AI Chatbot Companion

AI Chatbot Companion — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Virtual Woman

Virtual Woman is a software program that has elements of a chatbot, virtual reality, artificial intelligence, a video game, and a virtual human. It claims to be the oldest form of virtual life in existence, as it has been distributed since the late 1980s. Recent releases of the program can update their intelligence by connecting online and downloading newer personalities and histories. == Program play == When Virtual Woman starts, the user is presented with a list of options and then may choose their Virtual Woman's ethnic type, personality, location, clothing, etc. or load a pre-built Virtual Woman from a Digital DNA file. Once the options are determined, the user is presented with a 3-D animated Virtual Woman of their selection and then can engage them in conversation, progressing in a manner similar to that of its predecessor, ELIZA and its successors, the chatbots. In most versions of Virtual Woman, this is done through the keyboard, but some versions also support voice input. == In popular culture == Software sales and usage statistics from private companies are difficult to verify. WinSite, an independent Internet shareware distribution site that does publish public download counts, has for some time now listed some version of Virtual Woman in their top three shareware downloads of all time with well over seven hundred thousand downloads. == Compadre == The group of beta testers and advisers for Virtual Woman are referred to as Compadre and have their own beta testing site and forum. == Criticisms == As Virtual Woman has developed the ability to conduct longer and more realistic interactions, particularly in recent beta releases, criticism has arisen that this may lead some users to social isolation, or to use the program as a substitute for real human interaction. However, these are criticisms that have been leveled at all video games and at the use of the Internet itself. == Release history == Versions of Virtual Woman with rough release dates and PC platforms for which they were designed: Virtual Woman (????) (DOS) Virtual Woman for Windows (1991) (Windows 3.0) Virtual Woman 95 (1995) (Windows 3X, Windows 95) Virtual Woman 98 (1998) (Windows 3X, Windows 95) Virtual Woman 2000 (2000) (Windows 95+) Virtual Woman Millennium (Windows 95, XP) Virtual Woman Net ( Windows XP/Vista specific)
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AI Art Generators Reviews: What Actually Works in 2026

Comparing the best AI art generator? An AI art generator is software that uses machine learning to help you get more done — it lowers the barrier so anyone can produce professional output. Privacy matters too: check whether your data trains the model and whether a no-log or enterprise tier is available. Whether you are a beginner or a pro, the right AI art generator slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
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Daniel J. Hulme

Daniel Hulme (born 21 February 1980) is a British businessman, investor, academic and commentator, working in the field of Artificial Intelligence (AI), applied technology and ethics. He is the CEO and founder of Satalia that exited to WPP plc in 2021 for a rumoured $100M where he is also Chief AI Officer. Hulme is also an angel investor in emerging technology companies. In 2024 Hulme co-founded Conscium, an AI Safety company which tests AI Agents and verifies that they do what they are supposed to do. It is also investigating whether AIs will soon become conscious, and how to test for that, and developing more efficient approaches to AI development using neuromorphic computing. Alongside building and scaling Satalia, Hulme was also a Co-Founding Director of Faculty (company) AI - previously ASI Data-Science. In 2026, Accenture announced it had agreed to acquire Faculty for $1bn. Hulme founded Satalia in 2008, a company that provides AI products and consultancy for governments and companies such as Tesco,DFS Furniture,PwC and the BBC. He received a masters and doctorate in AI from University College London (UCL), and is now their Computer Science Entrepreneur in residence, where he teaches how AI can be applied to solve business and social problems. After exiting Satalia to WPP plc Hulme took the dual role of Chief AI Officer at WPP where he is responsible for informing and coordinating AI across the group. In 2026 Hulme was elected as a Founding Fellow of the Academy for the Mathematical Sciences, in recognition of his contributions at the intersection of AI and applied mathematics. Hulme is an angel investor and also a frequent public speaker and writer on the topics of AI, ethics, technology, innovation, decentralization and organisational design. == Early life and education == Hulme was born in 1980. He grew up in the seaside town of Morecambe in north west England. After completing secondary school, Hulme moved to London to study at University College London. On completing his under graduate degree, Hulme stayed at UCL to complete a master's degree and then an EngD. All three degrees were in subjects related to AI. In 2009 Hulme was awarded a Kauffman Global Entrepreneur Scholarship, which saw him visit institutes in the United States to better understand their culture of innovation, and what UK business people could learn from it. This included a tour of Stanford, MIT, Berkeley and Harvard, along with a placement at Cisco Systems HQ in Silicon Valley. == Career == === Satalia === Hulme founded NPComplete Limited in 2007, and incorporated it in 2008, a few months before completing his PhD. NPComplete Limited trades as Satalia. The London-based company provides full-stack AI consultancy and products, helping organisations harness data science, machine learning and AI to solve complex problems, including real-time optimisation. NPComplete refers to mathematical NP-completeness, which describes a class of exponential problems in the field of computational complexity theory. The trading name of NPComplete, Satalia, is a portmanteau of SAT (Short for satisfiability, as in the Boolean satisfiability problem) and the Latin phrase Et alia. Satalia seeks to solve hard problems, in particular the class of exponentially hard problems found in academia and industry known as NP-hardness. In 2016, Satalia was the only UK company to appear in the Gartner Cool Vendors list for data science. In November 2019, City A.M. reported that Satalia was the 39th fastest growing tech firm in the UK, with three year growth at 886%. Satalia was acquired by WPP plc in August 2021 for a rumored $100,000,000, where Hulme was the majority shareholder. === Conscium === Conscium is the World's first commercial organisation dedicated to the understanding, verification and validation of conscious AI and its implications for developing safe, efficient neuromorphic models. Conscium is an AI safety company with three workstreams: AI agent verification. Verification of AI agents developed by third parties to ensure they are beneficial and not harmful. Development of neuromorphic systems. Neuromorphic computing refers to technologies that can process information more like a biological brain compared to existing approaches, making them far more adaptive, scalable and efficient than current AI. Research into artificial consciousness. This workstream is led by Mark Solms, Chair of Neuropsychology at the University of Cape Town. This research aims to better understand what consciousness in AI systems and machines would look like, and, if and when machines do reach consciousness, what the moral and ethical implications would be. Conscium was founded in 2024 in London by a team including Hulme, Ed Charvet, Calum Chace, Ted Lappas, and Panagiotis Repoussis. Conscium has recruited some of the world’s leading neuroscientists and computer scientists to its advisory board, including Anil Seth, Mark Solms, Karl J. Friston, Anthony Finkelstein, Benjamin Rosman, David Wood, Jonathan Shock, Megan Peters, Moran Cerf, Nicholas Humphrey, Nicky Clayton, Nikola Kasabov, Steve Furber, and Suzanne Livingston. Supported by these world-leading experts, Conscium is creating a neuromorphic computing lab to research and validate the capacity of machines to acquire consciousness, making them safer for humanity. Conscium has published an open letter warning of the risks of AI suffering if care is not taken to mitigate against that possibility when and if AI becomes conscious. Signatories of the letter included Stephen Fry, Karl Friston and Anthony Finkelstein. === The Partnership for Research Into Sentient Machines (PRISM) === Hulme is one of the founding partners of PRISM - The Partnership for Research Into Sentient Machines, a non-profit set up to help prepare society for a future with conscious, or seemingly conscious, artificial intelligence. === Academia === Hulme's master's degree topic was on simulating artificial life, where he used Evolutionary algorithm's to generate emergent intelligence in AI agent's with Artificial Neural Network brains. His PhD spanned modelling bumblebee brains and mathematical optimization. Hulme maintained his connection with UCL after completing his doctorate, staying on in various teaching positions. From 2014 to Oct 2019 he was the Director of UCL's Business analytics MSc, which dealt with the application of AI to government, social, and business problems. As of 2020, Hulme is UCL's (University College London) Entrepreneur-in-Residence. He is also a faculty member and lecturer at Singularity University, and a visiting lecturer at London School of Economics's Marshall Institute. === Public engagement === Hulme frequently speaks for TEDx, Google and at various other events. He specialises in Artificial Intelligence, Decentralization, Organisational Design, and Innovation. He has written numerous articles and contributed to several books, largely concerning AI, as well as applied technology and related ethical issues. In 2017, along with Elon Musk, Stuart J. Russell, Geoffrey Hinton and Demis Hassabis, Hulme was one of the 116 founders of robotics and AI companies to sign an open letter to the United Nations, warning against the use of AI in autonomous weapons. Hulme also consults with various companies, governments and other organisations, independently of Satalia.
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Markovian discrimination

Markovian discrimination is a class of spam filtering methods used in CRM114 and other spam filters to filter based on statistical patterns of transition probabilities between words or other lexical tokens in spam messages that would not be captured using simple bag-of-words naive Bayes spam filtering. == Markovian Discrimination vs. Bag-of-Words Discrimination == A bag-of-words model contains only a dictionary of legal words and their relative probabilities in spam and genuine messages. A Markovian model additionally includes the relative transition probabilities between words in spam and in genuine messages, where the relative transition probability is the likelihood that a given word will be written next, based on what the current word is. Put another way, a bag-of-words filter discriminates based on relative probabilities of single words alone regardless of phrase structure, while a Markovian word-based filter discriminates based on relative probabilities of either pairs of words, or, more commonly, short sequences of words. This allows the Markovian filter greater sensitivity to phrase structure. Neither naive Bayes nor Markovian filters are limited to the word level for tokenizing messages. They may also process letters, partial words, or phrases as tokens. In such cases, specific bag-of-words methods would correspond to general bag-of-tokens methods. Modelers can parameterize Markovian spam filters based on the relative probabilities of any such tokens' transitions appearing in spam or in legitimate messages. == Visible and Hidden Markov Models == There are two primary classes of Markov models, visible Markov models and hidden Markov models, which differ in whether the Markov chain generating token sequences is assumed to have its states fully determined by each generated token (the visible Markov models) or might also have additional state (the hidden Markov models). With a visible Markov model, each current token is modeled as if it contains the complete information about previous tokens of the message relevant to the probability of future tokens, whereas a hidden Markov model allows for more obscure conditional relationships. Since those more obscure conditional relationships are more typical of natural language messages including both genuine messages and spam, hidden Markov models are generally preferred over visible Markov models for spam filtering. Due to storage constraints, the most commonly employed model is a specific type of hidden Markov model known as a Markov random field, typically with a 'sliding window' or clique size ranging between four and six tokens.
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Sprite multiplexing

Sprite multiplexing is a computer graphics technique where additional sprites (moving images) can be drawn on the screen, beyond the nominal maximum. It is largely historical, applicable principally to older hardware, where limited resources (such as CPU speed and memory) meant only a relatively small number of sprites were supported. On the other hand, it is also true that without multiplexing, the sprite circuitry would be idle much of the time, and limited resources were wasted. == Description == The sprite multiplexing technique is based on the idea that while the hardware may only support a finite number of sprites, it is sometimes possible to re-use the same sprite "slots" more than once per frame or scan line. The program will first use the hardware to draw one or more sprite(s), as normal. Before the next frame (or next scanline) needs to be drawn, the software reprograms the hardware to display additional sprites, in other positions. For example, the Nintendo Entertainment System explicitly supports hardware sprite multiplexing, where it has 64 hardware sprites, but is only capable of rendering 8 of them per scanline. On the older Atari 2600, sprite multiplexing was not intentionally designed in, but programmers discovered they could reset the TIA graphics chip to draw additional sprites on the same scanline. The sprite multiplexing technique relies on the program being able to identify what part of the video screen is being drawn at the moment, or being triggered by the video hardware to run a subroutine at the crucial moment. The programmer must carefully consider the layout of the screen. If the video graphics hardware is not reprogrammed in time for the extra sprites to be displayed, they will not appear, or will be drawn incorrectly. Modern video graphics hardware typically does not use hardware sprites, since modern computer systems do not have the kind of limitations that sprite hardware is designed to circumvent. == Implementations == Systems that allow the programmer to employ the sprite multiplexing technique include: Atari 2600 Atari 8-bit computers Amiga Commodore 64 MSX Nintendo Entertainment System Super Nintendo Entertainment System Master System Sega Genesis/Mega Drive
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Bixby (software)

Bixby ( ) is a virtual assistant developed by Samsung Electronics that runs on various Samsung-branded appliances, primarily mobile devices but also some refrigerators televisions and PCs. It uses voice commands and a natural-language user interface to answer questions and perform tasks, while adapting to the users' preferences and behavior. Samsung first launched Bixby in 2017. Along with Bixby voice assistant, its other main component currently is Bixby Vision, a contextual and visual search augmented reality camera app. Formerly, the Bixby suite consisted of a number of other tools, but these have since been renamed, such as Bixby Routines (now Modes and Routines). == History == On 20 March 2017, Samsung announced the voice-powered digital assistant named "Bixby" as a replacement of the S Voice assistant. It was introduced alongside the Galaxy S8 and S8+ and the Galaxy Tab A (2017) during the Galaxy Unpacked 2017 event. Although released for these devices, it could also be sideloaded on older Galaxy devices running Android Nougat. Before the phone's release, the Bixby Button was reprogrammable and could be set to open other applications or assistants, such as Google Assistant. However, near the phone's release, this ability was removed with a firmware update. Remapping remained possible through third-party apps. Bixby was launched in Korean on 1 May 2017 (KST). Bixby Voice was intended to be made available in the US later that spring. However, Samsung postponed the release, as Bixby had issues understanding English. The English version was finally rolled out in July 2017, followed by a Chinese language version later that year. In October 2017, Samsung announced the release of Bixby 2.0 during its annual developer conference in San Francisco. The new version was rolled out across the company's line of connected products, including smartphones, TVs, and refrigerators. Third parties were allowed to develop applications for Bixby using the Samsung Developer Kit. In August 2018, Samsung announced the Bixby-integrated Galaxy Home smart speaker. In 2019, UX developers at Samsung stated that they intended to use AR Emoji avatars as a personified Bixby assistant. At SDC19, Samsung displayed the Galaxy Home Mini speaker, which also supported Bixby. Bixby 3.0 was released with One UI 3 at the start of 2021. With version 3.0, Home and Reminders features were separated from Bixby. In June 2021, screenshots surfaced for what some thought as a replacement for Bixby. The three-dimensional virtual assistant, Sam, was popular on social media, though it was not intended as a replacement for Bixby. Bixby launched for Microsoft Windows in October 2021, with distribution through the Microsoft Store. This version of Bixby was optimized for Samsung's Galaxy Book computers. Samsung launched an AI Bixby custom voice creator in 2023, allowing users to record their own voice commands. Most recently, in July 2024, Samsung confirmed that it plans to launch an upgraded version of Bixby later that year. This new Bixby would be powered by Samsung's proprietary large language model (LLM) technology, promising a significant boost to Bixby's capabilities with the help of generative AI. In January 2025, with the announcement of Galaxy S25 and the One UI 7 update, Bixby was no longer the default voice assistant, having been replaced by Google Gemini. Despite this, Bixby still continued to be developed and expanded by Samsung and was revamped at the same time with new AI capabilities. Samsung brought the "smarter" Bixby to Samsung televisions, allowing users to speak to their TV sets and control their homes with it. A visual refresh was planned for One UI 8.5. == Functionality == Bixby is a voice assistant developed by Samsung that provides device control, information retrieval, and task automation using voice input and artificial intelligence. It can answer contextual queries, adjust system settings, perform searches, and manage reminders or schedules. The service also personalizes responses by recognizing individual user voices. Bixby itself was also formerly called Bixby Voice to differentiate from other Bixby tools in the suite. === Bixby Vision === Bixby Vision is a visual recognition feature that analyzes images captured through the device camera and provides context-specific information or actions. It combines on-device processing with cloud-based AI resources to identify objects, detect text, and interpret scenes within supported applications. It comes pre-installed on Samsung Galaxy phones. It is considered to be the imaging component of Bixby. ==== Translate ==== Detects foreign text in the camera view and provides real-time translation by overlaying translated text on the preview. ==== Text ==== Uses optical character recognition(OCR) to extract printed or handwritten text for copying, searching, or sharing. ==== Discover ==== Identifies consumer products, fashion items, or furniture and retrieves visually similar items or related online information. ==== Wine ==== Recognizes wine labels and provides information such as variety, region of origin, average price, and reviews. ==== Scene Describer ==== Generates written and spoken descriptions of captured scenes, supporting accessibility for users with visual impairments. ==== Object Identifier ==== Identifies plants, animals, food items, or landmarks and displays corresponding names or classification details. ==== Text Reader ==== Converts detected text into spoken audio using text-to-speech functionality. ==== Color Detector ==== Identifies and names colors within the frame, displaying or reading the recognized color aloud. === Former Bixby tools === Bixby Home was a vertically scrolling home screen displaying cards of information such as weather, fitness activity, and smart home controls. It was renamed Samsung Daily with the release of One UI 2.1 in 2020, then replaced by Samsung Free in One UI 3.0. Samsung Free was eventually discontinued in some markets. Its successor, Samsung News, now functions as a news aggregation service with optional home-screen integration similar to Bixby Home. Bixby Routines was an automation feature that allowed users to create custom rules based on triggers such as time, location, or device conditions. Beginning with One UI 5.0, it was renamed Modes and Routines. Bixby Text Call, introduced in One UI 5.0 (2022) in select regions, enabled users to handle incoming calls via speech-to-text conversion and vice versa. It is now named simply Text Call and can be found in the Phone app settings. Bixby Touch allowed users to trigger context-aware actions by touching on-screen content. It analyzed images, text, and other visual elements displayed on the device and provided related options such as translation, image search, product lookup, or other content-based information. Several of its capabilities overlapped with, or were later superseded by, features offered through Bixby Vision. Other legacy components including Bixby Touch, Bixby Global Action, Bixby Dictation, and Bixby Wakeup, formed part of the early Bixby suite and have since been phased out, though exact discontinuation details vary by region. == Regions and languages == As of April 2018, Bixby is available in over 195 countries, but only in Korean, English (American), and Chinese (Mandarin). The limitation is that the models not intended for the Japanese market, like S10e, are not allowed to login to Bixby services from Japan; therefore Bixby becomes blocked. The choice of languages has since expanded: Samsung has deployed Bixby's voice command function in French, and on 20 February 2019 Samsung announced the addition of further languages: English (British), German, Italian and Spanish (Spain). On 22 February 2020, Samsung announced the addition of Portuguese (Brazil), for Galaxy S10 & Note10, in Beta, and later for other models. == Compatible devices == === Flagship series === Galaxy S series: All models since Galaxy S7 Galaxy Tab S: All models since Galaxy Tab S4 Galaxy Note: All models since Galaxy Note FE and Galaxy Note 8 Galaxy Z series: All models === Other series === Galaxy A Galaxy A6/A6+ (Bixby Home, Reminder and Vision) Galaxy A7 (2017) (available to users in South Korea only; Bixby Home and Reminder only) Galaxy A7 (2018) (Bixby Home, Reminder and Vision only) Galaxy A8 (2018) (including A8 Star; Bixby Home, Reminder and Vision only; S Voice used instead) Galaxy A8s (Bixby Home, Reminder and Vision only) Galaxy A9 (2018)/A9s/A9 Star Pro (including A9 Star and A9 Star Lite; Bixby Home, Reminder and Vision only; S Voice used instead) Galaxy A9 Pro (2019) (Bixby Home, Reminder and Vision only) Galaxy A20 (Bixby Home and Service) Galaxy A21s Galaxy A30s (Bixby Home, Vision, Reminder and Routines) Galaxy A40 (Bixby Home and Reminder) Galaxy A41 (Bixby Home, Vision, Routines and Reminder) Galaxy A50 (Bixby Home, Voice, Vision, Reminder and Routines) Galaxy A50s (Bixby Home, Voice, Vision, Reminder and Routines) G
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How to Choose an AI Logo Maker

Trying to pick the best AI logo maker? An AI logo maker is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI logo maker slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.
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The Best Free AI Content Generator for Beginners

Looking for the best AI content generator? An AI content generator is software that uses machine learning to help you get more done — it can save you hours every week by automating repetitive work. Most options offer a generous free tier, with paid plans unlocking higher limits, faster processing, and team features. Whether you are a beginner or a pro, the right AI content generator slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.
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Multimodal representation learning

Multimodal representation learning is a subfield of representation learning focused on integrating and interpreting information from different modalities, such as text, images, audio, or video, by projecting them into a shared latent space. This allows for semantically similar content across modalities to be mapped to nearby points within that space, facilitating a unified understanding of diverse data types. By automatically learning meaningful features from each modality and capturing their inter-modal relationships, multimodal representation learning enables a unified representation that enhances performance in cross-media analysis tasks such as video classification, event detection, and sentiment analysis. It also supports cross-modal retrieval and translation, including image captioning, video description, and text-to-image synthesis. == Motivation == The primary motivations for multimodal representation learning arise from the inherent nature of real-world data and the limitations of unimodal approaches. Since multimodal data offers complementary and supplementary information about an object or event from different perspectives, it is more informative than relying on a single modality. A key motivation is to narrow the heterogeneity gap that exists between different modalities by projecting their features into a shared semantic subspace. This allows semantically similar content across modalities to be represented by similar vectors, facilitating the understanding of relationships and correlations between them. Multimodal representation learning aims to leverage the unique information provided by each modality to achieve a more comprehensive and accurate understanding of concepts. These unified representations are crucial for improving performance in various cross-media analysis tasks such as video classification, event detection, and sentiment analysis. They also enable cross-modal retrieval, allowing users to search and retrieve content across different modalities. Additionally, it facilitates cross-modal translation, where information can be converted from one modality to another, as seen in applications like image captioning and text-to-image synthesis. The abundance of ubiquitous multimodal data in real-world applications, including understudied areas like healthcare, finance, and human-computer interaction (HCI), further motivates the development of effective multimodal representation learning techniques. == Approaches and methods == === Canonical-correlation analysis based methods === Canonical-correlation analysis (CCA) was first introduced in 1936 by Harold Hotelling and is a fundamental approach for multimodal learning. CCA aims to find linear relationships between two sets of variables. Given two data matrices X ∈ R n × p {\displaystyle X\in \mathbb {R} ^{n\times p}} and Y ∈ R n × q {\displaystyle Y\in \mathbb {R} ^{n\times q}} representing different modalities, CCA finds projection vectors w x ∈ R p {\displaystyle w_{x}\in \mathbb {R} ^{p}} and w y ∈ R q {\displaystyle w_{y}\in \mathbb {R} ^{q}} that maximizes the correlation between the projected variables: ρ = max w x , w y w x ⊤ Σ x y w y w x ⊤ Σ x x w x w y ⊤ Σ y y w y {\displaystyle \rho =\max _{w_{x},w_{y}}{\frac {w_{x}^{\top }\Sigma _{xy}w_{y}}{{\sqrt {w_{x}^{\top }\Sigma _{xx}w_{x}}}{\sqrt {w_{y}^{\top }\Sigma _{yy}w_{y}}}}}} such that Σ x x {\displaystyle \Sigma _{xx}} and Σ y y {\displaystyle \Sigma _{yy}} are the within-modality covariance matrices, and Σ x y {\displaystyle \Sigma _{xy}} is the between-modality covariance matrix. However, standard CCA is limited by its linearity, which led to the development of nonlinear extensions, such as kernel CCA and deep CCA. ==== Kernel CCA ==== Kernel canonical correlation analysis (KCCA) extends traditional CCA to capture nonlinear relationships between modalities by implicitly mapping the data into high dimensional feature spaces using kernel functions. Given kernel functions K x {\displaystyle K_{x}} and K y {\displaystyle K_{y}} with corresponding Gram matrices K x ∈ R n × n {\displaystyle K_{x}\in \mathbb {R} ^{n\times n}} and K y ∈ R n × n {\displaystyle K_{y}\in \mathbb {R} ^{n\times n}} , KCCA seeks coefficients α {\displaystyle \alpha } and β {\displaystyle \beta } that maximize: ρ = max α , β α ⊤ K x K y β α ⊤ K x 2 α β ⊤ K y 2 β {\displaystyle \rho =\max _{\alpha ,\beta }{\frac {\alpha ^{\top }K_{x}Ky\beta }{{\sqrt {\alpha ^{\top }K_{x}^{2}\alpha }}{\sqrt {\beta ^{\top }K_{y}^{2}\beta }}}}} To prevent overfitting, regularization terms are typically added, resulting in: ρ = max α , β α T K x K y β α T ( K x 2 + λ x K x ) α β T ( K y 2 + λ y K y ) β {\displaystyle \rho =\max _{\alpha ,\beta }{\frac {\alpha ^{T}K_{x}K_{y}\beta }{{\sqrt {\alpha ^{T}\left(K_{x}^{2}+\lambda _{x}K_{x}\right)\alpha }}{\sqrt {\;\beta ^{T}\left(K_{y}^{2}+\lambda _{y}K_{y}\right)\beta }}}}} where λ x {\displaystyle \lambda _{x}} and λ y {\displaystyle \lambda _{y}} are regularization parameters. KCCA has proven effective for tasks such as cross-modal retrieval and semantic analysis, though it faces computational challenges with large datasets due to its O ( n 2 ) {\displaystyle O(n^{2})} memory requirement for sorting kernel matrices. KCCA was proposed independently by several researchers. ==== Deep CCA ==== Deep canonical correlation analysis (DCCA), introduced in 2013, employs neural networks to learn nonlinear transformations for maximizing the correlation between modalities. DCCA uses separate neural networks f x {\displaystyle f_{x}} and f y {\displaystyle f_{y}} for each modality to transform the original data before applying CCA: max W x , W y , θ x , θ y corr ⁡ ( f x ( X ; θ x ) , f y ( Y ; θ y ) ) {\displaystyle \max _{W_{x},W_{y},\theta _{x},\theta _{y}}\operatorname {corr} \left(f_{x}(X;\theta _{x}),f_{y}(Y;\theta _{y})\right)} where θ x {\displaystyle \theta _{x}} and θ y {\displaystyle \theta _{y}} represent the parameters of the neural networks, and W x {\displaystyle W_{x}} and W y {\displaystyle W_{y}} are the CCA projection matrices. The correlation objective is computed as: corr ⁡ ( H x , H y ) = tr ⁡ ( T − 1 / 2 H x T H y S − 1 / 2 ) {\displaystyle \operatorname {corr} (H_{x},H_{y})=\operatorname {tr} \left(T^{-1/2}H_{x}^{T}H_{y}S^{-1/2}\right)} where H x = f x ( X ) {\displaystyle H_{x}=f_{x}(X)} and H y = f y ( Y ) {\displaystyle H_{y}=f_{y}(Y)} are the network outputs, T = H x T H x + r x I {\displaystyle T=H_{x}^{T}H_{x}+r_{x}I} , S = H y T H y + r y I {\displaystyle S=H_{y}^{T}H_{y}+r_{y}I} and r x , r y {\displaystyle r_{x},r_{y}} are the regularization parameters. DCCA overcomes the limitations of linear CCA and kernel CCA by learning complex nonlinear relationships while maintaining computational efficiency for large datasets through mini-batch optimization. === Graph-based methods === Graph-based approaches for multimodal representation learning leverage graph structure to model relationships between entities across different modalities. These methods typically represent each modality as a graph and then learn embedding that preserve cross-modal similarities, enabling more effective joint representation of heterogeneous data. One such method is cross-modal graph neural networks (CMGNNs) that extend traditional graph neural networks (GNNs) to handle data from multiple modalities by constructing graphs that capture both intra-modal and inter-modal relationships. These networks model interactions across modalities by representing them as nodes and their relationships as edges. Other graph-based methods include Probabilistic Graphical Models (PGMs) such as deep belief networks (DBN) and deep Boltzmann machines (DBM). These models can learn a joint representation across modalities, for instance, a multimodal DBN achieves this by adding a shared restricted Boltzmann Machine (RBM) hidden layer on top of modality-specific DBNs. Additionally, the structure of data in some domains like Human-Computer Interaction (HCI), such as the view hierarchy of app screens, can potentially be modeled using graph-like structures. The field of graph representation learning is also relevant, with ongoing progress in developing evaluation benchmarks. === Diffusion maps === Another set of methods relevant to multimodal representation learning are based on diffusion maps and their extensions to handle multiple modalities. ==== Multi-view diffusion maps ==== Multi-view diffusion maps address the challenge of achieving multi-view dimensionality reduction by effectively utilizing the availability of multiple views to extract a coherent low-dimensional representation of the data. The core idea is to exploit both the intrinsic relations within each view and the mutual relations between the different views, defining a cross-view model where a random walk process implicitly hops between objects in different views. A multi-view kernel matrix is constructed by combining these relations, defining a cross-view diffusion process and associ
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Distributional–relational database

A distributional–relational database, or word-vector database, is a database management system (DBMS) that uses distributional word-vector representations to enrich the semantics of structured data. As distributional word-vectors can be built automatically from large-scale corpora, this enrichment supports the construction of databases which can embed large-scale commonsense background knowledge into their operations. Distributional-Relational models can be applied to the construction of schema-agnostic databases (databases in which users can query the data without being aware of its schema), semantic search, schema-integration and inductive and abductive reasoning as well as different applications in which a semantically flexible knowledge representation model is needed. The main advantage of distributional–relational models over purely logical or semantic web models is the fact that the core semantic associations can be automatically captured from corpora, in contrast to the definition of manually curated ontologies and rule knowledge bases. == Distributional–relational models == Distributional–relational models were first formalized as a mechanism to cope with the vocabulary/semantic gap between users and the schema behind the data. In this scenario, distributional semantic relatedness measures, combined with semantic pivoting heuristics can support the approximation between user queries (expressed in their own vocabulary), and data (expressed in the vocabulary of the designer). In this model, the database symbols (entities and relations) are embedded into a distributional semantic space and have a geometric interpretation under a latent or explicit semantic space. The geometric aspect supports the semantic approximation between entities from different databases, or between a query term and a database entity. The distributional relational model then becomes a double layered model where the semantics of the structured data provides the fine-grained semantics intended by the database designer, which is extended by the distributional semantic model which contains the semantic associations expressed at a broader use. These models support the generalization from a closed communication scenario (in which database designers and users live in the same context, e.g. the same organization) to an open communication scenario (e.g. different organizations, the Web), creating an abstraction layer between users and the specific representation of the conceptual model.
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Bin Yang

Bin Yang (Chinese: 杨彬; Pinyin: Yáng Bīn) is a professor of computer science the department of computer science, Aalborg University. His research interests include data management and machine learning. == Education and career == Bin Yang received his bachelor and master degrees from Northwestern Polytechnical University, China in 2004 and 2007, respectively, and his Ph.D. from Fudan University in China in 2010. From 2010 to 2011, he worked at the Databases and Information Systems department at Max-Planck-Institut für Informatik in Germany. From 2011 to 2014, he was employed at the department of computer science, Aarhus University. He has been employed at Aalborg University since 2014. At the present moment, he works on a number of different projects: Time Series Analytics and Spatio-temporal Data Management, funded by Huawei, 2020 - 2022. Light-AI for Cognitive Power Electronics, funded by Villum Synergy Programme, 2020 - 2022. Advance: A Data-Intensive Paradigm for Dynamic, Uncertain Networks, funded by Independent Research Fund Denmark, 2019 - 2023. Algorithmic Foundations for Data-Intensive Routing, funded by The Danish Agency for Science and Higher Education, 2019 - 2021. Astra: AnalyticS of Time seRies in spAtial networks, funded by Independent Research Fund Denmark, 2018 - 2021. Distinguished Scholar, funded by The Technical Faculty of IT and Design, Aalborg University, 2018 - 2021. == Awards == Bin Yang has received a series of awards throughout his career: Sapere Aude Research Leader, Independent Research Fund Denmark, 2018. Distinguished Scholar, The Technical Faculty of IT and Design, Aalborg University, 2018. Early Career Distinguished Lecturer, 20th IEEE International Conference on Mobile Data Management (MDM), 2019. Distinguished Program Committee Member, 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019 Best paper award at IEEE 14th International Conference on Mobile Data Management (MDM2013), Milan, Italy Best demo award at IEEE 14th International Conference on Mobile Data Management (MDM2013), Milan, Italy 2015 best paper in Pervasive and Embedded Computing, Shanghai Computer Academy == Selected publications == Sean Bin Yang, Chenjuan Guo, Jilin Hu, Jian Tang, and Bin Yang. Unsupervised Path Representation Learning with Curriculum Negative Sampling. IJCAI 2021. Razvan-Gabriel Cirstea, Tung Kieu, Chenjuan Guo, Bin Yang, and Sinno Jialin Pan. EnhanceNet: Plugin Neural Networks for Enhancing Correlated Time Series Forecasting. ICDE 2021. Sean Bin Yang, Chenjuan Guo, and Bin Yang. Context-Aware Path Ranking in Road Networks. TKDE 2021. Simon Aagaard Pedersen, Bin Yang, and Christian S. Jensen. Anytime Stochastic Routing with Hybrid Learning. PVLDB 13(9): 1555-1567 (2020). Tung Kieu, Bin Yang, Chenjuan Guo, and Christian S. Jensen. Outlier Detection for Time Series with Recurrent Autoencoder Ensembles. IJCAI 2019, 2725–2732. Jilin Hu, Chenjuan Guo, Bin Yang, and Christian S. Jensen. Stochastic Weight Completion for Road Networks using Graph Convolutional Networks. ICDE 2019, 1274–1285. Chenjuan Guo, Bin Yang, Jilin Hu, and Christian S. Jensen. Learning to Route with Sparse Trajectory Sets. ICDE 2018, 1073–1084. Bin Yang, Jian Dai, Chenjuan Guo, Christian S. Jensen, and Jilin Hu. PACE: A PAth-CEntric Paradigm For Stochastic Path Finding. The VLDB Journal 27(2): 153-178 (2018). Jian Dai, Bin Yang, Chenjuan Guo, and Zhiming Ding. Personalized Route Recommendation using Big Trajectory Data. ICDE 2015, 543–554, Seoul, Korea, April 2015. Bin Yang, Manohar Kaul, and Christian S. Jensen. Using Incomplete Information for Complete Weight Annotation of Road Networks. TKDE 26(5):1267-1279. Bin Yang, Chenjuan Guo, and Christian S. Jensen. Travel Cost Inference from Sparse, Spatio-Temporally Correlated Time Series Using Markov Models. PVLDB 6(9):769-780. VLDB 2013, Riva del Garda, Trento, Italy, August 2013.
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The Best Free AI Chatbot for Beginners

Trying to pick the best AI chatbot? An AI chatbot is software that uses machine learning to help you get more done — it scales effortlessly from a single task to thousands. The best picks balance beginner-friendly simplicity with the depth power users need, and they ship updates often. Whether you are a beginner or a pro, the right AI chatbot slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.
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Quantum robotics

Quantum robotics is an interdisciplinary field that investigates the intersection of robotics and quantum mechanics. This field, in particular, explores the applications of quantum phenomena such as quantum entanglement within the realm of robotics. Examples of its applications include quantum communication in multi-agent cooperative robotic scenarios, the use of quantum algorithms in performing robotics tasks, and the integration of quantum devices (e.g., quantum detectors) in robotic systems. == Introduction == The free-space quantum communication between mobile platforms was proposed for reconfigurable quantum key distribution (QKD) applications using unmanned aerial vehicle (UAVs, a.k.a. drones) in 2017. This technology was later advanced in various aspects in mobile drone and vehicle platforms in several configurations such as drone-to-drone, drone-to-moving vehicle, and vehicle-to-vehicle systems. Some research has contributed to low-size, low-weight, and low-power quantum key distribution systems for small-form UAVs, the characterization of a polarization-based receiver for mobile free-space optical QKD, and optical-relayed entanglement distribution using drones as mobile nodes. The topic of free-space quantum communication between mobile platforms, initially developed to meet the need for free-space QKD and entanglement distribution using mobile nodes, was brought into the robotics domain as an emerging interdisciplinary mechatronics topic to investigate the interface between quantum technologies and the robotic systems domain. The main advantage of such integrated technology is the guaranteed security in communication between multi-agent and cooperative autonomous systems. Other advances are anticipated. == Quantum entanglement == According to quantum mechanics, entanglement occurs when more than one particle become connected. If the state of one particle changes then it will instantly change the state of other particles regardless of their distance. Entangled sensors do the same kind of work and achieve strong sensitivity. A group of quantum robots can measure magnetic fields, gravitational fields and other physical properties using entangled sensors with high rate of accuracy. Again the connection of one robot to other is increased (become strong) by quantum entanglement. == Quantum teleportation == Quantum teleportation is the transfer of quantum information (not physical objects). This is used in case of multi robot process. One robot is programmed with a complex quantum update. Then that robot can teleport that complex quantum information (the update) to other robots. This teleportation or communication is very secure because all the work is done in quantum state. == Kinematics == Quantum computing has been proposed as being optimal for calculating inverse kinematics values. == Alice and Bob robots == In the realm of quantum mechanics, the names Alice and Bob are frequently employed to illustrate various phenomena, protocols, and applications. These include their roles in QKD, quantum cryptography, entanglement, and teleportation. The terms "Alice Robot" and "Bob Robot" serve as analogous expressions that merge the concepts of Alice and Bob from quantum mechanics with mechatronic mobile platforms (such as robots, drones, and autonomous vehicles). For example, the Alice Robot functions as a transmitter platform that communicates with the Bob Robot, housing the receiving detectors.
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Devi Parikh

Devi Parikh is an American computer scientist. == Career == Parikh earned her PhD in Electrical and Computer Engineering at Carnegie Mellon University. She has served as a professor at Virginia Tech and Georgia Tech, and as of 2022 she is a research director at Meta. == Research == Parikh's research focuses on computer vision and natural language processing. In 2015, Parikh and her students at Virginia Tech worked on AI for Visual Question Answering (VQA). This technology allows users to ask questions about pictures, e.g. "Is this a vegetarian pizza?" Parikh's VQA dataset has been used to evaluate over 30 AI models. In 2017, Parikh published a conversational agent called ParlAI. In 2020, she developed an AI system that generates dance moves in sync with songs. In 2022, Parikh and a team at Meta developed Make-a-Video, a text-to-video AI model that is based on the diffusion algorithm. == Awards == 2017 IJCAI Computers and Thought Award 2011 ICCV Best-Paper Award ("Marr Prize")
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Is an AI Paraphrasing Tool Worth It in 2026?

Curious about the best AI paraphrasing tool? An AI paraphrasing tool is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right AI paraphrasing tool slots into your workflow and pays for itself fast. This guide breaks down the top picks, their pros and cons, and who each one is best for.
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