As you may know we recently launched a new service offering, our News API, and over the past week or so we've been using it to run some little experiments around analyzing news content. We wanted to use the News API to collect and analyze popular news headlines. We set out to find both similarities and differences in the way two journalists write headlines for their respective news articles and blog posts. Note: For a more technical, in-depth and interactive representation of this project, check out the Jupyter notebook we created. This includes sample code and more in depth descriptions of our approach.

When it comes to the future of artificial intelligence, the ultimate battle between man and machine may come to mind -- but that's really the stuff of science fiction. AI actually has a presence in our daily lives on a much more useful and less apocalyptic level. Think personal assistant devices and apps like Alexa, Cortana and Siri, web search predictions, movie suggestions on Netflix and self-driving cars. The term "artificial intelligence" was coined back in 1956. It describes a machine's ability to perform intelligent behavior such as decision-making or speech recognition. In the last two decades or so, big strides have been made in artificial intelligence -- from IBM's Deep Blue computer beating chess champion Garry Kasparov and the company's Watson computer winning "Jeopardy!" to social robots like Jibo and online chatbots.

Each quarter, the editors of SearchFinancialApplications recognize new software for innovation or market impact. This quarter, that product is Performance for Performance. Enterprises can use Kanjoya performance review software to obtain a synopsis of an employee's strengths, consensus on areas for improvement and a summary of all the feedback provided by people who review the employee. Elaine Chang, director of products at Kanjoya, based in San Francisco, said Kanjoya's natural language processing and machine learning algorithms continually analyze performance reviews as they are submitted inside the software. The cloud-based tool provides a simple dashboard that displays an employee's "opportunity areas," or items for improvement, and strengths.

With the face of SEO constantly evolving, machine learning has become a huge concern for Internet marketers. An exciting post on the Moz blog about machine learning persuaded me to dig deeper into it. Eric Enge very clearly explained how machine learning works and ways Google may be using it. Google has been dominating the search engine world for decades, but this new concept may spark a complete overhaul to the Google spam-fighting algorithm updates. Internet marketers will have to adapt the best SEO practices according to this latest development.

Did you know there is a 25% chance your cause of death will be due to environmental pollution? According the World Health Organization, some 12.6 million people--or nearly 1 in 4 worldwide--died in 2012 due to living or working in unhealthy conditions. In addition, environmental degradation seriously affects overall quality of life and the balance of Earth's ecosystems through loss of forests, open spaces, marine environments and biodiversity. While technological growth and industrialization historically contributed to such problems, the latest technologies--from robotics to artificial intelligence to biotechnology--will also help create healthier and greener industries benefiting both people and planet. While affordable electric and hybrid cars will help reduce pollution and use of fossil fuels, self-driving cars will make our whole transportation and logistics systems more efficient. Cars, trucks, ships, drones and jets that drive or pilot themselves and wirelessly communicate with each other can coordinate and optimize delivery of people and goods in ways requiring less energy.

Microsoft has declared that its Azure ML service for predictive analytics with machine learning has achieved certification for security and privacy standards including ISO 27001 and the EU Model Clauses, a move designed to reassure potential enterprise users that their data will be safe. Machine learning is a growing tool of interest for analysing large datasets and gleaning useful insights, and Microsoft launched its cloud-based Azure ML service a couple of years ago to take advantage of this. However, concerns over moving sensitive data to public cloud services could prove a barrier to adoption of services such as Azure ML, and Microsoft aims to address this by gaining certification under several security and privacy standards, comprising the US HIPAA, ISO 27001, ISO 27018 and the EU Model Clauses relating to transfer of personal data to countries outside Europe. "Enterprise customers often require that cloud services comply with specific security certifications. Compliance certifications provide assurance to customers that the security of these services has been verified by independent auditors," said Microsoft's principal programme manager for Azure, Krishna Anumalasetty.

You are reading a guest blog post by John Boitnott. Artificial Intelligence is not by any means a new concept. It's been the stuff of science fiction speculation for years, and has been a repeated point of debate in popular culture for quite some time. Humanity's relationship to AI really came to the forefront of contemporary technological debate when IBM's Deep Blue computer defeated world chess champion Garry Kasparov in 1996. The trend of man vs. machine has continued into the present day with such events as IBM's Watson trouncing Jeopardy champion Ken Jennings and, more recently, Google's AI machine defeating world "Go" champion Lee Sedol in four out of five games.

ICML 2016 is in New York City. I expect it to be the largest ICML by far given the destination--New York is the place which is perhaps easiest to reach from anywhere in the world and it has the largest machine learning meetup anywhere in the world. I am the general chair this year, which is light in work but heavy in responsibilities. Markus Weimer also points out the 2016 KDD Cup which has a submission deadline of December 6. KDD Cup datasets have become common reference for many machine learning papers, so this is a good way to get your problem solved well by many people.

The CNTK tutorial is 1 hour during the lunch break of the Optimization workshop while the VW tutorial is 1 hour during the lunch break of the Extreme Multiclass workshop. Consider dropping by either if interested. CNTK is a deep learning system started by the speech people who started the deep learning craze and grown into a more general platform-independent deep learning system. It has various useful features, the most interesting of which is perhaps efficient scalable training. Using GPUs with allreduce and one-bit sgd it achieves both high efficiency and scalability over many more GPUs than could ever fit into a single machine.

If you put water on the stove and heat it up, it will at first just get hotter and hotter. You may then conclude that heating water results only in hotter water. But at some point everything changes – the water starts to boil, turning from hot liquid into steam. Automation, driven by technological progress, has been increasing inexorably for the past several decades. Two schools of economic thinking have for many years been engaged in a debate about the potential effects of automation on jobs, employment and human activity: will new technology spawn mass unemployment, as the robots take jobs away from humans? Or will the jobs robots take over release or unveil – or even create – demand for new human jobs?