In a method, a computer and a medical computer for automatic failure analysis in order to provide a cause of failure of the medical imaging apparatus during operation, input data are read into the computer that include raw data or image data, acquired by the imaging apparatus. A set of performance indicators in the input data is calculated by the computer. A trained neural network system is accessed with the calculated performance indicators, in order to provide result data that, in the case of a failure, identify a failure source.

Automatic failure detection in magnetic resonance apparatuses

Systems and methods for building a response for a machine learning-based dialogue agent includes implementing machine learning classifiers that predict slot segments of the utterance data based on an input of the utterance data; predict a slot classification label for each of the slot segments of the utterance data; computing a semantic vector value for each of the slot segments of the utterance data; assessing the semantic vector value of the slot segments of the utterance data against a multi-dimensional vector space of structured categories of dialogue, wherein the assessment includes: for each of a distinct structured categories of dialogue computing a similarity metric value; selecting one structured category of dialogue from the distinct structured categories of dialogue based on the computed similarity metric value for each of distinct structured categories; and producing a response to the utterance data.

Systems and methods for automatically categorizing unstructured data and improving a machine learning-based dialogue system

A set of vectors may be obtained. The vectors may be multi-dimensional vectors that are associated with and describe tokens from a first set of tokens from a corpus of sources. The description may be based in part on the relationship of the token to at least a portion of the remainder of the corpus. A set of sentiment scores may be obtained. The sentiment scores in the set of sentiment scores may describe a sentiment associated with a corresponding token that is described by a vector from the set of vectors. The set of vectors and the set of sentiment scores may be input into a pattern-recognizer pathway in a first neural network. A probability value of a potential future event may then be generated by the first neural network. The probability value may be based on the set of vectors and the set of sentiment scores.

Predictive neural network with sentiment data

Fantasy sports allow fans to manage a team of their favorite athletes and compete with friends. The fantasy platform aligns the real-world statistical performance of athletes to fantasy scoring and has steadily risen in popularity to an estimated 9.1 million players per month with 4.4 billion player card views on the ESPN Fantasy Football platform from 2018-2019. In parallel, the sports media community produces news stories, blogs, forum posts, tweets, videos, podcasts and opinion pieces that are both within and outside the context of fantasy sports. However, human fantasy football players can only analyze an average of 3.9 sources of information. Our work discusses the results of a machine learning pipeline to manage an ESPN Fantasy Football team. The use of trained statistical entity detectors and document2vector models applied to over 100,000 news sources and 2.3 million articles, videos and podcasts each day enables the system to comprehend natural language with an analogy test accuracy of 100% and keyword test accuracy of 80%. Deep learning feedforward neural networks provide player classifications such as if a player will be a bust, boom, play with a hidden injury or play meaningful touches with a cumulative 72% accuracy. Finally, a multiple regression ensemble uses the deep learning output and ESPN projection data to provide a point projection for each of the top 500+ fantasy football players in 2018. The point projection maintained a RMSE of 6.78 points. The best fit probability density function from a set of 24 is selected to visualize score spreads. Within the first 6 weeks of the product launch, the total number of users spent a cumulative time of over 4.6 years viewing our AI insights. The training data for our models was provided by a 2015 to 2016 web archive from Webhose, ESPN statistics, and Rotowire injury reports. We used 2017 fantasy football data as a test set.

Deep Artificial Intelligence for Fantasy Football Language Understanding.

Even skilled fantasy football managers can be disappointed by their mid-season rosters as some players inevitably fall short of draft day expectations. Team managers can quickly discover that their team has a low score ceiling even if they start their best active players. A novel and diverse combinatorial optimization system proposes high volume and unique player trades between complementary teams to balance trade fairness. Several algorithms create the valuation of each fantasy football player with an ensemble of computing models: Quantum Support Vector Classifier with Permutation Importance (QSVC-PI), Quantum Support Vector Classifier with Accumulated Local Effects (QSVC-ALE), Variational Quantum Circuit with Permutation Importance (VQC-PI), Hybrid Quantum Neural Network with Permutation Importance (HQNN-PI), eXtreme Gradient Boosting Classifier (XGB), and Subject Matter Expert (SME) rules. The valuation of each player is personalized based on league rules, roster, and selections. The cost of trading away a player is related to a team's roster, such as the depth at a position, slot count, and position importance. Teams are paired together for trading based on a cosine dissimilarity score so that teams can offset their strengths and weaknesses. A knapsack 0-1 algorithm computes outgoing players for each team. Postprocessors apply analytics and deep learning models to measure 6 different objective measures about each trade. Over the 2020 and 2021 National Football League (NFL) seasons, a group of 24 experts from IBM and ESPN evaluated trade quality through 10 Football Error Analysis Tool (FEAT) sessions. Our system started with 76.9% of high-quality trades and was deployed for the 2021 season with 97.3% of high-quality trades. To increase trade quantity, our quantum, classical, and rules-based computing have 100% trade uniqueness. We use Qiskit's quantum simulators throughout our work.

Large Scale Diverse Combinatorial Optimization: ESPN Fantasy Football Player Trades.

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Predictive neural network with sentiment data

Deep Artificial Intelligence for Fantasy Football Language Understanding.

Large Scale Diverse Combinatorial Optimization: ESPN Fantasy Football Player Trades.