Tanagra is a free suite of machine learning software for research and academic purposes developed by Ricco Rakotomalala at the Lumière University Lyon 2, France. Tanagra supports several standard data mining tasks such as: Visualization, Descriptive statistics, Instance selection, feature selection, feature construction, regression, factor analysis, clustering, classification and association rule learning. Tanagra is an academic project. It is widely used in French-speaking universities. Tanagra is frequently used in real studies and in software comparison papers. == History == The development of Tanagra was started in June 2003. The first version was distributed in December 2003. Tanagra is the successor of Sipina, another free data mining tool which is intended only for supervised learning tasks (classification), especially the interactive and visual construction of decision trees. Sipina is still available online and is maintained. Tanagra is an "open source project" as every researcher can access the source code and add their own algorithms, as long as they agree and conform to the software distribution license. The main purpose of the Tanagra project is to give researchers and students a user-friendly data mining software, conforming to the present norms of the software development in this domain (especially in the design of its GUI and the way to use it), and allowing the analyzation of either real or synthetic data. From 2006, Ricco Rakotomalala made an important documentation effort. A large number of tutorials are published on a dedicated website. They describe the statistical and machine learning methods and their implementation with Tanagra on real case studies. The use of other free data mining tools on the same problems is also widely described. The comparison of the tools enables readers to understand the possible differences in the presentation of results. == Description == Tanagra works similarly to current data mining tools. The user can design visually a data mining process in a diagram. Each node is a statistical or machine learning technique, the connection between two nodes represents the data transfer. But unlike the majority of tools which are based on the workflow paradigm, Tanagra is very simplified. The treatments are represented in a tree diagram. The results are displayed in an HTML format. This makes it is easy to export the outputs in order to visualize the results in a browser. It is also possible to copy the result tables to a spreadsheet. Tanagra makes a good compromise between statistical approaches (e.g. parametric and nonparametric statistical tests), multivariate analysis methods (e.g. factor analysis, correspondence analysis, cluster analysis, regression) and machine learning techniques (e.g. neural network, support vector machine, decision trees, random forest).
Tiki Wiki CMS Groupware
Tiki Wiki CMS Groupware or simply Tiki, originally known as TikiWiki, is a free and open source Wiki-based content management system and online office suite written primarily in PHP and distributed under the GNU Lesser General Public License (LGPL-2.1-only) license. In addition to enabling websites and portals on the internet and on intranets and extranets, Tiki contains a number of collaboration features allowing it to operate as a Geospatial Content Management System (GeoCMS) and Groupware web application. Tiki includes all the basic features common to most CMSs such as the ability to register and maintain individual user accounts within a flexible and rich permission / privilege system, create and manage menus, RSS-feeds, customize page layout, perform logging, and administer the system. All administration tasks are accomplished through a browser-based user interface. Tiki features an all-in-one design, as opposed to a core+extensions model followed by other CMSs. This allows for future-proof upgrades (since all features are released together), but has the drawback of an extremely large codebase (more than 1,000,000 lines). Tiki can run on any computing platform that supports both a web server capable of running PHP 5 (including Apache HTTP Server, IIS, Lighttpd, Hiawatha, Cherokee, and nginx) and a MySQL/MariaDB database to store content and settings. == Major components == Tiki has four major categories of components: content creation and management tools, content organization tools and navigation aids, communication tools, and configuration and administration tools. These components enable administrators and users to create and manage content, as well as letting them communicate to others and configure sites. In addition, Tiki allows each user to choose from various visual themes. These themes are implemented using CSS and the open source Smarty template engine. Additional themes can be created by a Tiki administrator for branding or customization as well. == Internationalization == Tiki is an international project, supporting many languages. The default interface language in Tiki is English, but any language that can be encoded and displayed using the UTF-8 encoding can be supported. Translated strings can be included via an external language file, or by translating interface strings directly, through the database. As of 29 September 2005, Tiki had been fully translated into eight languages and reportedly 90% or more translated into another five languages, as well as partial translations for nine additional languages. Tiki also supports interactive translation of actual wiki pages and was the initial wiki engine used in the Cross Lingual Wiki Engine Project. This allows Tiki-based web sites to have translated content — not just the user interface. == Implementation == Tiki is developed primarily in PHP with some JavaScript code. It uses MySQL/MariaDB as a database. It will run on any server that provides PHP 5, including Apache and Microsoft's IIS. Tiki components make extensive use of other open source projects, including Zend Framework, Smarty, jQuery, HTML Purifier, FCKeditor, Raphaël, phpCAS, and Morcego. When used with Mapserver Tiki can become a Geospatial Content Management System. == Project team == Tiki is under active development by a large international community of over 300 developers and translators, and is one of the largest open-source teams in the world. Project members have donated the resources and bandwidth required to host the tiki.org website and various subdomains. The project members refer to this dependence on their own product as "eating their own dogfood", which they have been doing since the early days of the project. Tiki community members also participate in various related events such as WikiSym and the Libre Software Meeting. == History == Tiki has been hosted on SourceForge.net since its initial release (Release 0.9, named Spica) in October 2002. It was primarily the development of Luis Argerich (Buenos Aires, Argentina), Eduardo Polidor (São Paulo, Brazil), and Garland Foster (Green Bay, WI, United States). In July 2003, Tiki was named the SourceForge.net July 2003 Project of the Month. In late 2003, a fork of Tiki was used to create Bitweaver. In 2006, Tiki was named to CMS Report's Top 30 Web Applications. In 2008, Tiki was named to EContent magazine's Top 100 In 2009, Tiki adopted a six-month release cycle and announced the selection of a Long Term Support (LTS) version and the Tiki Software Community Association was formed as the legal steward for Tiki. The Tiki Software Association is a not-for-profit entity established in Canada. Previously, the entire project was run entirely by volunteers. In 2010, Tiki received Best of Open Source Software Applications Award (BOSSIE) from InfoWorld, in the Applications category. In 2011, Tiki was named to CMS Report's Top 30 Web Applications. In 2012, Tiki was named "Best Web Tool" by WebHostingSearch.com, and "People's Choice: Best Free CMS" by CMS Critic. In 2016, Tiki was named as one of the "10 Best Open Source Collaboration Software Tools" by Small Business Computing. == Name == The name TikiWiki is written in CamelCase, a common Wiki syntax indicating a hyperlink within the Wiki. It is most likely a compound word combining two Polynesian terms, Tiki and Wiki, to create a self-rhyming name similar to wikiwiki, a common variant of wiki. A backronym has also been formed for Tiki: Tightly Integrated Knowledge Infrastructure. == Release Information and History == In general, the Tiki Software Community Association releases a new major version of Tiki Wiki every 8 months where prior, non-LTS, major versions are supported until the first minor version release of the next major version (i.e., 16.0 ⇒ 17.1). Starting with version 12.x, Tiki Wiki LTS is supported for 5 years where it enters a security/maintenance release cycle upon the release of the next LTS version. Tiki Wiki's release history is outlined below.
IJCAI Computers and Thought Award
The IJCAI Computers and Thought Award is presented every two years by the International Joint Conference on Artificial Intelligence (IJCAI), recognizing outstanding young scientists in artificial intelligence. It was originally funded with royalties received from the book Computers and Thought (edited by Edward Feigenbaum and Julian Feldman), and is currently funded by IJCAI. It is considered to be "the premier award for artificial intelligence researchers under the age of 35". == Laureates == Terry Winograd (1971) Patrick Winston (1973) Chuck Rieger (1975) Douglas Lenat (1977) David Marr (1979) Gerald Sussman (1981) Tom Mitchell (1983) Hector Levesque (1985) Johan de Kleer (1987) Henry Kautz (1989) Rodney Brooks (1991) Martha E. Pollack (1991) Hiroaki Kitano (1993) Sarit Kraus (1995) Stuart Russell (1995) Leslie Kaelbling (1997) Nicholas Jennings (1999) Daphne Koller (2001) Tuomas Sandholm (2003) Peter Stone (2007) Carlos Guestrin (2009) Andrew Ng (2009) Vincent Conitzer (2011) Malte Helmert (2011) Kristen Grauman (2013) Ariel D. Procaccia (2015) Percy Liang (2016) for his contributions to both the approach of semantic parsing for natural language understanding and better methods for learning latent-variable models, sometimes with weak supervision, in machine learning. Devi Parikh (2017) Stefano Ermon (2018) Guy Van den Broeck (2019) for his contributions to statistical and relational artificial intelligence, and the study of tractability in learning and reasoning. Piotr Skowron (2020) for his contributions to computational social choice, and to the theory of committee elections. Fei Fang (2021) for her contributions to integrating machine learning with game theory and the use of these novel techniques to tackle societal challenges such as more effective deployment of security resources, enhancing environmental sustainability, and reducing food insecurity. Bo Li (2022) for her contributions to uncovering the underlying connections among robustness, privacy, and generalization in AI, showing how different models are vulnerable to malicious attacks, and how to eliminate these vulnerabilities using mathematical tools that provide robustness guarantees for learning models and privacy protection. Pin-Yu Chen (2023) for his contributions to consolidating properties of trust, robustness and safety into rigorous algorithmic procedures and computable metrics for improving AI systems. Nisarg Shah (2024) for his contributions to AI and society, in particular foundational work on the theory of algorithmic fairness using principles from social choice theory. Aditya Grover (2025) for his foundational contributions uniting deep generative models, representation learning, and reinforcement learning, and for their applications in advancing scientific reasoning.
Emi Kusano
Emi Kusano (Japanese: 草野 絵美, Hepburn: Kusano Emi; born August 4, 1990) is a Tokyobased Japanese multidisciplinary artist known for creating photography, video, and installations using generative AI technology. Her work explores themes of nostalgia, pop culture, and collective memory. Her work explores themes of nostalgia, pop culture, and collective memory. She is recognized as one of the early practitioners of generative AI art. Her work has been exhibited at the 21st Century Museum of Contemporary Art, Kanazawa, and screened at the M+ Museum’s Asian Avant-Garde Film Festival. Additionally, she has participated in prestigious international art fairs, including Paris Photo and Art Basel Hong Kong. In 2025, she was named one of the World Economic Forum's Young Global Leaders. In 2026, she was selected as a fellow for the AI x Arts Fellowship at Mohamed bin Zayed University of Artificial Intelligence. Kusano serves as a part-time lecturer at the Tokyo University of the Arts and is the producer and vocalist for the Synthwave music unit, Satellite Young. == Early life == === Photography === Kusano was born and raised in Tokyo. Kusano's career began during her high school years before 2008 when she became involved in street fashion photography. Her photographs, primarily taken in Harajuku, were published on "Japanese Streets", "Metropolis", CNN's travel guide magazine "CNN GO","WGSN". Her photography was exhibited at the FIT Museum in New York and the Victoria and Albert Museum in London. == Career == === Music and Installation work === Since 2014, in collaboration with BelleMaison Sekine, Kusano has led "Satellite Young," a synthwave music unit s the lead vocalist, she sings about blending 1980s idol culture with lyrics that tackle contemporary issues such as planned obsolescence ("Sony Timer"), online dating, artificial intelligence, and social media. Their music, known for its conceptual depth, has earned international niche recognition. "Satellite Young" has participated in music festivals, including "South by Southwest," showcasing their unique fusion of retro aesthetics and modern critiques. In 2018, she was selected to participate in "Art Hack Day," an interdisciplinary art hackathon held at The National Museum of Emerging Science and Innovation. where she presented "Singing Dream," a karaoke machine endowed with artificial life, earning the Jury Prize. "Instababy Generator," a 2019 installation co-created with Junichi Yamaoka, explored the concept of designer babies and received recognition at the SIGGRAPH Art Gallery. In October 2020, operating under the name Emi Satellite, she debuted as a solo singer with her first single "Glass Ceiling," an empowerment anthem that addresses the challenges faced by women and encourages progress towards the future. The music video for this song features a direction where strong women rewrite the roles of protagonists in a Bishōjo game, a type of dating simulation game. This concept later served as a prototype for Shinsei Galverse. === Challenge for Blockchain Art === In 2021, she explored the financial world through her single "IPO" and entered the NFT space with "Love Is an IPO," her first NFT work on Ethereum, sold on Foundation. In April 2022, she co-founded the crowdfunded anime project "Shinsei Galverse" with Ayaka Ohira, Devin Mancuso, and Jack Baldwin. serving as one of the executive directors overseeing the creative direction and story. The project's NFT collection of 8,888 ranked #1 on OpenSea's "Top NFTs" for several days, marking one of Japan's first globally successful blockchain art projects. In 2023, Shinsei Galverse produced the official "I like u" music video by Grammy-nominated singer Tove Lo as an initial anime endeavor. Kusano also contributed to discussions on Web3.0 and blockchain technology as a panelist in seminars organized by the Digital Agency of Japan. === AI art === In May 2023, Kusano's first AI art collection "Neural Fad" depicting imaginary fashion history sold out 100 pieces within 24 hours at the "Bright Moments Tokyo" In June, she created WWDJAPAN's first AI-generated magazine cover using her own face. It is the first AI cover in Japanese fashion media. She was also appointed t to the Cultural Affairs Agency's Copyright Subcommittee, she participates in discussions on generative AI and copyright. Her "Synthetic Reflections" self-portrait series debuted on SuperRare, with the first piece auctioned for 3.5 ETH (equivalent to 6,480 US dollars at the time). In July 2023, she co-exhibited a 3D AI-generated dress at Christie's "Future Frequencies" auction with Gucci, alongside Claire Silver. In September, her 30-piece "Pixelated Perception" exhibit at Art Blocks Marfa explored 1990s media and gender, also showcased at the 21st Century Museum of Contemporary Art, Kanazawa. In December, her "Techno-Animism" AI art collection fused Japanese animism with technology. Collaborating with a U.S. gallery, she unveiled 336 pieces during a two-week Art Basel world tour. Throughout the two-week tour, she sold a total of 336 pieces, generating 11.2 ETH (equivalent to 21,264 US dollars at the time). === Generative art === In February 2024, the generative art platform Art Blocks selected the work "Melancholic Magical Maiden," for its Curated category. This piece reconstructs the aesthetics of 1990s magical girl anime, offering a critique of past anime heroines. It sold out within an hour, with all 300 pieces going for a total of 57 ETH (equivalent to approximately 215,385US dollars at the time). In April 2024, Emi Kusano spoke at the Standing Committee on Copyright and Other Rights at the World Intellectual Property Organization (WIPO) in Geneva, Switzerland, where she presented AI-specific information for discussion. == Style and technique == Kusano draws inspiration from Japanese retro-futurism as a foundation for her artwork, which explores the cutting-edge of technology. This approach is fueled by nostalgia for the pre-internet era, specifically the postwar period when Japanese mass media held significant sway. By blending modern technology with retro-culture, she captures the complex feelings of love, hate, and ambivalence towards present and future accelerationism. While at university, Kusano was profoundly influenced by Naoki Sakai, the industrial designer responsible for igniting the retro-futurism movement. In her musical project "Satellite Young", Kusano dons the persona of an '80s female idol and sings about contemporary technology. In her installation piece "Singing Dream", she investigates the concept of an artificial life form inhabiting a karaoke machine, which has been popular since the 1980s, compelling people to sing. In the collaborative NFT art project "Shinsei Galverse", Kusano reimagines a cyberpunk anime primarily featuring female characters, incorporating elements of magical girls popular in the early Heisei period. == Personal life == Kusano has two sons. In August 2021, she minted her older son Zombie Zoo Keeper's pixel art on "OpenSea" as part of his summer research project. The artwork was purchased by notable figures including Brud CEO Trevor McFedries and Steve Aoki, who bought the piece for the equivalent of 21.82 thousand US dollars, highlighting the intersection of art, technology, and family in her work.
Agents of S.H.I.E.L.D. season 4
The fourth season of the American television series Agents of S.H.I.E.L.D., based on the Marvel Comics spy organization S.H.I.E.L.D., follows Phil Coulson and other S.H.I.E.L.D. agents and allies after the signing of the Sokovia Accords. It is set in the Marvel Cinematic Universe (MCU) and acknowledges the continuity of the franchise's films. The season was produced by ABC Studios, Marvel Television, and Mutant Enemy Productions, with Jed Whedon, Maurissa Tancharoen, and Jeffrey Bell serving as showrunners. Clark Gregg reprises his role as Coulson from the film series, starring alongside the returning series regulars Ming-Na Wen, Chloe Bennet, Iain De Caestecker, Elizabeth Henstridge, and Henry Simmons. They are joined by John Hannah who was promoted from his recurring guest role in the third season. The fourth season was ordered in March 2016, with production taking place from that July until the following April. Due to its broadcast schedule, the season was split into three "pods": Ghost Rider for the first eight episodes, featuring recurring guest star Gabriel Luna as the supernatural Robbie Reyes / Ghost Rider and exploring mysticism in the MCU alongside the film Doctor Strange (2016); LMD, referring to the new Life Model Decoy program, for the next seven episodes which focus on recurring guest star Mallory Jansen as the LMD Aida; and Agents of Hydra for the final seven episodes, partly set in a "what if" virtual reality that allowed the return of former series regular Brett Dalton as Grant Ward. The season is also affected by the events of the film Captain America: Civil War (2016), and continues storylines established in the canceled series Agent Carter. The first episode premiered at a screening on September 19, 2016, with the season then airing for 22 episodes on ABC, from September 20, 2016, until May 16, 2017. The premiere debuted to 3.58 million viewers, down from previous season premieres but average for the series. Critical response to the season was positive, with many feeling that each pod was better than the last and in particular praising the visual effects and tone of Ghost Rider, the writing and acting of LMD, and the character development and political commentary explored during Agents of Hydra. The season saw series low viewership, but was still considered to have solved ABC's problem during its new Tuesday night timeslot, and the series was renewed for a fifth season in May 2017. == Episodes == == Cast and characters == == Production == === Development === Agents of S.H.I.E.L.D. was renewed for a fourth season on March 3, 2016, earlier than usual for the series. Executive producer Jed Whedon said on this, "We're thrilled to know going into the end of [season three] with certainty that we will be returning, because we can build our story accordingly." Executive producer Maurissa Tancharoen also noted that logistics for hiring directors for the season in advance would be easier, "which is a very nice privilege to have...that's a luxury". The end of the episode "What If..." features an onscreen tribute to Bill Paxton, who died in February 2017 and had portrayed John Garrett in the series' first season. The series paid additional tribute to Paxton in "All the Madame's Men" with promos during The Bakshi Report news segment showcasing John Garrett as a fallen American hero. The end of "World's End" features a similar onscreen tribute to Powers Boothe, who died in May 2017 and had portrayed Gideon Malick in the series' third season. === Writing === The season shifted to the later 10 pm timeslot, allowing it to take on a darker, more mature tone than previous seasons. According to Tancharoen, "The whole tagline for this year is 'Agents of S.H.I.E.L.D. After Dark'". The timeslot gave the series the opportunity to present an increased level of violence and partial nudity, as well as take more risks and present edgier themes. Following the third-season finale, Tancharoen stated that the fourth season would explore the guilt Daisy Johnson has over Lincoln Campbell's death. Executive producer Jeffrey Bell noted the writers tried to continue the tradition of "finding new combinations and new conflicts" between different sets of characters, given "a lot of procedurals [see] the same people doing the same thing for five years". Pairings that would be explored included Coulson and Mack, continuing from the end of season three, who have a mutual respect for one another due to their relationships with Daisy, and Leo Fitz and Holden Radcliffe, who work together. The Fitz-Simmons relationship was also explored more, examining the new challenges it presented for the two "working together, loving each other and living together". Following the third season's dealing with the themes of Captain America: Civil War (2016), such as the opposing reactions to the Inhumans, Whedon said that the question of "How do you deal with a war with powered people at that level, a government level?" was one that they wanted to answer in the fourth season. Tancharoen called the Inhumans "a permanent part of our universe now", with Whedon adding, "we have a quick-fire way of introducing people with powers. It gives us a lot of leeway in our world, and it lets us explore the metaphors of what it is like to be different. We will never close that chapter." With the Inhumans film being removed from Marvel Studios' release schedule, the series had "a little more freedom" and were "able to do a little bit more" with the species, including the potential of introducing some of the "classic" Inhumans, though the series would focus less on Inhumans than the third season which saw "a real significant Inhuman agenda story". It was not intended to be a spin-off of Agents of S.H.I.E.L.D. On the evolution of S.H.I.E.L.D. to featuring so many powered characters, Whedon said "the dynamic in the world has changed. There was one person with powers, and then by The Avengers there were maybe six total ... now they're much more prevalent, so there's reaction from the public based on that." The season is structured into three "pods" based on its airing schedule: the first eight episodes, subtitled Ghost Rider; LMD (Life Model Decoy) for the subsequent seven episodes; and a third pod for the final seven episodes called Agents of Hydra. Elements and characters cross over between the different pods, but the sections "definitely have a different feel" from one another, as Bell explained that 22 episodes "is a long time to hold a big bad or a single plot line, especially for an audience", and for the past two seasons, the series was able to have two separated halves that "allows us to introduce a big bad. And then, something happens and we rise somebody new ... Now, there's three of those." "Financial considerations" were also taken into account in creating the pods for the season, as using LMDs does not "cost as much as setting a guy's head on fire via CGI". In terms of writing the "complicated season", Whedon said the writers were "aware that our fans are our fans and have spent some time with these characters and are clever and see things coming sometimes ... Part of our job is to create not just what we are presenting on plot, but letting the audience be one step ahead of us and being one step ahead of that." He added that the writers knew that they wanted to tell a Ghost Rider story, an LMD story, and a "what if" scenario, and the hardest part was making each pod still fit together as a single season. The major connection ultimately became the Darkhold, which leads from the magic of Ghost Rider to the advanced science of LMD and then the Framework in Agents of Hydra. Ghost Rider also reappears in the final episode of the season, "World's End", as an additional connection. ==== Ghost Rider ==== While planning the fourth season, Marvel suggested that the series introduce Ghost Rider, after the character's film rights had returned to Marvel from Sony in May 2013. Loeb felt that this made the season unquestionably "the series' biggest" with the "most ambitious story yet". He added that "one of the things that we talked about is, S.H.I.E.L.D. always looked out for the weird, the unusual, the things that were and could be a problem for the public", and Marvel realized that Ghost Rider's abilities, which are more mystical than anything seen in the series to date, opened up "a quarter of the universe that we haven't really spent a lot of time exploring ... what happens if our very real, our very grounded agents who are very much a family have to take on something that is as bizarre and powerful and unique as Ghost Rider." Bell added that the producers would have been willing to give an entire season of the show to a Ghost Rider arc if the season was 13 episodes or less, but 22 episodes seemed too long to "feel like one flavor". The Robbie Reyes version of Ghost Rider was chosen over other versions of the character from the comics because of his relationship with his brother Gabe, w
Application framework
In computer programming, an application framework consists of a software framework used by software developers to implement the standard structure of application software. Application frameworks became popular with the rise of graphical user interfaces (GUIs), since these tended to promote a standard structure for applications. Programmers find it much simpler to create automatic GUI creation tools when using a standard framework, since this defines the underlying code structure of the application in advance. Developers usually use object-oriented programming (OOP) techniques to implement frameworks such that the unique parts of an application can simply inherit from classes extant in the framework. == Examples == Apple Computer developed one of the first commercial application frameworks, MacApp (first release 1985), for the Macintosh. Originally written in an extended (object-oriented) version of Pascal termed Object Pascal, it was later rewritten in C++. Another notable framework for the Mac is Metrowerks' PowerPlant, based on Carbon. Cocoa for macOS offers a different approach to an application framework, based on the OpenStep framework developed at NeXT. Since the 2010s, many apps have been created with the frameworks based on Google's Chromium project. The two prominent ones are Electron and the Chromium Embedded Framework. Free and open-source software frameworks exist as part of the Mozilla, LibreOffice, GNOME, KDE, NetBeans, and Eclipse projects. Microsoft markets a framework for developing Windows applications in C++ called the Microsoft Foundation Class Library, and a similar framework for developing applications with Visual Basic or C#, named .NET Framework. Several frameworks can build cross-platform applications for Linux, Macintosh, and Windows from common source code, such as Qt, wxWidgets, Juce, Fox toolkit, or Eclipse Rich Client Platform (RCP). Oracle Application Development Framework (Oracle ADF) aids in producing Java-oriented systems. Silicon Laboratories offers an embedded application framework for developing wireless applications on its series of wireless chips.
Artificial intelligence in pharmacy
Artificial intelligence in pharmacy refers to the application of artificial intelligence (AI) techniques across pharmaceutical research and practice, including drug discovery, drug delivery, safety monitoring, clinical decision support, and pharmacy operations. Machine learning, deep learning, and natural language processing have been applied to tasks ranging from molecular design to patient adherence monitoring, with the aim of reducing development costs, improving accuracy, and personalizing treatment. Adoption has been uneven. Barriers include limited AI training among pharmacists, high infrastructure costs, and the risk of harm from models trained on unrepresentative data. Regulatory frameworks for AI-based pharmaceutical tools remain in active development across most jurisdictions. == Applications == === Drug discovery and development === Drug development is resource-intensive: bringing a single drug to market typically costs around $2.6 billion and takes 12–14 years. Machine learning algorithms have been applied to analyze molecular datasets to identify potential drug candidates, predict drug–target interactions, and optimize formulations. Artificial neural networks and generative adversarial networks have been used in drug discovery tasks including virtual screening, structure-activity relationship modeling, and de novo molecule generation. Peptides designed using AI methods have shown activity against multidrug-resistant bacteria, and transcriptomic data from human cell lines has been used to train deep learning models to classify drugs by therapeutic properties. Results in drug discovery have been mixed. AI models depend on the quality and diversity of their training data; those trained on narrow chemical libraries can fail to generalize to novel molecular scaffolds. The gap between high virtual screening hit rates and success in preclinical or clinical testing remains a persistent challenge, and the translation of computationally predicted candidates into approved drugs has been slower than early projections suggested. === Drug delivery systems === AI methods including neural networks, principal component analysis, and neuro-fuzzy logic have been applied to identifying biological targets for pharmaceuticals and analyzing genetic information relevant to drug design. Computational models can predict how a formulation will behave in biological systems, helping narrow the field before laboratory synthesis begins. Systems have been proposed that monitor patient response and adjust doses in real time based on individual physiology, with potential applications in chronic disease management. Research has also explored AI applications in targeted cancer treatments and oral vaccine delivery, areas where precise control over drug release kinetics is a design priority. === Drug safety === AI has been applied to predicting and detecting adverse drug reactions using techniques including knowledge graphs, logistic regression classifiers, and neural networks. A 2023 study developed a machine learning algorithm using knowledge graph analysis to classify known causes of adverse reactions. Natural language processing and deep learning models including long short-term memory (LSTM) networks have shown better performance than conventional methods for detecting opioid misuse, drawing on both structured data from electronic health records and unstructured sources such as clinical notes. AI-based pharmacovigilance systems can scan large volumes of electronic health records and social media for drug safety signals at a scale not feasible with manual review. Limitations include difficulty distinguishing drug-related adverse events from unrelated conditions in free-text data, and the need for validated benchmarks to measure model performance against existing safety monitoring standards. === Clinical decision support and personalized medicine === Machine learning systems trained on patient datasets can predict individual risk profiles, including potential allergies and drug–drug interactions, reducing the risk of harm in complex polypharmacy cases where the number of possible interactions exceeds what a clinician can readily assess. Personalized dosing models have been developed for drugs with narrow therapeutic windows — including anticoagulants and immunosuppressants — using patient-specific variables such as weight, renal function, and relevant genetic markers. Prospective clinical validation of these systems has lagged behind their technical development. Most published evaluations report performance on retrospective datasets, and the regulatory pathway for AI-based clinical decision support tools in pharmacy varies by jurisdiction. === Pharmacy operations and automation === Robotic and AI-driven systems have been applied to dispensing accuracy and pharmacy logistics. At the UCSF Medical Center, robotic technology produced 350,000 medication doses with no dispensing errors recorded. Robots such as TUG assist with preparing and transporting medications and laboratory samples within hospital settings. AI has also been applied to inventory management, with demand-forecasting systems predicting medicine requirements to reduce shortages and minimize waste from expired stock. In community pharmacy settings, AI tools have been used to flag potential prescription errors and alert pharmacists to drug–drug interactions before dispensing. === Medication adherence === Confirming that patients take prescribed medications as directed is a persistent challenge in healthcare. AI-enabled tools including smart pillboxes, RFID tags, ingestible sensors, and video check-ins have been applied to this problem. Smart pillboxes record when they are opened, providing real-time adherence data that can be reviewed remotely by care teams. Ingestible sensors transmit a signal after dissolution, offering direct confirmation of ingestion rather than proxy measures such as pill count or self-report. == Adoption challenges == === Barriers === Several barriers limit AI adoption in pharmacy practice. Many published evaluations report model performance on retrospective datasets rather than prospective clinical outcomes, making it difficult to assess real-world benefit. Pharmacists have reported limited AI training and knowledge, and research facilities often lack the computational infrastructure required for model development and validation. Models trained on biased or unrepresentative datasets can produce misleading results with direct patient safety consequences. === Regulatory frameworks === Regulatory frameworks for AI-based pharmaceutical tools are in active development. In the United States, the Food and Drug Administration (FDA) has issued guidance on AI and machine learning-based software as a medical device, addressing requirements for pre-market review and post-market performance monitoring. The European Medicines Agency has published discussion papers on the use of AI across the medicines development lifecycle, with particular attention to transparency in model training and validation. The absence of harmonized international standards creates compliance complexity for developers operating across multiple jurisdictions. === Ethical challenges === AI adoption raises data privacy and security concerns, including the risk of exposing sensitive patient information through data breaches. Algorithmic bias presents a related hazard: a model trained on an unrepresentative patient population may generate unsuitable treatment recommendations for patients not reflected in its training data, with potential for disparate outcomes across demographic groups. The opacity of some machine learning models, particularly deep neural networks, limits clinicians' ability to interpret or contest a recommendation, raising questions of accountability when a model-assisted decision results in patient harm. === Proposed solutions === Responses proposed in the literature include AI-focused education programs for pharmacists, increased public funding for healthcare AI research, encryption and governance frameworks for patient data, and regulatory requirements to prevent the use of biased training datasets. Greater transparency about training data provenance, model architecture, and validation methodology has also been recommended, including disclosure requirements in regulatory submissions. === Future directions === Research groups have called for tighter integration between AI systems and electronic health records to reduce healthcare costs and improve continuity of care across settings. International collaboration through shared AI frameworks and federated learning approaches has been proposed to address data scarcity in underrepresented patient populations and accelerate validation across institutions.