Most of existing methods for DNA motif discovery consider only a single set of sequences to find an over-represented motif. In contrast, we consider multiple sets of sequences where we group sets associated with the same motif into a cluster, assuming that each set involves a single motif. Clustering sets of sequences yields clusters of coherent motifs, improving signal-to-noise ratio or enabling us to identify multiple motifs. We present a probabilistic model for DNA motif discovery where we identify multiple motifs through searching for patterns which are shared across multiple sets of sequences. Our model infers cluster-indicating latent variables and learns motifs simultaneously, where these two tasks interact with each other.

Competition commissioner Margrethe Vestager aims to create a'transatlantic space for trustworthy AI', giving companies a single set of rules to follow. Brussels and Washington can create a common space for trustworthy AI so that companies can comply with both EU and US artificial intelligence guidelines by applying a single set of rules, the EU's competition commissioner has said. Margrethe Vestager's remarks, made in advance of the third EU-US Trade and Technology Council (TTC) meeting, which will take place on December 5, go further than ever before in aiming to align rules over the technology. Speaking in Brussels on 21 November, she said that progress made by the EU and US should "pave the way for a transatlantic sort of space for trustworthy AI." If the US and EU can agree on a common rulebook for AI it would become the de facto global standard, given the weight of the two economies.

The deep learning field is progressing rapidly, and the latest work from Deepmind is a good example of this. Their Gato model is able to learn to play Atari games, generate realistic text, process images, control robotic arms, and more, all with the same neural network. Inspired by large-scale language models, Deepmind applied a similar approach but extended beyond the realm of text outputs. This new AGI (after Artificial General Intelligence) works as a multi-modal, multi-task, multi-embodiment network, which means that the same network (i.e. a single architecture with a single set of weights) can perform all tasks, despite involving inherently different kinds of inputs and outputs. While Deepmind's preprint presenting Gato is not very detailed, it is clear enough in that it is strongly rooted in transformers as used for natural language processing and text generation.

Most of existing methods for DNA motif discovery consider only a single set of sequences to find an over-represented motif. In contrast, we consider multiple sets of sequences where we group sets associated with the same motif into a cluster, assuming that each set involves a single motif. Clustering sets of sequences yields clusters of coherent motifs, improving signal-to-noise ratio or enabling us to identify multiple motifs. We present a probabilistic model for DNA motif discovery where we identify multiple motifs through searching for patterns which are shared across multiple sets of sequences. Our model infers cluster-indicating latent variables and learns motifs simultaneously, where these two tasks interact with each other.

Artificial intelligence should treat all people fairly, empower everyone, perform reliably and safely, be understandable, be secure and respect privacy, and have algorithmic accountability. It should be aligned with existing human values, be explainable, be fair, and respect user data rights. It should be used for socially beneficial purposes, and always remain under meaningful human control. These are some of the high-level headings under which Microsoft, IBM, and Google-owned DeepMind respectively set out their ethical principles for the development and deployment of A.I. They're also, pretty much by definition, A Good Thing. Anything that insists upon technology's weighty real-world repercussions -- and its creators' responsibilities towards these -- is surely welcome in an age when automated systems are implicated in every facet of human existence.

Multiple seasonal patterns play a key role in time series forecasting, especially for business time series where seasonal effects are often dramatic. Previous approaches including Fourier decomposition, exponential smoothing, and seasonal autoregressive integrated moving average (SARIMA) models do not reflect the distinct characteristics of each period in seasonal patterns, such as the unique behavior of specific days of the week in business data. We propose a multi-dimensional hierarchical model. Intermediate parameters for each seasonal period are first estimated, and a mixture of intermediate parameters is then taken, resulting in a model that successfully reflects the interactions between multiple seasonal patterns. Although this process reduces the data available for each parameter, a robust estimation can be obtained through a hierarchical Bayesian model implemented in Stan. Through this model, it becomes possible to consider both the characteristics of each seasonal period and the interactions among characteristics from multiple seasonal periods. Our new model achieved considerable improvements in prediction accuracy compared to previous models, including Fourier decomposition, which Prophet uses to model seasonality patterns. A comparison was performed on a real-world dataset of pallet transport from a national-scale logistic network.