"When we met with the [Deci] team we found they were able to help companies, especially at the junction between training the data on deployment and deploying the models into production, there's so much pain at that junction and this company really helps close that gap." Square Peg has made several investments in the field like radiology AI startup Aidoc and has seen portfolio companies like weather forecasting startup ClimaCell increasingly use AI models. "It's very much in our sweet spot," Mr Schwartz said. "We are pretty much focused on, Australia, New Zealand Israel and Southeast Asia, so it fits our geographic purpose, and the size of the company... it's at the early stage of its commercialisation, it's pre-revenue or early revenue and beginning (to get) commercial traction, so all that fits pretty well with us." Mr Geifman said Deci was working to make AI more accessible for more companies.

Noisy pairwise comparison feedback has been incorporated to improve the overall query complexity of interactively learning binary classifiers. The \textit{positivity comparison oracle} is used to provide feedback on which is more likely to be positive given a pair of data points. Because it is impossible to infer accurate labels using this oracle alone \textit{without knowing the classification threshold}, existing methods still rely on the traditional \textit{explicit labeling oracle}, which directly answers the label given a data point. Existing methods conduct sorting on all data points and use explicit labeling oracle to find the classification threshold. The current methods, however, have two drawbacks: (1) they needs unnecessary sorting for label inference; (2) quick sort is naively adapted to noisy feedback and negatively affects practical performance. In order to avoid this inefficiency and acquire information of the classification threshold, we propose a new pairwise comparison oracle concerning uncertainties. This oracle receives two data points as input and answers which one has higher uncertainty. We then propose an efficient adaptive labeling algorithm using the proposed oracle and the positivity comparison oracle. In addition, we also address the situation where the labeling budget is insufficient compared to the dataset size, which can be dealt with by plugging the proposed algorithm into an active learning algorithm. Furthermore, we confirm the feasibility of the proposed oracle and the performance of the proposed algorithm theoretically and empirically.

Humans are efficient continual learning systems; we continually learn new skills from birth with finite cells and resources. Our learning is highly optimized both in terms of capacity and time while not suffering from catastrophic forgetting. In this work we study the efficiency of continual learning systems, taking inspiration from human learning. In particular, inspired by the mechanisms of sleep, we evaluate popular pruning-based continual learning algorithms, using PackNet as a case study. First, we identify that weight freezing, which is used in continual learning without biological justification, can result in over $2\times$ as many weights being used for a given level of performance. Secondly, we note the similarity in human day and night time behaviors to the training and pruning phases respectively of PackNet. We study a setting where the pruning phase is given a time budget, and identify connections between iterative pruning and multiple sleep cycles in humans. We show there exists an optimal choice of iteration v.s. epochs given different tasks.

Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identification of causal effects from observational data in the cases where the causal graph is identifiable, i.e., the data generating mechanism can be recovered from the joint distribution. However, no such studies have been done to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effect from observational data by augmenting expert knowledge in the model development phase and with a practical clinical application. Our clinical application entails a timely and important research question, i.e., the effect of oxygen therapy intervention in the intensive care unit (ICU); the result of this project is useful in a variety of disease conditions, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We used data from the MIMIC III database, a standard database in the machine learning community that contains 58,976 admissions from an ICU in Boston, MA, for estimating the oxygen therapy effect on morality. We also identified the covariate-specific effect to oxygen therapy from the model for more personalized intervention.

Honeywell to leverage Microsoft Azure cloud platform and connect Microsoft Dynamics 365 to Honeywell Forge, enabling predictive maintenance applications with closed-loop maintenance workflows in the buildings industry. Honeywell and Microsoft announced that Honeywell will bring to market its domain-specific applications built on the Microsoft cloud platform to drive new levels of productivity for industrial clients. With the integration of the AI-driven autonomous controls of the Honeywell Forge enterprise performance management software with Microsoft Dynamics 365 Field Service, customers will be able to access operating data that includes workflow management support to improve performance and energy efficiency within the enterprise environment. Workers in the field will benefit from real-time access to critical data that will help them prioritize, analyze and solve problems more quickly. The first area of focus will be in automating maintenance for building owners and operators.

Locked down singles in Britain looking for love on Tinder, one of the world's most popular dating apps, can now video chat with their matches. Tinder had announced it is rolling out its'Face to Face' feature to its global customer base today. To prevent creeps and weirdos exploiting the feature to berate or harass their matches, video calling only becomes available when both parties opt in. It is designed to be used to compliment and boost conversation once a spark has been established. Tinder had announced it is rolling out its'Face to Face' feature today to its users around the world.

The research report focuses on target groups of customers to help players to effectively market their products and achieve strong sales in the global Emotion Artificial Intelligence Market. Readers are provided with validated and revalidated market forecast figures such as CAGR, Emotion Artificial Intelligence market revenue, production, consumption, and market share. Our accurate market data equips players to plan powerful strategies ahead of time. The Emotion Artificial Intelligence report offers deep geographical analysis where key regional and country level markets are brought to light. The vendor landscape is also analysed in depth to reveal current and future market challenges and Emotion Artificial Intelligence business tactics adopted by leading companies to tackle them.

Identifying whole/part relationships between books in digital libraries can be a valuable tool for better understanding and cataloging the works found in bibliographic collections, irrespective of the form in which they were printed. However, this relationship is difficult to learn computationally because of limited ground truth availability. In this paper, we present an approach for data augmentation of whole/part training data through the use of artificially generated books. Artificial data is found to be a robust approach to training deep neural network classifiers on books with limited real ground truth, working to prevent over-fitting and improving classification by 91.0%. Modern cataloging standards support encoding complex work-level relationships, opening the possibility for bibliographic collections that better represent the complex ways that works are changed, iterated, and collated in library books.

The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits depends heavily on improving general circulation model based dynamical forecasting systems. To improve dynamical seasonal forecast, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge cost in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. In this study, we develop a probabilistic deep neural network model, drawing on a wealth of existing climate simulations to enhance seasonal forecast capability and forecast diagnosis. By leveraging complex physical relationships encoded in climate simulations, our probabilistic forecast model demonstrates favorable deterministic and probabilistic skill compared to state-of-the-art dynamical forecast systems in quasi-global seasonal forecast of precipitation and near-surface temperature. We apply this probabilistic forecast methodology to quantify the impacts of initialization errors and model formulation deficiencies in a dynamical seasonal forecasting system. We introduce the saliency analysis approach to efficiently identify the key predictors that influence seasonal variability. Furthermore, by explicitly modeling uncertainty using variational Bayes, we give a more definitive answer to how the El Nino/Southern Oscillation, the dominant mode of seasonal variability, modulates global seasonal predictability.

Contrastive Learning has recently received interest due to its success in self-supervised representation learning in the computer vision domain. However, the origins of Contrastive Learning date as far back as the 1990s and its development has spanned across many fields and domains including Metric Learning and natural language processing. In this paper we provide a comprehensive literature review and we propose a general Contrastive Representation Learning framework that simplifies and unifies many different contrastive learning methods. We also provide a taxonomy for each of the components of contrastive learning in order to summarise it and distinguish it from other forms of machine learning. We then discuss the inductive biases which are present in any contrastive learning system and we analyse our framework under different views from various sub-fields of Machine Learning. Examples of how contrastive learning has been applied in computer vision, natural language processing, audio processing, and others, as well as in Reinforcement Learning are also presented. Finally, we discuss the challenges and some of the most promising future research directions ahead.