In the final game of their historic match, Google's artificially intelligent Go-playing computer system has defeated Korean grandmaster Lee Sedol, finishing the best-of-five series with four wins and one loss.

When a RNN is trained on sequences of words, it learns to represent each word as a high dimensional vector which encodes the model's understanding of that word. If you take a step back and view the image as a whole, the large scale structure of the image is determined by words' part of speech. Nouns tend to lie in the center of the image, verbs tend to lie on the upper right side, and first names form a large orange cluster in the bottom left part of the image. The RNN learned all of this semantic understanding without a human ever having to code a definition of concepts like nouns, verbs, universities, cities, meetings, or social media.

A simple way to find great articles and resources on popular subjects such as data science, machine learning, deep learning, Python, R, data sets, dataviz, IoT, AI - or even Excel - is to use our data science search engine. This page, populated with pre-selected queries, is an excellent starting point. The search box can be found on DSC and all our channels, on all pages, at the very top, on the right-hand site. For instance, click here to find results for Python.

After all, a lot about design is telling a good story, and building a robot is an even purer version of that. It's probably rather empty, but it already has some familiar controls on it: an options menu, a settings button, a big button for starting something new. It's easy to assume our robot is operating inside a pure messaging or voice platform, but increasingly this is not the case: Amazon Echo is controlled by voice, but has a companion app. As users become more familiar with chat robots, they will form expectations about how these things should work and behave.

In the neo-noir sci-fi classic, protagonist Neo is able to learn kung fu in seconds after the martial art is'uploaded' straight to his brain. Researchers from HRL Laboratories, based in California, say they have found a way to amplify learning, only on a much smaller scale than seen in the Hollywood film. They studied the electric signals in the brain of a trained pilot and then fed the data into novice subjects as they learned to pilot an aeroplane in a realistic flight simulator. The study, published in the journal Frontiers in Human Neuroscience, found that subjects who received brain stimulation via electrode-embedded head caps improved their piloting abilities and learnt the task 33 per cent better than a placebo group.

Google will create business tools and products based on its own artificial intelligence technology, seeking to entice more companies to rent its cloud-computing services. The company also said it won several new large cloud customers, including the interactive division of Walt Disney Co., which now runs a web-subscription service on Google's cloud, and Coca-Cola Co., which rented Google servers for a World Cup marketing campaign. While modern cloud systems are based on "decades-old" technology, Google said, the company's forthcoming products and services are designed for the next wave of cloud computing. Google will charge for these capabilities, a departure from its typical consumer-focused approach in which AI technology supports free Web-based services and apps such as Photos and Translate.

SAN FRANCISCO--(BUSINESS WIRE)--Executives previously from IBM Watson, GE Digital, Infosys, and MicroStrategy announced today that they have joined forces with TPG Growth to launch Noodle Analytics, Inc. (Noodle.ai), the Enterprise Artificial Intelligence company. Today's artificial intelligence technologies include machine learning, predictive data analytics, and data science. He is joined by Dr. Matt Denesuk, previously Chief Data Science Officer for GE Digital; Raj Joshi, previously Senior Executive Vice President of Professional Services at MicroStrategy; and Dr. Ted Gaubert, previously Chief Technology Officer of Infosys Consulting. The team brings deep experience in AI, big data, data science, machine learning, and data analytics across industries.

Whenever I read articles about data science I feel like there is some important aspect missing: evaluating the performance and quality of a machine learning model. Consequently, the first post on this blog will deal with a pretty useful evaluation technique: lift analysis. When evaluating machine learning models there is a plethora of possible metrics to assess performance.

The only way to keep up with this IoT-generated data and gain the hidden insights it holds is using AI (Artificial Intelligence) as the last mile of IoT. John McCarthy, who coined the term in 1955, defines it as "the science and engineering of making intelligent machines" In an IoT situation, AI can help companies take the billions of data points they have and boil them down to what's really meaningful. The general premise is the same as in the retail applications – review and analyze the data you've collected to find patterns or similarities that can be learned from, so that better decisions can be made. The data collected, combined with AI, makes life easier with intelligent automation, predictive analytics and proactive intervention.

"Winning a data science competition needs skills that are not relevant to a data science job" That explains why I am very good at the job I don't have;)