In 2005, Hanna Wallach, a machine learning researcher, found herself bunking with colleagues to attend the Neural Information Systems Processing (NIPS) conference. Wallach had been working in the field since 2001 and had attended numerous conferences, but this was the first time she had roomed with other women who specialized in machine learning, a branch of artificial intelligence that researches how computer programs can learn and grow. As a discipline, it is overwhelmingly male: Wallach estimates that only 13.5% of the entire machine learning field is female. At the conference, Wallach and her roommates, Jennifer Wortman Vaughan, Lisa Wainer, and Angela Yu, began discussing their experiences and commiserating about the lack of female allies. "We couldn't believe that there were four of us [at the conference]," Wallach says.

Scientists in Japan reportedly saved a woman's life by applying artificial intelligence to help them diagnose a rare form of cancer. Faced with a 60-year-old woman whose cancer diagnosis was unresponsive to treatment, they supplied an AI system with huge amounts of clinical cancer case data, and it diagnosed the rare leukemia that had stumped the clinicians in just ten minutes. The Watson AI system from IBM matched the patient's symptoms against 20m clinical oncology studies uploaded by a team headed by Arinobu Tojo at the University of Tokyo's Institute of Medical Science that included symptoms, treatment and response. The Memorial Sloan Kettering Cancer Center in New York has carried out similar work, where teams of clinicians and data analysts trained Watson's machine learning capabilities with oncological data in order to focus its predictive and analytic capabilities on diagnosing cancers. IBM Watson first became famous when it won the US television game show Jeopardy in 2011.

To pinpoint Inbenta's proposition in the marketplace, Jordi begins by making the important distinction between human-to-computer communication and human-to-human communication: "When we have to communicate with computers, historically humans have used HTML, Java, XML, 'command line' and a variety of languages that we collectively call'Formal Languages'. But when humans communicate with each other, we use English, Spanish and thousands of other languages called'Natural Languages'". Natural Language Processing (NLP), of course, is an area of AI that allows a computer to understand a Natural Language – and it's in this field that Inbenta thrive. At Inbenta we have developed a true natural language understanding platform that takes care of user conversations in website sites and mobile apps, answering 99% of user questions automatically and also guiding customers through all sales and support processes using chatbots. Inbenta are already working with an impressive array of enterprises, as their website shows.

In a 5,200-square-foot office in Orlando, Edison "Eddy" Ortiz and his team are building the next generation of artificial intelligence-based products for Royal Bank of Canada. The objective is to have customers interacting with virtual tellers that will make suggestions and decisions based upon machine-based learning. Someday, years from now, UCF student Sylvia DiPaulo may look at a photograph snapped during a NASA launch and see her legacy. With her University of Central Florida classmates, DiPaulo helped design a working space for NASA engineers in the agency's firing room, the area where launches are orchestrated... Someday, years from now, UCF student Sylvia DiPaulo may look at a photograph snapped during a NASA launch and see her legacy. With her University of Central Florida classmates, DiPaulo helped design a working space for NASA engineers in the agency's firing room, the area where launches are orchestrated... "It will allow us to provide virtual banking," said Ortiz, a 51-year-old Ecuadorian who has worked for the company 19 years.

The biggest appeal of fastText appears to be speed and efficiency. According to Facebook, fastText is, as its name suggests, much quicker than other learning methods, and can train models "on more than 1 billion words in less than 10 minutes using a standard multicore CPU." In fact, FAIR claims that, compared to deep learning models, fastText can cut training delays from several days to a few seconds. For example, fastText can learn that the words "boy," "girl," "man" and "woman" refer to specific gendered nouns and store those values in a document. Then, when an AI program, like a bot, is interpreting a request, such as "Where my girls at," it can look into the fastText-generated document and understand that the user is asking for female names. It's easy to see how this move makes sense for the social network.

At Def Con, seven AI bots were pitted against one another in a game of capture the flag. The DARPA-sponsored event was more than just a fun exercise for hackers. It was meant to get more researchers and companies to focus on autonomous artificial intelligence. As part of the Department of Defense (DoD), DARPA is tasked with making sure the United States is at the forefront of this emerging field. While the country may currently be mired in a ground wars against insurgents and extremist groups, the DoD is looking at future skirmishes. The department's long-term artificial intelligence plans are focused more on conflicts with countries like Russia, China and North Korea than terrorism.

From picking our favorite restaurants to predicting weather and correcting global food shortages, artificial intelligence is already augmenting everyday life. Firmly rooted in the realm of science fiction, artificial intelligence (AI) has often felt external – something happening out there. In reality, AI is a huge part of our everyday lives. We just don't recognize it. Bank alerts of suspected fraudulent charges, smartphone notifications to exercise, Siri or Cortana's ability to recognize voices – are all examples of AI. "Artificial intelligence is basically where machines make sense, learn, interface with the external world, without human beings having to specifically program it," said Nidhi Chappell, director of machine learning at Intel. AI improves lives in many other areas too.

Some say over 60-70% time is spent in data cleaning, munging and bringing data to a suitable format such that machine learning models can be applied on that data. This post focuses on the second part, i.e., applying machine learning models, including the preprocessing steps. The pipelines discussed in this post come as a result of over a hundred machine learning competitions that I've taken part in. It must be noted that the discussion here is very general but very useful and there can also be very complicated methods which exist and are practised by professionals. Before applying the machine learning models, the data must be converted to a tabular form.

Kimera Systems announced the birth of Nigel – the world's first commercial human-like intelligence technology for connected devices. Nigel was delivered at a birthday party held last Friday in downtown Portland by its creator, Kimera co-founder and CEO Mounir Shita. The Nigel artificial general intelligence (AGI) technology began learning immediately in the same way humans do: by observing the behavior of people with Nigel-enabled devices. Shita began working on his single-algorithm, federated approach to artificial intelligence in 2005, and Kimera Systems was formally incorporated in 2012. The technology was dubbed "Nigel" to honor one of its principal architects, Nigel Deighton, a noted international expert on wireless technologies and a former Gartner research vice president, who passed away in 2013.