I would like to thank Feiwen, Neil and all other technical reviewers and readers for their informative comments and suggestions in this post. Deep Neural Network (DNN) has made a great progress in recent years in image recognition, natural language processing and automatic driving fields, such as Picture.1 shown from 2012 to 2015 DNN improved IMAGNET's accuracy from 80% to 95%, which really beats traditional computer vision (CV) methods. In this post, we will focus on fully connected neural networks which are commonly called DNN in data science. The biggest advantage of DNN is to extract and learn features automatically by deep layers architecture, especially for these complex and high-dimensional data that feature engineers can't capture easily, examples in Kaggle. Therefore, DNN is also very attractive to data scientists and there are lots of successful cases as well in classification, time series, and recommendation system, such as Nick's post and credit scoring by DNN.

What do Search, artificial intelligence (AI) and productivity all have in common? They've all found a comfortable home in Google's new search product, Springboard. For over a decade, Google has been selling its search solutions to businesses. Now it is parlaying that expertise into Google Springboard, a platform designed to help Google for Work users search data across their entire suite of Google productivity tools. This means that across all Google Apps--Drive, Gmail, Calendar, Docs, Sheets, Sites and more--users can quickly find desired information.

In an effort to take a lead in artificial intelligence, Google (NASDAQ:GOOG) has established a new AI research group in Europe. Based in Zurich, Switzerland, the group will mainly focus on making machines learn and understand the way humans do. Google, in one of its official blogs, mentions that the largest Google research facility outside the US is already in Zurich. It is the same research facility that came up with the conversation engine for Allo, Google's smart chat software. Adding a separate artificial intelligence division to it means Google has something cooking. According to Google's announcement, the research will follow three aspects: Machine intelligence, Natural Language Processing and Understanding, and Machine Perception.

We are looking to hire a results-oriented data scientist with experience in data analysis and predictive modeling. Experience in advertising or real time bidding is a plus. The job will involve research, analysis and coding to improve our current technology and develop innovative new solutions to the problems posed by real time bidding. This is an exciting opportunity for a skilled professional (junior or senior) to apply big data techniques to work within a very fast-growing industry.

Consider a toddler navigating her day, bombarded by a kaleidoscope of experiences. How does her mind discover what's normal happenstance and begin building a model of the world? How does she recognize unusual events and incorporate them into her worldview? How does she understand new concepts, often from just a single example? These are the same questions machine learning scientists ask as they inch closer to AI that matches -- or even beats -- human performance.

The best place to follow my thinking on AI and other technologies to sign-up to Exponential View. Well … it depends who you ask. There were announcements at Apple's developer event this week which suggest Apple is going to continue its investments that improve user experience through technologies machine intelligence technologies. These suggest that Apple is taking the opportunities of machine intelligence quite seriously. These seem like practical steps in using AI to improve products.

"Lawyers say nothing will ever replicate the trusted adviser role, but at the end of the day general counsel may decide to sacrifice those relationships if artificial intelligence can do the job," he said. "It will accelerate the trend of inhouse corporate teams to do more work for themselves. "Within 10-15 years, current buyers of legal services probably won't need law firms, as we currently understand them, at all." Advances in e-discovery, document automation, compliance and contract analysis were already claiming the responsibilities of junior lawyers but in another decade the number of senior lawyers would also likely shrink in response to artificial intelligence, such as IBM's legal robot "Ross" as well as machine-learning systems such as Google Brain and Google DeepMind, Mr Dwyer said. Rather than be on a back foot, director at law firm consultancy Janders Dean, Justin North said technology could also be an enabler that help firms get closer to clients.

There are countless internet quizzes that helps sort you into Hogwarts houses depending on your personality, but nothing comes quite close to this: an actual sorting hat powered by IBM Watson. The hat, created by IBM engineer Ryan Anderson, started off as a fun project for him and his two daughters to help expose the girls to STEM while bridging it with their interests in the Harry Potter series. The hat uses Watson's Natural Language Classifier and Speech to Text to let the wearer simply talk to the hat, then be sorted according to what he or she says. Anderson coded the hat to pick up on words that fit the characteristics of each Hogwarts house, with brainy and cleverness going right into Ravenclaw's territory and honesty a recognized Hufflepuff attribute. His daughter helped by adding lines of established "ground truths" for each of the four houses, with Watson using deep learning to figure out more attributes and expanding known qualities of each house every time the hat is worn.

I recently participated in a meeting of technologists, economists and European philosophers and theologians. Other attendees included Andrew McAfee, Erik Brynjolfsson, Reid Hoffman, Sam Altman, Father Eric Salobir. One of the interesting things about this particular meeting for me was to have a theological (in this case Christian) perspective to our conversation. Among other things, we discussed artificial intelligence and the future of work. The question about how machines will replace human beings and place many people out of work is well worn but persistently significant. Sam Altman and others have argued that the total increase in productivity will create an economic abundance that will enable us to pay out a universal "basic income" to those who are unemployed.

When you think of leaders in artificial intelligence, Netflix doesn't usually jump to the top of the list. But the streaming video service's VP of Product Innovation Carlos Uribe-Gomez and Chief Product Officer Neil Hunt published a paper that says some of its AI algorithms save Netflix 1 billion each year. In their paper, the two Netflix execs detail how the company's recommendation engine impacts its churn rate. Netflix no longer reports its churn rate, but the paper notes that Netflix's "retention rates are already high enough that it takes a very meaningful improvement to make a retention difference of even 0.1%." Let's dive into how the recommendation engine saves Netflix money -- and what the return on investment looks like.