The Amazon Echo Dot is essentially all the bits of an Amazon Echo that make it interesting, but without the speaker beneath it – and so it costs just one-third of the price. The Dot is one of three Alexa-enabled products from Amazon that puts the company's voice assistant front and centre. Only two, the Echo Dot and the Echo are available in the UK: the third, the portable Bluetooth speaker called Echo Tap, is only available in the US. The Dot is a small black or white puck with a ring of lights at the top, four buttons and a seven-microphone array – the same system that makes the Echo speaker so good at hearing you from pretty much anywhere. It listens out for a "wake word", which by default is the name of the voice assistant contained within the Dot: "Alexa".

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In recent weeks we've seen incredible action in the intelligent assistant market. Google announcing the Google Assistant and associated devices to take on Amazon's Alexa, Microsoft at Ignite 2016 touting a new and improved Cortana, Salesforce launching Einstein, and Viv -- a start-up by the developers of Siri -- bought by Samsung. These AI-driven enhancements are becoming ubiquitous -- from customer service to marketing, from the home to the car, and from the factory to the community. They all have one thing in common -- they use predictions to deliver results which help you. Predictions are the result of predictive analysis, which, like data science, is red hot in the minds of executives and CMO's.

The experiment with having a robot in my home was going well – useful exchanges, mutual learning, some bonding – right up until the robot thought I told it to "fuck off". But the robot was convinced. It flashed its blue light and scolded me in a tone mixing hurt, disappointment and reprimand: "That's not very nice to say." Or bristled, saying it had erred and should pay more attention before leaping to conclusions. I could have unplugged the thing. Instead, worried at hurt feelings and a vague possibility of retribution, I apologised.

Many of you probably know that I have my own bot -- but one of the more useful features of my bot is its ability to field private messages on my behalf. These messages get sent to me via Messenger, and I respond from within that context. Unlike email which I can and tend to put off indefinitely (sorry!), messages that I receive via MessinaBot stack up quickly, and therefore need to be addressed serially, as they're received. This forces me to respond in a relatively timely and brief fashion. Sometimes though, I get tougher questions that require a bit more thought. Given the frequency of questions that I receive related to bots, I asked Anne Cathrine Saarem whether she'd mind if I published my responses to her questions publicly.

In the climactic reveal of Interstellar, NASA pilot Joseph Cooper is sucked towards the singularity of a black hole. He falls into rows upon columns of unending prisms of bookshelves. Much book-thumping ensues and Cooper is able to communicate with his daughter across time, sending her precious data that saves the day. What's mind-bendingly fascinating about the bookshelf'place' (a Tesseract), is that it is infinite. A single event that occurs in any one bookshelf creates infinite consequences.

Mobile means that, for the first time, pretty much everyone on earth will have a camera, taking vastly more images than were ever taken on film ('How many pictures?'). This feels like a profound change on a par with, say, the transistor radio making music ubiquitous. Then, the image sensor in a phone is more than just a camera that takes pictures - it's also part of new ways of thinking about mobile UIs and services ('Imaging, Snapchat and mobile'), and part of a general shift in what a computer can do ('From mobile first to mobile native'). Meanwhile, image sensors are part of a flood of cheap commodity components coming out of the smartphone supply chain, that enable all kinds of other connected devices - everything from the Amazon Echo and Google Home to an August door lock or Snapchat Spectacles (and of course a botnet of hacked IoT devices). When combined with cloud services and, increasingly, machine learning, these are no longer just cameras or microphones but new endpoints or distribution for services - they're unbundled pieces of apps.

S. Somasegar is a venture partner at Madrona Venture Group and the former head of Microsoft's Developer Division. More posts by this contributor: Escaping the trough of disillusionment for virtual and augmented reality The intelligent app ecosystem (is more than just bots!) How to join the network Daniel Li is an investor with Madrona Venture Group. More posts by this contributor: The intelligent app ecosystem (is more than just bots!) How to join the network You can hardly talk to a technology executive or developer today without talking about artificial intelligence, machine learning or bots. While everyone agrees on the importance of machine learning to their company and industry, few companies have adequate expertise to do what they wanted the technology to do. Here are some insights into what we can expect in the coming years around ML and AI.

Now if these are not dominant steps toward a world of AI I don't know what is. Front this technology with a robot body and it becomes an intelligent being. Google Assistant, Amazon Echo, Apple Siri and the other personal virtual assistants are bringing all daily rituals, habits and requests together via one central point. You want to control the lights, heating, music, TV, or order the shopping, these tools are evolving to allow you to do any of the above firstly from an initial request, but then by learning what your habits are and when you require things. We are surrounded by AI, is it just a matter of time?

Here are a whole bunch of case studies and use cases, as a complete primer for AI in our industry. Predictive analytics allows Netflix to surface and finesse recommendations. This kind of clustering algorithm is continually improving suggestions, allowing users to make the most of their subscription. Uniting information from diverse datasets is a common use of AI. Under Armour is one of the many companies to have worked with IBM's Watson.