If you want to dive into an exciting career field that has loads of upside, you need to purchase The Complete Machine Learning Bundle, marked down by more than 90% for Pocketnow readers. There has never been a better time to enter the field of machine learning. Artificial intelligence, as it's more commonly referred, is just starting to break out into the mainstream through applications like self driving cars and analytical software. Those who have the most innovative ideas are primed to take this relatively young technology even further–and secure their own future success. The Complete Machine Learning Bundle offers access to 10 courses and more than 63 hours of content that can teach anyone how artificial intelligence works, and how to apply it in a variety of applications.

Chip maker Intel Corporation has acquired Itseez, a company specializing in computer learning and machine learning. Itseez is said to bring its expertise in advanced driver assistance systems (ADAS) for automobiles to Intel. The move is likely a part of Intel's growing IoT-related ambitions. The firm recently acquired Yogitech, an Italian company manufacturing safety measures for semiconductors. The value of the deal it's not been disclosed.

What is interesting to notice, is that the network doesn't seem to have learned detailed representations of faces. In e.g. the visualization featuring the collar, the face looks more like a spooky flesh-colored blob than a face. This might be an artifact of the visualization process, but it's not entirely unlikely that the network have either not found it necessary to learn the details, or not had the capacity to learn them. There also are a surprisingly large number of units that detect dog-related features. I counted somewhere around 50, out of 512 units in the layer in total, which means a surprising 10% of the network may be dedicated solely to dogs.

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.