查看更多>>摘要：This patent application has not been assigned to a company or institution.The following quote was obtained by the news editors from the background informa tion supplied by the inventors: “Aspects of the present invention relate general ly to machine learning models and, more particularly, to automatically categoriz ing machine learning models into risk tiers using a model-tiering machine learni ng model.“Machine learning models are used in several industry sectors, such as finance, health, telecom, etc.As the use of machine learning models continues to grow in business practices, the need for model risk management increases.Prior to depl oying a machine learning model in a production environment, model risk managemen t activities include categorizing the model according to a classification called a tier (also called a risk tier) and validating the model in accordance with ru les defined by or for the tier.Different tiers indicate different priorities fo r evaluating and deploying a model and may also indicate different levels of ris k or sensitive data.For example, model risk managers may use model risk tiers t o schedule validation activities and determine their scope.Even more specifical ly, a model’s risk tier may dictate the breadth, depth, priority, and frequency of validation activities, such as review of documentation, developmental data an d/or performance testing.Similarly, a model’s risk tier may dictate the extent of model validation testing, such as backtesting, benchmarking, sensitivity test ing, stability testing or others.
查看更多>>摘要：This patent application has not been assigned to a company or institution.The following quote was obtained by the news editors from the background informa tion supplied by the inventors: “A robotically enabled medical system is capable of performing a variety of medical procedures, including both minimally invasiv e procedures, such as laparoscopy, and non-invasive procedures, such as endoscop y (e.g., bronchoscopy, ureteroscopy, gastroscopy, etc.).“Such robotic medical systems may include robotic arms configured to control the movement of surgical tool(s) during a given medical procedure.In order to achi eve a desired pose of a surgical tool, a robotic arm may be placed into a partic ular pose during teleoperation.Some robotically enabled medical systems may inc lude an arm support (e.g., a bar) that is connected to respective bases of the r obotic arms and supports the robotic arms.”
查看更多>>摘要：The following quote was obtained by the news editors from the background informa tion supplied by the inventors: “The availability of an accurate model of the wo rking environment of a robot, i.e.the knowledge of the coordinates of physical or virtual surfaces which the robot should not touch or pass, is a prerequisite for collision-free motion planning in robotic systems.Conventionally, this envi ronmental model is derived from CAD models of the robotic system, used commonly for offline planning of the system, or is approximated using computer vision sys tems (e.g.RGBD cameras, or Lidar) mounted at/near the system.
查看更多>>摘要：Reporters obtained the following quote from the background information supplied by the inventors: “BACKGROUND ART “Robots including articulated arms have been gaining widespread use at manufactu ring sites, and the replacement of human labor by robots is in progress for task s that used to depend on human labor.On the other hand, introducing a robot inv olves not only bearing an introduction cost of the robot itself, but also prepar ing peripheral devices, including expensive jigs, according to the contents of t asks to construct a new production system that uses the robot, as well as learni ng how to operate a robot-specific teaching system.Unable to meet these conditi ons, many users such as companies that do not have a production technology divis ion, small and medium-sized companies, and the like stop short of introducing a robot.
查看更多>>摘要：The following quote was obtained by the news editors from the background informa tion supplied by the inventors: “Decision making is a problem-solving activity y ielding a solution deemed to be optimal or at least satisfactory.Decision-makin g problems usually involve the analysis of a finite set of alternatives describe d in terms of evaluative criteria to arrive at a solution.Typically, there does not exist a unique optimal solution for such problems and it is necessary to us e decision-makers’ preferences to differentiate between solutions.For example, in purchasing a car, cost, comfort, safety, and fuel economy may be some of the main criteria that are considered; however, it is unusual that the cheapest car is the most comfortable and the safest one.A solution to a decision-making prob lem may include ranking the alternatives in terms of how attractive they are to the decision-maker(s) when all the criteria are considered simultaneously, findi ng the best alternative, and/or determining the relative total priority of each alternative.”
查看更多>>摘要：News editors obtained the following quote from the background information suppli ed by the inventors: “There are known composite tanks for storing pressurized ga ses, generally comprising a cylindrical central portion and two end portions hav ing decreasing cross-section and rounded towards the outside, classically known as domes.In the case of type IV tanks, the tanks are formed by an inner shell, classically called a liner, defining a sealed storage chamber, and a reinforcing shell, surrounding the liner, which is obtained by filament winding of continuo us fibers around the liner, the fibers preferably being pre-impregnated with a r esin, generally thermosetting resin, each fiber being formed from a multitude of continuous filaments.
查看更多>>摘要：News editors obtained the following quote from the background information suppli ed by the inventors: “This invention relates generally to using artificial intel ligence (AI) to directly determine a patient specific treatment or management of a disease that will benefit that patient, rather than manual selection based on a diagnosis.Currently, autonomous AI systems use machine learning or other opt imization techniques to determine the diagnosis of a patient, and clinicians the n use this AI diagnosis, in addition to other relevant patient and population in formation, to subjectively determine patient specific management or prescribe a patient specific treatment (also referred to herein as an “intervention”).Thus, Autonomous AI diagnosis, while capable of determining a diagnosis without human oversight, still relies on a clinician to interpret the diagnosis in terms of t he patient’s entire case and then decide the intervention.However, this interme diate, subjective decision step is subject to high inter- and intra clinician va riability, temporal and other drift.Moreover, the interaction between artificia l intelligence and the clinician is variable with often unanticipated risks, and has been known to worsen rather than improve outcome.Moreover, obtaining the h ighest quality reference standard (‘ground truth’) to train autonomous AI models for producing a diagnosis can be ethically problematic, and expensive.Where th e reference standard is dependent on clinician expertise, such as subjective rea ding of images, the reference standard can be noisy.Instead, when clinical outc ome, (which combines the effects of both the accuracy of the diagnostic process as well as the precision of the treatment or management selection process) rathe r than the interim diagnosis can be used as reference standard for training the AI, this subjective, noisy step is eliminated, with the potential to have higher performance.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from bi orxiv.org: “Advancements in AI, particularly deep learning, have revolutionized protein fol ding modeling, offering insights into biological processes and accelerating drug discovery for protein misfolding diseases.“However, detecting misfolded proteins associated with neurodegenerative disorde rs, such as Alzheimer\’s, Parkinson\ ’s, ALS, and prion diseases, relies on Seed Amplification Assays (SAAs) analyzed through manual, time-consuming, and potentially inconsistent methods.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from bi orxiv.org: “The University of California–Irvine (UCI) Machine Learning (ML) Repository (UC IMLR) is consistently cited as one of the most popular dataset repositories, hos ting hundreds of high-impact datasets.“However, a significant portion, including 28.4% of the top 250, c annot be imported via the ucimlrepo package that is provided and recommended by the UCIMLR website.Instead, they are hosted as .zip files, containing nonstanda rd formats that are difficult to import without additional ad hoc processing.
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