; Supply chain and production optimisation; On-demand production Energy: Smart metering; More efficient grid operation and storage; Intelligent infrastructure maintenance Transport and logistics; Autonomous trucking and delivery: Traffic control and reduced congestion; Enhanced security Methodology: To estimate AI impact, our team conducted a dual-phased top-down and bottom-up analysis combining a detailed assessment of the current and future use of AI and an exploration of the economic impact in terms of new jobs, new products, and other secondary effects. Healthcare: Data-driven diagnostic support: Pandemic identification: Imaging diagnostics (radiology, pathology) Automotive: Autonomous fleets for ride sharing; Smart cars/driver assist; Predictive and autonomous maintenance Financial services: Personalised financial planning; Fraud detection and anti-money laundering; Transaction automation Retail: Personalised design and production; Customer insights generation; Inventory and delivery management Technology, communications and entertainment: Media archiving and search; Content creation (marketing, film, music, etc.); Personalized marketing and advertising Manufacturing; Enhanced monitoring and auto-correction; Supply chain and production optimisation; On-demand production Energy: Smart metering; More efficient grid operation and storage; Intelligent infrastructure maintenance Transport and logistics; Autonomous trucking and delivery: Traffic control and reduced congestion; Enhanced security Healthcare: Data-driven diagnostic support: Pandemic identification: Imaging diagnostics (radiology, pathology) Automotive: Autonomous fleets for ride sharing; Smart cars/driver assist; Predictive and autonomous maintenance Financial services: Personalised financial planning; Fraud detection and anti-money laundering; Transaction automation Retail: Personalised design and production; Customer insights generation; Inventory and delivery management Technology, communications and entertainment: Media archiving and search; Content creation (marketing, film, music, etc.


How Far Away Are We from Inventing True A.I.? - Dataconomy

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The famous inventor and computer scientist Ray Kurzweil has made some very bold predictions about the pace at which human technology is advancing toward the ultimate threshold. That epithet is a metaphor borrowed from physics terminology to express the point at which information technology--specifically artificial intelligence--becomes sufficiently advanced as to irreversibly alter the course of history on earth. Kurzweil's model predicts that by 2029 technological advancement will be occurring at such a rapid and explosive rate that humans will not be able to keep up without merging symbiotically with machines. And by 2045, AI is predicted to surpass human beings as the most intelligent and capable beings on the planet.


An Artificial Intelligence Roadmap For Contact Centers - Brand Quarterly

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From being able to offer an omnichannel customer experience across multiple channels to internet-enabled devices connecting directly to contact centers to provide proactive service, there is no doubt that the days of the single channel call center are long gone. An intelligent routing solution can rout interactions from multiple channels, including voice, email, chat, social, mobile, and more. AI technology is also being used now to create smart customer care solutions that mimic customer care agents with humanlike recommendations and high precision search. Virtual contact center assistants make it fast and efficient for customers to obtain the help they need.


Cheat Sheet of Machine Learning and Python (and Math) Cheat Sheets

@machinelearnbot

Eventually, I compiled over 20 Machine Learning-related cheat sheets. There are a handful of helpful flowcharts and tables of Machine Learning algorithms. If you really want to understand Machine Learning, you need a solid understanding of Statistics (especially Probability), Linear Algebra, and some Calculus. These cheat sheets provide most of what you need to understand the Math behind the most common Machine Learning algorithms.


The one law of robotics: Humans must flourish

BBC News

According to Prof Dame Ottoline Leyser, who co-chairs the Royal Society's science policy advisory group, human flourishing should be the key to how intelligent systems governed. The report calls for a new body to ensure intelligent machines serve people rather than control them. The report calls for safeguards to prioritise the interests of humans over machines. It suggests a "stewardship body" of experts and interested parties should build an ethical framework for the development of artificial intelligence technologies.


A Primer on Machine Learning Models for Fraud Detection - Simility

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One area of machine learning that's getting a lot of buzz in recent years is artificial neural networks (ANNs), aka "deep learning" models, which try to simulate how layers of neurons act together in the brain to make a decision. ANN models are highly versatile and can be used to solve highly complex problems like identifying account takeover using the device's sensor data. While other techniques often require limiting the number of features, multi-layer ANNs can train on thousands of features and scale easily. Training such models requires massive amounts of data (typically, millions of labeled transactions), so deep learning models are really only practical for large companies or those that generate a lot of data points.