Although 11.5 million is a large number, most readers probably had no idea what went into drawing meaningful conclusions from that huge cache of documents. In fact, it took some 400 journalists at more than 100 news organizations an entire year to peruse the 2.6 terabytes of data in those documents and piece together the story of a company that helped the world's wealthiest people set up offshore bank accounts. In a lecture hosted by the University of Delaware Cybersecurity Initiative on Wednesday, April 6, computer scientist James Nolan used the Panama Papers as an example of the need for new machine learning techniques to address the problems associated with living in a data-rich, information-poor world. "Why can't we put that 2.6 terabytes through an algorithm and spit out relationships in a few hours?" he asked. Nolan emphasized the distinction between raw data which is collected from cameras, phones, sensors, satellites, written documents, cyber-logs, and other sources and information, which is the knowledge gained from studying data and teasing out relationships, resolving ambiguities, understanding scenes, and labeling events.

Engineers, safety advocates and even automakers have a safety message for federal regulators eager to get self-driving cars on the road: slow down. Fully self-driving cars may be the future of the automotive industry, but they aren't yet up to the demands of real-world driving, several people told the National Highway Traffic Safety Administration during a public meeting Friday. A slower, more deliberative approach may be needed instead of the agency's rapid timetable for producing guidance for deploying the vehicles, according to an auto industry trade association. In January, the federal agency announced that it would begin work on writing guidance for deploying the vehicles. Officials have promised to complete that guidance by July.

The Machine Learning Handbook is an eBook that I wrote comprising of my notes from Stanford's online Machine Learning course on Coursera.

Chatterbot comes with a data utility module that can be used to train chat bots. At the moment there is two languages, English and Portuguese training data in this module. Contributions of additional training data or training data in other languages would be greatly appreciated. Take a look at the data files in the chatterbot/corpus directory if you are interested in contributing. Please make a pull request.

Read Eduardo Bonet's answer to How are big data, machine learning, and data science affecting the field of education? Your email address will not be published. You may use these HTML tags and attributes: a href "" title "" abbr title "" acronym title "" b blockquote cite "" cite code del datetime "" em i q cite "" strike strong

Aman's background is in the intersection of Business Applications and Artificial Intelligence, using both to drive the next generation of business applications Aman also founded and worked in various startups in search, social, trading systems, and enterprise software. His last startup was TopCorner, a political platform for micro-lobbying. Aman was the architect for IBM SuperSell Enterprise and Oracle CRM. He was previously the Director of Special Projects for the CEO's office at Oracle. Aman earned a MS in Computer Science with research focused on natural language processing (NLP) from Stanford.

Too much data and too few people: Firstly, this is a no surprise that machine learning algorithms will work at the pace not matching their counter scientist friends. If trained properly, machine could easily pacify majority of data preparation and analysis demand in data analytics world. Another cool thing about machine learning is that once code is prepped and machine is programmed, you could use it multiple times and multiple places and see the magic happen. The trick is to not overkill first but to use it for overhead tasks first and keep making it more and more sophisticated, so that it will start doing all the heavy lifting and pacifying the resource demand as a result. Hence, machine learning single handedly can reduce big-data resource crunch and make the resource distribution relevant and appropriately.

While companies such as Google chase the fully autonomous car, Toyota is taking a more measured approach toward a "guardian angel" car that would seize control only when an accident is imminent. But as starkly different as those approaches are, they both will require a wide range of data-intensive technologies, according to Gill Pratt (pictured), chief executive officer of the Toyota Research Institute, a research center focused on AI and robotics. He spoke at the GPU Technology Conference in San Jose today. Toyota has made a huge bet–a billion dollars over five years, in fact–not only on semiautonomous cars but robots that could help older people with indoor mobility. The Toyota Research Institute, which will have facilities near Stanford University and the Massachusetts Institute of Technology, is intended to focus both on what Toyota calls outdoor mobility (cars) as well as indoor mobility (robots).

Make room, stethoscope and otoscope. Artificial intelligence (AI) applications are increasingly among the physician's standard instruments,experts at the University of Toronto say. "With electronic records, you can use text algorithms to read a patient's history, review their genetic predispositions, and correlate the information to make predictions," says Dr. Frank Rudzicz. Rudicz is one of five experts exploring the issues of privacy, accuracy and accountability at The Robot Will See You Now – the Revolution in Artificial Intelligence and Medicine at U of T on April 5. A research scientist with the Toronto Rehab Institute and an assistant professor (status only) in the department of computer science at the University of Toronto, Rudzicz is also a project lead within a federally funded national research network in technology and aging known as AGE-WELL NCE.

Recently, the computer science field took a monumental leap forward when Google DeepMind's artificial intelligence (AI) program, AlphaGo, bested Go champion Lee Sedol, four matches to one. While programs that can master competitive games of logic and recall, such as chess, jeopardy and poker, have already been developed, the ancient Chinese game of Go has both enticed and frustrated computer scientists due to its complexity. With move combinations outnumbering the number of atoms that exist in the known universe, brute force algorithms that have been greatly successful in game mastery were deemed to be largely inapplicable for Go. However, through advanced tree searches and deep neural networks, researchers can narrow down Go's massive array of data trees into much more manageable combinations akin to that of a human brain's neural network. While the dream of conscious AI is still years away, there are still some potential investments in this fast-growing sector.