The hype around artificial intelligence is at its peak, while those interested in blockchain might be heading for disillusionment. That's according to Gartner's annual Hype Cycle, which tracks the progress of emerging technology trends, from early innovation to becoming a household must-have. Deep learning and machine learning are at what it calls the "peak of inflated expectation", but are just two to five years away from mainstream adoption. Cognitive computing is also at peak hype, but up to 10 years away, while general artificial intelligence remains more than a decade away and is still at the stage of early innovation. Mainstream blockchain technology, which is being tested everywhere from finance to food chains, remains between five and 10 years away.

THE human-like abilities of robots continue to develop at incredible pace – with droids now being seen to chase targets and even fire guns. But now scientists have taken the potential for human-like resemblance to the next level with the creation of an artificial self-healing "skin". Scientists at the Brije Universiteit Brussel have managed to give robots self-healing properties which allow them to "recover" even if they are stabbed or gashed with a knife. The development evokes vsisions of Arnold Schwarzenegger as the cyborg assassin in the Terminator movies, but fortunately for humankind, these self-healing robots aren't likely to go on a rampage anytime soon. For now, they're more likely to be found grabbing fruit, veg and soft products on factory floors.

The big promise of driverless cars is that they'll save lives by preventing crashes. Computers don't fall asleep, get drunk, or glance at that tweet. Robocar technology could save tens, even hundreds, of thousands of lives each year. Such cars remain years away, of course, but you can find an autonomous vehicle saving lives on the road right now, in Colorado. The irony is, this vehicle is designed to crash.

Raman spectroscopy is a ubiquitous method for characterisation of substances in a wide range of settings including industrial process control, planetary exploration, homeland security, life sciences, geological field expeditions and laboratory materials research. In all of these environments there is a requirement to identify substances from their Raman spectrum at high rates and often in high volumes. Whilst machine classification has been demonstrated to be an essential approach to achieve real time identification, it still requires preprocessing of the data. This is true regardless of whether peak detection or multivariate methods, operating on whole spectra, are used as input. A standard pipeline for a machine classification system based on Raman spectroscopy includes preprocessing in the following order: cosmic ray removal, smoothing and baseline correction.

Revealing a community structure in a network or dataset is a central problem arising in many scientific areas. The modularity function $Q$ is an established measure quantifying the quality of a community, being identified as a set of nodes having high modularity. In our terminology, a set of nodes with positive modularity is called a \textit{module} and a set that maximizes $Q$ is thus called \textit{leading module}. Finding a leading module in a network is an important task, however the dimension of real-world problems makes the maximization of $Q$ unfeasible. This poses the need of approximation techniques which are typically based on a linear relaxation of $Q$, induced by the spectrum of the modularity matrix $M$. In this work we propose a nonlinear relaxation which is instead based on the spectrum of a nonlinear modularity operator $\mathcal M$. We show that extremal eigenvalues of $\mathcal M$ provide an exact relaxation of the modularity measure $Q$, however at the price of being more challenging to be computed than those of $M$. Thus we extend the work made on nonlinear Laplacians, by proposing a computational scheme, named \textit{generalized RatioDCA}, to address such extremal eigenvalues. We show monotonic ascent and convergence of the method. We finally apply the new method to several synthetic and real-world data sets, showing both effectiveness of the model and performance of the method.

We invoke a Gaussian mixture model (GMM) to jointly analyse two traditional emission-line classification schemes of galaxy ionization sources: the Baldwin-Phillips-Terlevich (BPT) and $\rm W_{H\alpha}$ vs. [NII]/H$\alpha$ (WHAN) diagrams, using spectroscopic data from the Sloan Digital Sky Survey Data Release 7 and SEAGal/STARLIGHT datasets. We apply a GMM to empirically define classes of galaxies in a three-dimensional space spanned by the $\log$ [OIII]/H$\beta$, $\log$ [NII]/H$\alpha$, and $\log$ EW(H${\alpha}$), optical parameters. The best-fit GMM based on several statistical criteria suggests a solution around four Gaussian components (GCs), which are capable to explain up to 97 per cent of the data variance. Using elements of information theory, we compare each GC to their respective astronomical counterpart. GC1 and GC4 are associated with star-forming galaxies, suggesting the need to define a new starburst subgroup. GC2 is associated with BPT's Active Galaxy Nuclei (AGN) class and WHAN's weak AGN class. GC3 is associated with BPT's composite class and WHAN's strong AGN class. Conversely, there is no statistical evidence -- based on four GCs -- for the existence of a Seyfert/LINER dichotomy in our sample. Notwithstanding, the inclusion of an additional GC5 unravels it. The GC5 appears associated to the LINER and Passive galaxies on the BPT and WHAN diagrams respectively. Subtleties aside, we demonstrate the potential of our methodology to recover/unravel different objects inside the wilderness of astronomical datasets, without lacking the ability to convey physically interpretable results. The probabilistic classifications from the GMM analysis are publicly available within the COINtoolbox (https://cointoolbox.github.io/GMM\_Catalogue/).

Summary: Recently we've been profiling Automated Machine Learning (AML) platforms, both of the professional variety, and particularly those proprietary one-click-to-model variety that are being pitched to untrained analysts and line-of-business managers. Since our first article, readers have suggested some additional companies we should look at which are profiled here along with some interesting observations about who is buying and why. Recently we've written a series of articles on Automated Machine Learning (AML) which are platforms or packages designed to take over the most repetitive elements of preparing predictive models. Typically these cover cleaning, preprocessing, some feature engineering, feature selection, and then model creation using one or several algorithms including hyperparameter optimization. Most will then offer code export and an API for scoring. These are grouped into two major schools.

Facebook's intelligent assistant M is now available for Facebook Messenger users in Australia, Canada, South Africa, and the U.K. M in Facebook Messenger first became available in April. M uses machine learning to scan words used in conversations to recommend actions or services. Tell someone good night and M may suggest a good night sticker. Chat about a plan and M may suggest you create a calendar event. M also suggests you do things like share your location, save a URL or video, initiate a voice or video call, or send best wishes when you're speaking to someone on their birthday.

Facebook has today launched its AI assistant'M' in the UK, four months after the service was made available in the US. M, which is run through Messenger, uses machine learning to recognise what you are talking about and offers suggestions that it thinks you might like. M will pop up and present helpful actions in the chat window of the app, perhaps sending a fun sticker, sharing your location or making plans to meet friends. It also allows you to save content from chats to view later, including URLs, videos, Facebook posts, events, and pages. A digital assistant launched by Facebook for Messenger is coming to the UK from today.

Amazon has patented a way for its delivery drones to drop off packages without ever having to land. The new patent describes an accordion-like tube that would extend from the drone to a drop-off point such as a porch and let the package slide through to a safe landing. Additionally, the technology could make it easier to deliver packages when conditions aren't optimal for landing and also cut down on noise pollution. The new patent describes an accordion-like tube that would extend from the drone to a drop-off point such as a porch and let the package slide through to a safe landing. Amazon has patented a way for its delivery drones to drop off packages without ever having to land.