There is a widespread need for effective forecasting of financial risk using readily available financial measures, but the complicated environment facing financial practitioners and business institutions makes this very challenging. The concept of financial volatility, a required parameter for pricing many kinds of financial assets and derivatives, is critical, because it is widely expected that financial volatility implies financial risk. Therefore, accurate prediction of financial volatility is extremely important. Efficient prediction of financial volatility has been an extremely difficult task, but we can now offer a scalable and customizable mathematical model to achieve this goal, employing two approaches to forecast the volatility using financial information available online. First, we carry out a comparative study between two different machine-learning techniques -- artificial neural networks (ANN) and support vector machines (SVM) -- to forecast trading volume volatility.

Finding insight in oceans of data is one of enterprises' most pressing challenges, and increasingly AI is being brought in to help. Now, a new tool for Apache Spark aims to put machine learning within closer reach. Announced on Friday, Sparkling Water 2.0 is a major new update from H2O.ai that's designed to make it easier for companies using Spark to bring machine-learning algorithms into their analyses. It's essentially an API (application programming interface) that lets Spark users tap H2O's open-source artificial-intelligence platform instead of -- or alongside -- the algorithms included in Spark's own MLlib machine-learning library. Among the highlights of the new software is the ability to run Spark and Scala through H2O's Flow user interface.

Bots have officially taken over, and they're about to make our lives a whole lot easier. In April, Facebook introduced bots for Messenger, but the world's most popular social media platform is not the only company to open a "bot store" with consumer functions, and virtual assistants like Amazon's Alexa are steadily increasing in both popularity and functionality. With Kik, you can chat with Michelangelo and see the climate conditions through Yahoo! With Operator, shopping is as easy as sending a text, and Pana, the online travel agency, turns a simple chat conversation via text into real bookings. In fact, everyone from 1–800-Flowers and the NBA to Taco Bell is jumping on the chatbot bandwagon.

As the automation of physical and knowledge work advances, many jobs will be redefined rather than eliminated--at least in the short term. The potential of artificial intelligence and advanced robotics to perform tasks once reserved for humans is no longer reserved for spectacular demonstrations by the likes of IBM's Watson, Rethink Robotics' Baxter, DeepMind, or Google's driverless car. Just head to an airport: automated check-in kiosks now dominate many airlines' ticketing areas. Pilots actively steer aircraft for just three to seven minutes of many flights, with autopilot guiding the rest of the journey. Passport-control processes at some airports can place more emphasis on scanning document bar codes than on observing incoming passengers.

In the not-so-distant future, children will be amazed by stories that we allowed relative strangers to steer one tonne machines, relying on human skill and instinct over the capabilities of computers. The field of artificial intelligence has a transformative capability over many things that govern our daily lives. Consider self-driving cars: about 33,000 people are killed in automobile accidents in the US every year and most fatalities can be traced back to humans. Companies across technology and automobile industries are pouring millions of dollars into using AI to drive new breakthroughs in this field. Already, cars by Tesla have limited self-drive capabilities and we will soon see this rapidly increasing.

Computer vision engineers have used machine learning techniques for decades to detect objects of interest in images and to classify or identify categories of objects. They extract features representing points, regions, or objects of interest and then use those features to train a model to classify or learn patterns in the image data. In traditional machine learning, feature selection is a time-consuming manual process. Feature extraction usually involves processing each image with one or more image processing operations, such as calculating gradient to extract the discriminative information from each image. Deep learning algorithms can learn features, representations, and tasks directly from images, text, and sound, eliminating the need for manual feature selection.

Sam Abrahams is a TensorFlow evangelist and a kickass storyteller, programmer, and a statistician!

Note: The title of this article is a play on the term "Rube Goldberg machine". Acccording to Wikipedia, a Rube Goldberg machine is a contraption, invention, device, or apparatus that is deliberately over-engineered to perform a simple task in a complicated fashion, generally including a chain reaction. Keep that in your mind. Everyone (that cares about ML) knows about supervised / unsupervised / semi-supervised learning pipelines. I have now come across an entirely new class of ML pipelines that I shall call "Rube Goldberg Machine Learning" pipelines.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

If you so choose, you can delete voice items one at a time or purge all of them from the same page, which can be found in the depths of your Google account online. Actually, deleting all your voice clips doesn't purge them from Google's system. On my Voice & Activity page, I find each spoken item presented with a button next to it, allowing me to play the voice command back or delete it entirely. I can see why companies like Apple and Google want to work with spoken commands because speech recognition can only get better when computers confront more and more speech.