new For example, Descartes Labs collects huge volumes of geospatial data derived from satellites, aircraft, and other sources and uses AWS cloud technologies like AI/ML, and high-performance computing (HPC) to store, process, and rapidly analyze vast volumes of raw data, and deliver analysis to customers, which helps them make timely decisions about complex issues such as climate change, food security, and the protection of natural resources. — Geospatial World, 16h ago

new The Institute is developing a set of missions to sit under the Defence and National Security Grand Challenge, to be launched soon. These will focus on scientific areas such as multi-modal data and signals analysis, human machine teaming, behavioural analysis, cyber security, reinforcement learning, AI explainability, model security, privacy technology, and digital twins. — The Alan Turing Institute, 8h ago

new ..., Epinote deploys its workforce and technology in to companies to manage tasks which can not ordinarily be automated - arduous repetitive time consuming tasks. With its mix of human hands and technology Epinote project manages jobs like data annotation and enrichment for analytics and AI development, lead generation and validation, CRM management, customer acquisition, market research, analysis and monitoring, as well as support for internal processes, including document management. — Tech.eu, 9h ago

new Predictive analytics is a powerful tool that utilizes data to anticipate the future, from predicting customer behavior to forecasting market trends. By leveraging sophisticated statistical algorithms and machine learning techniques, this practice uses information from past events in order to identify meaningful patterns and correlations that can be used for innovative decision-making about what lies ahead. — martechexec.com, 7h ago

new Alternative data sources, such as social media activity, satellite imagery, and geolocation data, can give significant insights into customer behavior, market trends, and industry dynamics that traditional data sources cannot easily capture. Yet, interpreting this data can be difficult since it is frequently unstructured and requires specialized techniques to extract valuable insights. This is where machine learning can play a crucial role. — IoT Worlds, 1d ago

new Data science is now being leveraged to help disentangle revenue and yield management, which has long been a tricky issue for the airline sector. Other operational areas that would benefit from intelligent automation include crew scheduling (recall the Southwest Airlines meltdown at the beginning of January); maintenance, repair and operations (MRO); fuel optimization and even mapping routes and destinations. Next-generation talent platforms and ecosystems that leverage cloud, AI and big data analytics can smartly connect enterprises with a specialized, globally accessible workforce. — Hospitality Technology, 1d ago

new Data & Analytics is one of the most rapidly evolving areas of data management. From initiatives to democratize data for citizen users to enabling A.I. and intelligent decision making, the pressure to connect enterprise data and make it actionable has never been stronger. — stardog.com, 5h ago

new AI and Machine learning are becoming increasingly important in clinical trials, as they provide a way to analyze large amounts of data and make predictions about outcomes. From identifying potential trial participants to predicting patient responses to treatment, Machine Learning can help make clinical trials more efficient and effective. Additionally, these technologies can help automate routine tasks, freeing up time and resources that can be used to focus on other aspects of the trial. Some of the AI & ML applications in clinical trials include eProtocol Design, eCRF Design, Medical Coding, DB Creation, Data Analytics, CSR Automation, SDV, Site Selection, SDTM Mapping, RBM, Query Management and Chatbots. — Pubs - Clinical Research News Online, 9h ago

new There is the need for new approaches to data science and AI that appreciate inter-relations between drivers of health, and are informed by deep biomedical understanding, clinical expertise and wider societal and environmental context in which they relate. — The Alan Turing Institute, 8h ago

new IBM is one of the leading providers of IT and business services to clients in 175 countries. It operates through six business segments: cognitive solutions, technology services, cloud platforms, Global Business Services (GBS), systems, and global financing. IBM caters to various verticals, such as aerospace and defense, education, healthcare, oil and gas, automotive, electronics, insurance, retail and consumer products, banking and finance, energy and utilities, life sciences, telecommunications, media and entertainment, chemicals, government, manufacturing, travel and transportation, construction, and metals and mining. It offers the Watson IoT platform in the market, which provides comprehensive IoT services, such as connectivity, information management, real-time analytics, and risk and security management. The Watson IoT platform?provides support to the main IoT product, such as connecting, storing, analyzing, and managing data along with monitoring support through the Watson IoT platform dashboard. IBM provides the Cognitive IoT for Healthcare solution that focuses on analyzing data collected from patients or individuals. The data is also collected through the signals generated from sensors in everyday objects, environmental sensors, and wearables. — marketsandmarkets.com, 9h ago

new There are several advantages to business process automation (BPA), including improved accuracy and efficiency, but there are also a number of implementation difficulties. Implementing BPA can be hampered by a lack of knowledge for which operations to automate, employee opposition, and a learning curve associated with new automated systems. Using data and analytics can assist in overcoming these difficulties. Businesses can find patterns and trends that can be leveraged to automate specific processes by looking at historical data. This saves companies time and money and enables them to concentrate on more crucial things. Businesses can also leverage data to determine which operations should be automated first and assess the performance of BPA projects. Companies can overcome BPA-related obstacles and benefit from automating their processes using data and analytics. — ReadITQuik, 11h ago

new Looking into the future, the research team expect instrumentation advances will continue to increase the data throughput on temporal, spatial and spectral dimensions. They should provide more features on data structures, such as sparsity and correlation. Meanwhile, new computational methods can be harnessed to break the design space trade-offs and provide enriched chemical compositions for biomedical research. With rapid advances in computational optical microscopy, we expect more ideas to infiltrate CRS. — phys.org, 1d ago

new From the most basic data tasks, such as formatting, to the most complex, such as entity resolution or extraction, Numbers Station enables all information workers to leverage AI to transform data. With Numbers Station, data workers can connect to their data warehouse, use a conversational interface to prototype intelligent data transformations powered by proprietary foundation model technology, and then deploy their pipelines back into their data warehouse. Customers can apply these new automated pipelines to business use cases spanning any department managing heavy analytics. — FinSMEs, 1d ago

new Consider, for example, paper-based records. Historically, from drug discovery through to product release, a significant amount of time was spent interacting with systems, recording, transferring and sharing data within cumbersome manual, paper-based systems. Digitizing, automating and streamlining those processes makes that information available automatically and immediately to relevant stakeholders, enabling scientists, for example, to focus on actual science! In a market increasingly driven by positive patient outcomes, freeing human imagination, creativity and curiosity to advance innovation and discovery in medicine, science and technology is key to delivering advanced therapeutics that can tangibly improve patients’ lives. — Contract Pharma, 8h ago

new We partnered with Exo-Space, an innovative Edge AI company, to leverage their FeatherEdge AI platform to provide near real time intelligence derived from Earth observation data. Processing will occur in space prior to transmitting data back to Earth, and will enable our constellation to learn to prioritize the retrieval of useful intelligence so we can deliver efficient actionable insights for critical data driven decisions. We entered into an agreement with SkyWatch, an industry leader in space intelligence platforms, to provide end-to-end data management and distribution capabilities. We announced the successful teaming with Collins Aerospace on the 10 year exploration extra vehicular activity services contract and began manufacturing hardware and supported the program. — Insider Monkey, 1d ago

new ...is currently leading the overall delivery of cloud-based data analytics products in the life sciences clinical domain at Saama. She has a proven track record of account management and delivery of complex IT programs in the clinical data analytics space with expertise in custom implementation of clinical data platform projects in data analytics and AI/ML. — Analytics India Magazine, 17h ago

new With the help of new Internet of Things analytics technologies, machine learning could be combined with new algorithms, architectures, and data structures. Decentralized analytics architecture can help make IoT networks safe without sacrificing the ability to share knowledge. — Blockchain Magazine, 1d ago

new Data from numerous transactional systems, such as CRM (Customer Relationship Management), ERP (Enterprise Resource Planning), and SCM (Supply Chain Management) systems, has traditionally been loaded, extracted, and turned into data warehouses. The data is then methodically arranged and stored to make it more efficient for analysis and searches. Dimensional modeling techniques are used in data warehouses to assist organize the data into dimensions and hierarchies such as time, product, region, and customer. — MarTech Series, 1d ago

new Tasks that Epinote is already performing for their clients include, among other things, data annotation and enrichment for analytics and AI development, lead generation and validation, CRM management, customer acquisition, market research, analysis and monitoring, as well as support for internal processes, including document management. — EU-Startups, 14h ago

new These data lake frameworks help you store data more efficiently and enable applications to access your data faster. Unlike simpler data file formats such as Apache Parquet, CSV, and JSON, which can store big data, data lake frameworks organize distributed big data files into tabular structures that enable basic constructs of databases on data lakes. — Zephyrnet, 1d ago

new Advanced technologies like AI, Machine Learning, demand forecasting and predictive analytics can analyze complex data from both internal and external sources, resulting in precise predictions and valuable insights. — SAPinsider, 1d ago

new Concerted education and culture change are necessary but can be difficult to achieve because of entrenched ways of doing things and the high critical mass of technical expertise required for enterprise data science. Here are steps that CDAOs can take to develop in-house talent and improve data science and machine learning literacy:... — SiliconANGLE, 1d ago

new The past few years have been marked by a literal exponential increase in the number of publications with the words “machine learning,” “artificial intelligence,” and “deep learning” in their titles. These tools now pervade materials science workflows and have been integrated with experimental/computational automation to form autonomous research agents, capable of planning, executing, and analyzing entire scientific campaigns. Lurking beneath the surface truly amazing accomplishments are serious questions around trust, bias, reproducibility, and equity which will ultimately determine the overall adoption of AI and autonomy by the broader community. Here, I will speak to recent work done by our group to systematically (1) remove human bias from experimental data analysis, (2) identify and actively remediate bias in large datasets , and (3) foster and promote a community of equity and reproducibility within the materials AI sub-domain. Specific case studies will center around standard electrochemical impedance spectroscopy analysis, building stability model predictions for complex alloys from large theoretical datasets, and maximizing the amount of information extracted from imaging techniques. — Faculty of Applied Science & Engineering, 1d ago

new ..., that is used to do data wrangling. We had disparate data across the organization, and you can't get analytics or insights unless the data is square. It drove our data science business. In cybersecurity, we had... — Digital Insurance, 1d ago

new ChaosSearch primarily targets enterprises with large and growing volumes of cloud data that need to improve analytics efficiency and performance. It also focuses on digital-native companies that are just driving their business based on data. In all these cases, the teams that manage IT environments, such as DevOps, CloudOps and SecOps engineers, need to analyze high volumes of logs and demand fast and efficient log analytics to... — SiliconANGLE, 1d ago

new Using pioneering privacy enhancing technologies, PrivaSapien enables DPOs/ CISOs/ Data Analytics teams to visualise and mitigate privacy risk in data pipelines and data lakes so that data can be unlocked for business value creation through analytics and strategic business decision making. — YourStory.com, 1d ago

new What: The multidimensional subject of data science uses statistical and computational methods to extract knowledge and information from enormous and complex data sets. To address real-world problems, create predictions, and enhance decision-making, it combines a variety of approaches including data mining, machine learning, predictive modeling, and data visualization. — onlineprnews.com, 2d ago

new Perfios' core data platform has been built to aggregate and analyze both structured and unstructured data and provide vertical solutions combining both consented and public data for the BFSI space catering to their stringent Scale Performance, Security, and other SLA requirements. — prnewswire.co.uk, 1d ago

new About Cognitive SpaceCognitive Space is the market leader in AI/ML-driven, automated satellite mission operations. Cognitive Space enables evolution of satellite operations at scale through effective and dynamic constellation management. The company’s flagship CNTIENT® platform allows commercial space operators and government operation centers to optimize and tailor remote sensing mission planning, scale without disruption, and automate payload scheduling, link management, bus management, and prioritized tasking. More information is available at... — Cognitive Space, 2d ago

new Perfios’ core data platform has been built to aggregate and analyze both structured and unstructured data and provide vertical solutions combining both consented and public data for the BFSI space catering to their stringent Scale Performance, Security, and other SLA requirements. — TechNode Global, 2d ago

new Web scraping is used to scrape data from webpages automatically on a large scale. Web scraping is done to convert data in complex HTML structures to structured format such as a spreadsheet or database, and used for various purposes such as research, analysis, and automation. — CoinGenius, 1d ago

new As already indicated, the 4th Gen Intel® Xeon® Scalable Processors are well suited for artificial intelligence (AI) and machine learning (ML) applications. They handle complex computations effortlessly and at scale. The prevalence of such AI and ML applications is driven by the need for insights into customer behavior, market trends, product usage patterns, and more. Almost every area of the corporate decision-making process can now be addressed in this way. Additionally, and for the same technology reasons, these processors provide excellent support for high-performance computing (HPC) workloads, like simulations and scientific research projects, which require substantial computing resources. — 7wData, 2d ago

new In addition to visualizing and simulating scientific concepts, 3D modeling technology can also be used to explore scientific data in a more interactive way. Scientists often use data visualization tools to represent large sets of data, but these representations are often static and lack the ability to explore data in depth. 3D models, on the other hand, can be used to represent scientific data in a more interactive and dynamic way. These models allow students to explore scientific data in new and exciting ways, enabling them to discover patterns and relationships that might not be visible in traditional representations. — eLearning Industry, 1d ago

new You can use data analytics to improve the success of your store down the road. Many platforms allow you to integrate Google Analytics or other analytics solutions. — Zephyrnet, 2d ago

new ETL and ELT have their pros and cons, so businesses need to think carefully about their data processing needs before deciding which one to use. But ETL is better than ELT for complex data transformations, cleaning data from multiple sources, keeping track of the past, and automating tasks. With the right ETL tools and expertise, businesses can improve their data processing capabilities, automate data processing and integration, and accelerate time-to-insight, enabling them to stay ahead of the competition and meet customer needs. — dzone.com, 2d ago

new The Data Cloud integrates multiple workloads that have traditionally required numerous point products, allowing businesses to ingest, store, and analyze the data spread across their IT ecosystems. The platform also helps collaboration by facilitating the secure sharing of data, and it supports data science by facilitating that transformation of data for use cases like machine learning and advanced statistical analysis. Additionally, the Powered by Snowflake program allows organizations to build and operate data-driven applications in the Data Cloud. — The Motley Fool, 2d ago

new Marketers are turning to data management tools such as warehouses, lakes, and visualization solutions in order to discover powerful insights from their vast amounts of customer information. By using these innovative systems strategically for analysis purposes, marketers can craft more successful campaigns that target the right audiences with maximum impact. — martechexec.com, 2d ago