Fears that new machines would gobble up jobs and turn society upside down were widespread as the Industrial Revolution unfolded two centuries ago. Back then the controversy over the dangers posed by machines was known as the "machinery question". Now a very similar debate is under way once again, thanks to advances in artificial intelligence (AI), which allow machines to perform tasks that could previously be done only by humans. The field of artificial intelligence was founded in the 1950s, and for decades it promised far more than it could deliver. But in the past five years it has taken off, thanks to a versatile technique called "deep learning", which can be applied to a vast range of tasks.

A cleaning robot was the example used by Google to future challenges of Artificial Intelligence. Google released a new paper on a highly controversial topic: safety rules for Artificial Intelligence. Even though in the last years the general opinion shifted as a result of more engagement with technology, Google seems to keep its ethical edge and consider all issues surrounding the overpowering ability a robot may have over the human existence. For this, Google went into the mundane and the paper treats a highly philosophical subject in relatively simplistic terms. While being highly practical, the issues raised are important to be considered.

A drone successfully delivered medical supplies to the New Jersey coastline straight from the deck of a ship, marking the first ship-to-shore delivery in the US. The flight was designed to test whether drones could be used to carry human medical supplies to and from areas that cannot be access during major storms, earthquakes or other disasters. The test was run by disaster preparedness non-profit Field Innovation Team. Drone-firm Flirtey, which managed the first land-based drone delivery of medical supplies to a rural health clinic in July 2015, flew medical samples to Camp May in partnership with Dr Timothy Amukele, assistant professor of pathology at Johns Hopkins University School of Medicine. While drones have already been muted as one way to deliver goods, such as Amazon's Air Prime drones, Amukele said that biological samples "are not like a shoe or a book, they are pretty fragile items".

Samsung Medison's new ultrasound system quickly screens for abnormalities. Artificial intelligence may be the new face of medical diagnostics. For the first time, a flavor of A.I. called deep learning is being implemented in new ultrasound imaging equipment to aid in breast exams and help patients avoid unnecessary biopsies. A new feature in Samsung Medison's ultrasound system uses a deep-learning algorithm to make recommendations about whether a breast abnormality is benign or cancerous. The "S-Detect for Breast" feature is now included in an upgrade to the company's RS80A ultrasound system and is commercially available in parts of Europe, the Middle East and Korea and is pending FDA approval in the U.S., according to PR manager Doug Kim.

Computers could soon be helping to diagnose cancer in patients with the help of artificial intelligence that has been trained to spots the early signs of the disease. An AI machine capable of accurately diagnosing breast cancer 92 per cent of the time has been developed by researchers. While it is still not quite as good as human specialists – who are correct 96 per cent of the time – it suggests that AI could soon be used to speed up and improve cancer screening. Scientists have used machine learning to create an artificial intelligence system capable of diagnosing breast cancer from lymph node biopsies with 92 per cent accuracy (cancer cells in a lymph node pictured). When combined with a human pathologist this accuracy increased to 99.5 per cent The system was developed by computer scientists at Harvard Medical School gave a machine learning algorithm slides of lymph nodes from breast cancer patients.

The takeaway here is the machine learning allows companies to build better applications that interact with things people create: pictures, speech, text, and other messy things. Interfaces powered by machine learning will make computing omnipresent. Like their other products, both Google Search and Facebook Photos demonstrate how RDAs generate significant network effects. Or, you could have a giant install base which only occasionally codes data (Facebook, whose users tag photos usually when they're uploaded).

Columbus, Ohio – The area of the brain responsible for recognizing facial expressions appears to be on the right side, just behind the ear. A study published Tuesday in the Journal of Neuroscience found that nerve patterns within this area, which is known as the posterior superior temporal sulcus (pSTS), seem to be programmed to identify movement in certain areas of the face. While one neural pattern detects a furrowed brow, another recognizes the upturned lips of a smile, according to the paper. "That suggests that our brains decode facial expressions by adding up sets of key muscle movements in the face of the person we are looking at," study author Aleix Martinez said in a university press release, as reported by CBS News. Martinez, a cognitive scientist who teaches electrical and computer engineering at Ohio State, added that humans use a significant number of facial movements to express emotion, as well as other non-verbal communications signals and language.

"The tagline is, Do you want to be a machine learning ninja?" says Christine Robson, a product manager for Google's internal machine learning efforts, who helps administer the program. "So we invite folks from around Google to come and spend six months embedded with the machine learning team, sitting right next to a mentor, working on machine learning for six months, doing some project, getting it launched and learning a lot." For Holgate, who came to Google almost four years ago with a degree in computer science and math, it's a chance to master the hottest paradigm of the software world: using learning algorithms ("learners") and tons of data to "teach" software to accomplish its tasks. For many years, machine learning was considered a specialty, limited to an elite few. That era is over, as recent results indicate that machine learning, powered by "neural nets" that emulate the way a biological brain operates, is the true path towards imbuing computers with the powers of humans, and in some cases, super humans.

There is definitely an established machine learning ecosystem, or, perhaps more accurately, a small set of established machine learning ecosystems. For research it would seem that the undisputed champion of machine learning ecosystems is centered on Python and its many libraries which support the data preparation and subsequent machine learning process itself, whether it be via scikit-learn, one of the many deep learning libraries available, or home-spun and highly specialized tools for achieving the same goals. This says nothing of the great support tools that grow up around the edges of the ecosystem, some of which become polished and useful enough to carve out their own eventual niche. As those in industry would be the first to let me know, Python is not the only option. There are Java-based tools (Deeplearning4j, Weka), those integrated with Apache Spark and/or Hadoop (MLlib, Mahout), C solutions (TensorFlow is written in C, as are many others in the Python ecosystem), and even those for Clojure, F#, Rust, and a whole host of other languages, environments, and ecosystems.

Virtual patients are becoming a useful tool for medical students and a resource for medical schools. The obvious reality that students can make mistakes with no risk to the "patient" is part of the attraction to this technology. "Virtual patients allow students to learn without putting real patients at risk," said Norm Berman, MD, professor of pediatrics at the Geisel School of Medicine at Dartmouth and the lead author of a perspective piece recently published by the journal Academic Medicine. "No actual patients are harmed in the process of learning from virtual patients." The authors outlined the role of virtual patients in relation to the challenges and opportunities within medical education.