Missing value imputation is a fundamental problem in spatiotemporal modeling, from motion tracking to the dynamics of physical systems. Deep autoregressive models suffer from error propagation which becomes catastrophic for imputing long-range sequences. In this paper, we take a non-autoregressive approach and propose a novel deep generative model: Non-AutOregressive Multiresolution Imputation (NAOMI) to impute long-range sequences given arbitrary missing patterns. NAOMI exploits the multiresolution structure of spatiotemporal data and decodes recursively from coarse to fine-grained resolutions using a divide-and-conquer strategy. We further enhance our model with adversarial training.

A learned generative model often produces biased statistics relative to the underlying data distribution. A standard technique to correct this bias is importance sampling, where samples from the model are weighted by the likelihood ratio under model and true distributions. When the likelihood ratio is unknown, it can be estimated by training a probabilistic classifier to distinguish samples from the two distributions. We employ this likelihood-free importance weighting method to correct for the bias in generative models. We find that this technique consistently improves standard goodness-of-fit metrics for evaluating the sample quality of state-of-the-art deep generative models, suggesting reduced bias.

The usage of deep generative models for image compression has led to impressive performance gains over classical codecs while neural video compression is still in its infancy. Here, we propose an end-to-end, deep generative modeling approach to compress temporal sequences with a focus on video. Our approach builds upon variational autoencoder (VAE) models for sequential data and combines them with recent work on neural image compression. The approach jointly learns to transform the original sequence into a lower-dimensional representation as well as to discretize and entropy code this representation according to predictions of the sequential VAE. Rate-distortion evaluations on small videos from public data sets with varying complexity and diversity show that our model yields competitive results when trained on generic video content.

The best way to calculate statistics from medical data is to use the data of individual patients. In some settings, this data is difficult to obtain due to privacy restrictions. In Germany, for example, it is not possible to pool routine data from different hospitals for research purposes without the consent of the patients. The DataSHIELD software provides an infrastructure and a set of statistical methods for joint analyses of distributed data. The contained algorithms are reformulated to work with aggregated data from the participating sites instead of the individual data. If a desired algorithm is not implemented in DataSHIELD or cannot be reformulated in such a way, using artificial data is an alternative. We present a methodology together with a software implementation that builds on DataSHIELD to create artificial data that preserve complex patterns from distributed individual patient data. Such data sets of artificial patients, which are not linked to real patients, can then be used for joint analyses. We use deep Boltzmann machines (DBMs) as generative models for capturing the distribution of data. For the implementation, we employ the package "BoltzmannMachines" from the Julia programming language and wrap it for use with DataSHIELD, which is based on R. As an exemplary application, we conduct a distributed analysis with DBMs on a synthetic data set, which simulates genetic variant data. Patterns from the original data can be recovered in the artificial data using hierarchical clustering of the virtual patients, demonstrating the feasibility of the approach. Our implementation adds to DataSHIELD the ability to generate artificial data that can be used for various analyses, e. g. for pattern recognition with deep learning. This also demonstrates more generally how DataSHIELD can be flexibly extended with advanced algorithms from languages other than R.

If you go back a few hundred years, what we take for granted today would seem like magic – being able to talk to people over long distances, to transmit images, flying, accessing vast amounts of data like an oracle. These are all things that would have been considered magic a few hundred years ago.

Follow the latest updates of the outbreak on our timeline. Artificial intelligence (AI) technology provider Iktos and research centre SRI International have partnered to discover and develop drugs to treat various viruses, including the novel coronavirus that causes Covid-19 and influenza. Iktos will combine its generative modelling technology with SRI's fully automated synthetic chemistry platform called SynFini to design compounds and speed-up the identification of drug candidates. The Iktos AI technology leverages deep generative models for the accelerated drug discovery process, made possible via the automatic design of virtual molecules with the required characteristics of a new drug candidate. Iktos co-founder and CEO Yann Gaston-Mathé said: "Iktos generative AI technology has proven its value and potential to accelerate drug discovery programs in multiple collaborations with renowned pharmaceutical companies.

Molecule generation is a challenging open problem in cheminformatics. Currently, deep generative approaches addressing the challenge belong to two broad categories, differing in how molecules are represented. One approach encodes molecular graphs as strings of text, and learns their corresponding character-based language model. Another, more expressive, approach operates directly on the molecular graph. In this work, we address two limitations of the former: generation of invalid and duplicate molecules. To improve validity rates, we develop a language model for small molecular substructures called fragments, loosely inspired by the well-known paradigm of Fragment-Based Drug Design. In other words, we generate molecules fragment by fragment, instead of atom by atom. To improve uniqueness rates, we present a frequency-based masking strategy that helps generate molecules with infrequent fragments. We show experimentally that our model largely outperforms other language model-based competitors, reaching state-of-the-art performances typical of graph-based approaches. Moreover, generated molecules display molecular properties similar to those in the training sample, even in absence of explicit task-specific supervision.

The retail giant said today that it will deploy the generative chatbot as an aid to human agents for the time being but plans to eventually have it deal with customers directly. The company is also rolling out a separate consumer-facing chatbot that uses a neural network to better match human-authored response templates to customer queries. The project marks one of the first commercial tests of a state-of-the-art new natural language processing technology that researchers think has the potential to supercharge progress in the field. The model, which has also powered cutting-edge systems like OpenAI's GPT-2, draws on massive training datasets and predictive text to generate realistic-sounding copy or dialogue. "It is difficult to determine what types of conversational models other customer service systems are running, but we are unaware of any announced deployments of end-to-end, neural-network-based dialogue models like ours," wrote Jared Kramer, an applied-science manager on Amazon's Customer Service Tech team, in a blog post. Despite these advances in machine learning, most chatbots on the market today still run on automation rather than true AI.

AI researchers continue to develop larger and more complicated models that can tackle more complicated language-related tasks. In the past year, we've seen the release of state-of-the-art language models such as OpenAI's GPT-2 and Google's Meena. While we're still pretty far from developing AI that can truly understand human language, practical uses will emerge from continued advances in natural language processing. AI will do the legwork, gathering import data and highlighting trends in text data, making it easier and less costly for insurers to piece that information together and address their clients' needs.

Tesla CEO Elon Musk wants to see all artificial intelligence better regulated, even at his own company, he tweeted Monday (via TechCrunch). He made the remark in response to a piece about OpenAI by MIT Technology Review, which claimed that the AI organization, co-founded by Musk, has shifted from its mission of developing and distributing AI safely and equitably into a secretive company obsessed with image and driven to constantly raise more money. Musk has a history of expressing serious concerns about the negative potential of AI. He tweeted in 2014 that it could be "more dangerous than nukes," and told an audience at an MIT Aeronautics and Astronautics symposium that year that AI was "our biggest existential threat," and humanity needs to be extremely careful: With artificial intelligence we are summoning the demon. In all those stories where there's the guy with the pentagram and the holy water, it's like yeah he's sure he can control the demon.