AI Art Video

AI Art Video — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

SUPS

In computational neuroscience, SUPS (for Synaptic Updates Per Second) or formerly CUPS (Connections Updates Per Second) is a measure of a neuronal network performance, useful in fields of neuroscience, cognitive science, artificial intelligence, and computer science. == Computing == For a processor or computer designed to simulate a neural network SUPS is measured as the product of simulated neurons N {\displaystyle N} and average connectivity c {\displaystyle c} (synapses) per neuron per second: S U P S = c × N {\displaystyle SUPS=c\times N} Depending on the type of simulation it is usually equal to the total number of synapses simulated. In an "asynchronous" dynamic simulation if a neuron spikes at υ {\displaystyle \upsilon } Hz, the average rate of synaptic updates provoked by the activity of that neuron is υ c N {\displaystyle \upsilon cN} . In a synchronous simulation with step Δ t {\displaystyle \Delta t} the number of synaptic updates per second would be c N Δ t {\displaystyle {\frac {cN}{\Delta t}}} . As Δ t {\displaystyle \Delta t} has to be chosen much smaller than the average interval between two successive afferent spikes, which implies Δ t < 1 υ N {\displaystyle \Delta t<{\frac {1}{\upsilon N}}} , giving an average of synaptic updates equal to υ c N 2 {\displaystyle \upsilon cN^{2}} . Therefore, spike-driven synaptic dynamics leads to a linear scaling of computational complexity O(N) per neuron, compared with the O(N2) in the "synchronous" case. == Records == Developed in the 1980s Adaptive Solutions' CNAPS-1064 Digital Parallel Processor chip is a full neural network (NNW). It was designed as a coprocessor to a host and has 64 sub-processors arranged in a 1D array and operating in a SIMD mode. Each sub-processor can emulate one or more neurons and multiple chips can be grouped together. At 25 MHz it is capable of 1.28 GMAC. After the presentation of the RN-100 (12 MHz) single neuron chip at Seattle 1991 Ricoh developed the multi-neuron chip RN-200. It had 16 neurons and 16 synapses per neuron. The chip has on-chip learning ability using a proprietary backdrop algorithm. It came in a 257-pin PGA encapsulation and drew 3.0 W at a maximum. It was capable of 3 GCPS (1 GCPS at 32 MHz). In 1991–97, Siemens developed the MA-16 chip, SYNAPSE-1 and SYNAPSE-3 Neurocomputer. The MA-16 was a fast matrix-matrix multiplier that can be combined to form systolic arrays. It could process 4 patterns of 16 elements each (16-bit), with 16 neuron values (16-bit) at a rate of 800 MMAC or 400 MCPS at 50 MHz. The SYNAPSE3-PC PCI card contained 2 MA-16 with a peak performance of 2560 MOPS (1.28 GMAC); 7160 MOPS (3.58 GMAC) when using three boards. In 2013, the K computer was used to simulate a neural network of 1.73 billion neurons with a total of 10.4 trillion synapses (1% of the human brain). The simulation ran for 40 minutes to simulate 1 s of brain activity at a normal activity level (4.4 on average). The simulation required 1 Petabyte of storage.
Read more →
Luca Maria Gambardella

Luca Maria Gambardella (born 4 January 1962) is an Italian computer scientist and author. He is the former director of the Dalle Molle Institute for Artificial Intelligence Research in Lugano, in the Ticino canton of Switzerland. He is currently the prorector of Università della Svizzera italiana, where he directs the Master of Science in Artificial Intelligence degree course. Several of his papers have been extensively cited, with his collaborators including Marco Dorigo, with whom he has published papers on the application of ant colony optimization theory to the traveling salesman problem, and Jürgen Schmidhuber with whom he has published research on deep neural networks.. Beside working in research, Gambardella explores the potentials of AI applied for the generation of art. Some of his artistic installations received significant media coverage. As a novelist, the genres he approached broad from Bildungsroman of his first book "Sei vite" ("Six lives"), to romance of his second book "Il suono dell'alba" ("The sound of sunrise").
Read more →
Eurotra

Eurotra was a machine translation project established and funded by the European Commission from 1978 until 1992. == History == In 1976, the European Commission started using the commercially developed machine translation system SYSTRAN with a plan to make it work for further languages than originally developed for (Russian-English and English-French), which however turned out to be difficult. This and the potential in existing systems within European research center, led to the decision in 1978 to start the project Eurotra, first through a preparatory Eurotra Coordination Group. Four years later, the European Commission and coordination group gained the approval of the European Parliament. The goal of the project as to create machine translation system for the official languages of the European Community, which at the time were Danish, Dutch, German, English, French, Italian, later including Greek, Spanish and Portuguese. However, as time passed, expectations became tempered; "Fully Automatic High Quality Translation" was not a reasonably attainable goal. The true character of Eurotra was eventually acknowledged to be in fact pre-competitive research rather than prototype development. The project was motivated by one of the founding principles of the EU: that all citizens had the right to read any and all proceedings of the Commission in their own language. As more countries joined, this produced a combinatorial explosion in the number of language pairs involved, and the need to translate every paper, speech and even set of meeting minutes produced by the EU into the other eight languages meant that translation rapidly became the overwhelming component in the administrative budget. To solve this problem Eurotra was devised. The project was unusual in that rather than consisting of a single research team, it had member groups distributed around the member countries, organised along language rather than national lines (for example, groups in Leuven and Utrecht worked closely together), and the secretariat was based at the European Commission in Luxembourg. The actual design of the project was unusual as MT projects go. Older systems, such as SYSTRAN, were heavily dictionary-based, with minor support for rearranging word order. More recent systems have often worked on a probabilistic approach, based on parallel corpora. Eurotra addressed the constituent structure of the text to be translated, going through first a syntactic parse followed by a second parse to produce a dependency structure followed by a final parse with a third grammar to produce what was referred to internally as Intermediate Representation (IR). Since all three modules were implemented as Prolog programs, it would then in principle be possible to put this structure backwards through the corresponding modules for another language to produce a translated text in any of the other languages. However, in practice this was not in fact how language pairs were implemented. The first "live" translation occupied a 4Mb Microvax running Ultrix and C-Prolog for a complete weekend some time in early 1987. The sentence, translated from English into Danish, was "Japan makes computers". The main problem faced by the system was the generation of so-called "Parse Forests" - often a large number of different grammar rules could be applied to any particular phrase, producing hundreds, even thousands of (often identical) parse trees. This used up huge quantities of computer store, slowing the whole process down unnecessarily. While Eurotra never delivered a "working" MT system, the project made a far-reaching long-term impact on the nascent language industries in European member states, in particular among the southern countries of Greece, Italy, Spain, and Portugal. There is at least one commercial MT system (developed by an academic/commercial consortium in Denmark) derived from Eurotra technology.
Read more →
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.
Read more →
Blobotics

Blobotics is a term describing research into chemical-based computer processors based on ions rather than electrons. Andrew Adamatzky, a computer scientist at the University of the West of England, Bristol used the term in an article in New Scientist March 28, 2005 [1]. The aim is to create 'liquid logic gates' which would be 'infinitely reconfigurable and self-healing'. The process relies on the Belousov–Zhabotinsky reaction, a repeating cycle of three separate sets of reactions. Such a processor could form the basis of a robot which, using artificial sensors, interact with its surroundings in a way which mimics living creatures. The coining of the term was featured by ABC radio in Australia [2].
Read more →
Lenhart Schubert

Lenhart Karl Otto Schubert is a professor of Computer Science at the University of Rochester, as well as a member of the Center for Language Sciences and the Center for Computation and the Brain. Schubert is a prominent researcher in the field of common sense reasoning. == Biography == Schubert received his Ph.D. from the University of Toronto in 1970. He was on the faculty of the University of Alberta between 1973 and 1988 and joined the faculty at the University of Rochester in 1988. He was elected fellow of Association for Advancement of Artificial Intelligence in 1993 for "fundamental contributions in NLP, esp. in the formalization, representation, and practical implementation of non-first order concepts".
Read more →
How to Choose an AI Coding Assistant

Looking for the best AI coding assistant? An AI coding assistant 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 coding assistant 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.
Read more →
How to Choose an AI Photo Editor

In search of the best AI photo editor? An AI photo editor is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI photo editor slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
Read more →
Open Threat Exchange

Open Threat Exchange (OTX) is a crowd-sourced computer-security platform. It has more than 180,000 participants in 140 countries who share more than 19 million potential threats daily. It is free to use. Founded in 2012, OTX was created and is run by AlienVault (now AT&T Cybersecurity), a developer of commercial and open source solutions to manage cyber attacks. The collaborative threat exchange was created partly as a counterweight to criminal hackers successfully working together and sharing information about viruses, malware and other cyber attacks. == Components == OTX is cloud-hosted. Information sharing covers a wide range of security-related issues, including viruses, malware, intrusion detection and firewalls. Its automated tools cleanse, aggregate, validate and publish data shared by participants. The OTX platform validates the data, then strips the information identifying the participating contributor. In 2015, OTX 2.0 added a social network, enabling members to share, discuss and research security threats, including via a real-time threat feed. Users can share the IP addresses or websites from where attacks originated or look up specific threats to see if anyone has already left such information. Users can subscribe to a “Pulse,” an analysis of a specific threat, including data on IoC, impact, and the targeted software. Pulses can be exported as STIX, JSON, OpenloC, MAEC and CSV, and can be used to update local security products automatically. Users can up-vote and comment on specific pulses to assist others in identifying the most important threats. OTX combines social contributions with automated machine-to-machine tools that integrate with major security products such as firewalls and perimeter security hardware. The platform can read security reports in .pdf, .csv, .json and other open formats. Relevant information is extracted automatically, assisting IT professionals in analyzing data more readily. Specific OTX components include a dashboard with details about the top malicious IPs around the world and to check the status of specific IPs; notifications should an organization's IP or domain be found in a hacker forum, blacklist or be listed by OTX; and a feature to review log files to determine if there has been communication with known malicious IPs. In 2016, AlienVault released a new version of OTX, allowing participants to create private communities and discussion groups to share information on threats only within the group. The feature is intended to facilitate more in-depth discussions on specific threats, particular industries, and different regions worldwide. Threat data from groups can also be distributed to subscribers of managed service providers using OTX." == Technology == OTX is a large data platform that integrates natural language processing and machine learning. It uses these features to facilitate the collection and correlation of data from many sources, including third-party threat feeds, websites, external APIs and local agents. == Partners == In 2015, AlienVault partnered with Intel to coordinate real-time threat information on OTX. A similar deal with Hewlett Packard was announced the same year. == Competitors == Both Facebook and IBM have threat exchange platforms. The Facebook ThreatExchange is in beta and requires an application or invitation to join. IBM launched IBM X-Force Exchange in April 2015.
Read more →
AI Image Generators: Free vs Paid (2026)

Looking for the best AI image generator? An AI image 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 image 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.
Read more →
Eric Xing

Eric Poe Xing (Chinese: 邢波) is an American computer scientist who has been serving as president of Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) since January 2021. He is also a professor in the Carnegie Mellon University School of Computer Science where he founded the SAILING Lab in 2004, and is the co-founder of the AI companies Petuum and GenBio AI. Xing's research focuses on statistical machine learning, probabilistic graphical models, and systems for distributed machine learning. He was elected a Fellow of the Institute of Electrical and Electronics Engineers in 2019 for "contributions to machine learning algorithms and systems" and a Fellow of the Association for Computing Machinery in 2022 for "contributions to algorithms, architectures, and applications in machine learning." == Education == Xing earned a B.Sc. in physics from Tsinghua University in 1993, and an M.Sc. in computer science from Rutgers University in 1998. He earned a Ph.D. in molecular biology and biochemistry from Rutgers in 1999, supervised by molecular cancer researcher Chung S. Yang. His dissertation examined the inactivation of the Rb and p53 pathways in human esophageal squamous cell carcinoma. He earned a second Ph.D. in computer science from the University of California, Berkeley in 2004, supervised by Richard Karp, Michael I. Jordan, and Stuart J. Russell. His thesis applied probabilistic graphical models to motif identification and haplotype inference in genomic data. == Career == Xing joined Carnegie Mellon University (CMU) as a faculty member in 2004, where he created the Statistical Artificial Intelligence and Integrative Genomics (SAILING) Lab. He held visiting appointments from 2010 to 2011, serving as a visiting research professor at Facebook Inc. and as a visiting associate professor in the Department of Statistics at Stanford University. He served as co-Program Chair of the International Conference on Machine Learning (ICML) in 2014 and General Chair in 2019. Xing served as the founding director of CMU’s Center for Machine Learning and Health, established in 2015 as part of the Pittsburgh Health Data Alliance, a collaboration between CMU, the University of Pittsburgh, and the University of Pittsburgh Medical Center. In 2016, Xing co-founded Petuum Inc., a US-based startup. In 2017, Petuum raised $93 million in a round of venture funding from SoftBank. In 2018 Petuum was named a World Economic Forum Technology Pioneer. In 2019, Xing received the Carnegie Science Award for Startup Entrepreneurs in recognition of his leadership of Petuum. On 29 November 2020, Xing was appointed president of the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), with the appointment taking effect in January 2021. In 2024, Xing co-founded GenBio AI where he is chief scientist. The US-based startup, which he co-founded with David Baker, Ziv Bar-Joseph, Emma Lundberg, Le Song and Fred Hu, aims to create AI-driven digital organisms (AIDO) for the purposes of modeling medical treatments. Xing has overseen the launch of the MBZUAI Institute of Foundation Models (IFM), which focuses on research and development of large-scale foundation models. In 2025–2026, IFM released the open-source reasoning model K2 Think, which was covered internationally as part of the UAE’s push to develop domestically controlled (“sovereign”) AI capabilities. IFM presented PAN as a “world model” research project and demonstrated related systems publicly. MBZUAI also collaborated with G42 and Cerebras Systems on the Jais language model, an open-source Arabic–English large language model released in 2023, according to Reuters. == Awards and honors == Xing is a recipient of the National Science Foundation (NSF) Career Award and the Alfred P. Sloan Research Fellowship. Xing is an elected Fellow of the following institutes and associations: Association for the Advancement of Artificial Intelligence (AAAI) 2016 Institute of Electrical and Electronics Engineers (IEEE) 2019 for "contributions to machine learning algorithms and systems" American Statistical Association (ASA) 2022 Association for Computing Machinery (ACM) 2022 for "contributions to algorithms, architectures, and applications in machine learning" Institute of Mathematical Statistics (IMS) 2023 International Society for Computational Biology (ISCB) 2026 == Selected publications == Eric P. Xing; Michael I. Jordan; Stuart J. Russell; Andrew Y. Ng (2003). "Distance Metric Learning with Application to Clustering with Side-Information" (PDF). Advances in Neural Information Processing Systems 15. Advances in Neural Information Processing Systems. Wikidata Q77691192. Edoardo M. Airoldi; David M. Blei; Stephen E Fienberg; Eric P Xing (1 September 2008). "Mixed Membership Stochastic Blockmodels". Journal of Machine Learning Research. 9: 1981–2014. ISSN 1533-7928. PMC 3119541. PMID 21701698. Wikidata Q35058357. Eric P. Xing; Michael I. Jordan; Richard M. Karp (28 June 2001), Feature selection for high-dimensional genomic microarray data, vol. 18, pp. 601–608, Wikidata Q138678867 Xing EP; Karp RM (1 January 2001). "CLIFF: clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts". Bioinformatics. 17 Suppl 1: S306-15. doi:10.1093/BIOINFORMATICS/17.SUPPL_1.S306. ISSN 1367-4803. PMID 11473022. Wikidata Q30657299.
Read more →
Gato (DeepMind)

Gato is a deep neural network for a range of complex tasks that exhibits multimodality. It can perform tasks such as engaging in a dialogue, playing video games, controlling a robot arm to stack blocks, and more. == Overview == Gato was created by researchers at London-based AI firm DeepMind. It is a transformer, like GPT-3. According to MIT Technology Review, the system "learns multiple different tasks at the same time, which means it can switch between them without having to forget one skill before learning another" whereas "[t]he AI systems of today are called “narrow,” meaning they can only do a specific, restricted set of tasks such as generate text", and according to The Independent, it is a "'generalist agent' that can carry out a huge range of complex tasks, from stacking blocks to writing poetry". It uses supervised learning with 1.2B parameters. The technology has been described as "general purpose" artificial intelligence and a "step toward" artificial general intelligence.
Read more →
Qloo

Qloo (pronounced "clue") is a company that uses artificial intelligence (AI) to understand taste and cultural correlations. It provides companies with an application programming interface (API). It received funding from Leonardo DiCaprio, Elton John, Barry Sternlicht, Pierre Lagrange and others. Qloo establishes consumer preference correlations via machine learning across data spanning cultural domains including music, film, television, dining, nightlife, fashion, books, and travel. The recommender system uses AI to predict correlations for further applications. == History == Qloo was founded in 2012 by chief executive officer Alex Elias and chief operating officer Jay Alger. Qloo initially launched an app designed for consumers, allowing them to understand their own tastes and receive personalized recommendations. The company amassed several million users and built a large catalog of cultural entities and corresponding user sentiment. In 2012, Qloo raised $1.4 million in seed funding from investors including Cedric the Entertainer, and venture capital firm Kindler Capital. Qloo had a public beta release in November 2012 after its initial funding. In 2013, the company raised an additional $1.6 million from Cross Creek Pictures founding partner Tommy Thompson, and Samih Toukan and Hussam Khoury, founders of Maktoob, an Internet services company purchased by Yahoo! for $164 million in 2009. On November 14, 2013, a website and an iPhone app were announced. The company later released an Android app, and tablet versions, in mid-2014. In 2015, Twitter approached Qloo about powering personalized social feeds and targeted eCommerce ads on the platform based on what users were posting. Qloo developed an enterprise-grade API to support Twitter’s needs. Twitter ended up pivoting to enable brands to use the social platform for customer service and support, but Qloo was able to sell access to its cultural intelligence via API to many other enterprise clients, marking the official transition from a B2C company to a B2B company. In 2016, Qloo secured $4.5 million in venture capital investment. The $4.5 million was split between a number of investors, including Barry Sternlicht, Pierre Lagrange, and Leonardo DiCaprio. In July 2017, Qloo raised $6.5 million in funding rounds from AXA Strategic Ventures, and Elton John. Following the investment, the founders stated in an interview with Tech Crunch that they would use the investment to expand Qloo's database. They hoped the move would secure larger contracts with corporate clients. At the time, clients already included Fortune 500 companies such as Twitter, PepsiCo, and BMW. In 2019, the company announced that it had acquired cultural recommendation service TasteDive, with Alex Elias becoming chairman of TasteDive. In September 2019, Qloo was named among the Top 14 Artificial Intelligence APIs by ProgrammableWeb. In 2022, Qloo raised $15M in Series B funding from Eldridge and AXA Venture Partners, enabling the privacy-centric AI leader to expand its team of world-class data scientists, enrich its technology, and build on its sales channels in order to continue to offer premier insights into global consumer taste for Fortune 500 companies across the globe. Qloo was recognized as the "Best Decision Intelligence Company" at the 2023 AI Breakthrough Awards. Also in 2023, the company was awarded a Top Performer Award by SourceForge. As of 2024, Qloo is a three-time Inc. 5000 honoree: No. 360 (2022), No. 344 (2021), No. 187 (2020). Qloo raised $25 million Series C round on February 21, 2024. The round was led by AI Ventures with participation from AXA Venture Partners, Eldridge, and Moderne Ventures, allowing Qloo to address new commercial surface areas for Taste AI, including on-device learning and foundational models leveraging Qloo, as well as introduce self-service platform to make consumer and taste analytics available to small and mid-sized enterprises and individuals. Qloo also announced pursuing opportunistic M&A using its balance sheet along the lines of the TasteDive acquisition completed, which expanded Qloo's first-party data moat and corpus of cultural learning. This latest financing brought the total amount raised since the company's founding in 2012 to over $56 million. == Services and features == Qloo calls itself a cultural AI platform to provide real-time correlation data across domains of culture and entertainment including: film, music, television, dining, nightlife, fashion, books, and travel. Each category contains subcategories. Qloo’s knowledge of a user's taste in one category can be utilized to offer suggestions in other categories. Users then rate the suggestions, providing it with feedback for future suggestions. Qloo has partnerships with companies such as Expedia and iTunes. == Technology == Qloo’s Taste AI technology uses machine learning to decode and predict consumers’ interests, maintaining user anonymity. It is powered by 3.7 billion lifestyle entities (brands, music, film, TV, dining, nightlife, fashion, books, travel, and more) and trillions of anonymized consumer behavioral signals. Through AI, Qloo identifies patterns in these data signals, making predictions about how much interest a person or group has in a concept or thing. Central to Qloo’s technology are algorithms designed to detect and mitigate biases within datasets and models, allowing Qloo to assess the fairness of its AI systems with a focus on attributes such as age, gender, and race, enabling the company to fine-tune its AI models to align with their ethical standards. They also use visualization tools to probe the behavior of their AI models for conducting counterfactual analyses and for comparing the performances of the AI models across diverse demographic segments. Qloo’s Taste AI doesn’t collect or use any Personally Identifiable Information (PII). Instead, it derives recommendations for audience segments based on co-occurrences between lifestyle entities and anonymized behavioral signals. == Applications == Starbucks uses Qloo to create in-store music playlists tailored to specific neighborhoods. Hershey’s uses Qloo to customize the content of assorted candy bags. Michelin uses Qloo to serve recommendations in its Michelin Guide app. Netflix leverages Qloo’s technology to enhance merchandising by identifying actors who resonate with certain demographics. Qloo also works with PepsiCo, Samsung, The New York Mets, BuzzFeed, and Ticketmaster, Universal Music Group, and OOH advertising company JCDecaux.
Read more →
Top 10 AI Blog Writers Compared (2026)

Comparing the best AI blog writer? An AI blog writer 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 blog writer slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.
Read more →
The Best Free AI Paraphrasing Tool for Beginners

Trying to pick the best AI paraphrasing tool? An AI paraphrasing tool 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 paraphrasing tool slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.
Read more →