Whilst the term artificial intelligence (AI) may conjure images of futuristic utopia and modern-day visionary technologies; the concept has actually been a societal forethought for longer than we think. Meanwhile, turn the clocks back considerably further to Ancient Egypt, and you'll find robotic-inspired animated ceremonial statues. Artificial intelligence is, in fact, centuries-old, and its implementation has long been a desire of the human race. Fast-forward to today and the omnipresence of robotics is remarkable. Not only are robots applied to large-scale industrial manufacturing chains (both assembling cars and integrated with vehicles themselves), but they're also found much closer to home on a smaller scale; hoovering our floors, mowing our lawns and, in some cases, stocking our shelves at local supermarkets1.

We've all seen stories about artificial intelligence in the news and on social media. Chat bots, speech recognition, machine translation and self-driving cars are just a few of the real-life examples you may have heard about or even experienced first-hand. The impact that AI decision-making has on the economy, society, education and our emotional well-being is tremendous. This begs the question: how well equipped are today's teachers to prepare their students for a world increasingly impacted by artificial intelligence and machine learning, and what opportunities and concerns do these developments bring to education? All educators are most welcome to join any time after the event.

We extend the idea of word pieces in natural language models to machine learning tasks on opaque ids. This is achieved by applying hash functions to map each id to multiple hash tokens in a much smaller space, similarly to a Bloom filter. We show that by applying a multi-layer Transformer to these Bloom filter digests, we are able to obtain models with high accuracy. They outperform models of a similar size without hashing and, to a large degree, models of a much larger size trained using sampled softmax with the same computational budget. Our key observation is that it is important to use a multi-layer Transformer for Bloom filter digests to remove ambiguity in the hashed input. We believe this provides an alternative method to solving problems with large vocabulary size.

We outline an inherent weakness of tensor factorization models when latent factors are expressed as a function of side information and propose a novel method to mitigate this weakness. We coin our method \textit{Kernel Fried Tensor}(KFT) and present it as a large scale forecasting tool for high dimensional data. Our results show superior performance against \textit{LightGBM} and \textit{Field Aware Factorization Machines}(FFM), two algorithms with proven track records widely used in industrial forecasting. We also develop a variational inference framework for KFT and associate our forecasts with calibrated uncertainty estimates on three large scale datasets. Furthermore, KFT is empirically shown to be robust against uninformative side information in terms of constants and Gaussian noise.

The analysis of multidimensional data is becoming a more and more relevant topic in statistical and machine learning research. Given their complexity, such data objects are usually reshaped into matrices or vectors and then analysed. However, this methodology presents several drawbacks. First of all, it destroys the intrinsic interconnections among datapoints in the multidimensional space and, secondly, the number of parameters to be estimated in a model increases exponentially. We develop a model that overcomes such drawbacks. In particular, we proposed a parsimonious tensor regression based model that retains the intrinsic multidimensional structure of the dataset. Tucker structure is employed to achieve parsimony and a shrinkage penalization is introduced to deal with over-fitting and collinearity. An Alternating Least Squares (ALS) algorithm is developed to estimate the model parameters. A simulation exercise is produced to validate the model and its robustness. Finally, an empirical application to Foursquares spatio-temporal dataset and macroeconomic time series is also performed. Overall, the proposed model is able to outperform existing models present in forecasting literature.

Elephants mourn their dead even if they did not have a close bond and continue to take an interest long after their bodies start to decay, a new study finds. Experts from the San Diego Zoo Institute for Conservation Research looked at 32 wild elephant carcasses from 12 different sources across Africa. They monitored the way in which the animals interacted with the carcasses and found that, in all cases, they would touch and examine the remains. They were also seen vocalising and attempting to lift or pull fallen elephants that had just died, according to researchers. New research has shown they mourn their dead even if they don't know them well (stock image) The idea that elephants have a'unique relationship' with the dead has been touted for a number of years, but this new study is the first to examine it in detail.

GNW • Which global factors are expected to impact the artificial intelligence market for automotive and transportation industry in the future? What are the key market strategies being adopted by them? Global Artificial Intelligence Market for Automotive and Transportation Industry Forecast, 2019-2029 In terms of value, the global artificial intelligence market for automotive and transportation industry is expected to grow at a CAGR of 13.12% during the forecast period 2019-2029. The growth in the market is attributable to the ongoing demand for innovative and technologically advanced automotive solutions. Moreover, intelligent solutions which reduce the incidences of human mistakes while driving, along with providing additional features for enhancing ease-of-driving, have driven the market.

AI to counter unmet clinical demand 3.5.1.5

We work in the area of specialized algorithms in ML/AI for Life Sciences and the financial markets.

We study the problem of community detection in multi-layer networks, where pairs of nodes can be related in multiple modalities. We introduce a general framework, i.e., mixture multi-layer stochastic block model (MMSBM), which includes many earlier models as special cases. We propose a tensor-based algorithm (TWIST) to reveal both global/local memberships of nodes, and memberships of layers. We show that the TWIST procedure can accurately detect the communities with small misclassification error as the number of nodes and/or the number of layers increases. Numerical studies confirm our theoretical findings. To our best knowledge, this is the first systematic study on the mixture multi-layer networks using tensor decomposition. The method is applied to two real datasets: worldwide trading networks and malaria parasite genes networks, yielding new and interesting findings.