If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The Global Machine Learning as a Service (MLaaS) Market 2032 Industry Report is a professional and in-depth study on the current state of the Machine Learning as a Service (MLaaS) Market by QMI. The Machine Learning as a Service (MLaaS) Market is supposed to demonstrate a considerable growth during the forecast period of 2023 – 2032. The company profiles of all the key players and brands that are dominating the market have been given in this report. Their moves like product launches, joint ventures, mergers and acquisitions and the respective effect on the sales, import, export, revenue and CAGR values have been studied completely in the report. The scope of this Machine Learning as a Service (MLaaS) Market report can be expanded from market scenarios to comparative pricing between major players.
Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. GEE also provides access to the vast majority of freely available, public, multi-temporal RS data and offers free cloud-based computational power for geospatial data analysis. Artificial intelligence (AI) methods are a critical enabling technology to automating the interpretation of RS imagery, particularly on object-based domains, so the integration of AI methods into GEE represents a promising path towards operationalizing automated RS-based monitoring programs. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review.
Founded at Stanford University, Walaris develops autonomous AI-based solutions focused on enhancing situational awareness. Our AirScout software platform uses artificial intelligence, sensor fusion, and edge computing to detect, classify, and track unwanted drones in protected airspace. Our cutting-edge technology is utilized by commercial, government, and military users to increase airspace awareness in and around sensitive locations. Computer Vision Engineers at Walaris have great responsibility, designing and developing deep learning, neural networks architecture and computer vision algorithms to continuously bring innovative solutions. They work together in cross-functional teams to exceed the expectations of our customers.
As highlighted by the intensity of recent discussion in online forums, artificial intelligence (AI) is an emerging innovation which is likely to significantly impact on knowledge management (KM). Other emerging innovations such as big data and gamification have also been receiving considerable attention within the KM community. However, there are other emerging innovative concepts that should also be an important focus for KM practitioners, but these are not yet receiving the attention they deserve. These concepts can be seen in a paper1 presented by Johannes Schenk at the recent 56th Hawaii International Conference on System Sciences. This gap between research findings and practice reinforces the need for KM practitioners to use research evidence in their work.
Chesapeake Conservancy's data science team developed an artificial intelligence deep learning model for mapping wetlands, which resulted in 94% accuracy. This method for wetland mapping could deliver important outcomes for protecting and conserving wetlands. "We're happy to support this exciting project as it explores new methods for wetlands delineation using satellite imagery," said EPRI Principal Technical Leader Dr. Nalini Rao. "It has the potential to save natural resource managers time in the field by using a GIS tool right from their desks. Plus, it can help companies and the public manage impacts to wetlands as infrastructure builds are planned to meet decarbonization targets."
The Bosch Research and Technology Center North America with offices in Sunnyvale, California, Pittsburgh, Pennsylvania and Cambridge, Massachusetts is a part of the global Bosch Group (www.bosch.com), The Research and Technology Center North America (RTC-NA) is dedicated to providing technologies and system solutions for various Bosch business fields, primarily in the field of artificial intelligence (for example, human-assisted AI, natural language processing, robotics, 3D perception, and AI platform), energy technologies, internet technologies, circuit design, semiconductors and wireless, as well as advanced MEMS design. Our global research on human-machine intelligence focuses on Big Data Visual Analytics, Explainable AI (XAI), Audio Analytics, Natural Language Processing, Knowledge Engineering, XR/AR/MR, 3D Perception, and Cloud Robotics. We develop intelligent and trustworthy AIoT solutions to enable inspiring UX for Bosch products and services in application areas such as autonomous driving, driver assistance systems (ADAS), robotics, smart manufacturing, health care, smart home and building solutions. As a part of our global research unit, our Mixed Reality and Autonomous System group is responsible for shaping the future user experience of Bosch products by developing cutting-edge technologies and prototype systems in the field of mixed reality and robotics, including object detection, segmentation, tracking and pose estimation, 3D reconstruction and understanding, visual localization, sensor fusion, reinforcement learning and adaptive robot control.
Abstract:: NP-hard problems are not believed to be exactly solvable through general polynomial time algorithms. Hybrid quantum-classical algorithms to address such combinatorial problems have been of great interest in the past few years. Such algorithms are heuristic in nature and aim to obtain an approximate solution. Significant improvements in computational time and/or the ability to treat large problems are some of the principal promises of quantum computing in this regard. The hardware, however, is still in its infancy and the current Noisy Intermediate Scale Quantum (NISQ) computers are not able to optimize industrially relevant problems.
In algorithms, as in life, negativity can be a drag. Consider the problem of finding the shortest path between two points on a graph -- a network of nodes connected by links, or edges. Often, these edges aren't interchangeable: A graph could represent a road map on which some roads are slower than others or have higher tolls. Computer scientists account for these differences by pairing each edge with a "weight" that quantifies the cost of moving across that segment -- whether that cost represents time, money or something else. Since the 1970s, they've known how to find shortest paths essentially as fast as theoretically possible, assuming all weights are positive numbers. But on some graphs weights can be negative -- traveling along one segment can offset the cost of traversing another.
Over the last decade, machine learning has revolutionized entire areas of science ranging from drug discovery to autonomous driving, to medical diagnostics, to natural language processing and many others. Despite this impressive progress, it has become increasingly evident that modern machine learning models suffer from several issues which, if not resolved, could prevent their widespread adoption. Example challenges include lack of robustness guarantees to slight distribution shifts, reinforcing unfair bias present in training data, leakage of sensitive information through the model, and others. Addressing these issues by inventing new methods and tools for establishing that machine learning models enjoy certain desirable guarantees, is critical, especially for domains where safety and security are paramount. Indeed, over the last few years there has been substantial research progress in new techniques aiming to address the above issues with most work so far focusing on perturbations applied to inputs of the model.