It's no joke that Artificial Intelligence is turning the technology industry (and others besides) on its ear. The latest advances in AI are regularly making headlines and either inspiring thoughtful dialogue or dramatic entertainment. This truly is the stuff of science fiction. But there are also a lot of misconceptions, requiring much-needed myth-busting around the topic. We recently spoke with AI aficionado (and author of Disruptive Marketing) Geoff Colon for some clarification on the subject.

If you've recently had your first interaction with a voice-based personal assistant like Amazon's Alexa or Apple's Siri, you might get the sense that artificial intelligence is just a few years away from being able to talk and act like a human. It will soon be capable of managing our schedules, troubleshooting technical issues, or even holding conversation. According to a recent Wall Street Journal piece titled "Alexa and Cortana May Be Heading to the Office," many businesses share that hope. One startup profiled in the piece uses "an Amazon Echo attached to the office ceiling for such tasks as adding events to their calendars," while another is building a virtual assistant to set meetings on behalf of human users. The belief that natural language processing is right around the corner seems to be widespread: About half of IT professionals in the Spiceworks survey cited in the article said they plan to use intelligent assistants in a corporate setting in the next three years.

For the last 15 years we've had to stare at screens to interact with the magic inside. But machine learning is changing the way we communicate with our devices, and our relationship with them is becoming more real, and downright emotional. Before you shrug off the notion of a humanized machine, or shake your head at its potential dangers, it is important to recognize that the industry has always attempted to provide an emotional input to our virtual ecosystem. Take Clippit, Microsoft's creepy but helpful talking paper clip--or even the smiling Mac. If you were to open up a '90s version of Microsoft Office, Clippit would be there to make you happy (or angry).

Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chat bots, or search engines. Given high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google's Tensorflow). Today, machine learning has come to play an integral role in many phases of the financial ecosystem, from approving loans, to managing assets, to assessing risks. Yet, few technically-savvy professionals have an accurate view of just how many ways machine learning finds its way into their daily financial lives.

Why? Townsend's friend had searched for Harry Potter a few days ago. And when he put Allo on the spot with a question about its existence, it spit out a random link it had uttered recently. Allo had done something very frightening: It had aired his private search results. Google called the error an "issue." And I'm sure to any software developer, that's exactly what it was–another bug to be squashed.

The South By Southwest (SXSW) festival is a whirlwind best described as "trying to drink from a firehose". There is so much happening at any one moment you have a real fear of missing out. Even though this was my third visit, the challenge of navigating the schedule to ensure you find the gems while making time to meet some of the most brilliant minds in tech, digital and content creation can be overwhelming. Robotics is moving at a rapid pace but still the sheer oddness and impracticality of what I saw was at times astounding. The best example for me was presented by Japanese Communication giant NTTS's Dr Higashinaka and roboticist Dr Ishiguro who have built life-sized robotic humanoid models that can have basic conversations with humans.

"Hey Cortana, play my favorite song." Smart AI is no longer a novelty worthy of science fiction; it's now ingrained into our everyday lives. You could probably recognize Siri or Alexa's voice in a crowded room, and chances are good that you've requested their services on more than one occasion. As tech giants like Apple, Amazon, and Microsoft continue to enhance their products using AI techniques to keep up with real life demands – integrating with third-party apps like Uber or Spotify, for example – the capabilities of these smart bots seems endless. In fact, more and more companies are using smart bots and even chatbots to create customized experiences for their shoppers.

The U.S. Defense Advanced Research Projects Agency (DARPA) has come up with some crazy ideas in the past, and its latest idea is to create computers that are always learning and adapting, much like humans. Mobile devices, computers, and gadgets already have artificial intelligence features, with notable examples being Apple's Siri, Microsoft's Cortana, and Amazon's Alexa. But these devices can only learn and draw conclusions within the scope of information pre-programmed into systems. Existing machine-learning techniques don't allow computers to think outside the box, so to speak, or think dynamically based on the situations and circumstances. The goal of a new DARPA project is to create computers that think like biological entities and are continually learning.

From October 2016 to January 2017, the Outbrain Click Prediction competition challenged Kagglers to navigate a huge dataset of personalized website content recommendations with billions of data points to predict which links users would click on. In this winners' interview, team brain-afk shares a deep dive into their second place strategy in this competition where heavy feature engineering gave a competitive edge over stacking methods. Darragh Hanley: I am a part time OMSCS student at Georgia Tech and a data scientist at Optum, using AI to improve healthcare. Marios Michailidis: I am a Part-Time PhD student at UCL, data science manager at dunnhumby and fervent Kaggler. Mathias Müller: I have a Master's in computer science (focus areas cognitive robotics and AI) and I'm working as a machine learning engineer at FSD. Alexey Noskov: I have an MSc in computer science and work as a software engineer at Evil Martians.

Tech giants are in a race to see who can build the most powerful voice-activated assistant, but there's a side effect that we haven't considered: Kids who grow up asking Amazon's Alexa questions or summoning Siri might lose some social skills.…