My clients are looking for computer scientists, statisticians, biostatisticians, physicists, computational social scientists, economists, engineers, operations researchers- in short they are looking for Data Scientist with strong hands on experience in "Big Data" as well as predictive modeling, optimization, machine learning, neural networks using a range of advanced technical tools (Python, SAS, Hadoop, R, Matlab, Machine learning, natural language processing, CPlex, C, etc.) This is a position that will be responsible for helping develop quantitative solutions to solve complex applications. The candidates will be involved in developing leading-edge, "out of the box" advanced analytic solutions and processes. Substantial data analysis experience utilizing standard tools such as SAS, SPSS, or R In depth knowledge of standard algorithms such as linear regression, logistic regression, clustering, decision trees, and affinity analysis Very strong SQL skills including complex query structures Comfort with relational database theory, third normal form, and data models Previous presales experience in the software industry and an understanding of sales cycles: partnering with account executives, sales approaches, and sales support for large customers. Sufficient depth and breadth of technical knowledge to design and scope multiple deliverables across a number of technologies Demonstrated innovation and communication of new deliverables and offerings.

Google says its Translate app now spits back more natural translations. Google said Tuesday it has vastly improved its Google Translate app, available on phones and the web. The search giant said it's now incorporating "neural machine translation" into the software, which means it can translate whole sentences at a time, instead of breaking the text down to smaller chunks and translating those pieces. The result is translations coming out more natural, with better syntax and grammar, Google said. "It has improved more in one single leap than in 10 years combined," Barak Turovsky, the product lead for Google Translate, said during a press event at Google's San Francisco office.

When you hear the term "artificial intelligence," you may think of tech giants Amazon, Google, IBM, Microsoft, or Facebook. Industrial powerhouse General Electric is now aiming to be included on that short list. It may not have a chipper digital assistant like Cortana or Alexa. It won't sort through selfies, but it will look through X-rays. It won't recommend movies, but it will suggest how to care for a diesel locomotive.

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Facebook said that it has bought facial recognition startup FacioMetrics, potentially using the technology for photo or video effects to better challenge rival Snapchat. "How people share and communicate is changing and things like masks and other effects allow people to express themselves in fun and creative ways," a Facebook spokesperson said in an email reply to an AFP inquiry. "We're excited to welcome the FacioMetrics team who will help bring more fun effects to photos and videos and build even more engaging sharing experiences on Facebook." Silicon Valley-based Facebook did not disclose financial terms of the deal to buy FacioMetrics, which was spun out of Carnegie Mellon University in Pennsylvania. FacioMetrics was founded in 2015 and specializes in using artificial intelligence to give facial image analysis capabilities to applications that run on smartphones.

Few industries are as competitive as ecommerce. Not only are online retailers competing with other online stores and brick-and-mortar locations, but also the overall noise that is the Internet. We live in a world where consumer attention span is getting shorter and shorter: 40 percent of people abandon a website that takes more than three seconds to load, and the average shopping cart is abandoned more than 68 percent of the time. I'm hard pressed to find an ecommerce site that is not constantly scrambling to engage more and drive more sales. Technology is finally helping with those efforts in a big way.

Google's amazing AI experiments let you play with neural networks Google is known for its funky experiments with web technology -- just take a look at its Chrome Experiments page, where the company has accumulated over a thousand creative web apps using its web technology. A lot of Google's products today use machine learning to better serve its users. For example, when searching for'pizza' in Google Photos shows all the pictures in your library of pizza. It knows what the dish looks like by analyzing thousands of pictures of the food and recognizing patterns between them. The technology might be complex, but the company is now making it easy to play around with it.

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Tesla might have got an early start when it comes to self-driving car technology but other companies are also stepping on the gas as far as the technology is concerned. While the technology is nowhere near being fully autonomous, with more and more automakers and even companies like Google stepping in, self-driving cars have transformed from a mere fantasy to slowly on the way to becoming a mainstream auto technology. Google's self-driving car project has been in the works for some time. The company has been testing a fleet of modified Lexus SUVs and new prototype cars. The car uses mapping and sensors to determine location and route.

With the growth of Machine Learning in the past few years, many tasks are being done with the help of machine learning algorithms. Unfortunately or fortunately, there has been little work done on security with machine learning algorithms. So I thought of presenting some at Fsecurify. A few days ago, I had this idea about what if we could detect a malicious URL from a non-malicious URL using some machine learning algorithm. There has been some research done on the topic so I thought that I should give it a go and implement something from scratch.