With many companies still stuck to take advantage of data, analytics has to be number one question because this is a key stage to implement successfully AI. For employees in the analytics business, there is a natural progression from analytics to artificial intelligence. There is a sequence of evolution in analytics, starting from descriptive to prescriptive. Many enterprises use the descriptive analytics, applying BI techniques: combine all your data to get a quick review on what's going on in the company. Diagnostic analytics includes data discovery, data mining, drill-down, and correlations.

CustomerMatrix is a New York company, but Mounier is calling from Paris today – that's where the company first started. Paris is also the location of 40 R&D people working on maintaining the company's artificial intelligence engine – you could also call it machine learning or cognitive computing. In any case, what the CustomerMatrix platform does is attempt to perform tasks in a manner similar to the human brain: take in lots of information, identify patterns, draw out meaning, and make decisions. IBM's Watson is the headline example for how this can work, but Mounier says the CustomerMatrix approach is more focused: "We're already connected to all the customer data sources on the internet. For example, we go on LinkedIn, we go on FactSet, we have relationship with Factiva, and in the US we even have a relationship with eBay."

Over the past few years technology has been finding a way to produce more and more innovative ways to diagnose disease, but now it has a new party trick – disease detection by voice analysis – and it's just as it sounds, if you excuse the pun. You talk, it analyses your voice and screens for illnesses. We're all used to being able to tell when someone's ill by just listening to their voice – on the phone for example. If someone has a cold then they sound nasal, and we know that and we can ask them if they have a cold. If they're slurring then again we can hear it and we can ask them what's wrong.

I'm a software engineer by training and I've had little interaction with AI. I had always wanted to delve deeper into machine learning, but never really found my "in". That's why when Google open sourced TensorFlow in November 2015, I got super excited and knew it was time to jump in and start the learning journey. Not to sound dramatic, but to me, it actually felt kind of like Prometheus handing down fire to mankind from the Mount Olympus of machine learning. In the back of my head was the idea that the entire field of Big Data and technologies like Hadoop were vastly accelerated when Google researchers released their Map Reduce paper.

Everyone today talks about AI, big data and machine learning, yet most do not delve into the fundamental properties of how they will operate and how they might be an actual threat...

Artificial intelligence has taught itself to create its own encryption and produced its own universal'language'. A neural network, called DeepCoder, developed by Microsoft and University of Cambridge computer scientists, has learnt how to write programs without a prior knowledge of code. First reported by the New Scientist, the system works by taking lines of code from existing programs and combining them. The system is only able to produce short, five-line, pieces of code at present but this has been enough to test it against real-world problems used by trainee developers. "We have found several problems in real online programming challenges that can be solved with a program in our language," the research paper says.

With AI in a full-fledged mania, 2017 will be the year of reckoning. Pure hype trends will reveal themselves to have no fundamentals behind them. Paradoxically, 2017 will also be the year of breakout successes from a handful of vertically-oriented AI startups solving full-stack industry problems that require subject matter expertise, unique data, and a product that uses AI to deliver its core value proposition. Over the past year a mania has risen up around'bots.' In the technical community, when we talk about bots, we usually mean software agents which tend to be defined by "four key notions that distinguish agents from arbitrary programs; reaction to the environment, autonomy, goal-orientation and persistence." Enterprises have decided to usurp the term'bot' to be mean'any form of business process automation' and create the term'RPA', robotic process automation.

In advertising and marketing specifically, brands might not be completely overhauling their existing ad tech and martech stacks to make room for AI just yet, but many are getting a feel for it by experimenting with single-touch AI solutions that focus on isolated tasks, like recommendations, ad buying and optimization. The coming wave of AI in marketing will be defined by the automation of complex, multi-step processes -- not just one-off aspects of a larger campaign. For brands, this will mean relinquishing control, trusting the technology to come in and quickly understand processes comprised of numerous tasks, channels, people and procedures, without messing things up. Before handing over the reins, it's helpful to understand how AI works -- and how entire human thought processes are converted into algorithms. For all its complexity, here's a simplified look at seven steps to introducing an AI that can automate holistic digital marketing programs from start to finish.

The AImotive office is in a small converted house at the end of a quiet residential street in sunny Mountain View, spitting distance from Google's headquarters. Outside is a branded Toyota Prius covered in cameras, one of three autonomous cars the Hungarian company is testing in the sleepy neighbourhood. While other autonomous car projects, including those from Waymo and Uber, rely on an expensive (but very useful) radar-like system called Lidar for depth perception and obstacle detection (as well as cameras for seeing the colour of traffic lights and signs), AImotive is trying to do the same using regular cameras combined with artificial intelligence. This means the company can convert a regular car into a driverless one for a fraction of the price – around $6,000 – as opposed to $70,000-$100,000. "The whole traffic system is based on the visual system," explained founder and CEO Laszlo Kishonti. "Drivers don't have bat ears and sonars, you just look around and drive."

There's a great deal of concern over artificial intelligence; what it means for our jobs, whether robots will one day replace us in the workplace, whether it will one day lead to robot wars. But current research projects show that artificial intelligence (AI) can also be used for the greater good. Here are five global problems that machine learning could help us solve. One of the biggest benefits of AI is its ability to trawl through massive amounts of data in record time. This helps researchers pinpoint areas of focus for their own research.