Artificial intelligence (AI), security orchestration and the Internet of Things (IoT) are disrupting our industry. Some bombastic pundits are even predicting a Jetsons-like world where computers and IoT devices replace workers with machines. Yet most futurists believe computing will only evolve to the level of a digital assistant. Tasks, they say, will be split between artificial and biologic intelligence. Forensics is a prime example of a human compliment to both AI and security automation.

As was also clearly stated by Vladimir Putin on September 4, 2017: "whichever country leads the way in Artificial Intelligence research will be the ruler of the world". According to Thomas Kuhn's old, but still useful, epistemological model, every change of the scientific paradigm – rather than the emergence of new material discoveries – radically changes the visions of the world and hence strategic equilibria. Hence, first of all, what is Artificial Intelligence? It consists of a series of mathematical tools, but also of psychology, electronic technology, information technology and computer science tools, through which a machine is taught to think as if it were a human being, but with the speed and security of a computer. The automatic machine must representman's knowledge, namely show it, thus enabling an external operator to change the process and understand its results within the natural language. In practice, AI machines imitate the perceptual vision, the recognition and the reprocessing of language -and even of decision-making – but only when all the data necessary to perform it are available.

For centuries, scientists have assembled and maintained extensive information on plants and stored it in what are known as herbaria -- vast numbers of cabinets and drawers – at natural history museums and research institutions across the globe. They've used them to discover and confirm the identity of organisms and catalog their characteristics. Over the past two decades, much of this data has been digitized, and this treasure of text, imagery and samples has become easier to share around the world. Now, complementary projects at the Smithsonian Institution in the U.S. and the Costa Rica Institute of Technology (ITCR) are tapping the combination of big data analytics, computer vision and GPUs to deepen science's access -- and understanding -- of botanical information. Their use of GPU-accelerated deep learning promises to hasten the work of researchers, who discover and describe about 2,000 species of plants each year, and need to compare them against the nearly 400,000 known species.

The Department of Veterans Affairs has announced a research partnership with Alphabet subsidiary DeepMind that will tackle issues concerning patient deterioration during hospital care. Using a dataset comprised of 700,000 historical, de-personalized health records, the machine learning platform will help the VA identify risk factors for deterioration while predicting its onset. "Medicine is more than treating patients' problems," VA Secretary David J. Shulkin said in a statement. "Clinicians need to be able to identify risks to help prevent disease. This collaboration is an opportunity to advance the quality of care for our nation's veterans by predicting deterioration and applying interventions early."

Artificial intelligence, upskilling, and an older workforce are a few of the workplace trends we're likely to see in 2018. With unemployment at a 42-year low of 4.3pc, the jobs market remains competitive: the Office for National Statistics recently revealed that UK businesses are struggling to find workers as the pool of potential staff dries up. As a result, employers are having to evolve and develop new strategies to attract top talent. Set against this, the broader outlook is relatively uncertain, with sluggish wages, rising inflation and concerns about Britain leaving the EU dominating the debate.

Since the DARPA Robotics Challenge Finals in June of 2015, roboticists at the Italian Institute of Technology (IIT) have been working to improve the capabilities of their custom made humanoid disaster robot, WALK-MAN. WALK-MAN is part of a much larger European Commission-funded project, which involves a variety of research institutes and universities all contributing to the development of different aspects of the robot, from simulation to perception to locomotion to manipulation. After a solid five years of work, the WALK-MAN project is now at its final validation phase, and it's gotten one last major upgrade to help it prepare to be helpful in the disasters we're certain to have in the future. For background on WALK-MAN, make sure and check out this in-depth article that we posted in 2015, just before the DRC. The version that IIT is announcing today has a number of hardware improvements, starting with a redesigned frame made of aluminum, magnesium alloys, and titanium.

Across moral and non-moral domains, humans improve their expertise by learning from personal experience, by learning from being told, and by observing the outcomes when others face similar decisions. Children start with little experience and a small number of simple rules they have been taught by parents and teachers. Over time, they accumulate a richer and more nuanced understanding of when particular actions are right or wrong. The complexity of the world suggests the only way to acquire adequately complex decision criteria is through learning. Robots, however, are manufactured artifacts, whose computational state can be stored, copied, and retrieved. Even if mature moral and ethical expertise can only be created through experience and observation, it is conceivable this expertise can then be copied from one robot to another sufficiently similar one, unlike what is possible for humans.

Matthias Felleisen (matthias@ccs.neu.edu) is a Trustee Professor in the College of Computer Science at Northeastern University, Boston, MA, USA. Robert Bruce Findler (robby@eecs.northwestern.edu) is a professor of computer science at Northwestern University, Evanston, IL, USA. Matthew Flatt (mflatt@cs.utah.edu) is a professor of computer science at the University of Utah, Salt Lake City, UT, USA. Shriram Krishnamurthi (sk@cs.brown.edu) is a professor of computer science at Brown University, Providence, RI, USA. Eli Barzilay (eli@barzilay.org) is a research scientist at Microsoft Research, Cambridge, MA, USA. Jay McCarthy (jay.mccarthy@gmail.com) is an associate professor of computer science at the University of Massachusetts, Lowell, MA, USA. Sam Tobin-Hochstadt (samth@cs.indiana.edu) is an assistant professor of computer science at Indiana University, Bloomington, IN, USA.

This viewpoint is about differences between computer science and social science, and their implications for computational social science. Spoiler alert: The punchline is simple. Despite all the hype, machine learning is not a be-all and end-all solution. We still need social scientists if we are going to use machine learning to study social phenomena in a responsible and ethical manner. I am a machine learning researcher by training.

An image of a dog created by a deep convolutional generative adversarial network (GAN) algorithm. Back in 1999, Hany Farid was finishing his postdoctoral work at the Massachusetts Institute of Technology (MIT) and was in a library when he stumbled on a book called The Federal Rules of Evidence. The book caught his eye, and Farid opened to a random page, on which was a section entitled "Introducing Photos into a Court of Law as Evidence." Since he was interested in photography, Farid wondered what those rules were. While Farid was not surprised to learn that a 35mm negative is considered admissible as evidence, he was surprised when he read that then-new digital media would be treated the same way.