It was one of those amazing "we're living in the future" moments. In an October 2013 press release, IBM declared that MD Anderson, the cancer center that is part of the University of Texas, "is using the IBM Watson cognitive computing system for its mission to eradicate cancer." Well, now that future is past. The partnership between IBM and one of the world's top cancer research institutions is falling apart. The project is on hold, MD Anderson confirms, and has been since late last year.

Apple has acquired Tel Aviv, Israel-based Realface, a machine learning startup whose facial recognition technology can be used to authenticate users, according to reports from Calcalist (in Hebrew) and The Times Of Israel. Founded in 2014, RealFace says it has developed an innovative way of recognising people, self-reportedly building A.I software that "brings back human perception to digital process". RealFace's software enables authentication, automated on-boarding & mobile conversion optimisation. The company, led by co-founder and Columbia Business School-trained Adi Eckhouse Barzilai, says it has proprietary IP in the field of'frictionless face recognition' and effective learnings from facial features. The Israeli startup is behind the Pickeez app, which automatically choses a user's best photos from various platforms using the RealFace recognition software.

UK-based security technology company Sophos (OTC:SPHHF) (LSE: SOPH) has announced an agreement to acquire Invincea's software business for up to $120 million in total consideration. Invincea has developed security software that utilizes machine learning and behavioral modeling to quickly detect and react to endpoint, perimeter threats and malware attacks. The technology is a necessary upgrade to the Sophos Central product line and also provides middle market focused Sophos with a foothold in the small business market through the addition of Invincea's customer base. Fairfax, Virginia-based Invincea was founded in 2009 by CEO Anup Ghosh to focus on endpoint threat detection, prevention, and analysis. The company provides security software to more than 25,000 small and medium enterprise customers through its antivirus'X' system that'combines machine learning and behavioral monitoring to eliminate endpoint security blind spots without sacrificing usability.'

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.