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Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. Such machines may be called AIs.
In machine learning, a neural network is a model inspired by the structure and function of biological neural networks in animal brains.
Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Quick progress in the field of deep learning, beginning in 2010s, allowed neural networks to surpass many previous approaches in performance.
A facial recognition system is a technology potentially 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.
Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene.
An age verification system, also known as an age gate, is any technical system that externally verifies a person's age. These systems are used primarily to restrict access to content classified, either voluntarily or by local laws, as being inappropriate for users under a specific age, such as alcohol, tobacco, gambling, video games with objectionable content, pornography, or to remain in compliance with online privacy laws that regulate the collection of personal information from minors, such as COPPA in the United States.
Human image synthesis is technology that can be applied to make believable and even photorealistic renditions of human-likenesses, moving or still. It has effectively existed since the early 2000s. Many films using computer generated imagery have featured synthetic images of human-like characters digitally composited onto the real or other simulated film material. Towards the end of the 2010s deep learning artificial intelligence has been applied to synthesize images and video that look like humans, without need for human assistance, once the training phase has been completed, whereas the old school 7D-route required massive amounts of human work .
Private biometrics is a form of encrypted biometrics, also called privacy-preserving biometric authentication methods, in which the biometric payload is a one-way, homomorphically encrypted feature vector that is 0.05% the size of the original biometric template and can be searched with full accuracy, speed and privacy. The feature vector's homomorphic encryption allows search and match to be conducted in polynomial time on an encrypted dataset and the search result is returned as an encrypted match. One or more computing devices may use an encrypted feature vector to verify an individual person or identify an individual in a datastore without storing, sending or receiving plaintext biometric data within or between computing devices or any other entity. The purpose of private biometrics is to allow a person to be identified or authenticated while guaranteeing individual privacy and fundamental human rights by only operating on biometric data in the encrypted space. Some private biometrics including fingerprint authentication methods, face authentication methods, and identity-matching algorithms according to bodily features. Private biometrics are constantly evolving based on the changing nature of privacy needs, identity theft, and biotechnology.
The Facial Recognition Technology (FERET) program was a government-sponsored project that aimed to create a large, automatic face-recognition system for intelligence, security, and law enforcement purposes. The program began in 1993 under the combined leadership of Dr. Harry Wechsler at George Mason University (GMU) and Dr. Jonathon Phillips at the Army Research Laboratory (ARL) in Adelphi, Maryland and resulted in the development of the Facial Recognition Technology (FERET) database. The goal of the FERET program was to advance the field of face recognition technology by establishing a common database of facial imagery for researchers to use and setting a performance baseline for face-recognition algorithms.
Léon Bottou is a researcher best known for his work in machine learning and data compression. His work presents stochastic gradient descent as a fundamental learning algorithm. He is also one of the main creators of the DjVu image compression technology, and the maintainer of DjVuLibre, the open source implementation of DjVu. He is the original developer of the Lush programming language.
DeepFace is a deep learning facial recognition system created by a research group at Facebook. It identifies human faces in digital images. The program employs a nine-layer neural network with over 120 million connection weights and was trained on four million images uploaded by Facebook users. The Facebook Research team has stated that the DeepFace method reaches an accuracy of 97.35% ± 0.25% on Labeled Faces in the Wild (LFW) data set where human beings have 97.53%. This means that DeepFace is sometimes more successful than human beings. As a result of growing societal concerns Meta announced that it plans to shut down Facebook facial recognition system, deleting the face scan data of more than one billion users. This change will represent one of the largest shifts in facial recognition usage in the technology's history. Facebook planned to delete by December 2021 more than one billion facial recognition templates, which are digital scans of facial features. However, it did not plan to eliminate DeepFace which is the software that powers the facial recognition system. The company has also not ruled out incorporating facial recognition technology into future products, according to Meta spokesperson.
Emotion recognition is the process of identifying human emotion. People vary widely in their accuracy at recognizing the emotions of others. Use of technology to help people with emotion recognition is a relatively nascent research area. Generally, the technology works best if it uses multiple modalities in context. To date, the most work has been conducted on automating the recognition of facial expressions from video, spoken expressions from audio, written expressions from text, and physiology as measured by wearables.
This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.
Artificial empathy or computational empathy is the development of AI systems—such as companion robots or virtual agents—that can detect emotions and respond to them in an empathic way.
Artificial intelligence in healthcare is the application of artificial intelligence (AI) to copy human cognition in the analysis, presentation, and understanding of complex medical and health care data. It can also augment and exceed human capabilities by providing faster or new ways to diagnose, treat, or prevent disease. Using AI in healthcare has the potential improve predicting, diagnosing and treating diseases. Through machine learning algorithms and deep learning, AI can analyse large sets of clinical data and electronic health records and can help to diagnose the disease more quickly and precisely. In addition, AI is becoming more relevant in bringing culturally competent healthcare practices to the industry.
Beauty.AI is a mobile beauty pageant for humans and a contest for programmers developing algorithms for evaluating human appearance. The mobile app and website created by Youth Laboratories that uses artificial intelligence technology to evaluate people's external appearance through certain algorithms, such as symmetry, facial blemishes, wrinkles, estimated age and age appearance, and comparisons to actors and models.
Algorithmic bias describes systematic and repeatable errors in a computer system that create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm.
Deepfakes are images, videos, or audio which are edited or generated using artificial intelligence tools, and which may depict real or non-existent people. They are a type of synthetic media.
Virtual assistants are software technology that assist users complete various tasks. Well known virtual assistants include Amazon Alexa, and Siri, produced by Apple. Other companies, such as Google and Microsoft, also have virtual assistants. There are privacy issues concerning what information can go to the third party corporations that operate virtual assistants and how this data can potentially be used.
References
- ↑ Bekhouche, Salah Eddine; Benlamoudi, Azeddine; Dornaika, Fadi; Telli, Hichem; Bounab, Yazid (January 14, 2024). "Facial Age Estimation Using Multi-Stage Deep Neural Networks". Electronics . 13 (16): 3259. doi: 10.3390/electronics13163259 .
- ↑ "Contrôle de l'âge pour l'accès aux sites pornographiques" [Age control for access to pornographic sites]. Commission nationale de l'informatique et des libertés .
- ↑ "How facial age-estimation tech can help protect children's privacy for COPPA and beyond". International Association of Privacy Professionals .
- ↑ Punyani, Prachi; Gupta, Rashmi; Kumar, Ashwani (June 1, 2020). "Neural networks for facial age estimation: a survey on recent advances". Artificial Intelligence Review. 53 (5): 3299–3347. doi:10.1007/s10462-019-09765-w – via Springer Link.
- ↑ "Face Analysis Technology Evaluation (FATE) Age Estimation & Verification". National Institute of Standards and Technology . Retrieved 2024-10-14.
- ↑ "How facial age estimation is creating age-appropriate experiences". THINK Digital Partners. July 4, 2023.
- ↑ "Supermarket cameras to guess age of alcohol buyers". BBC News . 2022-02-02. Retrieved 2024-10-14.
- ↑ Hymas, Charles (2024-01-24). "AI face-scanning technology to be rolled out at supermarkets to check age of shoppers". The Telegraph . ISSN 0307-1235 . Retrieved 2024-10-14.