Machine Learning Sample Clauses
A Machine Learning clause defines the terms under which machine learning technologies, data, or outputs are used, shared, or developed within the context of an agreement. It typically addresses issues such as ownership of training data, rights to use machine learning models, and responsibilities for accuracy or bias in outputs. For example, it may specify whether one party can use data provided by another to train algorithms, or who retains rights to improvements made through machine learning processes. The core function of this clause is to clarify intellectual property rights and responsibilities related to machine learning, thereby reducing disputes and ensuring both parties understand how such technologies and data can be utilized.
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Machine Learning. The parties acknowledge that machine learning (‘ML’) is a subset of AI that enables machines to develop algorithms, including via deep learning (as defined below), based on statistical inferences drawn from patterns in submitted training data, including, but not limited to, diffusion models and large language models, for the purpose of performing tasks. Such tasks include, but are not limited to, predicting human behaviors, disseminating information and generating content.
Machine Learning. We introduce the essential concepts and terminology related to machine learning and its application to the cybersecurity domain, and more specifically, to malware detection.
Machine Learning. Usage Data and Customer Content may be used to develop, train, or enhance artificial intelligence or machine learning models that are part of Provider's products and services, including third-party components of the Product, and Customer authorizes Provider to process its Usage Data and Customer Content for such purposes. However, (a) Usage Data and Customer Content must be aggregated before it can be used for these purposes, and (b) Provider will use commercially reasonable efforts consistent with industry standard technology to de-identify Usage Data and Customer Content before such use. Nothing in this section will reduce or limit Provider's obligations regarding Personal Data that may be contained in Usage Data or Customer Content under Applicable Data Protection Laws. Due to the nature of artificial intelligence and machine learning, information generated by these features may be incorrect or inaccurate. Product features that include artificial intelligence or machine learning models are not human and are not a substitute for human oversight.
Machine Learning. Certain Subscription Content may include machine learning, which are taught and trained largely from Customer’s internal data sets. Therefore, the quality of the results and outputs of the machine learning portions of the Subscription Content (such as optimized price proposals and recommended store order quantities, etc.) (“Machine Learning Outputs”) is heavily reliant on the quality of the Customer Data.
Machine Learning. Customer acknowledges that a fundamental component of the Moveworks Product is the use of machine learning for the purpose of improving and providing Moveworks’ products and services. Notwithstanding anything to the contrary, Customer agrees that Moveworks is hereby granted the right to use (during and after the term hereof) IT and employee service helpdesk ticket information submitted hereunder to train its algorithms internally through machine learning techniques for such purpose.
Machine Learning. Hands-On for Developers and Technical Professionals. ▇▇▇▇ ▇▇▇▇▇ & Sons. ▇▇▇▇▇▇, ▇. ▇., ▇▇▇▇▇▇, K., ▇▇▇▇▇, J., ▇▇▇▇▇▇▇▇, T., & ▇▇▇▇▇▇, ▇. ▇. (2016). Implementation of Web-Based Autism Screening in an Urban Clinic. Clinical Pediatrics, 55(10), 927–934. ▇▇▇▇▇▇▇▇▇▇▇, ▇. ▇., ▇▇▇▇, J., Van Naarden ▇▇▇▇▇, K., Bilder, D., ▇▇▇▇▇▇▇, J., ▇▇▇▇▇▇▇▇▇▇▇, ▇. ▇., … Centers for Disease Control and Prevention (CDC). (2016). Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years--Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012. Morbidity and Mortality Weekly Report. Surveillance Summaries , 65(3), 1–23. ▇▇▇▇▇▇▇▇, ▇. ▇., Akshoomoff, N., & ▇▇▇▇▇▇▇, ▇. ▇. (2013). Diagnosis of autism spectrum disorders in 2-year-olds: a study of community practice. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 54(2), 178–185. ▇▇▇▇▇▇▇, A. M., Halladay, A. K., Shih, A., ▇▇▇▇▇, ▇. ▇., & ▇▇▇▇▇▇, G. (2014). Approaches to enhancing the early detection of autism spectrum disorders: a systematic review of the literature. Journal of the American Academy of Child and Adolescent Psychiatry, 53(2), 141–152. ▇▇▇▇ ▇▇▇▇▇, ▇▇▇▇ ▇. ▇▇▇▇, and ▇▇▇ ▇. ▇▇▇▇▇▇. (2016). Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques.” Data Mining: Practical Machine Learning Tools and Techniques. ▇▇▇▇ ▇▇▇▇▇▇▇. Retrieved from ▇▇▇▇://▇▇▇.▇▇.▇▇▇▇▇▇▇.▇▇.▇▇/ml/weka/Witten_et_al_2016_appendix.pdf Falkmer, T., ▇▇▇▇▇▇▇▇, K., Falkmer, M., & Horlin, C. (2013). Diagnostic procedures in autism spectrum disorders: a systematic literature review. European Child & Adolescent Psychiatry, 22(6), 329–340. ▇▇▇▇▇▇-▇▇▇▇▇▇, E., ▇▇▇▇▇▇, J., & Peacock, G. (2016). Whittling Down the Wait Time: Exploring Models to Minimize the Delay from Initial Concern to Diagnosis and Treatment of Autism Spectrum Disorder. Pediatric Clinics of North America, 63(5), 851–859. ▇▇▇▇▇▇▇▇▇▇, ▇. ▇., Bai, R., & ▇▇▇▇▇▇▇, A. M. (2013). Screening children for autism in an urban clinic using an electronic M-CHAT. Clinical Pediatrics, 52(1), 35–41. How Is Autism Diagnosed? (n.d.). Retrieved February 8, 2017, from ▇▇▇▇▇://▇▇▇.▇▇▇▇▇▇▇▇▇▇▇▇.▇▇▇/what-autism/diagnosis ▇▇▇▇▇▇▇, ▇. ▇., & ▇▇▇▇▇▇▇▇▇▇, ▇. ▇. (2004). Evaluation of reporting timeliness of public health surveillance systems for infectious diseases. BMC Public Health, 4, 29. ▇▇▇▇▇, M. (2017). CDC: Autism rates unchanged at 1 in 68 children. AAP News. Retrieved from ▇▇▇▇://▇▇▇.▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇.▇▇▇/news/2016/03/3...
Machine Learning. Client agrees and instructs that Heyday may use Conversational Data to develop and improve the capabilities of the Services and Heyday's machine-learning technologies, both during and after the term of the Agreement, provided that (i) such Conversational Data shall be anonymized so that no individual can be specifically identified; and (ii) such Conversational Data shall not be shared with any other client or customer.
Machine Learning. Machine learning is the process of learning patterns from available data to make predictions that generalize to “future unseen” data. It is generally divided into two major types: supervised and unsupervised learning. When labels are available for the dataset, a supervised learning approach is often used to learn how to predict these labels from the features provided. When labels are not available, an “unsupervised” approach is used, where there is no phenotype or outcome to predict, but a supposed underlying structure of the data is being discovered. A fundamental concept in machine learning is data separation and the quest for generalization. Before making any predictions, the data is divided into training and testing sets. The testing set, also known as the “held out” set, is used to test how generalizable the trained model would be if it were to be used on future unseen data. A simple example to illustrate this is polynomial fitting. Suppose we have two synthetically-generated random variables (X1 and X2), which when drawn in a scatter plot (with X1 and X2 being the two axes) have no underlying pattern. Given a polynomial fitting algorithm, it is possible to explain much of the variance of the data with a very high-degree polynomial, given enough training iterations. In other words, without restraint on model complexity, it is possible to explain almost any dataset to an arbitrary level of accuracy. This does not mean, of course, that the model will have any meaning or generalization, and indeed our high degree polynomial is very unlikely to be even close to accurate when it is applied to the testing data. This is known as model “overfitting”. There is a well-known trade-off between model fitting and generalization, and there almost always exists a “sweet spot” where the model fits the training data well enough to have any meaning, but is generalizable enough to allow for utility over future unseen data. Most of the machines learning algorithms require tuning of model “hyperparameters.” In regularized linear models, for example, it is necessary to determine how much to penalize the weights, and in neural networks, it is necessary to determine what network architecture and learning behavior, including the number of nodes per layer (width), the number of layers (depth), the learning rate, the type of non-linearity and the type of optimizer to use. If we were to tune these parameters on the testing set, we would be defeating the purpose of an independent,...
Machine Learning. Random Forest Machine Learning – Neural Networks
Machine Learning the End-User is entitled to access or use the Processing Blocks for the purpose of developing or training machine learning algorithms.