Machine Learning Tools Unlock the Most Critical Insights from Unstruc…

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Feel free to share but we would appreciate a Health Catalyst citation. Prof, Dept of Family Medicine, Indiana University School of Medicine This report is based on a 2018 Healthcare Analytics Summit presentation given by Shaun Grannis, MD, MS, FAAFP, FACMI, Director Regenstrief Center for Biomedical Informatics; Assoc. Prof, Dept of Family Medicine, Indiana University School of Medicine, entitled "Real-World Examples of Leveraging NLP, Big Data, and Data Science to Improve Population Health and Individual Care Outcomes." Feel free to share but we would appreciate a Health Catalyst citation. Many healthcare leaders operate on the premise that health system caregivers and stakeholders are more effective and better at what they do with the aid of thoughtful IT. This concept drives data analytics and technology integration in healthcare. But what does thoughtful IT mean? Thoughtful IT occurs when health systems use the right technology to lead to accurate data to deliver better patient care and improve outcomes. Feel free to share but we would appreciate a Health Catalyst citation.


Avoiding Carsickness When the Cars Drive Themselves

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The day is approaching when commuters stuck in soul-crushing traffic will be freed from the drudgery of driving. Companies are investing billions to devise sensors and algorithms so motorists can turn our attention to where we like it these days: our phones. But before the great promise of multitasking on the road can be realized, we need to overcome an age-old problem: motion sickness. "The autonomous-vehicle community understands this is a real problem it has to deal with," said Monica Jones, a transportation researcher at the University of Michigan. "That motivates me to be very systematic."


Cruise CTO Says Safety Reporting For Self-Driving Cars Is Broken

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California requires all companies that test self-driving cars on public roads in the state to report miles driven and the number of "disengagements," or times a human driver takes over control. Cruise co-founder and CTO Kyle Vogt believes this reporting method is a poor metric for comparing companies, and is causing companies to test and demo in easier environments in order to reduce reported disengagements. In a post on Medium, Vogt says that at Cruise, disengagements are sometimes used as a courtesy to other drivers, or as a cautious reaction from the driver to a situation that could have been handled by the vehicle. He explains, "Have you ever been in the backseat of a human-driven car and felt the urge to grab the wheel when something crazy happens on the road? Autonomous vehicle (AV) companies need to be extra careful when it comes to safety, as well as the perception of safety. AVs working correctly is not news, but disengagements, running red lights, and crashes are very much news that could affect the perception of AV safety, independent of their actual safety record. This leads to the well-controlled demos that Vogt takes issue with. "Companies carefully curate demo routes, avoid urban areas with cyclists and pedestrians, constrain geofences and pickup/dropoff locations, and limit the kinds of maneuvers the AV will attempt during the ride -- all in order to limit the number of disengagements.


Artificial Intelligence is completely reinventing media and marketing.

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When artificial intelligence is fully operational, it will transform the media and marketing industries. In particular, I believe that synthetic personalities powered by AI will change the way we learn about new products and how to use them. In my previous article, I showed how the collapse of broadcast TV exposed a huge weakness in the advertising industry. And I pointed to the nascent field known as Influencer Media, and especially Virtual Influencers, as a harbinger of the future of engagement brand-building. What happens when artificial intelligence is available to any app, any advertising campaign, and any brand marketer? How will that change things? Here's my answer: the media landscape will be transformed so deeply that it will be completely unrecognizable. All the leftover junk from the 20th century will be kaputt, including one-size-fits-all video programs for mass audiences, appointment viewing of a TV schedule and the very concept of TV channels, and the outdated intrusion of interruption advertising. Personalized programming and fully-responsive adbots will be the new norm.


AI in storytelling: Machines as cocreators

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Sunspring debuted at the SCI-FI LONDON film festival in 2016. Set in a dystopian world with mass unemployment, the movie attracted many fans, with one viewer describing it as amusing but strange. But the most notable aspect of the film involves its creation: an artificial-intelligence (AI) bot wrote Sunspring's screenplay. "Maybe machines will replace human storytellers, just like self-driving cars could take over the roads." A closer look at Sunspring might raise some doubts, however.


Can you think for me, please? The AI behind everyday apps

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Below I'm going to talk about two of the most used digital products and how they use AI and machine learning in order to provide the best user experience. So, before we dive into Google's wonder search platform, we have to clear something off, since there's a lot of confusion around this. Most of my friends call it Google suggestions, but it's not the case. Since they integrated AI and machine learning in their search algorithms, those are not suggestions anymore, but predictions. "What's the difference between those two?" you may ask.


How to Fix k-Fold Cross-Validation for Imbalanced Classification

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Model evaluation involves using the available dataset to fit a model and estimate its performance when making predictions on unseen examples. It is a challenging problem as both the training dataset used to fit the model and the test set used to evaluate it must be sufficiently large and representative of the underlying problem so that the resulting estimate of model performance is not too optimistic or pessimistic. The two most common approaches used for model evaluation are the train/test split and the k-fold cross-validation procedure. Both approaches can be very effective in general, although they can result in misleading results and potentially fail when used on classification problems with a severe class imbalance. In this tutorial, you will discover how to evaluate classifier models on imbalanced datasets.


Artificial Intelligence: What Tasks Can Self-Learning Systems Really Perform For Finance?

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Go to wherever you listen to your podcasts. But, while podcasters, software creators, journalists, meme-creators, etc., postulate on the potential ramifications – robot takeovers and the like – we hardly ever stop to talk about what a future with AI could look like on a more reasonable scale. Broadly speaking, artificial intelligence is the application of computer algorithms to task automation, often in a way that mimics how a human might respond to or manage a process. Of these categories, machine learning (ML) has the broadest available applications and is most likely to impact an accountant's day-to-day. Some vendors/providers will tout the availability of ML technology, but their solutions may be little more than applied automation to repetitive tasks and workflows.


Can Simple Neuron Interactions Capture Complex Linguistic Phenomena?

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Deep neural machine translation (NMT) can learn representations containing linguistic information. And despite the differences between various models, they all tend to learn similar properties. This phenomena got researchers wondering whether the learned information is fully distributed and embedded to individual neurons. Recent research results confirmed that hypothesis, revealing that simple properties such as coordinating conjunctions and determiners can be attributed to individual neurons, while more complex linguistic properties such as syntax and semantics are distributed across multiple neurons. Following on this, researchers from The Chinese University of Hong Kong, Tencent AI Lab and University of Macau have proposed a new neuron interaction based representation composition for NMT.


Complete Machine Learning and Data Science: Zero to Mastery

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Complete Machine Learning and Data Science: Zero to Mastery Get udemy course coupon code Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more! What you'll learn Become a Data Scientist and get hired Master Machine Learning and use it on the job Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0 Use modern tools that big tech companies like Google, Apple, Amazon and Facebook use Present Data Science projects to management and stakeholders Learn which Machine Learning model to choose for each type of problem Real life case studies and projects to understand how things are done in the real world Learn best practices when it comes to Data Science Workflow Implement Machine Learning algorithms Learn how to program in Python using the latest Python 3 How to improve your Machine Learning Models Learn to pre process data, clean data, and analyze large data. Build a portfolio of work to have on your resume Developer Environment setup for Data Science and Machine Learning Supervised and Unsupervised Learning Machine Learning on Time Series data Explore large datasets using data visualization tools like Matplotlib and Seaborn Explore large datasets and wrangle data using Pandas Learn NumPy and how it is used in Machine Learning A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided Learn to use the popular library Scikit-learn in your projects Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry Learn to perform Classification and Regression modelling Learn how to apply Transfer Learning Description Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 180,000 developers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. This is a brand new Machine Learning and Data Science course just launched January 2020!