India's patent laws allow for reverse-engineering of certain technologies. A prime example of this reverse-engineering is in the pharmaceutical space, where Indian pharma companies are allowed to reverse-engineer drugs, especially life-saving ones. These drugs may have been developed by pharma majors in other parts of the world--and then introduced into western markets--after India-based outsourcing firms had helped them out with clinical trials, data gathering and reporting to the US Food and Drug Administration (FDA) or its equivalent to get these drugs passed. Indian courts have continued to allow such reverse-engineering of drugs--famously prompting Bayer AG's then CEO Martin Dekkers to say at a conference a few years ago, "We did not develop this medicine for Indians. We developed it for western patients who can afford it."

The world's first artificially intelligent attorney, Ross, has been hired by its first official law firm- Baker & Hostetler. Other firms will shortly be making their announcements on hiring Ross as well. The law firm Baker & Hostetler have announced they will be hiring the world's first artificially intelligent attorney, Ross, for its bankruptcy practice- where almost 50 lawyers are employed. According to Baker & Hostetler's CEO and co-founder Andrew Arruda, other law firms have also signed licenses with Ross and will soon be making their announcements as well. Baker & Hostetler chief information officer Bob Craig explains why they hired the AI: "At BakerHostetler, we believe that emerging technologies like cognitive computing and other forms of machine learning can help enhance the services we deliver to our clients."

Do you think you can tell a piece of music composed by artificial intelligence (AI) from one created by a human composer? Before you read any further, let's find out. The following audio consists of two fragments, one written by AI, the other by a human. Last year, Facebook's VP of Design thought the TNW Conference main stage was the best she'd ever been on. If you didn't get it right the first time, no worries--we'll have a couple more mini-quizzes like this below.

AI has become such a huge topic that late last year the White House released a report on the Future of Artificial Intelligence, which focused on the opportunities, considerations, and challenges of AI. As more and more industries, including healthcare and financial services, adopt AI technology, the technology's value will increase its impact on society as a whole. AI, machine learning and other technologies are already making a significant impact in several industries, including e-commerce, hospitality, and retail. While there has always been a slight tension among workers about robots taking over and claiming jobs, there is a lot to be said about how these types of technologies will, in fact, add more value, contribute to economic growth and augment the workplace so employees can work more effectively. It starts with the huge volumes of contracts that define business relationships, capture the rules of engagement and are the foundation of business transactions every day.

Last Fall when Salesforce introduced Einstein, its artificial intelligence initiative, it debuted with some intelligence built into the core CRM tool, but with a promise that it would expand into other products over time. Today it announced it was adding Einstein AI to its Service Cloud customer service platform. The goal is to make life easier for customer service reps and their managers. For the reps, it gives information that is supposed to help them better understand the needs of the customer they're dealing with. For the managers, it's been designed to help give deeper insight into their customer service center operation.

Deep learning techniques can be used to extract facial imaging biomarkers of human health status and to track the effects of cosmetic interventions. At the Deep Learning in Healthcare Summit, Research Scientist, Anastasia Georgievskaya from Beauty.AI, will be presenting a set of tools for analysis of perception of human age and health status. She will also demonstrate that when certain population groups are under-represented in the training sets, these populations are left out or may be subject to higher error rates. This is why Youth Laboratories launched Diversity.AI, a think tank for anti-discrimination by the deep-learned systems. The presentation describes the strategies for evaluating human appearance for machine-human interaction and reveals the risks and dangers of deep-learned biomarkers.

Last October, 11-year-old Evan Spisak wandered down to his father's basement workshop to help out on a weekend project, a time-honored tradition in homes across the country. But Evan's father, Mike, is an IBM master inventor. And what they came up with was no birdhouse or pinewood derby car. It was Havyn, a homegrown voice assistant that taps into IBM's enormous cybersecurity infrastructure, putting Watson's AI smarts at their literal beck and call. Think of Havyn, instead, as a highly specific analog to Amazon's Alexa voice assistant.

Data integration requires merging date from different sources, stored using technologies. Companies build a "data warehouse where aggregated data can be stored and retrieved. This is particularly useful for researchers looking to big data to aid in their investigation and corporations usually during the merging with other companies. Users can access all systems of different sources or interface of web pages but without viewing consolidated data. This organizational level requires particular applications to integrate data.

Jeffrey Tarrant is a Wall Street guy. He spent the last thirty years investing in new hedge funds. As the founder and CEO of a firm called Protege Partners, he compares himself to Sam Altman, the president of Y Combinator. What Altman does for Silicon Valley tech startups, Tarrant does for hedge funds. "I help seed them and incubate them," he says.

In this post we will focus on configuring the elasticsearch bit. I have chosen the Wikipedia people dump for the dataset. This is the wiki pages of a subset of people on Wikipedia. This dataset consists of three columns – URI, name, text. As the column names suggest, URI is the actual wiki link to that person's page, name is the person's name.