When Cross-Validation is More Powerful than Regularization

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Regularization is a way of avoiding overfit by restricting the magnitude of model coefficients (or in deep learning, node weights). A simple example of regularization is the use of ridge or lasso regression to fit linear models in the presence of collinear variables or (quasi-)separation. The intuition is that smaller coefficients are less sensitive to idiosyncracies in the training data, and hence, less likely to overfit. Cross-validation is a way to safely reuse training data in nested model situations. This includes both the case of setting hyperparameters before fitting a model, and the case of fitting models (let's call them base learners) that are then used as variables in downstream models, as shown in Figure 1.


Minecraft Earth is live, so get tapping – TechCrunch

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Microsoft's big experiment in real-world augmented reality gaming, Minecraft Earth, is live now for players in North America, the U.K., and a number of other areas. The pocket-size AR game lets you collect blocks and critters wherever you go, undertake little adventures with friends, and of course build sweet castles. I played an early version of Minecraft Earth earlier this year, and found it entertaining and the AR aspect surprisingly seamless. The gameplay many were first introduced to in Pokemon GO is adapted here in a more creative and collaborative way. You still walk around your neighborhood, rendered in this case charmingly like a Minecraft world, and tap little icons that pop up around your character.


Minecraft Earth is live, so get tapping – TechCrunch

#artificialintelligence

Microsoft's big experiment in real-world augmented reality gaming, Minecraft Earth, is live now for players in North America, the U.K., and a number of other areas. The pocket-size AR game lets you collect blocks and critters wherever you go, undertake little adventures with friends, and of course build sweet castles. I played an early version of Minecraft Earth earlier this year, and found it entertaining and the AR aspect surprisingly seamless. The gameplay many were first introduced to in Pokemon GO is adapted here in a more creative and collaborative way. You still walk around your neighborhood, rendered in this case charmingly like a Minecraft world, and tap little icons that pop up around your character.


What is Edge AI? Machine Learning IoT

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The term "Edge AI" might be the new buzzword of 2019/2020, much like "Internet of Things" was in 2016/2017. To understand this growing new trend, we need to provide a solid definition of what constitutes "Artificial Intelligence on the Edge." With the invention of digital computers in the early 1900s, researchers began to theorize that the functions of a human (or other animal) brain could be recreated in digital form. The name "Artificial Intelligence" came from John McCarthy's 1956 proposal to host a conference for academics to discuss the possibility of programming a computer to mimic higher functions of the human brain. "Intelligence is the computational part of the ability to achieve goals in the world."


The Good, Bad and Ugly of Automation - Problems it is Solving Now and Trouble it Will Cause Tomorrow

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Let's look at the latest face of automation - the good, the bad, and the ugly! It solves some of today's problems and is starting to create new ones. Find out if your job is at risk .of My books on Amazon: The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python: Find Your Market Bearings with Python, Jupyter Notebooks, and Freely Available Data: https://amzn.to/2DERG3d Create Income Streams with Online Classes: Design Classes That Generate Long-Term Revenue: https://amzn.to/2VToEHK


Artificial Intelligence and the Customer Journey

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Virtual assistants--like Alexa and Siri--have gained traction in the consumer environment and are likely to continue to spur an uptick in voice-activated search and other customer activities, from purchases to providing feedback to seeking service and support. In short, voice technology can aid consumers at many points along the customer journey. "Digital customer care is on a major upswing with the expectation that 85% of customer relationships with an enterprise will be managed without human contact by 2020, according to Gartner," says Chris Bergh, CEO of DataKitchen Inc., a DataOps consultancy and platform provider. "Conversational AI is critical and plays an integral role in improving customer service, reducing agent-assisted service costs, and cutting down on fraud." Unfortunately, consumer expectations may outpace technological reality at this point, challenging both brands and tech vendors to meet those expectations.


Making Artificial Intelligence Work in a Changing Environment

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Machine learning (ML) is changing our lives. We can instantly translate from one language to another, search entire libraries in a matter of seconds, and even prevent credit card fraud. ML's success is mostly due to the power of artificial neural networks -- a machine learning model inspired by how the brain works -- massive datasets, and a lot of computational power. However, while these ML applications are making our lives easier, we have not really solved the artificial intelligence (AI) agent. A true AI agent should be able to perform well in a broad range of tasks.


InterVision 2020 Predictions: Solving for Data in States of Storage, Deployment and Usage : @VMblog

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As I look back at 2019 and the changes that shaped the IT landscape this year, we saw more widespread adoption of solutions that both run on and regurgitate datasets in efforts to improve business intelligence. We are increasingly seeing the role of data in enabling futuristic technologies like data analytics, machine learning, Artificial Intelligence (AI) and the Internet of Things (IoT). With 2020 on the horizon, everything is trending toward how and why companies use their data to serve consumers, make decisions for competitiveness, and iterate operations to improve profitability. For this reason, I think that one of the biggest challenges of 2020 will be how companies can secure a reliable storage, deployment and usage posture for their datasets. Datasets need to move around fast during usage and deployment stages, but also securely.


The problem with artificial intelligence is that it will do exactly what we ask it to do

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Janelle Shane is an AI researcher. In this TED talk she explains that we should not be afraid that AIs are going to rebel against us. We should be afraid of AIs because they are going to do exactly what we tell them to do. "It's really easy to accidentally give AI the wrong problem to solve," she says, "and often we don't realize that until something has actually gone wrong."


Impact of AI on Work - Jobs Are Changing, MIT-IBM Watson AI Lab Says

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IBM has always believed that 100% of jobs will ultimately change due to the impact of AI. Recent empirical research conducted by the MIT-IBM Watson AI Lab provides insights into the prediction and explains how it's going to happen. The joint research by Massachusetts Institute of Technology and IBM scrutinized the probable applications of Machine Learning in 170 million online job postings between 2010 and 2017 and came up with a report "The Future of Work: How New Technologies Are Transforming Tasks." The research examined the impact of Artificial Intelligence on employment and inferred that the result will be a significant decrease in the number of tasks. It additionally stated that work that would require "soft skills" would be given more focus on.