Recently, artificial intelligence ('AI'), has been booming, and with good reason. The reasons behind this can be explained fairly simply, as technology advances we see advancements in particular sectors, in this instance, AI. AI has been around for a long time, with earliest known uses in the 1950s. But the tech has evolved to a point where it can be used on the mainstream today. As a technology advances, it becomes much more accurate, which is the case here in AI as we see Apple launching AI in its Photos app and within Siri.

Earlier this year Facebook shared its vision of a chatbot-filled future - AI-powered programs working through Messenger to help us order pizza, find support with a wonky gadget or get the latest football scores. Well, developers apparently love the idea: there are already 11,000 bots available on Facebook, the head of Messenger David Marcus says. The benefits of being able to outsource your ordering system to chatbots who never take a break and never get grumpy are obvious, but whether users are going to embrace this new era of artificial intelligence quite so enthusiastically remains to be seen. If you've heretofore been chatting with real human beings on Facebook and fancy a change, head to this listings page and have a browse - anything with a Messenger icon next to its name has a bot, so you can start chatting with the business or brand and see if you're able to spot the signs of artificial intelligence. Facebook is by no means the only tech company investing in a bot-filled future: you can find them inside Microsoft's Skype apps and they're coming to Google's new Allo messenger app too, in a slightly different form.

Based on this thesis statement, Turing devised the "Turing test", which is now considered the standard for qualifying computer programs (chatbots) as intelligent. The test today involves a series of 5 minute-long text conversations with judges, during which the program must convince them that it is human on average at least 30% of the time. This is based on Turing's assumption that by the year 2000, machines would be capable of fooling 30% of human judges after five minutes of questioning. Whether a bot is actually able to pass the Turing test or if Turing did in fact intend the test to be passed is still debatable. Regardless, there are a few bots that have an uncanny humanness to them that will convince a good many people that they are human.

This has been the year of the chatbot. Siri opened up to developers recently. The Facebook Messenger bots arrived. The Slack App Store continues to evolve quickly with hundreds of bots. Chatbots have been in many ways disappointing to me, but they are an important step in the evolution of conversational technology.

Few environmental limits are as obvious to people today as water availability. Particularly in drier climates, availability can be a pretty unforgiving equation. Even there, a family might pay less for water than for cell phones, but there is often a pretty complex system behind your tap that keeps it running. The challenge of water availability rises beyond engineering. It becomes a delicate dance managing demand, forecasting supply, and sustaining ecosystems. Decisions have to be made based on information that is never complete, so any opportunity to obtain more useful information is liable to get a thirsty look from water managers.

More than 1.5 billion people use Facebook. And only half speak English. The rest speak so many dozens of other languages, effectively silo'd off from the English speakers and, in many cases, from each other. If you stumble onto a Facebook post in a foreign language, Facebook lets you instantly translate it--in a semi-effective way. And beginning today, millions of people will have the option of instantly translating their own posts into any one of 44 other languages, so that they will automatically show up in your News Feed in your native tongue.

Enterprises today are finding it exceedingly meaningful and resourceful in the massive amounts of data they generate and save every day. The required algorithms, applications and frameworks to bring greater predictive accuracy and value to enterprises' data sets are available; therefore, businesses need to make sure they have data sets of sufficient size and quality. It is due to the excessive need to do a better job in capturing and utilizing data. The rise of deep learning and neural networks has spread in everyday lives. It took about six years for neural nets to show impressive results, first in speech recognition, then computer vision, images, image detection and diagnostics, and more recently, in natural language processing.

Studies suggest that relying on your smartphone heart monitor may not be the wisest move. The widespread use of smartphones to collect healthcare data has been thrown into doubt by a couple of recent studies. In some fields -- such as the management of chronic diseases and mental health monitoring smartphones have enabled a greater level of patient self-control, and personalised clinical intervention. Success in one area, however, does not imply success in all – especially as smartphone health apps are often rolled out before evidence of their effectiveness has been rigorously analysed. A major study into the use of mobile phone data as a tool for predicting clinical decisions, released in June, came to a scathing conclusion, characterising the practice as "voodoo machine learning".

A recent Evans Data report shows that 36 percent of developers working with big data and analytics are also using machine learning.

The United States Air Force wants robots and service members to be best buds on the battlefield. Last Friday, the Air Force announced a grant of 7.5 million for research on ways to make humans trust artificial intelligence (AI) so that people and machines can collaborate on missions. Soon service members in every branch of the Armed Forces will be working with AI on a daily basis--be it unmanned ariel vehicles, underwater drones, or robot soldiers (the U.S. Military had to shelve the Boston Dynamic L3 "robotic mule" because it was too loud, but last week the company revealed the much stealthier SpotMini). On November 1, 2014, (one week after Elon Musk compared developing AI to "summoning the demon") Undersecretary of Defense Frank Kendall issued a memo asking the Defense Science Board to study what issues must be solved in order to expand the use of AI "across all war-fighting domains." But robotic weapons and soldiers wont be as effective if their human counterparts don't trust them.