Boeing Company has announced that it will partner again with the Qatar Computing Research Institute (QCRI), part of Hamad Bin Khalifa University, to host the fourth annual Machine Learning and Data Analytics Symposium (MLDAS) in Qatar. The event, to be held at the Qatar National Convention Centre on March 13 and 14, will feature top global experts discussing the latest solutions in the fields of machine learning, data mining, applied machine learning techniques and data analytics solutions. This year's event will focus on the growing use of deep learning and its potential impacts on the human condition, such as through autonomous vehicles and medical advances. Bernard Dunn, president of Boeing Middle East, North Africa and Turkey, said, "Boeing is proud of the partnership with QCRI as we are congregating expertise from around the world to help elevate the already high benchmark being set in Qatar in the field of data analytics and machine learning. "In an increasingly digitised and automated world, bringing together such expertise will only continue to inspire innovation and regional talent." Dr Ahmed Elmagarmid, executive director, QCRI, said the symposium had enjoyed an extraordinary response from the scientific community in the past three years and the research institute looked forward to working with Boeing to expand its success. "The event is a huge success for Qatar and the region and we are grateful for the outstanding role that Boeing has played.

This week's milestones in the history of technology include Alan Turing anticipating today's deep learning by intelligent machines and concerns about the impact of AI on jobs, Clifford Stoll anticipating Mark Zuckerberg, and establishing the FCC and NPR. Alan Turing gives a talk at the London Mathematical Society in which he declares that "what we want is a machine that can learn from experience." Anticipating today's enthusiasm about machine learning and deep learning, Alan Turing described how intelligent machines will work: Let us suppose we have set up a machine with certain initial instruction tables, so constructed that these tables might on occasion, if good reason arose, modify those tables. One can imagine that after the machine had been operating for some time, the instructions would have altered out of all recognition, but nevertheless still be such that one would have to admit that the machine was still doing very worthwhile calculations. Possibly it might still be getting results of the type desired when the machine was first set up, but in a much more efficient manner.

On December 2nd, 1942, a team of scientists led by Enrico Fermi came back from lunch and watched as humanity created the first self-sustaining nuclear reaction inside a pile of bricks and wood underneath a football field at the University of Chicago. Known to history as Chicago Pile-1, it was celebrated in silence with a single bottle of Chianti, for those who were there understood exactly what it meant for humankind, without any need for words. Now, something new has occurred that, again, quietly changed the world forever. Like a whispered word in a foreign language, it was quiet in that you may have heard it, but its full meaning may not have been comprehended. However, it's vital we understand this new language, and what it's increasingly telling us, for the ramifications are set to alter everything we take for granted about the way our globalized economy functions, and the ways in which we as humans exist within it. The language is a new class of machine learning known as deep learning, and the "whispered word" was a computer's use of it to seemingly out of nowhere defeat three-time European Go champion Fan Hui, not once but five times in a row without defeat.

A page from the notebook of British mathematician and pioneer in computer science Alan Turing, the World War II code-breaking genius, is displayed in front of his portrait during an auction preview in Hong Kong Thursday, March 19, 2015. This week's milestones in the history of technology include Alan Turing anticipating today's deep learning by intelligent machines and concerns about the impact of AI on jobs, Clifford Stoll anticipating Mark Zuckerberg, and establishing the FCC and NPR. Alan Turing gives a talk at the London Mathematical Society in which he declares that "what we want is a machine that can learn from experience." Anticipating today's enthusiasm about machine learning and deep learning, Alan Turing described how intelligent machines will work: Let us suppose we have set up a machine with certain initial instruction tables, so constructed that these tables might on occasion, if good reason arose, modify those tables. One can imagine that after the machine had been operating for some time, the instructions would have altered out of all recognition, but nevertheless still be such that one would have to admit that the machine was still doing very worthwhile calculations. Possibly it might still be getting results of the type desired when the machine was first set up, but in a much more efficient manner.

It's officially winter, so what could be better than drinking hot chocolate while querying the new Stack Overflow dataset in BigQuery? It has every Stack Overflow question, answer, comment, and more -- which means endless possibilities of data crunching. Inspired by Felipe Hoffa's post on how response time varies by tag, I wanted to look at the comments table (53 million rows!). To measure happy comments I looked at comments with "thank you", "thanks", "awesome" or ":)" in the body. I limited the analysis to tags with more than 500,000 comments.

Researchers have developed an algorithm to accurately predict which patients diagnosed with acute myelogenous leukemia (AML), a cancer of the blood and bone marrow, will go into remission following treatment and which ones will relapse. Using bone marrow data and medical histories of AML patients, as well as blood data from healthy individuals, researchers were able to teach a standard 64-bit computer workstation running Windows to predict remission with 100 percent accuracy, while relapse was correctly predicted in 90 percent of relevant cases. "It's pretty straightforward to teach a computer to recognize AML, once you develop a robust algorithm, and in previous work we did it with almost 100 percent accuracy," said Murat Dundar, associate professor of computer science in the School of Science at Indiana University-Purdue University Indianapolis. "What was challenging was to go beyond that work and teach the computer to accurately predict the direction of change in disease progression in AML patients, interpreting new data to predict the unknown--which new AML patients will go into remission and which will relapse," adds Dundar. Ultimately, Bartek Rajwa, research assistant professor of computational biology in the Bindley Bioscience Center at Purdue University who collaborated with Dundar, contends that the machine-learning algorithm was better at extracting knowledge from complex data than humans performing manual analysis of cytometry data.

At SVDS, our R&D team has been investigating different deep learning technologies, from recognizing images of trains to speech recognition. We needed to build a pipeline for ingesting data, creating a model, and evaluating the model performance. However, when we researched what technologies were available, we could not find a concise summary document to reference for starting a new deep learning project. One way to give back to the open source community that provides us with tools is to help others evaluate and choose those tools in a way that takes advantage of our experience. We offer the chart below, along with explanations of the various criteria upon which we based our decisions.

Since resources across the internet vary in terms of their pre-requisites and general accessibility, it is useful to give attributes to them so that it is easy to understand where a resource fits into the wider machine learning scope.

Last year computer scientists unveiled the first song to be composed by artificial intelligence, the Beatles-esque ditty'Daddy's Car'. But it's not the first sign of AI infiltrating music-making – from self-generating soundtracks to unique albums created on demand, the robots are on the march. Jack Needham asks if we're ready for the AI revolution to reach our ears. When we think of the early relationship between humans, machines and music, we might think of Kraftwerk's analog pop or Delia Derbyshire's Radiophonic soundscapes – yet our fascination with machine music goes back much further than that. Late last year, University of Canterbury professor Jack Copeland and composer Jason Long restored the first piece of recorded machine music created in 1951 by Alan Turing, the British mathematician and artificial intelligence pioneer. The single-sided 12" acetate disc captures three melodies played by a primitive computer that filled most of the ground floor of Turing's laboratory.

Libratus, an artificial intelligence developed by Carnegie Mellon University, made history by defeating four of the world's best professional poker players in a marathon 20-day poker competition, called "Brains Vs. Once the last of 120,000 hands of Heads-up, No-Limit Texas Hold'em were played on Jan. 30, Libratus led the pros by a collective $1,766,250 in chips. The developers of Libratus -- Tuomas Sandholm, professor of computer science, and Noam Brown, a Ph.D. student in computer science -- said the sizable victory is statistically significant and not simply a matter of luck. "The best AI's ability to do strategic reasoning with imperfect information has now surpassed that of the best humans," Sandholm said. This new milestone in artificial intelligence has implications for any realm in which information is incomplete and opponents sow misinformation, said Frank Pfenning, head of the Computer Science Department in CMU's School of Computer Science. Business negotiation, military strategy, cybersecurity and medical treatment planning could all benefit from automated decision-making using a Libratus-like AI. "The computer can't win at poker if it can't bluff," Pfenning said. "Developing an AI that can do that successfully is a tremendous step forward scientifically and has numerous applications.