Artificial Intelligence is the buzzword of the year with immense anticipation and excitement attached to it, but also often a fear of the unknown. So much so, that tech behemoths Facebook, IBM, Google's parent company Alphabet, Amazon, and Microsoft recently announced a partnership to discuss AI best practices. While there are the science fiction-driven angles of AI, like robots, self-driving cars, Internet of Things, and augmented reality, there are also more practical applications that affect business owners every day, especially those working in the virtual customer service world of online retail. Gartner predicts that by 2020, 85% of interaction between customers and retailers will be through artificial intelligence customer service programs. Brands are rushing to build out their customer service approaches leveraging AI to create accurate product catalogs, fine-tuned search capabilities, and truly personalized online experiences.

AI for recruiting is on everyone's mind these days with a lot of talk on how it's going to transform recruiting. Artificial intelligence for recruiting is the next generation of software designed to improve or automate some part of the recruiting workflow. The improving economy: The recent economic gains have created a candidate-driven market that's made competing for talent tougher than ever. This competition will only continue to increase – 56% talent acquisition leaders surveyed by LinkedIn believe their hiring volume will grow in 2017. The need for better technology: Although hiring is predicted to increase, 66% of talent acquisition leaders state their recruiting teams will stay the same size or even shrink.

Facebook is releasing tools to help improve Messenger chatbots. Earlier this year at F8, the social network's most important conference, CEO Mark Zuckerberg detailed a big investment in chatbots, which use artificial intelligence to allow people to text with the software to help them do things like schedule calendar meetings or buy a pair of shoes. Now Facebook wants to help software developers who create bots make them better. On Monday, the company said it would bring free analytics tools to its Facebook Messenger platform, where the bots live. The company says businesses have built more than 33,000 bots on Messenger. With the new tools, bot makers will be able to measure the demographics of bot users, like age, gender, education level, relationship status, household income or retail spending.

Some scientists, such as Stephen Hawking and Stuart Russell, believe that if advanced AI someday gains the ability to re-design itself at an ever-increasing rate, an unstoppable "intelligence explosion" could lead to human extinction (Technological Singularity). There is no doubt that the studies and research on Artificial Intelligence, if only targeted to commercial profit, military power and technological speculation paradigms, represent today one of the greatest existential threat to humanity. There are many laboratories in the world developing technologies associated with AI, but only some of them make their progress known. But from the signals – albeit fragmented – we get, it is extremely easy to understand an exposure to significant risks, even if masked by the opaque veil of "modernity". In order to clarify and avoid generalizations: simple positive implementations of AI are already part of our everyday since long time. Every time we turn on a last generation washing machine, or browse on Google or Facebook, complex adaptive algorithms guide us and facilitate the task, learning our behavior or our needs.

Of the vast wealth of information unlocked by the Internet, most is plain text. The data necessary to answer myriad questions--about, say, the correlations between the industrial use of certain chemicals and incidents of disease, or between patterns of news coverage and voter-poll results--may all be online. But extracting it from plain text and organizing it for quantitative analysis may be prohibitively time consuming. Information extraction--or automatically classifying data items stored as plain text--is thus a major topic of artificial-intelligence research. Last week, at the Association for Computational Linguistics' Conference on Empirical Methods on Natural Language Processing, researchers from MIT's Computer Science and Artificial Intelligence Laboratory won a best-paper award for a new approach to information extraction that turns conventional machine learning on its head.

Other than maybe the NSA, nobody knows more about you than Google. It's got a read on where you are, what you're doing, what you're thinking and watching and searching for and chatting with your friends about. Which means nobody should be better equipped to soundtrack every second of your life than Google Play Music. Starting today, the company's taking full advantage of its smarts to deliver you the sounds you want, when you want them. All you have to do is press play.

Apple announcements are like holidays in our office, and the new iOS release in September was no exception. Since then, AI assistant news has moved at warp speed. Every Alexa update is a new opportunity for fintech integration. Google, with its latest releases, is also now in the running. Add to this Bank of America's launch of a new AI chatbot, Erica, at Money 20/20 in Las Vegas, and the bank bot race is on.

Developed back in the 50s by Rosenblatt and colleagues, this extremely simple algorithm can be viewed as the foundation for some of the most successful classifiers today, including suport vector machines and logistic regression, solved using stochastic gradient descent. The convergence proof for the Perceptron algorithm is one of the most elegant pieces of math I've seen in ML. Most useful: Boosting, especially boosted decision trees. This intuitive approach allows you to build highly accurate ML models, by combining many simple ones. Boosting is one of the most practical methods in ML, it's widely used in industry, can handle a wide variety of data types, and can be implemented at scale.

A collection of minimal and clean implementations of machine learning algorithms. This project is targeting people who wants to learn internals of ml algorithms or implement them from scratch. The code is much easier to follow than the optimized libraries and easier to play with. All algorithms are implemented in Python, using numpy, scipy and autograd.

Report discusses "special responsibilities for government which follow from its use of artificial intelligence and big data" A report by the Government Office for Science has warned that making the most of artificial intelligence, including in the public sector, will require the government to pay strong attention to ethics and governance. The report, Artificial intelligence: opportunities and implications for the future of decision making by government chief scientific advisor, Sir Mark Walport and Home Office permanent secretary Mark Sedwill, says it is important that the government actively works to bring this about. "Reaping the benefits of this revolution in information technology will require an approach to ethics and governance that enables innovation, builds trust among citizens, establishes a stable environment for businesses and investors, and fosters appropriate access to the data necessary for computer science to develop this technology still further," the report said. "The right form of governance for artificial intelligence, and indeed for the use of digital data more widely, is not self-evident. It is important to consider forms of data governance that cover all elements of the increasingly complex space, from responsibly generating data from people's behaviour to remaining accountable for autonomous software agents. Additionally, any approach adopted must be flexible, able to adapt to new uses and more advanced forms of artificial intelligence. There are many models that can be considered. But the important task is to set out what needs to be done before considering how it is to be achieved."