Shift Technology, a software-as-a-service (SaaS) platform that leans on artificial intelligence (AI) to help companies combat insurance fraud, has raised 10 million in a round led by Accel, with participation from existing investors Iris Capital and Elaia Partners. Founded out of Paris in 2013, Shift Technology taps machine learning smarts to combat insurance fraud, using what it calls a "decision support platform" that automates the process of detecting fraud and helps humans prioritize which cases to follow up on. In addition to using big data to highlight spurious claims, Shift Technology has tools to explain the kind of fraud that's likely involved and can point to specific facets of a claim that are worth exploring further. As with most machine-learning systems, Shift Technology's system is designed to improve over time by tapping user feedback and additional data. The company claims to have processed more than 50 million claims for insurers around the world since it was launched in 2014.

In a previous post I advised data scientists in training to build stuff. This post gets more specific. Here's what I mean when I say I'm doing data science. The raw stuff of data science is a collection of numbers and names. Measurements, prices, dates, times, products, titles, actions--everything is fair game. You can use images, text, audio, video and other complex data too, as long as you have a way to reduce it to numbers and names.

A few weekends ago, on a snowy Saturday in April (not uncommon in Denver), I signed into Kaggle for the first time in several months, looking to play around with some competition data in order to while away the chilly day. My kids' endless chatter and my wife's disapproving looks faded into the background, and I blissfully wrangled data from the Expedia Hotel Recommendation competition for several hours. I submitted a few entries, slowly climbing the leaderboard until I got to the top 1/3 of scores, and then finally I got up to help with my family duties. That night in bed, my mind whirled with possibilities for what I could do with the data to improve my score – different variables I could use, several time-related features I could engineer, and thoughts about how to ensemble a couple dissimilar models together. I woke up early Sunday and fired up my project in RStudio.

This project is part of the Human Data Interaction project at CSAIL, MIT. The Deep Mining project aims at finding the best hyperparameter set for a Machine Learning pipeline. A pipeline example for the handwritten digit recognition problem is presented below. Some hyperparameters indeed need to be set carefully, as the degree for the polynomial kernel of the SVM. Choosing the value of such hyperparameters can be a very difficult task and this project's goal is to make it much easier.

AN ALARMING app that can scan a person's face and identify who they are and what they are into has become very popular in Russia. This is the opposite of great, and represents a threat to privacy that has staggered even The Guardian newspaper, which has reported that the FindFace app is very accurate and can even be used to recognise whole crowds. It is the sort of thing that we might have expected to report alongside words like'Snowden' and'National Security Agency', but actually it's a consumer thing that draws on Vkontakte, the Russian equivalent of Facebook, to match mugs with, well, mugs. "In the short time since the launch, FindFace has amassed 500,000 users and processed nearly three million searches," said a report in The Guardian. "Some have sounded the alarm about the potentially disturbing implications. Already the app has been used by a St Petersburg photographer to snap and identify people on the city's metro, as well as by online vigilantes to uncover the social media profiles of female porn actors and harass them."

Google yesterday confirmed rumors that it has been working on a custom chip designed to speed up computing related to its artificial intelligence efforts. The result, it said at its I/O developer conference, is a chip it calls a Tensor Processing Unit. It's designed to work with TensorFlow, an open source software library for developing AI applications. The TPU chips, Google says, are designed to be built into its existing computing infrastructure and are already in use boosting the performance of services like Street View and voice recognition. They also played a part in Google's AlphaGo software that defeated the human champion at the board game Go.

Artificial technology has recently been viewed as an asset of precision medicine that can outsmart several diseases. With the advancement and sophistication of modern technology, artificial intelligence has seamlessly coalesced into the field of medicine. In fact, AI technology has recently been viewed as an asset of precision medicine that can outmaneuver tough medical problems. The presence of artificial intelligence in the field of medicine is nothing new. Last month, a team of scientists at the California NanoSystems Institute at UCLA has developed a new technique using artificial intelligence to efficiently detect cancer cells without damaging blood samples, as previously reported.

It is hard to miss the warnings. In the race to make computers more intelligent than us, humanity will summon a demon, bring forth the end of days, and code itself into oblivion. Instead of silicon assistants we'll build silicon assassins. The doomsday story of an evil AI has been told a thousand times. But behind the apocalyptic words of Stephen Hawking, the Oxford philosopher Nick Bostrom, and Elon Musk – who compared AI to nukes even as he launched an AI company – lies a more numbing existential threat.

Google's finally interweaving the deep treasure troves of information it holds about the world at large and you, specifically. Google Assistant is a conversational digital assistant built around the company's strengths in deep learning and natural language recognition, summoned at an utterance of "Ok Google" to respond to any queries you may have. It can understand context depending on the topic at hand, too: Holding your camera at a famous sculpture and asking "Who designed this?" will get an answer, as will queries like "Show me his other movies" after asking something like "Who directed The Revenant?" Google Assistant will be able to play music playlists, file reminders, help you buy movie tickets, and a whole lot more. Interestingly, it doesn't appear to be a discrete app; instead, it appears tied to be baked right into Google-y gadgets, in what Google CEO Sundar Pichai calls "an ambient experience that extends across devices." Think of it as a supercharged version of Google Now, mixed with Amazon's Alexa.

Big Data has been a hot topic since the Information Age took the world by storm in the 21st century. What quickly followed was the birth of the "data scientist" as an occupation to manage this influx of information, a job title our past VP of Product, DJ Patil coined in 2008. Having witnessed the growth of data science as a discipline, our Chief Data Scientist, Vitaly Gordon is excited for its future. Vitaly currently leads our data science team at SalesforceIQ, where they are developing sophisticated machine learning tools for the intelligent Customer Success Platform. We sat down with Vitaly to get his perspective on what he envisions for the future of data science.