Artificial intelligence (AI) is here to stay. The AI revolution is changing lives but scaring millions. AIs are already everywhere, supporting the human mind in the pursuit to make the world better and our way of life even easier. They're in your phones and favorite apps, helping you stay connected and do what you do best in the shortest possible time. The opportunities are endless and uncertain.

Microsoft's attempt at engaging millennials with artificial intelligence has backfired hours into its launch, with waggish Twitter users teaching its chatbot how to be racist. The company launched a verified Twitter account for "Tay" – billed as its "AI fam from the internet that's got zero chill" – early on Wednesday. The chatbot, targeted at 18- to 24-year-olds in the US, was developed by Microsoft's technology and research and Bing teams to "experiment with and conduct research on conversational understanding". Related: How much should we fear the rise of artificial intelligence? "Tay is designed to engage and entertain people where they connect with each other online through casual and playful conversation," Microsoft said.

Mauna Kea Technologies (Euronext: MKEA, FR0010609263; OTCQX: MKEAY), inventor of Cellvizio(R), the multidisciplinary confocal laser endomicroscopy platform, today announced its full-year results for the financial year ended December 31, 2015, as approved by the Board of Directors at its meeting on March 23, 2016. Benoît Jacheet, Chief Financial Officer of Mauna Kea Technologies, stated: "2015 was a critical year in the implementation of our updated strategic plan and this affected our sales performance. Even so, we successfully delivered a significant improvement in our gross margin and operating performance by streamlining our cost structure to reduce fixed operating costs. Increased financial flexibility is expected to support the continued growth of our global user base." As previously reported, Mauna Kea Technologies recorded a 22% decline in its full-year 2015 sales to EUR8,547 thousand.

You may know Watson for its ability to dominate Jeopardy, but did you know it has impressive movie skills too? IBM has hinted at Watson's movie-watching capabilities, like in its commercial with film director Ridley Scott, but we've never seen it in practice. So we asked Watson to tell us what it thought of the "Harry Potter" original book series and movies -- and the results are really interesting. By analyzing written text, Watson can identify different tones such as fear, joy, confidence, and openness. It can also analyze written text to assess personality traits based on the Big Five test, one of the most common, preferred ways psychologists use to measure personality.

Thursday 24 March 2016 03.00 EDT Last modified on Thursday 24 March 2016 03.03 EDT

Microsoft's attempt at engaging millennials with artificial intelligence has backfired hours into its launch, with waggish Twitter users teaching its chatbot how to be racist. The company launched a verified Twitter account for "Tay" – billed as its "AI fam from the internet that's got zero chill" – early on Wednesday. The chatbot, targeted at 18- to 24-year-olds in the US, was developed by Microsoft's technology and research and Bing teams to "experiment with and conduct research on conversational understanding". Related: How much should we fear the rise of artificial intelligence? "Tay is designed to engage and entertain people where they connect with each other online through casual and playful conversation," Microsoft said.

The IoT has been influencing the creation of a connected world where unbelievable amounts of data are generated from just about every imaginable thing. These connected, and previously dormant, things are now going to be able to communicate- from my dog's collar, the milk carton, my car, my thermostat, my watch, my washer and dryer (not that I would particularly enjoy that conversation), you name it. This connectivity and mass data production is exciting because it is defining the future by creating an eco-system where a huge variety of technologies have to work together, thus breaking down silos. Coming from the telco world, that is refreshing. So what are we supposed to do with this influx of data and connected devices?

The objective of my final project at Metis from weeks 9 to 12, is to categorize drivers based on their behaviour on the roads - their driving style and the type of roads that they follow. The challenge associated with this objective is to identify uniquely a driver (and hence his proper "driving behaviour") based on the GPS log of a mobile phonelocated inside the car. My idea to solve this issue is to experiment Topic Modeling techniques especially Latent Semantic Indexing/Analysis (LSI/LSA) and Latent Dirichlet Allocation(LDA) and explain the observed trips by the unobserved behaviour of drivers. The following is an executive summary … you can also browse throught the ppt that I am presenting at Metis on the 7th of April 2015 during the Career Event, or check the Python code available on my blog: http://nasdag.org The raw data received for each trip is a csv file of (x,y) coordinates logged every one second.

Recently, I began a series on exploratory data analysis; so far, I have written about computing descriptive statistics and creating box plots in R for a univariate data set with missing values. Today, I will continue this series by introducing the underlying concepts of kernel density estimation, a useful non-parametric technique for visualizing the underlying distribution of a continuous variable. In the second half of this blog post that will be published later here on AnalyticBridge, I will show how to construct kernel density estimates and plot them in R. I will also introduce rug plots and show how they can complement kernel density plots. Before defining kernel density estimation, let's define a kernel. A kernel is a special type of probability density function (PDF) with the added property that it must be even.

What would be the best way to deliver nutrition to the 7.1 billion odd people on this planet? Science would tell you that it is not the animals. Researchers at the Not Company (NotCo) which is a food-tech startup based at Chile have developed food products that is no longer based on animal ingredients but entirely based on plants. They use machine learning technology to develop tasty, nutritious and affordable plant-based food. However, the food looks and tastes like the classic (animal-based) food.