Astronomers require efficient automated detection and classification pipelines when conducting large-scale surveys of the (optical) sky for variable and transient sources. Such pipelines are fundamentally important, as they permit rapid follow-up and analysis of those detections most likely to be of scientific value. We therefore present a deep learning pipeline based on the convolutional neural network architecture called $\texttt{MeerCRAB}$. It is designed to filter out the so called 'bogus' detections from true astrophysical sources in the transient detection pipeline of the MeerLICHT telescope. Optical candidates are described using a variety of 2D images and numerical features extracted from those images. The relationship between the input images and the target classes is unclear, since the ground truth is poorly defined and often the subject of debate. This makes it difficult to determine which source of information should be used to train a classification algorithm. We therefore used two methods for labelling our data (i) thresholding and (ii) latent class model approaches. We deployed variants of $\texttt{MeerCRAB}$ that employed different network architectures trained using different combinations of input images and training set choices, based on classification labels provided by volunteers. The deepest network worked best with an accuracy of 99.5$\%$ and Matthews correlation coefficient (MCC) value of 0.989. The best model was integrated to the MeerLICHT transient vetting pipeline, enabling the accurate and efficient classification of detected transients that allows researchers to select the most promising candidates for their research goals.

Vetter says the use of artificial intelligence will become ever more specific in the biopharma production space and plans to use robotics in aseptic processes. German fill/finish and pharma services firm Vetter has completed a pilot project using a dual-arm robot in secondary packaging. "Robotics have been widespread in pharmaceutical manufacturing for many years. At Vetter, for example, we have been using classical robotics in our pharmaceutical production since the 1990's," Vetter spokesperson Markus Kirchner told this publication. "We see the usage of this technology becoming ever-more specific and evolving over time. This is evidenced in our example of collaborative work, which is essentially a close interaction of humans and machines."