AI Code Ui

AI Code Ui — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • Roposo

    Roposo

    Roposo is an Indian video-sharing social media service, owned by Glance, a subsidiary of InMobi. Roposo provides a space where users can share posts related to different topics like food, comedy, music, poetry, fashion and travel. It is a platform where people express visually with homemade videos and photos. The app offers a TV-like browsing experience with user-generated content on its channels. Users can also use editing tools on the platform and upload their content. == History == Established in July 2014 under Relevant E-solutions Pvt. Ltd., Roposo is the brainchild of three IIT Delhi alumni – Mayank Bhangadia, Avinash Saxena, and Kaushal Shubhank. Under Bhangadia's leadership, the company pivoted from a fashion-based network into a short-form video platform with AI-powered moderation, and its journey was featured as a Harvard Business Publishing case study. In November 2019, Roposo was acquired by InMobi's Glance Digital Experience Pvt. Ltd.(the mobile content platform and part of the InMobi Group). When the Chinese-owned video-sharing app TikTok was banned on 30 June 2020, the app saw a huge spike in users with several TikTok users registering on Roposo. == Technology == The open platform has some features such as a TV-like browsing, different channels, a chat feature that lets buyers and sellers converse directly through the platform, and creation tools such as an option to add voice-over, music and GIF stickers for videos and photos.

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  • Linguistic Systems

    Linguistic Systems

    Linguistic Systems, Inc., also known as LSI, provides language translation services (conversion) for all media in over 115 languages. LSI focuses on the translation of legal, medical, business, institutional, academic, government and personal documents. LSI is headquartered in Cambridge, Massachusetts. == About LSI == Linguistic Systems, Inc. (LSI) was founded in 1967 by Martin Roberts. LSI's translates to/from 115 languages, DTP, audio-visual conversions, software localization, consecutive and simultaneous interpreting services, foreign brand name analysis, and machine translation with post-editing. LSI has provided translation services to over half of the Fortune 500 companies and most of the Fortune 100. Among its clients are AT&T, Boeing, Citigroup, Coca-Cola, DuPont, Exxon-Mobil, General Electric, General Motors, Hewlett-Packard, IBM, Johnson & Johnson, Pfizer, Procter & Gamble, Simon & Schuster, Time Warner, Verizon, and Walmart. As of 2013, LSI had a network of more than 7,000 translators who translate into their native languages; These include lawyers, scientists, engineers, and other bilingual professionals.

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  • Aslı Çelikyılmaz

    Aslı Çelikyılmaz

    Aslı Çelikyılmaz is an engineer specializing in natural language processing, and particularly in natural language generation for software agents with advanced reasoning and real-world modeling capabilities. Educated in Turkey and Canada, she works in the US as senior research lead at Fundamentals AI Research, Meta. She also holds an affiliate faculty position in computer science at the University of Washington, and is co-editor-in-chief of the journal Transactions of the Association for Computational Linguistics. == Education and career == Çelikyılmaz is a 1997 graduate of Istanbul Technical University, where she studied industrial engineering. After a 2002 master's degree in computer and information science from Seneca Polytechnic in Toronto, and a second master's degree in information science from the University of Toronto in 2005, she completed a Ph.D. in information science at the University of Toronto in 2008. She worked as a postdoctoral researcher in California, at the University of California, Berkeley, from 2008 to 2010. In 2010 she joined Microsoft in Sunnyvale, California, where she became a senior scientist and later a senior principal researcher in Redmond, Washington. She added her affiliation with the University of Washington in 2018, and moved to Meta in Seattle in 2021. == Recognition == Çelikyılmaz was named to the 2026 class of IEEE Fellows, "for contributions to conversational systems and language generation".

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  • Indic OCR

    Indic OCR

    Indic OCR refers to the process of converting text images written in Indic scripts into e-text using Optical character recognition (OCR) techniques. Broadly, it can also refer to the OCR systems of Brahmic scripts for languages of South Asia and Southeast Asia, not just the scripts of the Indian subcontinent, which are all written in an abugida-based writing system. OCR for Latin characters is still not 100% accurate but a relatively high degree of accuracy in conversion has been able to be achieved. Such accuracy has not yet been able to be achieved for Indic scripts using OCR. This is due in part to the writing systems of Indic languages as well as a lack of standard representation, encoding, and support among operating systems and keyboards. The Centre for Development of Advanced Computing (C-DAC) and Technology Development for Indian Languages, the premier R&D organisation of the Ministry of Electronics and Information Technology (also known as MeitY) of India have carried out many projects relating to OCR. Their projects include OCR for Malayalam, Odia, Punjabi, Telugu and Devanagari script. == Properties of Indian writing systems == There are 22 officially recognised languages in India. Of these, Hindi, Bengali and Punjabi are the most widely spoken Indo-Aryan languages and are also the fourth, seventh and tenth most widely spoken languages in the world respectively. Two or more languages can be written with same script. For example, Devanagari is used to write Hindi, Marathi, Rajasthani, Sanskrit, Bhojpuri and others, while Eastern Nagari is used to write Bengali, Assamese, Manipuri and others. Apart from basic characters as consonants and vowels, most Indic languages combine 2 or more basic characters to form compound characters. The shape of a compound character is more complex than the constituent basic characters. Some Indo-Aryan languages (including Hindi and Punjabi) have a horizontal line over the characters, while other languages (including Gujarati) and Dravidian languages (Malayalam, Kannada, Tamil, and Telugu) do not. These are some of the main challenges for creating a single OCR for all Indic languages. Indic OCR also generally includes support for recently invented scripts in India like Ol Chiki, Warang Citi, Mundari Bani, etc. which are mainly created for writing Munda languages of Austroasiatic family. The concept of upper/lower case is absent in Indic scripts. Apart from Urdu, Sindhi, Kashmiri and Thaana, all other Indic languages are written from left to right. == Examples == SanskritOCR - OCR software for Sanskrit, Hindi and other Indo-Aryan languages based on the Devanagari script. Sanskrit OCR is developed by a Sanskrit scholar from Germany - Dr. Oliver Hellwig of Department for Languages and Cultures of Southern Asia, Freie Universität Berlin. The official website is in German. The interface of earlier versions of the software was also in German, but later versions have an English interface too. E-aksharayan - Optical character recognition engine for Indian languages Chitrankan - This technology was developed by ISI, Kolkata, and transferred to C-DAC. It processes printed Hindi text from a scanner or from an image. Indic OCR models for Tesseract (software) == OCR in use == OCR has been used for Wikisource and other projects.

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  • How Data Happened

    How Data Happened

    How Data Happened: A History from the Age of Reason to the Age of Algorithms is a 2023 non-fiction book written by Columbia University professors Chris Wiggins and Matthew L. Jones. The book explores the history of data and statistics from the end of the 18th century to the present day. == Content == The book starts at the end of the 18th century, when European states began tabulating physical resources, and ends at the present day, when algorithms manipulate our personal information as a commodity. It looks at the rise of data and statistics, and how early statistical methods were used to justify eugenics, quantify supposed racial differences, and develop military and industrial applications. The authors also discuss the impact of the internet and e-commerce on data collection, the rise of data science, and the consequences of government-run surveillance systems collecting vast amounts of personal data for customized, targeted advertising. They emphasize the importance of privacy and democracy and propose remedies to the problems caused by mass data collection, including stronger regulation of the tech industry and collective action by its employees. The book is a historical analysis that provides context for understanding the debates surrounding data and its control. The book has 336 pages and was published in 2023 by W. W. Norton & Company.

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  • Devi Parikh

    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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  • Dissociated press

    Dissociated press

    Dissociated press is a parody generator (a computer program that generates nonsensical text). The generated text is based on another text using the Markov chain technique. The name is a play on "Associated Press" and the psychological term dissociation (although word salad is more typical of conditions like aphasia and schizophrenia – which is, however, frequently confused with dissociative identity disorder by laypeople). An implementation of the algorithm is available in Emacs. Another implementation is available as a Perl module in CPAN, Games::Dissociate. == The algorithm == The algorithm starts by printing a number of consecutive words (or letters) from the source text. Then it searches the source text for an occurrence of the few last words or letters printed out so far. If multiple occurrences are found, it picks a random one, and proceeds with printing the text following the chosen occurrence. After a predetermined length of text is printed out, the search procedure is repeated for the newly printed ending. Considering that words and phrases tend to appear in specific grammatical contexts, the resulting text usually seems correct grammatically, and if the source text is uniform in style, the result appears to be of similar style and subject, and takes some effort on the reader's side to recognize as not genuine. Still, the randomness of the assembly process deprives it of any logical flow - the loosely related parts are connected in a nonsensical way, creating a humorously abstract, random result. == Examples == Here is a short example of word-based Dissociated Press applied to the Jargon File: wart: n. A small, crocky feature that sticks out of an array (C has no checks for this). This is relatively benign and easy to spot if the phrase is bent so as to be not worth paying attention to the medium in question. Here is a short example of letter-based Dissociated Press applied to the same source: window sysIWYG: n. A bit was named aften /bee´t@/ prefer to use the other guy's re, especially in every cast a chuckle on neithout getting into useful informash speech makes removing a featuring a move or usage actual abstractionsidered interj. Indeed spectace logic or problem! == History == The dissociated press algorithm is described in HAKMEM (1972) Item #176. The name "dissociated press" is first known to have been associated with the Emacs implementation. Brian Hayes discussed a Travesty algorithm in Scientific American in November 1983. The article provided a garbled William Faulkner passage: When he got on the table, he come in. He never come out of my own pocket as a measure of protecting the company against riot and bloodshed. And when he said. "You tell me a bus ticket, let alone write out no case histories. Then the law come back with a knife!" Hugh Kenner and Joseph O'Rourke of Johns Hopkins University discussed their frequency table-based Travesty generator for microcomputers in BYTE in November 1984. The article included the Turbo Pascal source for two versions of the generator, one using Hayes' algorithm and another using Claude Shannon's Hellbat algorithm. Murray Lesser offered a compiled BASIC version in the magazine in July 1985, in September 1985 Peter Wayner offered a version that used tree data structures instead of frequency tables, and in December 1985 Neil J. Rubenking offered a version written in Turbo Pascal that stored frequency information in a B-tree.

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  • Conversational AI Platforms Reviews: What Actually Works in 2026

    Conversational AI Platforms Reviews: What Actually Works in 2026

    Shopping for the best conversational AI platform? An conversational AI platform is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right conversational AI platform slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.

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  • FedRAMP

    FedRAMP

    The Federal Risk and Authorization Management Program (FedRAMP) is a United States federal government-wide compliance program that provides a standardized approach to security assessment, authorization, and continuous monitoring for cloud products and services. The US government describes FedRAMP as FISMA for the cloud. == Overview == The FedRAMP PMO mission is to promote the adoption of secure cloud services across the federal government by providing a standardized approach to security and risk assessment. Per the OMB memorandum, any cloud services that hold federal data must be FedRAMP authorized. FedRAMP prescribes the security requirements and processes that cloud service providers must follow in order for the government to use their service. There are two ways to authorize a cloud service through FedRAMP: a Joint Authorization Board (JAB) provisional authorization (P-ATO), and through individual agencies. FedRAMP provides accreditation for cloud services for the various cloud offering models which are Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service, (SaaS). == History == In 2011, the Office of Management and Budget (OMB) released a memorandum establishing FedRAMP "to provide a cost-effective, risk-based approach for the adoption and use of cloud services to Executive departments and agencies." The General Services Administration (GSA) established the FedRAMP Program Management Office (PMO) in June 2012. Before the introduction of FedRAMP, individual federal agencies managed their own assessment methodologies following guidance set by the Federal Information Security Management Act of 2002. == Governance and applicable laws == FedRAMP is governed by different Executive Branch entities that collaborate to develop, manage, and operate the program. These entities include: The Office of Management and Budget (OMB): The governing body that issued the FedRAMP policy memo, which defines the key requirements and capabilities of the program The Joint Authorization Board (JAB): The primary governance and decision-making body for FedRAMP comprises the chief information officers (CIOs) from the Department of Homeland Security (DHS), General Services Administration (GSA), and Department of Defense (DOD) The National Institute of Standards and Technology (NIST): Advises FedRAMP on FISMA compliance requirements and assists in developing the standards for the accreditation of independent 3PAOs The Department of Homeland Security (DHS): Manages the FedRAMP continuous monitoring strategy including data feed criteria, reporting structure, threat notification coordination, and incident response The Federal Chief Information Officers (CIO) Council: Disseminates FedRAMP information to Federal CIOs and other representatives through cross-agency communications and events The FedRAMP PMO: Established within GSA and responsible for the development of the FedRAMP program, including the management of day-to-day operations There are several laws, mandates, and policies that are foundational to FedRAMP. FISMA–the Federal Information Security Modernization Act–requires that agencies authorize the information systems that they use. The US government describes FedRAMP as FISMA for the cloud. The FedRAMP Policy Memo requires federal agencies to use FedRAMP when assessing, authorizing, and continuously monitoring cloud services in order to aid agencies in the authorization process as well as save government resources and eliminate duplicative efforts. FedRAMP's security baselines are derived from NIST SP 800-53 (as revised) with a set of control enhancements that pertain to the unique security requirements of cloud computing. == Third-party assessment organizations == Third-party assessment organizations (3PAOs) play a critical role in the FedRAMP security assessment process, as they are the independent assessment organizations that verify cloud providers' security implementations and provide the overall risk posture of a cloud environment for a security authorization decision. Accredited by the American Association for Laboratory Accreditation (A2LA), these assessment organizations must demonstrate independence and the technical competence required to test security implementations and collect representative evidence. == FedRAMP Marketplace == The FedRAMP Marketplace provides a searchable, sortable database of Cloud Service Offerings (CSOs) that have achieved a FedRAMP designation. 3PAOs, accredited auditors that can perform the FedRAMP assessment, are listed within the Marketplace. The FedRAMP Marketplace is maintained by the FedRAMP Program Management Office (PMO). == Security and authorization concerns == A 2026 ProPublica investigation found that FedRAMP entered into a partnership with Microsoft despite considerable concerns about the security of its cloud technology.

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  • Top 10 AI Website Builders Compared (2026)

    Top 10 AI Website Builders Compared (2026)

    Curious about the best AI website builder? An AI website builder 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 website builder slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.

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  • Mehryar Mohri

    Mehryar Mohri

    Mehryar Mohri is a professor and theoretical computer scientist at the Courant Institute of Mathematical Sciences. He is also heading the Machine Learning Theory (ML Theory) team at Google Research. == Career == Prior to joining the Courant Institute, Mohri was a research department head and later technology leader at AT&T Bell Labs, where he was a member of the technical staff for about ten years. Mohri has also taught as an assistant professor at the University of Paris 7 (1992-1993) and Ecole Polytechnique (1992-1994). == Research == Mohri's main area of research is machine learning, in particular learning theory. He is also an expert in automata theory and algorithms. He is the author of several core algorithms that have served as the foundation for the design of many deployed speech recognition and natural language processing systems. == Publications == Mohri is the author of the reference book Foundations of Machine Learning used as a textbook in many graduate-level machine learning courses. Mohri is also a member of the Lothaire group of mathematicians with the pseudonym M. Lothaire and contributed to the book on Applied Combinatorics on Words. He is the author of more than 250 conference and journal publications. == Organizational affiliations == Mohri is currently the President of the Association for Algorithmic Learning Theory (AALT) and the Steering Committee Chair for the ALT conference. He is also Editorial Board member of Machine Learning and TheoretiCS, Action Editor of the Journal of Machine Learning Research (JMLR) and a member of the advisory board for the Journal of Automata, Languages and Combinatorics.

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  • Danqi Chen

    Danqi Chen

    Danqi Chen (Chinese: 陈丹琦; pinyin: Chén Dānqí, IPA: [ʈ͡ʂʰə̌n tan t͡ɕʰǐ]; born in Changsha, China) is a Chinese computer scientist and assistant professor at Princeton University specializing in the AI field of natural language processing (NLP). In 2019, she joined the Princeton NLP group, alongside Sanjeev Arora, Christiane Fellbaum, and Karthik Narasimhan. She was previously a visiting scientist at Facebook AI Research (FAIR). She earned her Ph.D. at Stanford University and her BS from Tsinghua University. Chen is the author of Neural Reading Comprehension and Beyond, a dissertation on using artificial intelligence to access knowledge in ordinary and structured documents. She is the author or co-author of a number of journal articles, including Reading Wikipedia to Answer Open-Domain Questions. Google's SyntaxNet is based on algorithms developed by Danqi Chen and Christopher Manning at Stanford. Her primary research interests are in text understanding and knowledge representation and reasoning. She won a gold medal at the 2008 International Informatics Olympiad. She is known among friends as CDQ. A well known algorithm in competitive programming, CDQ Divide and Conquer, is named after this acronym. She is married to Huacheng Yu, an assistant professor in theoretical computer science at Princeton University.

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  • Biohybrid microswimmer

    Biohybrid microswimmer

    A biohybrid microswimmer also known as biohybrid nanorobot, can be defined as a microswimmer that consist of both biological and artificial constituents, for instance, one or several living microorganisms attached to one or various synthetic parts. In recent years nanoscopic and mesoscopic objects have been designed to collectively move through direct inspiration from nature or by harnessing its existing tools. Small mesoscopic to nanoscopic systems typically operate at low Reynolds numbers (Re ≪ 1), and understanding their motion becomes challenging. For locomotion to occur, the symmetry of the system must be broken. In addition, collective motion requires a coupling mechanism between the entities that make up the collective. To develop mesoscopic to nanoscopic entities capable of swarming behaviour, it has been hypothesised that the entities are characterised by broken symmetry with a well-defined morphology, and are powered with some material capable of harvesting energy. If the harvested energy results in a field surrounding the object, then this field can couple with the field of a neighbouring object and bring some coordination to the collective behaviour. Such robotic swarms have been categorised by an online expert panel as among the 10 great unresolved group challenges in the area of robotics. Although investigation of their underlying mechanism of action is still in its infancy, various systems have been developed that are capable of undergoing controlled and uncontrolled swarming motion by harvesting energy (e.g., light, thermal, etc.). Over the past decade, biohybrid microrobots, in which living mobile microorganisms are physically integrated with untethered artificial structures, have gained growing interest to enable the active locomotion and cargo delivery to a target destination. In addition to the motility, the intrinsic capabilities of sensing and eliciting an appropriate response to artificial and environmental changes make cell-based biohybrid microrobots appealing for transportation of cargo to the inaccessible cavities of the human body for local active delivery of diagnostic and therapeutic agents. == Background == Biohybrid microswimmers can be defined as microswimmers that consist of both biological and artificial constituents, for instance, one or several living microorganisms attached to one or various synthetic parts. The pioneers of this field, ahead of their time, were Montemagno and Bachand with a 1999 work regarding specific attachment strategies of biological molecules to nanofabricated substrates enabling the preparation of hybrid inorganic/organic nanoelectromechanical systems, so called NEMS. They described the production of large amounts of F1-ATPase from the thermophilic bacteria Bacillus PS3 for the preparation of F1-ATPase biomolecular motors immobilized on a nanoarray pattern of gold, copper or nickel produced by electron beam lithography. These proteins were attached to one micron microspheres tagged with a synthetic peptide. Consequently, they accomplished the preparation of a platform with chemically active sites and the development of biohybrid devices capable of converting energy of biomolecular motors into useful work. One of the most fundamental questions in science is what defines life. Collective motion is one of the hallmarks of life. This is commonly observed in nature at various dimensional levels as energized entities gather, in a concerted effort, into motile aggregated patterns. These motile aggregated events can be noticed, among many others, as dynamic swarms; e.g., unicellular organisms such as bacteria, locust swarms, or the flocking behaviour of birds. Ever since Newton established his equations of motion, the mystery of motion on the microscale has emerged frequently in scientific history, as famously demonstrated by a couple of articles that should be discussed briefly. First, an essential concept, popularized by Osborne Reynolds, is that the relative importance of inertia and viscosity for the motion of a fluid depends on certain details of the system under consideration. The Reynolds number Re, named in his honor, quantifies this comparison as a dimensionless ratio of characteristic inertial and viscous forces: R e = ρ u l μ {\displaystyle \mathrm {Re} ={\frac {\rho ul}{\mu }}} Here, ρ represents the density of the fluid; u is a characteristic velocity of the system (for instance, the velocity of a swimming particle); l is a characteristic length scale (e.g., the swimmer size); and μ is the viscosity of the fluid. Taking the suspending fluid to be water, and using experimentally observed values for u, one can determine that inertia is important for macroscopic swimmers like fish (Re = 100), while viscosity dominates the motion of microscale swimmers like bacteria (Re = 10−4). The overwhelming importance of viscosity for swimming at the micrometer scale has profound implications for swimming strategy. This has been discussed memorably by E. M. Purcell, who invited the reader into the world of microorganisms and theoretically studied the conditions of their motion. In the first place, propulsion strategies of large scale swimmers often involve imparting momentum to the surrounding fluid in periodic discrete events, such as vortex shedding, and coasting between these events through inertia. This cannot be effective for microscale swimmers like bacteria: due to the large viscous damping, the inertial coasting time of a micron-sized object is on the order of 1 μs. The coasting distance of a microorganism moving at a typical speed is about 0.1 angstroms (Å). Purcell concluded that only forces that are exerted in the present moment on a microscale body contribute to its propulsion, so a constant energy conversion method is essential. Microorganisms have optimized their metabolism for continuous energy production, while purely artificial microswimmers (microrobots) must obtain energy from the environment, since their on-board-storage-capacity is very limited. As a further consequence of the continuous dissipation of energy, biological and artificial microswimmers do not obey the laws of equilibrium statistical physics, and need to be described by non-equilibrium dynamics. Mathematically, Purcell explored the implications of low Reynolds number by taking the Navier-Stokes equation and eliminating the inertial terms: μ ∇ 2 u − ∇ p = 0 {\displaystyle {\begin{aligned}\mu \nabla ^{2}\mathbf {u} -{\boldsymbol {\nabla }}p&={\boldsymbol {0}}\\\end{aligned}}} where u {\displaystyle \mathbf {u} } is the velocity of the fluid and ∇ p {\displaystyle {\boldsymbol {\nabla }}p} is the gradient of the pressure. As Purcell noted, the resulting equation — the Stokes equation — contains no explicit time dependence. This has some important consequences for how a suspended body (e.g., a bacterium) can swim through periodic mechanical motions or deformations (e.g., of a flagellum). First, the rate of motion is practically irrelevant for the motion of the microswimmer and of the surrounding fluid: changing the rate of motion will change the scale of the velocities of the fluid and of the microswimmer, but it will not change the pattern of fluid flow. Secondly, reversing the direction of mechanical motion will simply reverse all velocities in the system. These properties of the Stokes equation severely restrict the range of feasible swimming strategies. Recent publications of biohybrid microswimmers include the use of sperm cells, contractive muscle cells, and bacteria as biological components, as they can efficiently convert chemical energy into movement, and additionally are capable of performing complicated motion depending on environmental conditions. In this sense, biohybrid microswimmer systems can be described as the combination of different functional components: cargo and carrier. The cargo is an element of interest to be moved (and possibly released) in a customized way. The carrier is the component responsible for the movement of the biohybrid, transporting the desired cargo, which is linked to its surface. The great majority of these systems rely on biological motile propulsion for the transportation of synthetic cargo for targeted drug delivery/ There are also examples of the opposite case: artificial microswimmers with biological cargo systems. Over the past decade, biohybrid microrobots, in which living mobile microorganisms are physically integrated with untethered artificial structures, have gained growing interest to enable the active locomotion and cargo delivery to a target destination. In addition to the motility, the intrinsic capabilities of sensing and eliciting an appropriate response to artificial and environmental changes make cell-based biohybrid microrobots appealing for transportation of cargo to the inaccessible cavities of the human body for local active delivery of diagnostic and therapeutic agents. Active locomotion, targeting and steering of concentrated therape

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  • Jaime Carbonell

    Jaime Carbonell

    Jaime Guillermo Carbonell (July 29, 1953 – February 28, 2020) was a computer scientist who made seminal contributions to the development of natural language processing tools and technologies. His research in machine translation resulted in the development of several state-of-the-art language translation and artificial intelligence systems. He earned his B.S. degrees in Physics and in Mathematics from MIT in 1975 and did his Ph.D. under Dr. Roger Schank at Yale University in 1979. He joined Carnegie Mellon University as an assistant professor of computer science in 1979 and moved to Pittsburgh. He was affiliated with the Language Technologies Institute, Computer Science Department, Machine Learning Department, and Computational Biology Department at Carnegie Mellon. His interests spanned several areas of artificial intelligence, language technologies and machine learning. In particular, his research focused on areas such as text mining (extraction, categorization, novelty detection) and in new theoretical frameworks such as a unified utility-based theory bridging information retrieval, summarization, free-text question-answering and related tasks. He also worked on machine translation, both high-accuracy knowledge-based MT and machine learning for corpus-based MT (such as generalized example-based MT). == Career == Carbonell was the Allen Newell Professor of Computer Science and head of the Language Technologies Institute at Carnegie Mellon University. He joined Carnegie Mellon in 1979, and became a key faculty member in the artificial intelligence area. He was appointed full professor in 1987, Newell Chair in 1995, and University Professor in 2012. He completed his undergraduate studies at MIT. He received dual degrees in Mathematics and Physics. He received his Ph.D. in computer science from Yale University in 1979. At the time of his appointment, Carbonell was the youngest chaired professor in the School of Computer Science at CMU. His research spanned several areas of computer science, mostly in artificial intelligence, including: machine learning, data and text mining, natural language processing, very-large-scale knowledge bases, translingual information retrieval and automated summarization. He wrote more than 300 technical papers and gave over 500 invited or refereed-paper presentations (colloquia, seminars, panels, conferences, keynotes, etc.). He died following a long illness on February 28, 2020. Mona Talat Diab became the director of CMU's Language Technologies Institute in 2023. == Research == Carbonell created MMR (maximal marginal relevance) technology for text summarization and informational novelty detection in search engines, invention of transformational analogy, a generalized method for case-based reasoning (CBR) to re-use, modify and compose past successful plans for increasingly complex problems and knowledge-based interlingual machine translation. He was instrumental in setting up the Computational Biolinguistics Program, a joint venture between Carnegie Mellon and the University of Pittsburgh, which combines Language Technologies and Machine Learning to model and predict genomic, proteomic and glycomic 3D structures. Carbonell also did work in machine learning. He organized the first four machine learning conferences, starting with CMU in 1981. The Language Technologies Institute (LTI), founded and directed by Carbonell, achieved top honors in multiple areas. These areas include machine translation, search engines (including founding of Lycos by Michael Mauldin, one of Carbonell’s PhD students), speech synthesis, and education. LTI remains the original, largest and best-known institute for language technologies, with over $12M in annual funding and 200 researchers (faculty, staff, PhD students, MS students, visiting scholars etc.). Carbonell made major technical contributions in several fields, including (1) Creation of MMR (maximal marginal relevance) technology for text summarization and informational novelty detection in search engines,(2) Proactive machine learning for multi-source cost-sensitive active learning, (3) Linked conditional random fields for predicting tertiary and quaternary protein folds, (4) Symmetric optimal phrasal alignment method for trainable example-based and statistical machine translation, (5) Series- anomaly modeling for financial fraud detection and syndromic surveillance, (6) Knowledge-based interlingual machine translation, (7) Robust case-frame parsing, (8) Seeded version-space learning and (9) Invention of transformational and derivational analogy, generalized methods for case-based reasoning (CBR) to re-use, modify and compose past successful plans for increasingly complex problems. The teams led by Carbonell achieved top honors in many areas such as first scalable high-accuracy interlingual machine translation (1991), first speech-to-speech machine translation (1992), first large-scale spider and search engine (1994), and first trainable, large-scale protein-structure topology predictor (2005). Modern machine learning, co-founded by Carbonell, Michalski and Mitchell, is a fundamental enabling technology in search engines, data mining and social networking. Starting in 1980, he co-edited the first three books on ML, launched the ML conferences and was a co-founder and editor-in-chief of ML Journal. Carbonell’s innovations have led to several successful start-ups: Carnegie Group (AI expertsystems), Lycos (web search), Wisdom (financial optimization & ML), Carnegie Speech (spoken-language tutoring), Dynamix (data mining and pattern discovery), and Meaningful Machines (context-based machine translation). Carbonell was the founding director of The Language Technology Institute, the preeminent global institution in language studies, unparalleled in size and scope and has since been adopted/imitated in Germany (DFKI), Japan (Tokyo Univ.), and the US (Johns Hopkins). == Awards and honors == Okawa Prize, 2015 Best paper award, “Translingual Search” w/Yang, International Joint Conference on AI, 1997 Allen Newell endowed chair, Carnegie Mellon University, 1995 Elected fellow of AAAI, 1991 Computer Science teaching award, Carnegie Mellon University, 1987 Sperry Fellowship for excellence in AI research, 1986 Herbert Simon teaching award, 1986 "Recognition of Service" award from the ACM for the SIGART presidency, 1983–1985 Provided congressional testimony on machine translation, 1990 == Selected works == === Books === 1983. (with Ryszard S. Michalski & Tom M. Mitchell, Eds.) Machine learning: An artificial intelligence approach. Los Altos, CA: Morgan Kaufmann. 1986. (with Ryszard S. Michalski & Tom Mitchell, Eds.) Machine learning: An artificial intelligence approach. Vol. II. Los Altos, CA: Morgan-Kaufmann. 1986. (with Ryszard S. Michalski & Tom Mitchell, Eds.) Machine Learning: A Guide to Current Research. Kluwer Academic Publishers. == Contributions == “Protein Quaternary Fold Recognition Using Conditional Graphical Models” IJCAI 2007 (w/Liu et al.) “Context-Based Machine Translation” AMTA 2006 (w/Klein et al.) “SCRFs: A New Approach for Protein Fold Recognition,’’ Journal of Computational Biology, 13,2, 2006 (w/Liu et al) “MT for Resource-Poor Languages Using Elicitation-Based Learning” Machine Translation, 2004 ‘‘Learning Approaches for Detecting and Tracking News Events,’’ IEEE Trans I.S., 14, 4, 2000 (w/Yang)

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  • Nicholas Carlini

    Nicholas Carlini

    Nicholas Carlini is an American researcher affiliated with Anthropic and previously with Google DeepMind who has published research in the fields of computer security and machine learning. He is known for his work on adversarial machine learning, particularly his work on the Carlini & Wagner attack in 2016. This attack was particularly useful in defeating defensive distillation, a method used to increase model robustness, and has since been effective against other defenses against adversarial input. In 2018, Carlini demonstrated an attack on Mozilla's DeepSpeech model, showing that hidden commands could be embedded in speech inputs, which the model would execute even if they were inaudible to humans. He also led a team at UC Berkeley that successfully broke seven out of nine defenses against adversarial attacks presented at the 2018 International Conference on Learning Representations. In addition to his work on adversarial attacks, Carlini has made significant contributions to understanding the privacy risks of machine learning models. In 2020, he revealed that large language models, like GPT-2, could memorize and output personally identifiable information. His research demonstrated that this issue worsened with larger models, and he later showed similar vulnerabilities in generative image models, such as Stable Diffusion. == Life and career == Nicholas Carlini obtained his Bachelor of Arts in Computer Science and Mathematics from the University of California, Berkeley, in 2013. He then continued his studies at the same university, where he pursued a PhD under the supervision of David Wagner, completing it in 2018. Carlini became known for his work on adversarial machine learning. In 2016, he worked alongside Wagner to develop the Carlini & Wagner attack, a method of generating adversarial examples against machine learning models. The attack was proved to be useful against defensive distillation, a popular mechanism where a student model is trained based on the features of a parent model to increase the robustness and generalizability of student models. The attack gained popularity when it was shown that the methodology was also effective against most other defenses, rendering them ineffective. In 2018, Carlini demonstrated an attack against Mozilla Foundation's DeepSpeech model where he showed that by hiding malicious commands inside normal speech input the speech model would respond to the hidden commands even when the commands were not discernible by humans. In the same year, Carlini and his team at UC Berkeley showed that out of the 11 papers presenting defenses to adversarial attacks accepted in that year's ICLR conference, seven of the defenses could be broken. Since 2021, he and his team have been working on large language models, creating a questionnaire where humans typically scored 35% whereas AI models scored in the 40%, with GPT-3 getting 38% which could be improved to 40% through few shot prompting. The best performer in the test was UnifiedQA, a model developed by Google specifically for answer questions and answer sets. Carlini has also developed methods to cause large language models like ChatGPT to answer harmful questions like how to construct bombs. He is also known for his work studying the privacy of machine learning models. In 2020, he showed for the first time that large language models would memorize some of the text data that they were trained on. For example, he found that GPT-2 could output personally identifiable information. He then led an analysis of larger models and studied how memorization increased with model size. Then, in 2022 he showed the same vulnerability in generative image models, and specifically diffusion models, by showing that Stable Diffusion could output images of people's faces that it was trained on. Following on this, Carlini then showed that ChatGPT would also sometimes output exact copies of webpages it was trained on, including personally identifiable information. Some of these studies have since been referenced by the courts in debating the copyright status of AI models. == Other work == Carlini received the Best of Show award at the 2020 IOCCC for implementing a tic-tac-toe game entirely with calls to printf, expanding on work from a research paper of his from 2015. The judges commented on his submission "This year's Best of Show (carlini) is such a novel way of obfuscation that it would be worth of a special mention in the (future) Best of IOCCC list!". [sic] == Awards == Best Student Paper Award, IEEE S&P 2017 ("Towards Evaluating the Robustness of Neural Networks") Best Paper Award, ICML 2018 ("Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples") Distinguished Paper Award, USENIX 2021 ("Poisoning the Unlabeled Dataset of Semi-Supervised Learning") Distinguished Paper Award, USENIX 2023 ("Tight Auditing of Differentially Private Machine Learning") Best Paper Award, ICML 2024 ("Stealing Part of a Production Language Model") Best Paper Award, ICML 2024 ("Considerations for Differentially Private Learning with Large-Scale Public Pretraining")

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