Google Lens is both a return to form for the search giant, and a tantalizing glimpse into what lies ahead. Google's early claim to fame was its ability to efficiently index the web and fetch search results quickly, bringing some much needed organization to the chaotic early days of the internet. Lens, similarly, uses computer vision and AI to make sense of your photos, videos and the real world. Most intriguingly, Lens is yet another way for Google to expand on its original mission statement: "to organize the world's information and make it universally accessible and useful." Though we've only seen brief a brief, pre-produced demonstration of Lens, it looks compelling.

Here's some excellent news: Roughly 53% of statisticians are women. This is up from 5.8% in 1978. So it is with some surprise that this isn't common knowledge. Some say that it's the fact that women are not well represented at conferences, giving the mis-impression that women are not well represented in the industry. So how can we get more women involved in conferences?

As PSD2 is set to open data sharing between banks and third parties, banking and fintech professionals should heed this warning: Don't confuse digitization with digital strategy. You need to build mobile apps and digital experiences for your customers. They expect it, so you should deliver. But a series of mobile experience projects doesn't constitute a digital strategy. To appreciate the difference, we'll look at a topic outside of banking and fintech--AI (artificial intelligence) assistants.

Over the last several decades, the evolution of artificial intelligence has followed an uncertain path – reaching incredible highs and new levels of innovation, often followed by years of stagnation and disillusionment as the technology fails to deliver on its promises. Today we are once again experiencing growing interest in the future possibilities for AI. From voice powered personal assistants like Google Home and Alexa, to Netflix's predictive recommendations, Nest learning thermostats and chatbots used by banks and retailers, there are countless examples of AI seeping into everyday life and the potential of future applications seem limitless . . . Despite the mounting interest and the proliferation of new technologies, is this current wave that much different than what we have seen in the past? Do the techniques of the modern AI movement – machine learning, data mining, deep learning, natural language processing and neural nets – deserve to be captured under the AI moniker, or is it just more of the same?

It's been quite an interesting journey for Factmata since we started in January and we're now about to launch a tool that puts factual context in the hands of the people. This will happen around the UK general election, and marks the completion of our Google Digital News Initiative (DNI) project. For 5 months, we've been working around the clock with a distributed team of NLP researchers, PhDs and scientists from around the world to build this, and now finishing off the final touches. As we prepare for launch, we wanted to tell the world about what's next and where we want to take Factmata in the future. Given our team's work in automated fact-checking in previous research, we are uniquely placed to build AI to solve the problem of online misinformation.

Music streaming service Spotify on Wednesday disclosed it has acquired the team and technology behind Niland, a French start-up with a service for delivering music recommendations. The move signals that Spotify wants to incorporate more artificial intelligence (AI) into its system as it fights off competition from alternatives like Apple Music. Niland is not well-known in the field of AI. But for years its CEO, Damien Tardieu, has done research on ways to extract meaningful information from raw music content in order to form connections with other music. This approach differs from collaborative filtering, one of the techniques that Spotify and others use.

While it's easier than ever to define and train deep neural networks (DNNs), understanding the learning process remains somewhat opaque. Monitoring the loss or classification error during training won't always prevent your model from learning the wrong thing or learning a proxy for your intended classification task. Regardless of the veracity of this tale, the point is familiar to machine learning researchers: training metrics don't always tell the whole story. And the stakes are higher than ever before: for rising applications of deep learning like autonomous vehicles, these kinds of training errors can be deadly [2]. Fortunately, standard visualizations like partial occlusion [3] and saliency maps [4] provide a sanity check on the learning process.

As Python has gained a lot of traction in the recent years in Data Science industry, I wanted to outline some of its most useful libraries for data scientists and engineers, based on recent experience. And, since all of the libraries are open sourced, we have added commits, contributors count and other metrics from Github, which could be served as a proxy metrics for library popularity. When starting to deal with the scientific task in Python, one inevitably comes for help to Python's SciPy Stack, which is a collection of software specifically designed for scientific computing in Python (do not confuse with SciPy library, which is part of this stack, and the community around this stack). This way we want to start with a look at it. However, the stack is pretty vast, there is more than a dozen of libraries in it, and we want to put a focal point on the core packages (particularly the most essential ones).

Summary: We are swept up by the rapid advances in AI and deep learning, and tend to laugh off AI's failures as good fodder for YouTube videos. But those failures are starting to add up. It's time to take a hard look at the weaknesses in AI and where that's leading us. Trying to spot new themes and directions I was trolling through some data and I found this. This is the Google Trends chart of searches for'Artificial Intelligence' for the last four years.

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