Another study shows how images that are used to train image-recognition software amplify gender biases. Two large image collections used for research purposes – including one supported by Microsoft and Facebook – were found to display predictable gender biases in photos of everyday scenes such as sport and cooking. Images of shopping and washing were linked to women, while coaching and shooting were tied to men. If a photo set generally associates women with housework, software trained by studying those photos and their labels create an even stronger association with it.

The development of artificial intelligence (AI) has had a huge influence on today's society, as ongoing discussions evaluate the impacts of creating machines and computer systems that can react and perform like humans. These systems can process information in a more cognitive way, making them capable of more human-like functions like learning, decision-making, and visual perception. Hollywood portrayals of hyper-intelligent robots taking over the planet might make artificial intelligence seem intimidating, but there is a lot that can be gained by through these advanced computer systems. Without the element of human error, intelligent machines are capable of unmatched precision and accuracy, and since they don't require fundamental human needs like oxygen or food, they can perform tasks with far fewer limitations. In fact, AI is already popping up everywhere in our daily lives – through social media recommendations, virtual assistants on our smartphones, and even self-driving cars.

Microsoft France proudly opened the doors to its newly unveiled AI School on 6 March. Located at the company's Paris-based offices, the Microsoft AI School will see 24 students from a variety of backgrounds embark on a free, intensive seven month-course, during which they will learn invaluable AI development skills. The course will prepare participants for future careers in AI, and will be followed by 12 months of employment at participating companies. The new AI School is an embodiment of Microsoft's mission to enable every person and organisation on the planet to achieve more, while contributing to the democratization of AI for people from all backgrounds. Carlo Purassanta, President of Microsoft France believes that "France has all the assets needed to be an AI powerhouse, thanks to the quality of its researchers and engineers. If companies want to accelerate the development of strong projects, we must train AI experts."

High-dimensional datasets can be very difficult to visualize. While data in two or three dimensions can be plotted to show the inherent structure of the data, equivalent high-dimensional plots are much less intuitive. To aid visualization of the structure of a dataset, the dimension must be reduced in some way. The simplest way to accomplish this dimensionality reduction is by taking a random projection of the data. Though this allows some degree of visualization of the data structure, the randomness of the choice leaves much to be desired.

Customer churn is a problem that all companies need to monitor, especially those that depend on subscription-based revenue streams. The simple fact is that most organizations have data that can be used to target these individuals and to understand the key drivers of churn, and we now have Keras for Deep Learning available in R (Yes, in R!!), which predicted customer churn with 82% accuracy. We're super excited for this article because we are using the new keras package to produce an Artificial Neural Network (ANN) model on the IBM Watson Telco Customer Churn Data Set! As for most business problems, it's equally important to explain what features drive the model, which is why we'll use the lime package for explainability. In addition, we use three new packages to assist with Machine Learning (ML): recipes for preprocessing, rsample for sampling data and yardstick for model metrics. These are relatively new additions to CRAN developed by Max Kuhn at RStudio (creator of the caret package). It seems that R is quickly developing ML tools that rival Python. Good news if you're interested in applying Deep Learning in R! We are so let's get going!! Customer churn refers to the situation when a customer ends their relationship with a company, and it's a costly problem. Customers are the fuel that powers a business. Further, it's much more difficult and costly to gain new customers than it is to retain existing customers. As a result, organizations need to focus on reducing customer churn. The good news is that machine learning can help. For many businesses that offer subscription based services, it's critical to both predict customer churn and explain what features relate to customer churn.

For more content like this, follow Insight and Emmanuel on Twitter. When trying to develop a scientific understanding of the world, most fields start with broad strokes before exploring important details. In Physics for example, we start with simple models (Newtonian physics) and progressively dive into more complex ones (Relativity) as we learn which of our initial assumptions were wrong. This allows us to solve problems efficiently, by reasoning at the simplest useful level. The exact same approach of starting with a very simple model can be applied to machine learning engineering, and it usually proves very valuable. In fact, after seeing hundreds of projects go from ideation to finished products at Insight, we found that starting with a simple model as a baseline consistently led to a better end product.

Machine learning is cool, but let's spend a few minutes talking process – the application of data science to derive business insights. Let's look in particular at capital markets, where news and mood drive trading strategies. Trading is highly competitive, yet traders like to talk, and StockTwits – "the largest social network for investors and traders" – is where they often do it. Traders flock to the platform to share assertions and perceptions, analyses and predictions. This activity produces a combination of hard data and subjective information that can be profitably modeled via natural language processing, sentiment analysis, and machine learning.

Trump opened the meeting with a highlight reel of clips from the last decade of gaming, ranging from goofy to excessively bloody violence. Some attendees didn't expect any significant resolution, Glixel reported, and saw the meeting as an opening foray into a larger conversation...on gun violence in America. Critics of the industry called for regulations that would make it difficult for youths to buy violent games, and some asked Trump to widen the discussion to include violent movies and TV shows. But beyond sharing opinions during the closed-door summit, there was no commitment from attendees or the White House on concrete action. Instead, it seemed a stage to reframe the post-Parkland debate around video games' influence on school shootings.

If you love Andrew Ng's first Coursera course on machine learning as much as I do, you were equally hyped when you heard that deeplearning.ai Since everybody's on a tight schedule, let's try the impossible and finish a course that is laid out to last one month in one week. Let's not rush through though, but actually understand the material. And of course, we'll do it while continuing our 40h/week job. What are the advantages of finishing the course quickly you ask?

Last week Google announced that it will be making its artificial intelligence (AI) and machine learning (ML) courses available to everyone. In order to help, everyone understands how AI can solve challenging problems and to make learning resources available, Google has created a new platform called Learn with Google AI. Zuri Kemp, who leads Google's machine learning education effort, wrote in a blog post that the aim of this initiative is making AI and its benefits accessible to everyone. "Part of Google AI's mission is to help anyone interested in machine learning succeed -- from researchers to developers and companies, to students," wrote Kemp. Learn with Google AI website provides ways to learn about core ML concepts, develop and hone ML skills, and apply ML to real-world problems.