From your water-logged phone to your smashed smart TV, those personal electronics headed for the landfill are a potential goldmine. Economists already knew that along with the swelling 44.7 million metric tons of electronic waste tossed each year we were throwing out billions of dollars in resources. But quantifying all the gold, copper, iron, plastic, and rare earths languishing in our landfills and recycling centers is only part of the problem. Figuring out whether it's worthwhile, financially speaking, to sift those resources out of the rubble--instead of continuing to extract them from traditional mines--is another issue entirely. A study published in Environmental Science & Technology this week finally has an answer, suggesting that'urban mining' of electronic waste for copper and gold in China was actually more cost-effective than digging those metals out of the ground.

Predictive modelling is a very exciting field with many different applications. Lots of algorithms have been developed in this area. According to many Kaggle competitions (Fogg, 2016), winning solutions are often obtained with elastic tools like random forest, gradient boosting or neural networks. These algorithms have many strengths but also share a major weakness, which is the lack of interpretability of a model structure. A single random forest, an xgboost model or a neural network may be parametrized with thousands of parameters which makes these models hard to understand.

Another such invention, 3-D printing, is now scaling up. All over the world, an impressive diversity of people and organizations, ranging from startups and hobbyists to construction and engineering firms, are successfully prototyping 3-D-printed buildings. The government of Dubai has set a goal of 3-D printing 25% of every new building by 2030. Prototype single-family dwellings have been 3-D-printed in China, Italy, Russia--and Texas. Global infrastructure firm AECOM ACM 2.59% uses 3-D printing to prefabricate jail cells and hospital rooms.

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.