During a typical day, we use a variety of applications that, by virtue of their artificial intelligence, automatically understand our speech and provide near-real-time feedback to support decision-making. But is machine learning, one of a number of AI techniques, ready for clinical applications, specifically to accelerate drug development and/or reduce development costs? Machine learning encompasses a variety of algorithmic techniques that clinical drug developers can use to identify and infer patterns to support enhanced/automated decision-making. One such technique is Natural Language Processing, which can be used to "read" scientific text and infer its semantic context in order to search and find information more easily. The main benefit of machine learning and natural language processing is that they can be used to either augment or replace the error-prone manual analysis work performed by people, and they can scale infinitely as the volume and variety of data grow.

Baidu's SVAIL used the ring allreduce algorithm to train state of the art speech recognition models, and now it hopes that others will take advantage of the group's implementation to do even more interesting things. The group released its ring allreduce implementation as both a standalone C library as well as a patch for TensorFlow.

Are you chatting with a human or a bot? Chatbots increasingly make it hard to tell. Chatbots are getting a lot of attention in retail these days. At the NRF Big Show in January, several vendors, startups and established alike, featured chatbots in their booths at the show. They could do everything from interact with you about new products and what you liked about them, to help you figure out the opening hours of your local store.

Artificial intelligence (AI) is here and it is getting smarter all the time. For many, mention of AI immediately conjures up images of The Terminator. At the very least it makes many think of robots and machines disrupting the workforce and replacing jobs. While the latter may not be completely out of the realm of possibility, datapine is leaving that side of AI to Silicon Valley and John Connor. Instead we are excitedly monitoring and utilizing artificial intelligence in business intelligence.

Artificial intelligence (AI) has the power to change how our workforce operates, and if you want your business to stay competitive, you need to get ahead of the AI revolution. Don't let yourself feel daunted by it as a buzzword. At my current company, we've seen firsthand the renaissance of AI, neural networking, big data and machine learning over the past five years. Not only do we provide automation and analytics for clients, we've now started to incorporate AI into our product development and operational automation. A study by McKinsey found that 45% of all on-the-job activities could be automated by deploying AI. Google's Jeff Dean estimates about 10% of its engineers know machine learning, and he'd like that number to be 100%.

Artificial intelligence has a new job: setting a good example for your kids. It seems that children's behaviour can be influenced by the personality of a robot companion – playing with an enthusiastic or attentive robot, for instance, made them engage more and work harder. Researchers ran a series of experiments with Tega, a companion robot that looks like a cross between a Furby and a Teletubby. To test how the robot's personality could affect the children's behaviour, they programmed the robot with different responses. "The goal is to have a companion that has all of the behaviours that we want to instil and promote in the child," says team member Goren Gorden at Tel Aviv University in Israel.

These are just a few data-driven ways to overcome the everyday challenges of practical machine learning. Alyssa Frazee is a machine learning engineer at Stripe, where she builds models to detect fraud in online credit card payments. Before Stripe, she did a PhD in biostatistics and fell in love with programming at the Recurse Center. Find her on Twitter at @acfrazee. Machine learning has long powered many products we interact with daily–from "intelligent" assistants like Apple's Siri and Google Now, to recommendation engines like Amazon's that suggest new products to buy, to the ad ranking systems used by Google and Facebook.

Machine learning coupled with the explosion of data offers the very real possibility of addressing the most intractable problems in healthcare. For the first time, data is helping to answer healthcare's toughest questions. Like Google's Research Director Peter Norvig, I too believe it's not better algorithms that are fueling our advancement. Instead, it's the surge in data sources and the innate ability of machine learning to automatically apply complex calculations to vast stores of data and derive rules that help us to understand the correlations, patterns and predictions within the data. In 2015, our country spent $3.2 trillion a year on healthcare, or $9,990 per person, and the actuaries at the Centers for Medicare & Medicaid Services project the total to rise to nearly $5.6 trillion in 2025.

Suddenly, it seems, every application and cloud service has been fortified with machine learning or artificial intelligence. They now can do magic. Much of the marketing around machine learning and AI is misleading, making promises that aren't realistic--and often using the terms when they don't apply. In other words, there's a lot of BS being peddled. Artificial intelligence is a wide range of cognitive technologies to enable ad hoc or situational reasoning, planning, learning, communication, perception, and the ability manipulate objects to an intended purpose.

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