Google announced last week it was adding two new engines, CrowdStrike and Invincea, to its malware scanning platform VirusTotal. Google limited access to the full VirusTotal API only for companies that had a product listed in its scanning service. The company said that any vendor can integrate its product in VirusTotal, and be granted access to the full API if they provided data back to the community, and join the Anti-Malware Testing Standards Organization (AMTSO). A day later, Invincea announced it was joining AMTSO and VirusTotal as well.

Google announced last week it was adding two new engines, CrowdStrike and Invincea, to its malware scanning platform VirusTotal. Both are part of the new wave of next-gen anti-malware products that rely on machine learning algorithms to analyze behavior and network activity in order to detect anomalies and flag malware. The news is of great importance if we take a look at how a Google announcement from May has changed the antivirus market in the last three months. On May 4, Google published new API access rules on the VirusTotal blog. Google kicked out all security companies that were using VirusTotal's API to scan suspicious files and present the results to their clients, as they would be a real antivirus.

We've had more than 150 speakers in the past three seasons. Two of them made the news this summer: Danny Bickson (E9 Season 1) one of the co-founders of Graphlab then Dato then Turi and Arjun Bansal from Nervana systems (E12 Season 3). Turi just got acquired by Apple for 300M, and Nervana got acquired for 350M by Intel. In a different direction, at the last meetup, Raymond Francis explained to us what got picked by the LA Times a month later, Curiosity now uses Machine Learning on Mars. This news is exciting on two levels: First, robots can now explore the universe better and second, it definitely brings some perspective when we talk about the dichotomy between exploration and exploitation in our discussions.

Interest in bots and artificial intelligence is on the rise. VC funds are looking at the space for new investments. Likewise, players such as Facebook, Amazon, Google, and Microsoft are hoping to find new revenue streams here. And at the same time, an increasing number of entrepreneurs are trying to make it big with Bots. MobileBeat 2016 was all about bots and artificial intelligence and this blog will give you the lowdown on the latest and greatest in this exciting field.

A group of Stanford researchers is using satellite imagery and artificial intelligence to track poverty reduction efforts around the world. By combining those satellite images with machine learning -- the ability for machines to learn things without being programmed to -- scientists hope to collect data that could help the U.N. achieve its 2030 goal to eradicate poverty in a cheap and efficient manner. Collecting that data in the past has been difficult for a number of reasons. "Most countries don't collect much data, and scaling up traditional household survey-based data collection efforts would be expensive," Stanford researchers explain in a short video explaining the project. But the researchers suggest that by using "less conventional data sources," such as algorithms and satellite imagery, they can put together an "accurate, inexpensive and scalable method for estimating consumption expenditure and asset wealth."

NASA scientists are engineering a form of artificial intelligence (AI) that they hope will help firefighters and other first responders escape dangerous situations. Set to launch next year, the system will help first responders through unpredictable fires and chemical leaks by giving them advice based on machine learning of past emergencies. The new system--called AUDREY--the Assistant for Understanding Data through Reasoning, Extraction and sYnthesis--is designed to be distributed to individual firefighters so it can collect a precise network of data directly from the field, and learn from that data for next time. No emergency is the same, which means first responders have to rely on extensive training and experience to stay safe in dangerous conditions that can change rapidly. The AUDREY system hopes to use distributed data collection and machine learning to better inform first responders about the situation at hand.

For the casual internet user, a quick Google search is often all it takes to find plenty of information on any particular topic. But for specialized financial research, analysts often find themselves laboriously searching proprietary databases, regulatory filings, and paywalled sources that aren't even indexed by the big search engines, says Jack Kokko, the founder and CEO of financial search engine company AlphaSense. That's why he and cofounder and CTO Raj Neervannan, created AlphaSense, which applies natural language processing and machine learning techniques to let users find relevant information in financial documents. "It started from my first job out of college as an analyst at Morgan Stanley, where I was, as every analyst, going through these huge piles of paper on my desk and trying to find information very manually--nights and days spent toiling through that information and still fearing that I'm missing a lot," Kokko says. The San Francisco-based company takes in information from thousands of licensed data sources, as well as public web sources like news reports, and automatically processes them to extract meaning on a sentence-by-sentence level.

How Many Arlington Asset Investment Corp (NYSE:AI)'s Analysts Are Bearish?

We had a pretty incredible line-up for Data Day Texas 2016 -- and we intend to exceed your expectations again for 2017. Tell us whom you want to see, what topics you want to learn about, and let us make it happen. Please share your thoughts at suggestions@datadaytexas.com. If you wish to propose a talk or workshop, please visit the Data Day Proposals page. Her commercial applications of data science include developing predictive maintenance models for oil and gas pipelines at Deep Signal, and designing/building a platform for real-time model application, data storage, and model building at WibiData.

COUNTRIES grow richer when they learn how to produce more valuable stuff per person. Sadly, many advanced economies seem to have lost the knack. Except for a brief spurt around the turn of the millennium, productivity has grown painfully slowly in rich countries over the last four decades (see chart)--a factor, economists reckon, that has contributed to stagnant pay. Labour productivity in America fell at a startling 2.2% annual pace in the fourth quarter of 2015; growth of 0.6% for the year as a whole was better, but hardly impressive. Orthodox explanations for the problem tend to fall into one of three categories.