Deep learning is a part of AI and machine learning that is "based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised," according to Wikipedia. Deep Learning, rather than following rigid hierarchies, is modeled on the neurons of the brain. Are our systems ready to learn? In a world that is just getting started with AI, deep learning is another leap in sophistication.

But a new project could change all that. Known as In Codice Ratio, it uses a combination of artificial intelligence and optical-character-recognition (OCR) software to scour these neglected texts and make their transcripts available for the very first time. If successful, the technology could also open up untold numbers of other documents at historical archives around the world. OCR has been used to scan books and other printed documents for years, but it's not well suited for the material in the Secret Archives. Traditional OCR breaks words down into a series of letter-images by looking for the spaces between letters.

Artificial intelligence may still be in its infancy, but it's moving fast. Nowhere is this more apparent than in the data-rich health sector. AI has the potential to provide more precise, personalised care, as well as help us to shift our focus from treatment to prevention and tackle some of the world's biggest global health issues. The WHO estimates that achieving the health-related targets under the Sustainable Development Goals – from ending tuberculosis to ensuring universal access to sexual and reproductive healthcare services by 2030 – will cost between $134bn-$371bn (£97bn-£270bn) a year over current health spending. AI startups raised $15.2bn last year alone, adding to investments made by tech giants like Google, Facebook, and Alibaba and a host of research institutions.

Many of the world's leading aircraft makers are in Germany for the ILA Berlin Air Show, with the focus this year on drone technology. Its development is helping spark a revolution in sustainable flight.

Engineers love the complex challenge they present. But right now, what are they for, really? In the weeks after a self-driving Uber hit and killed an Arizona woman, it's hard to remember. Our own Jack Stewart has something close to a solution: Autonomous developers need to figure out how to explain the point of these vehicles, in the near term, and it's not saving the world. Aim smaller, maybe, breaking the complex task of building a vehicle that can go anywhere into steps.

Amazon has updated the software so that now if a kid asks Alexa to do something with a "please" attached she'll say "thanks for asking nicely."

Japanese companies are turning to robots to help build their skyscrapers. No workers: The country has a labor shortage problem, and it's only getting worse, especially in the booming construction industry. So firms are turning to automation technologies, including AI and drones, to map and control construction sites. Bring in the robots: Shimizu Corporation, for example, is looking to offload a small amount of labor onto bots. Its automatons will weld beams, move supplies, and install ceiling panels, taking over about 1 percent of the total labor involved in a typical high-rise project.

One of the formidable challenges healthcare providers face is putting medical data to maximum use. Somewhere between the quest to unlock the mysteries of medicine and design better treatments, therapies, and procedures, lies the real world of applying data and protecting patient privacy. "Today, there are many barriers to putting data to work in the most effective way possible," observes Drew Harris, director of health policy and population health at Thomas Jefferson University's College of Population Health in Philadelphia, PA. "The goals of protecting patients and finding answers are frequently at odds." It is a critical issue and one that will define the future of medicine. Medical advances are increasingly dependent on the analysis of enormous datasets--as well as data that extends beyond any one agency or enterprise.

Communication with computing machinery has become increasingly'chatty' these days: Alexa, Cortana, Siri, and many more dialogue systems have hit the consumer market on a broader basis than ever, but do any of them truly notice our emotions and react to them like a human conversational partner would? In fact, the discipline of automatically recognizing human emotion and affective states from speech, usually referred to as Speech Emotion Recognition or SER for short, has by now surpassed the "age of majority," celebrating the 22nd anniversary after the seminal work of Daellert et al. in 199610--arguably the first research paper on the topic. However, the idea has existed even longer, as the first patent dates back to the late 1970s.41 Previously, a series of studies rooted in psychology rather than in computer science investigated the role of acoustics of human emotion (see, for example, references8,16,21,34). Blanton,4 for example, wrote that "the effect of emotions upon the voice is recognized by all people. Even the most primitive can recognize the tones of love and fear and anger; and this knowledge is shared by the animals. The dog, the horse, and many other animals can understand the meaning of the human voice. The language of the tones is the oldest and most universal of all our means of communication." It appears the time has come for computing machinery to understand it as well.28 This holds true for the entire field of affective computing--Picard's field-coining book by the same name appeared around the same time29 as SER, describing the broader idea of lending machines emotional intelligence able to recognize human emotion and to synthesize emotion and emotional behavior.