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.

If you don't mind Google knowing everything about your life, then there are some benefits to handing over your data to the big G. The overhauled Google Music, which will launch this week on Android, iOS and on the web, will now offer streaming mixes based on your location and activity. SEE ALSO: 7 reasons Google's Assistant stomps Siri For example, if you're heading out to the gym, Google will assume you want your workout music of preference to play. On the other hand, if you're commuting to work, the service might soothe you with tunes you like to listen to on the subway. Google's machine learning algorithms also take the weather into account, so you'll get a different set of tunes when it's raining than you would on a beautiful sunny day.

IBM and Nvidia said they are collaborating on a deep learning tool that trains computers faster and scales better. Specifically, IBM launched a software kit dubbed PowerAI that will run on its OpenPOWER LC servers for high performance computing. The Power S822LC for high performance computing (HPC) uses Nvidia NVLink technology to work with IBM's architecture. IBM and Nvidia said that the S822LC system can run AlexNet with Caffe faster than x86 servers with multiple configurations. Caffe is a deep learning framework created by Berkeley Vision and Learning Center (BVLC).

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.