prediction


AI: How we arrived at the 4th industrial revolution

#artificialintelligence

You could be forgiven for wondering why AI is so big all of a sudden. Hasn't humankind been dreaming about human-like robots for a long time? The first Star Wars film (with crowd-pleasing'droids' R2D2, C-3PO) was released in 1977; Terminator (starring Arnold Schwarzenegger as a cyborg assassin) was a massive success in the mid -1980s, a few years after Blade Runner (starring synthetic – or not? The idea of an intelligent machine is not exactly a new one, yet our ability to create something with Artificial Intelligence has increased dramatically in the last decade or so. There is now scope to use AI to make legal assessments, create games, predict purchases, navigate through traffic, translate words into different languages and diagnose diseases.


The Impact of Artificial Intelligence in 2018: Seven Predictions

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The AI debate shifts from "is it good or evil" to "is it ever going to be good enough": If 2017 was the year where the warnings from Elon Musk and Stephen Hawking about the potential evil from AI clashed with predictions from Mark Zuckerberg and Bill Gates on its potential good, 2018 will be the year when the debate shifts to its practical utility. Much like other technologies that were lauded for their world-changing potential and then fizzled as the fog of the hype cleared, early adopters will find themselves disappointed by AI's obvious limits. The broader public--familiar with Alexa, Siri, and Google Home--will be similarly disillusioned as the experts acknowledge that there is only so much that AI will be able to do, and for really complex problems, a new paradigm will be needed. Despite the hype, AI has demonstrated value in industries across the board - from agriculture to biotech to manufacturing. AI is just beginning to ingest data to power services and offerings, in turn providing information necessary for better decision-making.


The Impact of Artificial Intelligence in 2018: Seven Predictions

#artificialintelligence

The AI debate shifts from "is it good or evil" to "is it ever going to be good enough": If 2017 was the year where the warnings from Elon Musk and Stephen Hawking about the potential evil from AI clashed with predictions from Mark Zuckerberg and Bill Gates on its potential good, 2018 will be the year when the debate shifts to its practical utility. Much like other technologies that were lauded for their world-changing potential and then fizzled as the fog of the hype cleared, early adopters will find themselves disappointed by AI's obvious limits. The broader public--familiar with Alexa, Siri, and Google Home--will be similarly disillusioned as the experts acknowledge that there is only so much that AI will be able to do, and for really complex problems, a new paradigm will be needed. Despite the hype, AI has demonstrated value in industries across the board - from agriculture to biotech to manufacturing. AI is just beginning to ingest data to power services and offerings, in turn providing information necessary for better decision-making.


Enhancing Claims Experience With AI - Insurance Thought Leadership

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Insurance is now ready for an AI-based analytics platform that can help minimize claim costs and improve customers' claims experience. Insurtech and artificial intelligence (AI) have become the new buzz words and mantra in the insurance industry. Creativity and innovation are thriving in Silicon Valley with more than 1,600 technology companies in the insurtech space for underwriting and claims. If you remember, back in the 1990s, experts predicted that if your company was not an internet company, you would not be around for long. That prediction came true, but what about the current prediction that artificial intelligence for claims will change the insurance industry?


Using TensorFlow for Predictive Analytics with Linear Regression

@machinelearnbot

Since its release in 2015 by the Google Brain team, TensorFlow has been a driving force in conversations centered on artificial intelligence, machine learning, and predictive analytics. With its flexible architecture, TensorFlow provides numerical computation capacity with incredible parallelism that is appealing to both small and large businesses. TensorFlow, being built on stateful dataflow graphs across multiple systems, allows for parallel processing--data to be leveraged in a meaningful way without requiring petabytes of data. To demonstrate how you can take advantage of TensorFlow without having huge silos of data on hand, I'll explain how to use TensorFlow to build a linear regression model in this post. Linear modeling is a relatively simplistic type of mathematical method that, when used properly, can help predict modeled behavior.


Why AI Could Be Entering a Golden Age - Knowledge@Wharton

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The quest to give machines human-level intelligence has been around for decades, and it has captured imaginations for far longer -- think of Mary Shelley's Frankenstein in the 19th century. Artificial intelligence, or AI, was born in the 1950s, with boom cycles leading to busts as scientists failed time and again to make machines act and think like the human brain. But this time could be different because of a major breakthrough -- deep learning, where data structures are set up like the brain's neural network to let computers learn on their own. Together with advances in computing power and scale, AI is making big strides today like never before. Frank Chen, a partner specializing in AI at top venture capital firm Andreessen Horowitz, makes a case that AI could be entering a golden age.


3 A.I. Predictions for 2018: Emotion, Data, Ethics Xconomy

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In recent years, the smartphones, bots, and devices we spend so much of our time with could be accused of contributing to the desensitization of our society. When a fight breaks out, some teens' first reaction is to pull out their phones and take a video, rather than call for help. We can yell mean things at our Amazon Alexa device without any consequences. These are just a few examples. In 2018 and beyond, this will change.


AI and machine learning: Looking beyond the hype -- FCW

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In every federal agency, critical insights are hidden within the massive data sets collected over the years. But because of a shortage of data scientists in the federal government, extracting value from this data is time consuming, if it happens at all. Yet with advances in data science, artificial intelligence (AI) and machine learning, agencies now have access to advanced tools that will transform information analysis and agency operations. From predicting terror threats to detecting tax fraud, a new class of enterprise-grade tools, called automated machine learning, have the power to transform the speed and accuracy of federal decision-making through predictive modeling. Technologies like these that enable AI are changing the way the federal government understands and makes decisions.


Deloitte TMT Predictions: Machine Learning Deployments Will Continue to Drive Growth - DATAVERSITY

@machinelearnbot

Among the findings pertaining to the enterprise, this year's report indicates that business organizations will likely double their use of machine learning technology by the end of 2018. TMT Predictions highlights five key areas that Deloitte believes will unlock more intensive use of machine learning in the enterprise by making it easier, cheaper and faster. The most important key area is the growth in new semiconductor chips that will increase the use of machine learning, enabling applications to use less power, and at the same time become more responsive, flexible and capable."


Regression prediction intervals with XGBOOST

@machinelearnbot

Knowledge of the uncertainty in predictions of algorithms is paramount for anyone who wishes to make serious predictive analytics for his business. Predictions are never absolute, and it is imperative to know the potential variations. If one wishes to know the passengers volume for each flight, he also needs to know by how many passengers the prediction may differ. Another could decide to predict disembarking times. There is of course a difference between a prediction on a scale of a few hours with a 95% chance of correctness up to half an hour, and a potential error of 10 hours!