IBM is making machine learning technology available in the place where much of the world's enterprise data resides: the z System mainframe. Big Blue announced IBM Machine Learning, a cognitive platform for creating, training and deploying a high volume of analytic models in the private cloud. The platform draws on the core machine learning technology from its Watson Machine Learning service on its Bluemix public cloud offering. "Our mission is making data simple and accessible to clients," says Rob Thomas, general manager, IBM Analytics. "If you look at the data landscape today, over 90 percent of the data in the world today cannot be Googled. Most of that data resides behind corporate firewalls in private clouds."

Our group recently identified a ten gene pattern of differential expression in peripheral blood which may have utility for detection of stroke. The objective of this study was to assess the diagnostic capacity and temporal stability of this stroke-associated transcriptional signature in an independent patient population. Publically available whole blood microarray data generated from 23 ischemic stroke patients at 3, 5, and 24 hours post-symptom onset, along with 23 cardiovascular disease controls were obtained via the National Center for Biotechnology Information Gene Expression Omnibus. Expression levels of the ten candidate genes were extracted, compared between groups, and evaluated for their discriminatory ability at each time point. We observed a largely identical pattern of differential expression between stroke patients and controls across the ten candidate genes as reported in our prior work.

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.

In 2011, IBM Watson debuted on the TV show Jeopardy and impressed millions in a spectacle where the artificial intelligence defeated 2 of jeopardy's all time great champions. While Apple, Google, Facebook, Amazon, and Microsoft have been developing their AI to handle daily tasks such as shopping, calling an Uber, or playing music, IBM (NYSE: IBM) has been pushing Watson into tackling real world problems such as cancer diagnosis/treatment, traffic accident prevention, and tax reporting. IBM Watson, like the rest of its AI colleagues, is a product of machine learning, natural language processing, and statistical analysis. The difference lies in the types of data IBM is feeding Watson. Watson is being taught intellectual knowledge in the form of research papers, large industrial data reports, and other scholarly publishings.

Elon Musk believes that artificial intelligence will create a cyborg race. During the World Government Summit in Dubai, the SpaceX founder explains that deep artificial intelligence - the kind that is much smarter than the smartest man in the world - can be a dangerous thing. During his talk, Elon Musk believes that robot automation and artificial intelligence will be taking over 15 percent of the global workforce. The SpaceX, Tesla and Hyperloop authority believes that humans are becoming more and more dependent on technology. He sees a world where human beings are already cyborgs.

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.