Over the past week much has been made about the launch and (temporary) shutdown of Microsoft's chatbot Tay. For those of you who might not know, Tay is a machine learning project that was launched with the goal of conducting research and development in the field of conversational understanding. It's a bot that can chat with users online, and it has presence over several platforms, including Twitter, GroupMe and Kik. Tay is programmed to mimic the behavior of a young woman, tell jokes and offer comments on pictures, but she's also designed to repeat after users and learn from them in order to respond in personalized ways. Unfortunately, Tay was shut down shortly after her launch because she was found to make racist and offensive comments. Apparently, the quirks in the bot's behavior were capitalized by a subset of users to promote Nazism and attack other Twitter users.

Welcome to California Inc., the weekly newsletter of the L.A. Times Business Section. Expect financial markets to face headwinds today after the Federal Reserve reported Friday that U.S. industrial production fell more than expected in March. This is the latest sign that economic growth slowed significantly in the first quarter. On the plus side, though, many economists still forecast a rebound in growth as the year plods ahead. Tax deadline: Monday is the deadline for most Americans to submit their tax returns.

Oracle Corp. has acquired Israeli machine-learning Big Data startup Crosswise, Inc. The price of the acquisition was not officially disclosed but is believed to be 50 million according to local media. Founded in 2013, Crosswise provides an authoritative consumer device map to ad tech vendors, consumer brands, and premium publishers. The company's platform combines data science, Big Data and machine learning, to identify which PCs, phones, tablets, digital TVs and other connected devices are being used by individual consumers; by applying advanced data science and proprietary machine-learning techniques to this data, Crosswise constructs a new probabilistic Device Map matching multiple devices to individual users in an accurate, scalable and high-quality manner. According to Crosswise, the benefits in being able to provide this data is that it allows marketers and premium publishers to deliver advertising, personalization and analytics across different sorts of devices.

David Ledbetter, data scientist at the Children's Hospital Los Angeles, shares how his team is using TITAN X GPUs and deep learning to help provide better recommendations of drug treatments for children in their pediatric intensive care unit. To train their models, 13,000 patient snapshots were created from ten years of electronic health records at the hospital to understand the interactions between a patient's vital state, heart rate, blood pressure and the treatments they were given. By understanding the most important relationships in the data, they are then able to generate the probability of survival predictions for the patients moving forward as well as physiology predictions in order to simulate augmented treatments. David presented his research poster "Dr. Watch more scientists and researchers share how accelerated computing is benefiting their work at http://nvda.ly/X7WpH

While the technology to simulate conversation with a computer has been around for decades, bots -- or "chatbots" -- are an increasingly trendy model for software. This new obsession came on fast. Where did these bots come from? Is a fake conversation better than just clicking buttons? You'll be hearing a lot more about bots soon, so here's an overview.

We live in an age of disruption -- and that's a good thing. Old systems will collapse as entrepreneurs figure out how to optimize and reinvent inefficient businesses, products, and services to provide consumers (us) with all things better, faster and cheaper. According to the Olin School of Business, 40% of today's Fortune 500 companies will be gone in the next 10 years. This post is a quick look at three industries (healthcare, finance and insurance) that are ripe for disruption this decade due to big data and artificial intelligence. Clearly big data and AI will change almost every industry this decade...but none more than these. Healthcare is so massively broken, that its disruption will come easy and happen fast.

The terminators from Skynet, the agents of the Matrix, the Decepticons…Hollywood has done a good job portraying artificial intelligence (AI) as an existential threat to the human race. The scary thing is that this idea may not be purely science fiction. In fact, many leading technologists today seem to share the concern that at some point in the not-too-distant future, human kind could be beholden to super-intelligent computer overlords. That is a scary thing to think about, and even if the worse does not come to pass, AI will certainly impact everyone's life in some form or another. So let's look at some history of AI, its current state, and potential risks and possible outcomes.

Learn how to apply test-driven development (TDD) to machine-learning algorithms--and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks. Machine-learning algorithms often have tests baked in, but they can't account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If you're familiar with Ruby 2.1, you're ready to start.

"To me, the human should be more important than the technology," says Steve Wozniak. "The way we think, use our brains, interact with each other, get around with our lives – these should be more important than the technology. The technology should adapt to our ways, our customs, and our concepts of what is right and wrong." So, technology needs to fit in or push off – given the speaker, it's a fascinating statement of principle. Any dinner party discussion about who has most influenced life in the modern era is bound to include Steve Jobs, Bill Gates, Mark Zuckerberg, Jeff Bezos and Elon Musk.

Boosting is a machine learning meta-algorithm that aims to iteratively build an ensemble of weak learners, in an attempt to generate a strong overall model. For example, consider a problem of binary classification with approximately 50% of samples belonging to each class. Random guessing in this case would yield an accuracy of around 50%. So a weak learner would be any algorithm, however simple, that slightly improves this score – say 51-55% or more. Usually, weak learners are pretty basic in nature.