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Labeled Data
Labeled data is a group of samples that have been tagged with one or more labels. Labeling typically takes a set of unlabeled data and augments each piece of it with informative tags. For example, a data label might indicate whether a photo contains a horse or a cow, which words were uttered in an audio recording, what type of action is being performed in a video, what the topic of a news article is, what the overall sentiment of a tweet is, or whether a dot in an X-ray is a tumor. Labels can be obtained by asking humans to make judgments about a given piece of unlabeled data. Labeled data is significantly more expensive to obtain than the raw unlabeled data. Crowdsourced labeled data In 2006 Fei-Fei Li, the co-director of the Stanford Human-Centered AI Institute, set out to improve the artificial intelligence models and algorithms for image recognition by significantly enlarging the training data. The researchers downloaded millions of images from the World Wide Web and a team o ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sample (statistics)
In statistics, quality assurance, and survey methodology, sampling is the selection of a subset (a statistical sample) of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt to collect samples that are representative of the population in question. Sampling has lower costs and faster data collection than measuring the entire population and can provide insights in cases where it is infeasible to measure an entire population. Each observation measures one or more properties (such as weight, location, colour or mass) of independent objects or individuals. In survey sampling, weights can be applied to the data to adjust for the sample design, particularly in stratified sampling. Results from probability theory and statistical theory are employed to guide the practice. In business and medical research, sampling is widely used for gathering information about a population. Acceptance sampling is used to determ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Outline Of Object Recognition
Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes and scales or even when they are translated or rotated. Objects can even be recognized when they are partially obstructed from view. This task is still a challenge for computer vision systems. Many approaches to the task have been implemented over multiple decades. Approaches based on CAD-like object models * Edge detection * Primal sketch * Marr, Mohan and Nevatia * Lowe * Olivier Faugeras Recognition by parts * Generalized cylinders (Thomas Binford) * Geons ( Irving Biederman) * Dickinson, Forsyth and Ponce Appearance-based methods * Use example images (called templates or exemplars) of the objects to perform recognition * Objects look different ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Joy Buolamwini
Joy Adowaa Buolamwini is a Ghanaian-American-Canadian computer scientist and digital activist based at the MIT Media Lab. Buolamwini introduces herself as a poet of code, daughter of art and science. She founded the Algorithmic Justice League, an organization that works to challenge bias in decision-making software, using art, advocacy, and research to highlight the social implications and harms of artificial intelligence (AI). Early life and education Buolamwini was born in Edmonton, Alberta, grew up in Mississippi and attended Cordova High School. At age 9, she was inspired by Kismet, the MIT robot, and taught herself XHTML, JavaScript and PHP. She was a competitive pole vaulter. As an undergraduate, Buolamwini studied computer science at Georgia Institute of Technology, where she researched health informatics. Buolamwini graduated as a Stamps President's Scholar from Georgia Tech in 2012, and was the youngest finalist of the Georgia Tech InVenture Prize in 2009. Buo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Facial Recognition System
A facial recognition system is a technology capable of matching a human face from a digital image or a video frame against a database of faces. Such a system is typically employed to authenticate users through ID verification services, and works by pinpointing and measuring facial features from a given image. Development began on similar systems in the 1960s, beginning as a form of computer application. Since their inception, facial recognition systems have seen wider uses in recent times on smartphones and in other forms of technology, such as robotics. Because computerized facial recognition involves the measurement of a human's physiological characteristics, facial recognition systems are categorized as biometrics. Although the accuracy of facial recognition systems as a biometric technology is lower than iris recognition and fingerprint recognition, it is widely adopted due to its contactless process. Facial recognition systems have been deployed in advanced human–c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Representative Sample
In statistics, quality assurance, and survey methodology, sampling is the selection of a subset (a statistical sample) of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt to collect samples that are representative of the population in question. Sampling has lower costs and faster data collection than measuring the entire population and can provide insights in cases where it is infeasible to measure an entire population. Each observation measures one or more properties (such as weight, location, colour or mass) of independent objects or individuals. In survey sampling, weights can be applied to the data to adjust for the sample design, particularly in stratified sampling. Results from probability theory and statistical theory are employed to guide the practice. In business and medical research, sampling is widely used for gathering information about a population. Acceptance sampling is used to determi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Predictive Model
Predictive modelling uses statistics to predict outcomes. Most often the event one wants to predict is in the future, but predictive modelling can be applied to any type of unknown event, regardless of when it occurred. For example, predictive models are often used to detect crimes and identify suspects, after the crime has taken place. In many cases the model is chosen on the basis of detection theory to try to guess the probability of an outcome given a set amount of input data, for example given an email determining how likely that it is spam. Models can use one or more classifiers in trying to determine the probability of a set of data belonging to another set. For example, a model might be used to determine whether an email is spam or "ham" (non-spam). Depending on definitional boundaries, predictive modelling is synonymous with, or largely overlapping with, the field of machine learning, as it is more commonly referred to in academic or research and development contexts. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Stack Overflow
In software, a stack overflow occurs if the call stack pointer exceeds the stack bound. The call stack may consist of a limited amount of address space, often determined at the start of the program. The size of the call stack depends on many factors, including the programming language, machine architecture, multi-threading, and amount of available memory. When a program attempts to use more space than is available on the call stack (that is, when it attempts to access memory beyond the call stack's bounds, which is essentially a buffer overflow), the stack is said to ''overflow'', typically resulting in a program crash. Causes Infinite recursion The most-common cause of stack overflow is excessively deep or infinite recursion, in which a function calls itself so many times that the space needed to store the variables and information associated with each call is more than can fit on the stack. [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fei-Fei Li
Fei-Fei Li (; born 1976) is a Chinese-American computer scientist who is known for establishing ImageNet, the dataset that enabled rapid advances in computer vision in the 2010s. She is the Sequoia Capital Professor of Computer Science at Stanford University. Li is a Co-Director of the Stanford Institute for Human-Centered Artificial Intelligence, and a Co-Director of the Stanford Vision and Learning Lab. She served as the director of the Stanford Artificial Intelligence Laboratory (SAIL) from 2013 to 2018. In 2017, she co-founded AI4ALL, a nonprofit organization working to increase diversity and inclusion in the field of artificial intelligence. Her research expertise includes artificial intelligence (AI), machine learning, deep learning, computer vision and cognitive neuroscience. Li was elected a member of the National Academy of Engineering (NAE) in 2020 for contributions in building large knowledge bases for machine learning and visual understanding. She is also a me ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Piece Work
Piece work (or piecework) is any type of employment in which a worker is paid a fixed piece rate for each unit produced or action performed, regardless of time. Context When paying a worker, employers can use various methods and combinations of methods. Some of the most prevalent methods are: paid a wage by the hour (known as "time work"); paid an annual salary; salary plus commission (common in sales jobs); base salary or hourly wages plus gratuities (common in service industries); salary plus a possible bonus (used for some managerial or executive positions); salary plus stock options (used for some executives and in start-ups and some high tech firms); salary pool systems; gainsharing (also known as "profit sharing"); paid by the piece – the number of things they make, or tasks they complete (known as ‘output work’); or paid in other ways (known as ‘unmeasured work’). Some industries where piece rate pay jobs are common are agricultural work, cable installation, ca ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Online Marketplace
An online marketplace (or online e-commerce marketplace) is a type of e-commerce website where product or service information is provided by multiple third parties. Online marketplaces are the primary type of multichannel ecommerce and can be a way to streamline the production process. In an online marketplace, consumer transactions are processed by the marketplace operator and then delivered and fulfilled by the participating retailers or wholesalers. These type of websites allow users to register and sell single items to many items for a "post-selling" fee. In general, because marketplaces aggregate products from a wide array of providers, selection is usually more wide, and availability is higher than in vendor-specific online retail stores. Since 2014 online marketplaces have become abundant. Some online marketplaces have a wide variety of general interest products that cater to almost all the needs of the consumers, others are consumer specific and cater to a particular segme ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
