Jun'ichi Tsujii (辻井 潤一, Tsujii Jun'ichi; born 7 February 1949) is a Japanese computer scientist specializing in natural language processing and text mining, particularly in the field of biology and bioinformatics. == Education == Tsujii received his Bachelor of Engineering, Master of Engineering and PhD degrees in electrical engineering from Kyoto University in 1971, 1973, and 1978 respectively. He was Assistant Professor and Associate Professor at Kyoto University, before accepting a position as Professor of Computational Linguistics at the University of Manchester Institute of Science and Technology (UMIST) in 1988. He was President of the Association for Computational Linguistics (ACL) in 2006, and has been a permanent member of the International Committee on Computational Linguistics (ICCL) since 1992, and the chair of the committee since 2014. == Research == Since May 2015, Tsujii has been the director of the Artificial Intelligence Research Center at the National Institute of Advanced Industrial Science and Technology, Japan. Tsujii was previously a Principal Researcher at Microsoft Research Asia (MSRA). Before joining MSRA, he was a professor at the University of Tokyo, where he belonged to both the School of Inter-faculty Initiative on Informatics and the Graduate School of Information Science and Technology. Tsujii is also a Visiting Professor and Scientific Advisor at the National Centre for Text Mining (NaCTeM) at the University of Manchester in the United Kingdom. == Awards == On 14 May 2010, Tsujii was awarded the Medals of Honor with Purple Ribbon, one of Japan's highest awards, presented to influential contributors in the fields of art, academics or sports. In September 2014, Tsujii was awarded the FUNAI Achievement Award at the Forum on Information Technology (FIT), which took place at the University of Tsukuba. The award is presented to distinguished individuals engaged in research or related business activities in the field of Information Technology who have produced excellent achievements in the field, are still active in leading positions and have strong impact on young students and researchers. In December 2014, Tsujii was named as an ACL Fellow, in recognition of his significant contributions to MT, parsing by unification-based grammar and text mining for biology. In March 2016, Tsujii was awarded Okawa Prize for his contribution to the field of Natural Language Processing, Machine Translation and Text Mining, together with Professor Jaime Carbonnel of CMU. In August 2021, Tsujii received ACL Lifetime Achievement Award, which is considered the most prestigious award in the field of Computational Linguistics and Natural Language Processing. In May 2022, Tsujii received the Order of the Sacred Treasure, Gold Rays and Neck Ribbon, from the Japanese government. In October 2024, Tsujii was designated a Person of Cultural Merit. == Selected publications == Oiwa, Hidekazu; Tsujii, Jun'ichi (2014). Common Space Embedding of Primal-Dual Relation Semantic Spaces. COLING 2014. Dublin. pp. 1579–1590. Taura, K.; Matsuzaki, T.; Miwa, M.; Kamoshida, Y.; Yokoyama, D.; Dun, N.; Shibata, T.; Jun, C. S.; Tsujii, J. (2013). "Design and implementation of GXP make – A workflow system based on make". Future Generation Computer Systems. 29 (2): 662–672. doi:10.1016/j.future.2011.05.026. S2CID 31627886. Sun, X.; Zhang, Y.; Matsuzaki, T.; Tsuruoka, Y.; Tsujii, J. (2013). "Probabilistic Chinese word segmentation with non-local information and stochastic training". Information Processing & Management. 49 (3): 626–636. doi:10.1016/j.ipm.2012.12.003. Mu, T.; Goulermas, J. Y.; Tsujii, J.; Ananiadou, S. (2012). "Proximity-Based Frameworks for Generating Embeddings from Multi-Output Data". IEEE Transactions on Pattern Analysis and Machine Intelligence. 34 (11): 2216–2232. Bibcode:2012ITPAM..34.2216M. doi:10.1109/TPAMI.2012.20. PMID 23289130. S2CID 711467. Miwa, M.; Sætre, R.; Kim, J. D.; Tsujii, J. (2010). "Event Extraction with Complex Event Classification Using Rich Features". Journal of Bioinformatics and Computational Biology. 08 (1): 131–146. doi:10.1142/S0219720010004586. PMID 20183879. Kim, J. D.; Ohta, T.; Tsujii, J. (2008). "Corpus annotation for mining biomedical events from literature". BMC Bioinformatics. 9 10. doi:10.1186/1471-2105-9-10. PMC 2267702. PMID 18182099. Miyao, Y.; Tsujii, J. (2008). "Feature Forest Models for Probabilistic HPSG Parsing". Computational Linguistics. 34: 35–80. doi:10.1162/coli.2008.34.1.35. S2CID 885002. Sagae, Kenji; Tsujii, Jun'ichi (2007). Dependency Parsing and Domain Adaptation with LR Models and Parser Ensembles. EMNLP-CoNLL. pp. 1044–1050. Ananiadou, S; Pyysalo, S; Tsujii, J; Kell, D. B. (2010). "Event extraction for systems biology by text mining the literature". Trends in Biotechnology. 28 (7): 381–90. doi:10.1016/j.tibtech.2010.04.005. PMID 20570001. Tsuruoka, Y.; Tateishi, Y.; Kim, J. D.; Ohta, T.; McNaught, J.; Ananiadou, S.; Tsujii, J. (2005). "Developing a Robust Part-of-Speech Tagger for Biomedical Text". Advances in Informatics. Lecture Notes in Computer Science. Vol. 3746. p. 382. doi:10.1007/11573036_36. ISBN 978-3-540-29673-7. S2CID 206592413. Tsuruoka, Y.; Tsujii, J. (2005). Bidirectional inference with the easiest-first strategy for tagging sequence data. Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05. pp. 467–474. doi:10.3115/1220575.1220634. Tsujii, J.; Ananiadou, S. (2005). "Thesaurus or Logical Ontology, Which One Do We Need for Text Mining?". Language Resources and Evaluation. 39: 77–90. doi:10.1007/s10579-005-2697-0. S2CID 3204827. Kazama, J. I.; Tsujii, J. I. (2005). "Maximum Entropy Models with Inequality Constraints: A Case Study on Text Categorization". Machine Learning. 60 (1–3): 159–194. doi:10.1007/s10994-005-0911-3. hdl:10119/3305. Matsuzaki, T.; Miyao, Y.; Tsujii, J. I. (2005). Probabilistic CFG with latent annotations. 43rd Annual Meeting on Association for Computational Linguistics - ACL '05. p. 75. doi:10.3115/1219840.1219850. Kim, J. -D.; Ohta, T.; Tateisi, Y.; Tsujii, J. (2003). "GENIA corpus--a semantically annotated corpus for bio-textmining". Bioinformatics. 19: i180–i182. doi:10.1093/bioinformatics/btg1023. PMID 12855455. Hirschman, L.; Park, J. C.; Tsujii, J.; Wong, L.; Wu, C. H. (2002). "Accomplishments and challenges in literature data mining for biology". Bioinformatics. 18 (12): 1553–1561. doi:10.1093/bioinformatics/18.12.1553. PMID 12490438. Torisawa, K.; Tsujii, J. I. (1996). Computing phrasal-signs in HPSG prior to parsing. 16th conference on Computational linguistics -. Vol. 2. p. 949. doi:10.3115/993268.993332.
Mistral Vibe
Mistral Vibe or Vibe (Le Chat until May 2026), is a chatbot that uses generative artificial intelligence developed in France by Mistral AI. Mistral Vibe is available in iOS and Android. Its services are operated on a freemium model. == History == In February 2024, Mistral AI released Le Chat. In January 2025, Mistral AI made a content deal with Agence France-Presse (AFP) that lets Le Chat query AFP's entire archive dating back to 1983. On 6 February 2025, a mobile app for Le Chat was released for iOS and Android, and a subscription tier, Pro, was introduced at a cost of $14.99 per month. In July 2025, Mistral AI released Voxtral, an open-source language model that understands and generates audio. Mistral introduced a voice mode for chatting that uses Voxtral, and projects, which allows grouping chats and files. In September 2025, Le Chat introduced the capability to remember previous conversations. In May 2026, Mistral AI announced the rebrand from Le Chat to Mistral Vibe and new features were introduced at the same time.
Logic Programming Associates
Logic Programming Associates (LPA) is a company specializing in logic programming and artificial intelligence software. LPA was founded in 1980 and is widely known for its range of Prolog compilers, the Flex expert system toolkit and most recently, VisiRule. LPA was established to exploit research at the Department of Computing and Control at Imperial College London into logic programming carried out under the supervision of Prof Robert Kowalski. == History of LPA Prolog == One of the first Prolog implementations made available by LPA was micro-PROLOG which ran on popular 8-bit home computers such as the Sinclair ZX Spectrum and Apple II. The 8-bit micro-PROLOG interpreter was soon followed by micro-PROLOG Professional one of the first Prolog implementations for the IBM PC running MS-DOS. micro-PROLOG Professional could access all of the 640K memory available under MS-DOS and therefore manage much larger programs In 1985, LPA released LPA MacProlog which ran on the MacPlus and Mac II computers which could access up to 4 Mb memory. MacProlog was later licensed to Quintus for re-distribution in the USA. In 1989, LPA started work on a new 32-bit Prolog compiler which could use DOS-extender technology to access up to 4GB memory. This became the basis for LPA Prolog for Windows, aka WIN-PROLOG, which was then released for Windows 3.0 in 1990. LPA's core Prolog product is LPA Prolog for Windows, a compiler and development system for the Microsoft Windows platform. The current LPA software range comprises an integrated AI toolset which covers various aspects of Artificial Intelligence including Logic Programming, Expert Systems, Knowledge-based Systems, Data Mining, Agents and Case-based reasoning etc. As well as continuing with Prolog compiler technology development, LPA has a track record of creating innovative associated tools and products to address specific challenges and opportunities. == Flex Expert System toolkit == In 1989, in response to the rise of interest in Expert Systems and the emergence of products such as Crystal, GoldWorks, NExpert, LPA developed the Flex expert system toolkit, which incorporated frame-based reasoning with inheritance, rule-based programming and data-driven procedures. Flex has its own English-like Knowledge Specification Language (KSL) which means that knowledge and rules are defined in an easy-to-read and understand way. LPA supported Flex on Windows, DOS and Macintosh PCs, as an add-on toolkit to its various LPA Prolog systems and eanbled LPA to enter the then quick vibrant Expert Systems rules-market. Flex was quickly established as the leading Prolog-based expert system toolkit and was licensed to other Prolog providors on other hardware platforms including Telecomputing Plc to supplement Top One on IBM and ICL mainframes. Other implementations included Quintec-Flex, Quintus Flex, Poplog Flex and BIM Flex which were all running on Unix and/or Vax/VMS platforms. POPLOG-Flex was used to build BRAND EVALUATOR - an expert system to assist brand specialists in evaluating the worth of branded products Quintec-Flex was used to build a hybrid system for the non-linear dynamic analysis/design of coupled shear walls Flex was adopted by the Open University as part of its course T396, "Artificial intelligence for technology" which was designed by Prof Adrian Hopgood. Some of the teaching material is now available on his AI tookit website. Flex was also used by David A Ferrucci and Selmer Bringsjord in their storytelling machine, BRUTUS. == PVG == In 1992, LPA helped set up the Prolog Vendors Group, a not-for-profit organization whose aim was to help promote Prolog by making people aware of its usage in industry. == Business Integrity Ltd and Contract Express == Between 1996 and 1998, based on work co-funded through a DTI Smart award, LPA developed ScaffoldIT, a tool for building dynamic documents and intelligent web sites. This technology, built using the LPA Prolog engine and associated ProWeb Server, was able to generate complex, personalised documents such as insurance policy schedules, legal contracts, and complex sales proposals, over the Web. In 1999/2000, LPA helped set up Business Integrity Ltd, as a Joint Venture with Tarlo-Lyons, to bring the above document assembly technology to market. This product eventually became Contract Express. Contract Express became very popular amongst large law firms and was sold worldwide for both internal and external use. Partners and GCs liked Contract Express because lawyers were able to quickly and accurately automate and update their legal templates in Word without requiring IT specialists to convert them into programs. As a result of the commercial success of Contract Express, BIL was acquired by Thomson Reuters in 2015. The very early days of BIL are described by Clive Spenser here. == VisiRule == In 2004, LPA launched VisiRule a graphical tool for developing knowledge-based and decision support systems. VisiRule was described in IEEE Potentials in 2007 (see Drawing on your knowledge with VisiRule): VisiRule has been used in various sectors, to build legal expert systems, machine diagnostic programs, medical and financial advice systems, etc. In 2013, VisiRule was incorporated into Ecosystem Management Decision Support (EMDS) where it has been used to provide enhanced decision support capabilities. EMDS integrates state-of-the-art geographic information system (GIS) as well as logic programming and decision modeling technologies on multiple platforms (Windows, Linux, Mac OS X) to provide decision support for a substantial portion of the adaptive management process of ecosystem management. EMDS is actively used, extended, supported and maintained by Mountain View Business Group (for an in-depth reprise of EMDS see the article in Frontiers in Environmental Science). In 2023, VisiRule was listed as one of the 5 best decision support software for large enterprises in 2024. == Customers == For many years, LPA has worked closely with Valdis Krebs, an American-Latvian researcher, author, and consultant in the field of social and organizational network analysis. Valdis is the founder and chief scientist of Orgnet, and the creator of the popular Inflow software package. LPA Prolog and Flex were used to create Allergenius, an expert system for the interpretation of allergen microarray results. Rules representing the knowledge base (KB) were derived from the literature and specialized databases. The input data included the patient's ID and disease(s), the results of either a skin prick test or specific IgE assays and ISAC results. The output was a medical report.
Minimum information standard
Minimum information standards are sets of guidelines and formats for reporting data derived by specific high-throughput methods. Their purpose is to ensure the data generated by these methods can be easily verified, analysed and interpreted by the wider scientific community. Ultimately, they facilitate the transfer of data from journal articles (unstructured data) into databases (structured data) in a form that enables data to be mined across multiple data sets. Minimal information standards are available for a vast variety of experiment types including microarray (MIAME), RNAseq (MINSEQE), metabolomics (MSI) and proteomics (MIAPE). Minimum information standards typically have two parts. Firstly, there is a set of reporting requirements – typically presented as a table or a checklist. Secondly, there is a data format. Information about an experiment needs to be converted into the appropriate data format for it to be submitted to the relevant database. In the case of MIAME, the data format is provided in spreadsheet format (MAGE-TAB). Some of the communities that maintain minimum information standards also provide tools to help experimental researchers to annotate their data. == MI Standards == The individual minimum information standards are brought by the communities of cross-disciplinary specialists focused on the problematic of the specific method used in experimental biology. The standards then provide specifications what information about the experiments (metadata) is crucial and important to be reported together with the resultant data to make it comprehensive. The need for this standardization is largely driven by the development of high-throughput experimental methods that provide tremendous amounts of data. The development of minimum information standards of different methods is since 2008 being harmonized by "Minimum Information about a Biomedical or Biological Investigation" (MIBBI) project. === MIAPPE, Minimum Information About a Plant Phenotyping Experiment === MIAPPE is an open, community driven project to harmonize data from plant phenotyping experiments. MIAPPE comprises both a conceptual checklist of metadata required to adequately describe a plant phenotyping experiment. === MIQE, Minimum Information for Publication of Quantitative Real-Time PCR Experiments === Published in 2009 these guidelines for the basis of requirements by many journals when submitting QPCR data, sadly they are not adhered to enough. === MIAME, gene expression microarray === Minimum Information About a Microarray Experiment (MIAME) describes the Minimum Information About a Microarray Experiment that is needed to enable the interpretation of the results of the experiment unambiguously and potentially to reproduce the experiment and is aimed at facilitating the dissemination of data from microarray experiments. It was published by the FGED Society in 2001 and was the first published minimum information standard for high-throughput experiments in the life sciences. MIAME contains a number of extensions to cover specific biological domains, including MIAME-env, MIAME-nut and MIAME-tox, covering environmental genomics, nutritional genomics and toxogenomics, respectively. === MINI: Minimum Information about a Neuroscience Investigation === ==== MINI: Electrophysiology ==== Electrophysiology is a technology used to study the electrical properties of biological cells and tissues. Electrophysiology typically involves the measurements of voltage change or electric current flow on a wide variety of scales from single ion channel proteins to whole tissues. This document is a single module, as part of the Minimum Information about a Neuroscience investigation (MINI) family of reporting guideline documents, produced by community consultation and continually available for public comment. A MINI module represents the minimum information that should be reported about a dataset to facilitate computational access and analysis to allow a reader to interpret and critically evaluate the processes performed and the conclusions reached, and to support their experimental corroboration. In practice a MINI module comprises a checklist of information that should be provided (for example about the protocols employed) when a data set is described for publication. The full specification of the MINI module can be found here. === MIARE, RNAi experiment === Minimum Information About an RNAi Experiment (MIARE) is a data reporting guideline which describes the minimum information that should be reported about an RNAi experiment to enable the unambiguous interpretation and reproduction of the results. === MIACA, cell based assay === Advances in genomics and functional genomics have enabled large-scale analyses of gene and protein function by means of high-throughput cell biological analyses. Thereby, cells in culture can be perturbed in vitro and the induced effects recorded and analyzed. Perturbations can be triggered in several ways, for instance with molecules (siRNAs, expression constructs, small chemical compounds, ligands for receptors, etc.), through environmental stresses (such as temperature shift, serum starvation, oxygen deprivation, etc.), or combinations thereof. The cellular responses to such perturbations are analyzed in order to identify molecular events in the biological processes addressed and understand biological principles. We propose the Minimum Information About a Cellular Assay (MIACA) for reporting a cellular assay, and CA-OM, the modular cellular assay object model, to facilitate exchange of data and accompanying information, and to compare and integrate data that originate from different, albeit complementary approaches, and to elucidate higher order principles. Documents describing MIACA are available and provide further information as well as the checklist of terms that should be reported. === MIAPE, proteomic experiments === The Minimum Information About a Proteomic Experiment documents describe information which should be given along with a proteomic experiment. The parent document describes the processes and principles underpinning the development of a series of domain specific documents which now cover all aspects of a MS-based proteomics workflow. === MIMIx, molecular interactions === This document has been developed and maintained by the Molecular Interaction worktrack of the HUPO-PSI (www.psidev.info) and describes the Minimum Information about a Molecular Interaction experiment. === MIAPAR, protein affinity reagents === The Minimum Information About a Protein Affinity Reagent has been developed and maintained by the Molecular Interaction worktrack of the HUPO-PSI (www.psidev.info)in conjunction with the HUPO Antibody Initiative and a European consortium of binder producers and seeks to encourage users to improve their description of binding reagents, such as antibodies, used in the process of protein identification. === MIABE, bioactive entities === The Minimum Information About a Bioactive Entity was produced by representatives from both large pharma and academia who are looking to improve the description of usually small molecules which bind to, and potentially modulate the activity of, specific targets in a living organism. This document encompasses drug-like molecules as well as herbicides, pesticides and food additives. It is primarily maintained through the EMBL-EBI Industry program (www.ebi.ac.uk/industry). === MIGS/MIMS, genome/metagenome sequences === This specification is being developed by the Genomic Standards Consortium === MIFlowCyt, flow cytometry === === Minimum Information about a Flow Cytometry Experiment === The Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) is a standard related to flow cytometry which establishes criteria to record information on experimental overview, samples, instrumentation and data analysis. It promotes consistent annotation of clinical, biological and technical issues surrounding a flow cytometry experiment. === MINDR, dual gene expression reporters === Requires (1) reporting absolute values of reporter readouts, (2) list of positive and negative controls, and (3) sequences of all reporter constructs. === MISFISHIE, In Situ Hybridization and Immunohistochemistry Experiments === === MIAPA, Phylogenetic Analysis === Criteria for Minimum Information About a Phylogenetic Analysis were described in 2006. === MIRAGE, Glycomics === The MIRAGE project is supported and coordinated by the Beilstein-Institut to establish guidelines for data handling and processing in glycomics research [1] === MIAO, ORF === === MIAMET, METabolomics experiment === === MIAFGE, Functional Genomics Experiment === === MIRIAM, Minimum Information Required in the Annotation of Models === The Minimal Information Required In the Annotation of Models (MIRIAM), is a set of rules for the curation and annotation of quantitative models of biological systems. === MIASE, Minimum Information About a Simulation Experiment =
Minion (solver)
Minion is a solver for satisfaction problems. Unlike constraint programming toolkits, which expect users to write programs in a traditional programming language like C++, Java or Prolog, Minion takes a text file which specifies the problem, and solves using only this. This makes using Minion much simpler, at the cost of much less customization. Minion has been shown to be faster than major commercial constraint solvers including CPLEX (formerly IBM ILOG). == Overview == Minion was introduced in 2006 by researchers at the University of St Andrews as a “fast, scalable” solver for large and hard CSP instances. The project provides a compact input language and a low-overhead C++ implementation aimed at throughput and memory efficiency. == Design and features == Minion implements a range of variable and constraint types commonly used in CSP modelling, plus search heuristics and optimisation support. The solver architecture prioritises cache-friendly data structures and specialised propagators. Notably, the developers adapted watched literal techniques from SAT solving to speed up constraint propagation for, among others, Boolean sums, the element global constraint, and table constraints. The modelling approach relies on a plain-text format (parsed by Minion) rather than embedding models into a host programming language. This reduces overhead and supports rapid “model-and-run” experimentation for large benchmark sets. == Performance == In the original evaluation on standard benchmarks, the authors reported that Minion often ran between one and two orders of magnitude faster than state-of-the-art toolkits of the time (including ILOG Solver and Gecode) on large, hard instances, with smaller gains—or slowdowns—on easier problems. Subsequent research has used Minion as a baseline solver in empirical studies and test generation tasks, reflecting its adoption within parts of the constraint programming community. == Applications == Minion has been applied in academic work on combinatorial search, scheduling and test generation, and is available to other environments via wrappers (for example, from the R language).
Pandemonium architecture
Pandemonium architecture is a theory in cognitive science that describes how visual images are processed by the brain. It has applications in artificial intelligence and pattern recognition. The theory was introduced by the artificial intelligence pioneer Oliver Selfridge in his 1959 paper "Pandemonium - A Paradigm for Learning". It describes the process of object recognition as the exchange of signals within a hierarchical system of detection and association, the elements of which Selfridge metaphorically termed "demons". This model is now recognized as the basis of visual perception in cognitive science. Pandemonium architecture arose in response to the inability of template matching theories to offer a biologically plausible explanation of the image constancy phenomenon. Contemporary researchers praise this architecture for its elegancy and creativity; that the idea of having multiple independent systems (e.g., feature detectors) working in parallel to address the image constancy phenomena of pattern recognition is powerful yet simple. The basic idea of the pandemonium architecture is that a pattern is first perceived in its parts before the "whole". Pandemonium architecture was one of the first computational models in pattern recognition. Although not perfect, the pandemonium architecture influenced the development of modern connectionist, artificial intelligence, and word recognition models. == History == Most research in perception has been focused on the visual system, investigating the mechanisms of how we see and understand objects. A critical function of our visual system is its ability to recognize patterns, but the mechanism by which this is achieved is unclear. The earliest theory that attempted to explain how we recognize patterns is the template matching model. According to this model, we compare all external stimuli against an internal mental representation. If there is "sufficient" overlap between the perceived stimulus and the internal representation, we will "recognize" the stimulus. Although some machines follow a template matching model (e.g., bank machines verifying signatures and accounting numbers), the theory is critically flawed in explaining the phenomena of image constancy: we can easily recognize a stimulus regardless of the changes in its form of presentation (e.g., T and T are both easily recognized as the letter T). It is highly unlikely that we have a stored template for all of the variations of every single pattern. As a result of the biological plausibility criticism of the template matching model, feature detection models began to rise. In a feature detection model, the image is first perceived in its basic individual elements before it is recognized as a whole object. For example, when we are presented with the letter A, we would first see a short horizontal line and two slanted long diagonal lines. Then we would combine the features to complete the perception of A. Each unique pattern consists of different combination of features, which means those that are formed with the same features will generate the same recognition. That is, regardless of how we rotate the letter A, is still perceived as the letter A. It is easy for this sort of architecture to account for the image constancy phenomena because you only need to "match" at the basic featural level, which is presumed to be limited and finite, thus biologically plausible. The best known feature detection model is called the pandemonium architecture. == Pandemonium architecture == The pandemonium architecture was originally developed by Oliver Selfridge in the late 1950s. The architecture is composed of different groups of "demons" working independently to process the visual stimulus. Each group of demons is assigned to a specific stage in recognition, and within each group, the demons work in parallel. There are four major groups of demons in the original architecture. The concept of feature demons, that there are specific neurons dedicated to perform specialized processing is supported by research in neuroscience. Hubel and Wiesel found there were specific cells in a cat's brain that responded to specific lengths and orientations of a line. Similar findings were discovered in frogs, octopuses and a variety of other animals. Octopuses were discovered to be only sensitive to verticality of lines, whereas frogs demonstrated a wider range of sensitivity. These animal experiments demonstrate that feature detectors seem to be a very primitive development. That is, it did not result from the higher cognitive development of humans. Not surprisingly, there is also evidence that the human brain possesses these elementary feature detectors as well. Moreover, this architecture is capable of learning, similar to a back-propagation styled neural network. The weight between the cognitive and feature demons can be adjusted in proportion to the difference between the correct pattern and the activation from the cognitive demons. To continue with our previous example, when we first learned the letter R, we know is composed of a curved, long straight, and a short angled line. Thus when we perceive those features, we perceive R. However, the letter P consists of very similar features, so during the beginning stages of learning, it is likely for this architecture to mistakenly identify R as P. But through constant exposure of confirming R's features to be identified as R, the weights of R's features to P are adjusted so the P response becomes inhibited (e.g., learning to inhibit the P response when a short angled line is detected). In principle, a pandemonium architecture can recognize any pattern. As mentioned earlier, this architecture makes error predictions based on the amount of overlapping features. Such as, the most likely error for R should be P. Thus, in order to show this architecture represents the human pattern recognition system we must put these predictions into test. Researchers have constructed scenarios where various letters are presented in situations that make them difficult to identify; then types of errors were observed, which was used to generate confusion matrices: where all of the errors for each letter are recorded. Generally, the results from these experiments matched the error predictions from the pandemonium architecture. Also as a result of these experiments, some researchers have proposed models that attempted to list all of the basic features in the Roman alphabet. == Criticism == A major criticism of the pandemonium architecture is that it adopts a completely bottom-up processing: recognition is entirely driven by the physical characteristics of the targeted stimulus. This means that it is unable to account for any top-down processing effects, such as context effects (e.g., pareidolia), where contextual cues can facilitate (e.g., word superiority effect: it is relatively easier to identify a letter when it is part of a word than in isolation) processing. However, this is not a fatal criticism to the overall architecture, because is relatively easy to add a group of contextual demons to work along with the cognitive demons to account for these context effects. Although the pandemonium architecture is built on the fact that it can account for the image constancy phenomena, some researchers have argued otherwise; and pointed out that the pandemonium architecture might share the same flaws from the template matching models. For example, the letter H is composed of 2 long vertical lines and a short horizontal line; but if we rotate the H 90 degrees in either direction, it is now composed of 2 long horizontal lines and a short vertical line. In order to recognize the rotated H as H, we would need a rotated H cognitive demon. Thus we might end up with a system that requires a large number of cognitive demons in order to produce accurate recognition, which would lead to the same biological plausibility criticism of the template matching models. However, it is rather difficult to judge the validity of this criticism because the pandemonium architecture does not specify how and what features are extracted from incoming sensory information, it simply outlines the possible stages of pattern recognition. But of course that raises its own questions, to which it is almost impossible to criticize such a model if it does not include specific parameters. Also, the theory appears to be rather incomplete without defining how and what features are extracted, which proves to be especially problematic with complex patterns (e.g., extracting the weight and features of a dog). Some researchers have also pointed out that the evidence supporting the pandemonium architecture has been very narrow in its methodology. Majority of the research that supports this architecture has often referred to its ability to recognize simple schematic drawings that are selected from a small finite set (e.g., letters in the Roman alphabet). Evidence from these types of exper
Marco Camisani Calzolari
Marco Camisani Calzolari (born March 1969) is an Italian British university professor, author, and television personality specializing in digital communications, transformation, and artificial intelligence. He advises the Italian government and police on ethical AI and digital safety and hosts the digital segment of the Italian news show Striscia la Notizia. His research gained international attention in 2012 after creating an algorithm claiming to identify real Twitter users from fake users of bots. Marco Camisani Calzolari was awarded as an Honorary Police Officer by the Italian State Police and the Knight of the Italian Republic. == Biography == Camisani Calzolari was born in Milan, Italy where he began his television career, hosting on local provider LA7 in (2001). In 2008 Camisani Calzolari moved to the UK where he founded multiple digital start-ups. He is now a naturalised British citizen and applied to become a "Freeman of the City" in June 2022. In 2024, Marco Camisani Calzolari began serving as the Chair and Adjunct Professor of the elective course Cyber-Humanities within the Degree Programme in Medicine and Surgery at Università Vita-Salute S.Raffaele in Milan. On the 14th of May 2024, Camisani Calzolari was awarded the Knight of the Italian Republic (Order of the Star of Italy). In 2024, Marco Camisani Calzolari was awarded the title of Honorary Police Officer by the Italian State Police for his commitment to combating cybercrime and promoting digital security. He also received the Keynes Sraffa Award 2024 from the Italian Chamber of Commerce and Industry for the UK. Additionally, he was honored with the University Seal by Università degli Studi della Tuscia (Viterbo) for his efforts in disseminating knowledge both in Italy and abroad. == Academic career == Camisani Calzolari began his academic career at the Università Statale di Milano in 2007, until chairing a course on Corporate Communication and Digital Languages at the IULM University of Milan between 2007 and 2010. During this time Camisani Calzolari published his first written work under the title 'Impresa 4.0'. After moving to London, Camisani Calzolari focussed on digital start-ups including 'Digitalevaluation ltd' where he would publish the results of his Twitter algorithm study. Following its publication, he accepted a role as Affiliate Practitioner at the Centre for Culture Media & Regulation (CCMR), University of Brunel London, and subsequently another role at a British University as Lecturer in Digital Communication at the LCA Business School. Camisani Calzolari returned to Italy to lecture on Interactive Digital Communication at the University of Milan. From 2017 to 2023, he held various roles at the European University of Rome, including Adjunct Professor and Chair in Digital Communication, and published The Fake News Bible in 2018. In 2024 he became the Scientific Coordinator for a Master's program at Università San Raffaele in Milan. === Twitter fake followers study === In 2012, Camisani Calzolari's research came into the focus of the public eye following the publication of his findings in a study analysing the followers of high-profile public figures and corporations. He developed a computer algorithm claiming to be able to distinguish real followers from computer-generated "bots". The algorithm compiled data correlative of human activity such as having a name, image, physical address, using punctuation and cross-account activity. Genuine Twitter users were considered to have written at least 50 posts and possessed over 30 followers themselves. The findings led to scrutiny of several individuals and corporations for allegedly purchasing followers. === Publications === Camisani Calzolari is best for known for his work in improving accessibility to digital and tech solutions for everyday business and personal use. His work in digital and communications has been included in several publications including: Cyberhumanism (2023) The Fake News Bible (2018), First Digital Aid for Business (2015), The Digital World (2013), Escape from Facebook (2012), Enterprise 4.0. Camisani Calzolari was also the subject of a University College London (UCL) case study titled Marco Camisani-Calzolari: the Digital Renaissance Man. == Government work == Since 2023, he is a member of the Coordination Committee on Artificial Intelligence at the Presidency of the Council of Ministers and an advisor in Digital Skills and Designer of initiatives for the Department for Digital Transformation. He also serves as the official spokesperson for the State Police, educating the public on preventing digital threats, avoiding digital scams, and explaining criminal case. Since August 2024, Marco Camisani Calzolari has served as an expert for the Italian Agency for the National Cybersecurity (ACN). In October of the same year, he also became a member of the General-Purpose AI Code of Practice working group for the European Commission. == Television work == Camisani Calzolari hosts a digital segment for Striscia la Notizia, an Italian satirical television program on the Mediaset-controlled Canale 5. He presented on weekly segments that include: RAI 1 – Digital First Aid (TV Program – 2014 to 2017) in the program "Uno Mattina" as a digital expert; RTL 102.5 – Technology Space (Radio Program – 2012 to 2017) in the morning news program as a digital expert (100 episodes from 2012 to 2017); DIGITALK Talkshow (2004) as host of Digitalk; Misterweb (TV Program – 2001 to 2002), he presented the TV program “MisterWeb”, on "LA7". Marco Camisani Calzolari was a testimonial for several institutional communication campaigns by the Italian Department of Digital Transformation. These include initiatives promoting the Punti Digitale Facile, raising awareness about the NIS2 Directive for cybersecurity, and advocating for the adoption of the Electronic Identity Card (CIE).