Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. Moving data between applications and warehousing data for analysis are recurring issues for app builders, data engineers, and IT teams. But we all know our businesses can benefit in significant ways if we are smart with our data. There are plenty of options for moving data now.

This paper explores the use of factor graphs as an inference and analysis tool for Bayesian peer-to-peer decentralized data fusion. We propose a framework by which agents can each use local factor graphs to represent relevant partitions of a complex global joint probability distribution, thus allowing them to avoid reasoning over the entirety of a more complex model and saving communication as well as computation cost. This allows heterogeneous multi-robot systems to cooperate on a variety of real world, task oriented missions, where scalability and modularity are key. To develop the initial theory and analyze the limits of this approach, we focus our attention on static linear Gaussian systems in tree-structured networks and use Channel Filters (also represented by factor graphs) to explicitly track common information. We discuss how this representation can be used to describe various multi-robot applications and to design and analyze new heterogeneous data fusion algorithms. We validate our method in simulations of a multi-agent multi-target tracking and cooperative multi-agent mapping problems, and discuss the computation and communication gains of this approach.

The intricate detail is extremely complex and a patentable secret, but it's all about the use of algorithms that combine computational linguistics, contextual memory, deep learning. The AI parses words as input from voice or text from resources, so that it'understands' the relationship between words (as concepts or things) not just by simple keyword association (e.g. car transport) but by well-defined meta-labels, which refer to relationships between language, concepts, objects and questions from an infinite number of possible (and impossible) relationships. A limited set of relation types are used, using global identifiers with unambiguous denotations. This is combined with semantic'Extraction Transformation and Load' (ETL) processes from structured databases, forming strong associations and disassociations during the AI's training. An example of a Knowledge Graph (KG) to draw upon vast amounts of varied information might be a recommendation system for TV shows, movies, songs and albums from an online entertainment provider, to help find relationships between actors, artistes, titles and series.

Data fusion is a well-known technique, becoming more and more popular in the Artificial Intelligence for Earth Observation (AI4EO) domain mainly due to its ability of reinforcing AI4EO applications by combining multiple data sources and thus bringing better results. On the other hand, like other methods for satellite data analysis, data fusion itself is also benefiting and evolving thanks to the integration of Artificial Intelligence (AI). In this letter, four data fusion paradigms, based on Convolutional Neural Networks (CNNs), are analyzed and implemented. The goals are to provide a systematic procedure for choosing the best data fusion framework, resulting in the best classification results, once the basic structure for the CNN has been defined, and to help interested researchers in their work when data fusion applied to remote sensing is involved. The procedure has been validated for land-cover classification but it can be transferred to other cases.

Attention mechanism enables the Graph Neural Networks(GNNs) to learn the attention weights between the target node and its one-hop neighbors, the performance is further improved. However, the most existing GNNs are oriented to homogeneous graphs and each layer can only aggregate the information of one-hop neighbors. Stacking multi-layer networks will introduce a lot of noise and easily lead to over smoothing. We propose a Multi-hop Heterogeneous Neighborhood information Fusion graph representation learning method (MHNF). Specifically, we first propose a hybrid metapath autonomous extraction model to efficiently extract multi-hop hybrid neighbors. Then, we propose a hop-level heterogeneous Information aggregation model, which selectively aggregates different-hop neighborhood information within the same hybrid metapath. Finally, a hierarchical semantic attention fusion model (HSAF) is proposed, which can efficiently integrate different-hop and different-path neighborhood information respectively. This paper can solve the problem of aggregating the multi-hop neighborhood information and can learn hybrid metapaths for target task, reducing the limitation of manually specifying metapaths. In addition, HSAF can extract the internal node information of the metapaths and better integrate the semantic information of different levels. Experimental results on real datasets show that MHNF is superior to state-of-the-art methods in node classification and clustering tasks (10.94% - 69.09% and 11.58% - 394.93% relative improvement on average, respectively).

Learning meaningful representations of data that can address challenges such as batch effect correction, data integration and counterfactual inference is a central problem in many domains including computational biology. Adopting a Conditional VAE framework, we identify the mathematical principle that unites these challenges: learning a representation that is marginally independent of a condition variable. We therefore propose the Contrastive Mixture of Posteriors (CoMP) method that uses a novel misalignment penalty to enforce this independence. This penalty is defined in terms of mixtures of the variational posteriors themselves, unlike prior work which uses external discrepancy measures such as MMD to ensure independence in latent space. We show that CoMP has attractive theoretical properties compared to previous approaches, especially when there is complex global structure in latent space. We further demonstrate state of the art performance on a number of real-world problems, including the challenging tasks of aligning human tumour samples with cancer cell-lines and performing counterfactual inference on single-cell RNA sequencing data. Incidentally, we find parallels with the fair representation learning literature, and demonstrate CoMP has competitive performance in learning fair yet expressive latent representations.

Due to the growing awareness of driving safety and the development of sophisticated technologies, advanced driving assistance system (ADAS) has been equipped in more and more vehicles with higher accuracy and lower price. The latest progress in this field has called for a review to sum up the conventional knowledge of ADAS, the state-of-the-art researches, and novel applications in real-world. With the help of this kind of review, newcomers in this field can get basic knowledge easier and other researchers may be inspired with potential future development possibility. This paper makes a general introduction about ADAS by analyzing its hardware support and computation algorithms. Different types of perception sensors are introduced from their interior feature classifications, installation positions, supporting ADAS functions, and pros and cons. The comparisons between different sensors are concluded and illustrated from their inherent characters and specific usages serving for each ADAS function. The current algorithms for ADAS functions are also collected and briefly presented in this paper from both traditional methods and novel ideas. Additionally, discussions about the definition of ADAS from different institutes are reviewed in this paper, and future approaches about ADAS in China are introduced in particular.

Scale's customers process millions of tasks through our APIs, and we're looking for a talented Analytics Engineer to build scalable solutions to support this growth. You will have widespread purview, with responsibility for understanding, mining, aggregating, and exposing data across the entire business to support timely and efficient decision-making and data exploration. You will also implement Scale's data warehouse, data mart, and business intelligence reporting environments, and help users transition their workflows to these systems. You will: Work with analytics, infrastructure, finance, and other business partners to drive the development of reporting and analytics platform Establish business intelligence best practices, and build pipelines that provide single-source-of-truth foundational accuracy Partner with operations and sales teams to automate manual workflows Continually improve ongoing data pipelines and simplify self-service support for business stakeholders Perform regular system audits to ensure complete and accurate reporting of data/metrics Design and build visualization dashboards to accelerate information-to-action at scale Ideally you'd have: 5 years of relevant work experience in a role requiring application of data modeling and analytic skills A clear passion for learning new BI skills and techniques independently and continuously Experience with ETL tools and building / maintaining a data warehouse Experience in designing and building data infrastructure/automated reporting tools (Tableau) Ability to create extensible and scalable data schema that lay the foundation for downstream analysis Advanced data analysis knowledge and experience, with strong SQL, and data mining skills Advanced knowledge and hands-on experience leveraging Python, and/or R to perform in-depth data analysis Fluent in written and spoken English Nice to haves: Experience in using highly scalable data engineering technologies such as AWS, Airflow, Dagster, DBT Experience in best practices in table partitioning/data sharding strategies and query optimizationAbout Us:At Scale, we believe that the transition from traditional software to AI is one of the most important shifts of our time. Our mission is to make that happen faster across every industry, and our team is transforming how machine learning can build innovative products.

Big data is a big deal. Spotting trends in data enables business leaders and entrepreneurs to make better decisions, improve team performance and increase revenue. Sales, customer and operations data can make a night-and-day difference for your business. The most efficient method for extracting data is a process called ETL. Short for "extract, transform, load," ETL tools pull data from the various platforms you use and prepare it for analysis.

While causal models are becoming one of the mainstays of machine learning, the problem of uncertainty quantification in causal inference remains challenging. In this paper, we study the causal data fusion problem, where datasets pertaining to multiple causal graphs are combined to estimate the average treatment effect of a target variable. As data arises from multiple sources and can vary in quality and quantity, principled uncertainty quantification becomes essential. To that end, we introduce Bayesian Interventional Mean Processes, a framework which combines ideas from probabilistic integration and kernel mean embeddings to represent interventional distributions in the reproducing kernel Hilbert space, while taking into account the uncertainty within each causal graph. To demonstrate the utility of our uncertainty estimation, we apply our method to the Causal Bayesian Optimisation task and show improvements over state-of-the-art methods.