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) …
When it comes to dimensionality reduction, the Singular Value Decomposition (SVD) is a popular method in linear algebra for matrix factorization in machine learning. Such a method shrinks the space dimension from N-dimension to K-dimension (where K N) and reduces the number of features. SVD constructs a matrix with the row of users and columns of items and the elements are given by the users' ratings.
Many tasks in Machine Learning are setup as classification tasks. The name would imply you have to learn a classifier to solve such a task. However, there are many popular alternatives to solving classification tasks which do not involve training a classifier at all! For the purpose of illustrating these alternative methods, let's use a very simple, yet visual classification problem. Our dataset shall be a random image from the internet.
Tech advances coupled with AI can help insurers manage risk, improve underwriting and boost customer experience. In the wake of the pandemic, people have dramatically changed how they live, communicate, work and shop. COVID-19 has also changed how they interact with critical services such as healthcare and insurance. In a time of change and uncertainty, customers are seeking reassurance and easy transitions to the "new normal." Insurers that take advantage of the new data and customer insights this global digital shift has provided can better assess customer claims and applications, and deliver a better experience.
Computational learning theory, or statistical learning theory, refers to mathematical frameworks for quantifying learning tasks and algorithms. These are sub-fields of machine learning that a machine learning practitioner does not need to know in great depth in order to achieve good results on a wide range of problems. Nevertheless, it is a sub-field where having a high-level understanding of some of the more prominent methods may provide insight into the broader task of learning from data. In this post, you will discover a gentle introduction to computational learning theory for machine learning. A Gentle Introduction to Computational Learning Theory Photo by someone10x, some rights reserved.
Humans interact with each other through several means (e.g., voice, gestures, written text, facial expressions, etc.) and a natural human-machine interaction system should preserve the same modality. However, traditional Natural Language Processing (NLP) focuses on analyzing textual input to solve language understanding and reasoning tasks, and other modalities are only partially targeted. This workshop aims to be a forum for both academia and industry researchers where new and unfinished research in the area of Multi/Cross-Modal NLP can be discussed. In particular, the focus of this workshop are (i) studying how to bridge the gap between NLP on spoken and written language and (ii) exploring how NLU models can be empowered by jointly analyzing multiple input sources, including language (spoken or written), vision (gestures and expressions) and acoustic (paralingustic) modalities. All deadlines must be considered at 11.59pm GMT-12 (anywhere on Earth).
In a win for transparency, a state court judge ordered the California Department of Corrections and Rehabilitation (CDCR) to disclose records regarding the race and ethnicity of parole candidates. This is also a win for innovation, because the plaintiffs will use this data to build new technology in service of criminal justice reform and racial justice. In Voss v. CDCR, EFF represented a team of researchers (known as Project Recon) from Stanford University and University of Oregon who are attempting to study California parole suitability determinations using machine-learning models. This involves using automation to review over 50,000 parole hearing transcripts and identify various factors that influence parole determinations. Project Recon's ultimate goal is to develop an AI tool that can identify parole denials that may have been influenced by improper factors as potential candidates for reconsideration.