Chemical synthesis is a scientific procedure that uses simple chemical compounds to construct complex ones. It is the exact process used to create most of the substances we use in our daily life – from drugs to those components inside our electronic devices. However, the steps involved are often time intensive and extremely complicated that it may take years for chemists to master or achieve breakthroughs in certain procedures. Nonetheless, artificial intelligence has come to the rescue. To make it simple for everyone we can say, artificial intelligence is the new lab technician for chemical synthesis.

Marwin Segler, the lead author of the study, puts it in a nutshell: "Retrosynthesis is the ultimate discipline in organic chemistry. Chemists need years to master it -- just like with chess or Go. In addition to straightforward expertise, you also need a goodly portion of intuition and creativity for it. So far, everyone assumed that computers couldn't keep up without experts programming in tens of thousands of rules by hand. What we have shown is that the machine can, by itself, learn the rules and their applications from the literature available."

Google, Facebook and others fill their pockets with billions using our data. But artificial intelligence can do much more – for example ending world hunger. During my studies in artificial intelligence I didn't learn that much about how powerful computers are. Most of all, I learned that many things humans do are less complex than we think they are. Here you can find our introductory text on artificial intelligence by the physicist and neuroscientist David Hofmann (German) Artificial Intelligence – even though the term triggers associations such as Do we want that robots take care of us in the future, wonders Dirk Walbrühl here (German) humanoid robots, world supremacy and apocalypse, very often it means »no more« than data analysis. This might sound less exciting, but it is a powerful tool indeed – not only to understand what we click and buy, but also to find answers to questions that might improve the lives of many people.

Chief executives of highly innovative companies must figure out how to take bold risks while being stable enough to sustain an enterprise over the long term. Achieving this balance is even more difficult in Japan, where lifelong employment is a strong tradition, than elsewhere. Shin Sakane, founder and CEO of the Japanese startup Seven Dreamers Laboratories, has built the company's identity around resolving that conflict. Sakane is a member of a prominent Japanese business family, perhaps best known as the founders and owners of the I.S.T Corporation, a global producer of composite materials made from glass fiber and fluorine resin. After completing a Ph.D. in chemistry and biochemistry at the University of Delaware, he returned to Japan in 2000, joining I.S.T as a managing director. He succeeded his father as CEO in 2003. In 2008, I.S.T acquired Super Resin, a company making components for the aerospace, industrial, automotive, and semiconductor industries.

The rise of artificial intelligence (AI), is raising questions at the ethics watchdog for the world's biggest wealth fund. In particular, the threat posed by weapons systems guided by AI brings ethical "challenges," according to Johan H. Andresen, the chairman of the Norwegian Council on Ethics. "It won't necessarily only have to do with weapons, but many other applications," he said in an interview in Oslo. "It's hard to program empathy– we can't demand that– but everything we're looking at is under human control and humans are making the decisions, so both people and companies can be held responsible." Norway's $1 trillion wealth fund has been stepping up the scrutiny of its portfolio and has banned a swathe of companies, including nuclear weapons and cluster bomb producers as well as tobacco and coal companies.

The resolution and calibration of pure spectra of minority components in measurements of chemical mixtures without prior knowledge of the mixture is a challenging problem. In this work, a combination of band target entropy minimization (BTEM) and target partial least squares (T-PLS) was used to obtain estimates for single pure component spectra and to calibrate those estimates in a true, one-at-a-time fashion. This approach allows for minor components to be targeted and their relative amounts estimated in the presence of other varying components in spectral data. The use of T-PLS estimation is an improvement to the BTEM method because it overcomes the need to identify all of the pure components prior to estimation. Estimated amounts from this combination were found to be similar to those obtained from a standard method, multivariate curve resolution-alternating least squares (MCR-ALS), on a simple, three component mixture dataset. Studies from two experimental datasets demonstrate where the combination of BTEM and T-PLS could model the pure component spectra and obtain concentration profiles of minor components but MCR-ALS could not.

NCCR Researchers are using a new method for digital timber construction in a real project for the first time. The load-bearing timber modules, which are prefabricated by robots, will be assembled on the top two floors at the DFAB HOUSE construction site. Digitalisation has found its way into timber construction, with entire elements already being fabricated by computer-aided systems. The raw material is cut to size by the machines, but in most cases it still has to be manually assembled to create a plane frame. In the past, this fabrication process came with many geometric restrictions.

With an operating footprint of up to 50km from the mining pit to iron ore carriers, it was easy for Citic Pacific Mining, Australia's largest magnetite mining company, to lose track of its assets, such as light vehicles, buses and service trucks. Find out how to draw up a battle plan for securing connected devices and the key areas to target. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered.

User interfaces provide an interactive window between physical and virtual environments. A new concept in the field of human-computer interaction is a soft user interface; a compliant surface that facilitates touch interaction through deformation. Despite the potential of these interfaces, they currently lack a signal processing framework that can efficiently extract information from their deformation. Here we present OrbTouch, a device that uses statistical learning algorithms, based on convolutional neural networks, to map deformations from human touch to categorical labels (i.e., gestures) and touch location using stretchable capacitor signals as inputs. We demonstrate this approach by using the device to control the popular game Tetris. OrbTouch provides a modular, robust framework to interpret deformation in soft media, laying a foundation for new modes of human computer interaction through shape changing solids.

Proteins are used in various applications by pharmaceutical, food, fuel, and many other industries and their usage is growing steadily (Kirk et al., 2002; Sanchez and Demain, 2010). Proteins have important advantages over chemical catalysts, as they are derived from renewable resources, are biodegradable and are often highly selective (Cherry and Fidantsef, 2003). Protein engineering is used to further improve the properties of proteins, for example to enhance their catalytic activity, modify their substrate specificity or to improve their thermostability (Rapley and Walker, 2000). Increasing the stability is an important aspect of protein engineering, as the proteins used in industry should be stable in the industrial process conditions, which often involve higher than ambient temperature and non-aqueous solvents (Bommarius et al., 2011). The properties of a protein are modified by introducing alterations to its amino acid sequence. Mutations in general tend to be destabilising, and if too many destabilising mutations are implemented, the protein may not remain functional without compensatory stabilising mutations (Tokuriki and Tawfik, 2009). The stability of a protein can be defined as the difference in Gibbs energy G between the folded and unfolded (or native and denaturated) state of the protein.