With technology pioneers Facebook and Microsoft both throwing their multibillion hats into the chatbot ring, it is safe to say the dawn of the bot is upon us. But "what is a chatbot?" we hear you confusedly cry. Well, you're going to hear a lot of noise about them and while the name might explain a lot, let us clear up the many unanswered questions of what they're for, how they work and whether they're the harbinger of the inevitable robopocalypse. In basic terms, a chatbot is a rudimentary form of artificial intelligence software that can converse with humans to answer questions or simply natter to us in as lifelike a manner as possible. The scope and complexity of a chatbot is determined by the creator's algorithmic aptitude.

You may have heard that "conversational interfaces" are the new hotness in digital product design. Why open and close a bunch of apps on your phone to get stuff done when you can invoke a text-message-like window and just say what you want done to a chatbot? Well, here's one reason: what if the bot is annoying or tedious to talk to? In conversational UIs, personality is the new UX. "We want people to enjoy dealing with our software, but now we have a very limited palette with which to design the experience," says Ben Brown, co-founder of Howdy, a "digital coworker" chatbot that runs within the office communication tool Slack and automates things like project-status meetings and taking lunch orders.

At a tech conference recently, I was only partly joking when I said, "Everyone is working on Artificial Intelligence, what about just, Intelligence?" The true promise for the future of technology is for it to find a way to work with humanity such that the two are made better. Ideally, our best traits are amplified by technology. The best of technology is unlocked when humanity is woven into its DNA. I worked at Google when it was still a "startup," before it became a public company.

Microsoft certainly hasn't been scared off doing further experiments with artificial intelligence after the whole racist bot debacle. The tech firm is back with an AI powered API that can identify (or at least try) what's in a picture. Created by Microsoft's Cognitive Services which was also behind the how old robot and the twin or not robot, CaptionBot uses computer vision and natural language to come up with descriptions of the image. Test it out yourself, here (warning, the images you put in will be "held on to" by the bot to learn from).

Microsoft is notching up its Azure Media services platform by a couple of notches. The company is now going to implement its machine learning tools into its collection of cloud-based tools for video workflows. Now, you may wonder at the apparent non-existence of a relation between videos and machine learning. Machine learning after all, is used for data analysis. It can't be used with videos, right?

Something is wrong in the way ML is being taught to developers. Most ML teachers like to explain how different learning algorithms work and spend tons of time on that. For a beginner who wants to start using ML, being able to choose an algorithm and set parameters looks like the #1 barrier to entry, and knowing how the different techniques work seems to be a key requirement to remove that barrier. Many practitioners argue however that you only need one technique to get started: random forests. Other techniques may sometimes outperform them, but in general, random forests are the most likely to perform best on a variety of problems (see Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?), which makes them more than enough for a developer just getting started with ML.

In 2009, Hal Varian (Google's Chief Economist) famously joked that "the sexiest job in the next 10 years will be Statistics". Fast forward to 2016, and it's abundantly clear that he was right (and how!) Compare that with, say, what the average web developer gets paid: 67,097. Companies are churning out exponentially more data every day yet struggling to derive value from it. According to McKinsey, by 2018, the US alone will face a shortage of 150,000 data analysts and an additional 1.5 million data-savvy managers. But you know this stuff.

If you think chatbots are hot right now -- being used in psychotherapy, turning into racist trolls, and presenting an existential threat to Apple -- just wait until they turn into full-fledged personal assistants. In five years time, digital personal assistants will even more important than your smartphone, says University of Washington computer scientist Pedro Domingos, author of "The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World." "What you have right now on your smartphone is dozens of apps," Domingos tells Tech Insider, "with each app doing it's own thing." On any given Friday night, you use one app to find a restaurant, another to buy a movie ticket, another to figure out how to get to where you're going, and another to find a date to take out with you. "It's incredibly annoying," he says, since the apps "don't talk to each other and you have to learn all these different interfaces."

We hear about AI taking over our jobs. We hear about AI listening in on our conversations. We hear about AI becoming a substitute for our romantic partners. Here's what the real AI experts Guru Banavar, (IBM), Toby Walsh, (The University of New South Wales), and Roman Yampolskiy (University of Louisville), say about the subject, and why a lot of what you think you know is probably wrong. In 2015, GE inaugurated a new, Multi-Modal manufacturing facility in Chakan, India.

For artificial intelligence, 2016 has been called "like 2015 on steroids." Want to learn more about what that really means? Follow these 10 twitter users for an insider's take on the latest developments in AI. The brains behind Google's AI platform DeepMind, Hassabis is arguably one of the most important voices in the AI world today. AlphaGo, created by DeepMind, has surpassed expectations, winning in the game of Go ten years before experts predicted.