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Ganapathy, Apoorva, and Taposh Kumar Neogy. "Artificial Intelligence Price Emulator: A Study on Cryptocurrency." Global Disclosure of Economics and Business 6, no. 2 (2017): 115–22. http://dx.doi.org/10.18034/gdeb.v6i2.558.
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The cryptocurrency Artificial intelligence price emulator is a software programmed to collect cryptocurrency market data, analyze the data and predict the market price using the collected data. Computer emulators are programmed to mimic and copy behaviors or other software/hardware. The reason for emulation is to get to a particular result as quickly as possible. Machine learning is the ability of computers to read and process data while learning from the data with human interference or influence. This work focused majorly on how cryptocurrency market prices can be emulated using Artificial Intelligence with machine learning abilities. It also looked into the advantages of using the software for crypto investors. Some of which is the reduced time of research, reduction of risk, among others.
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Ali, Mohammed. "The Human Intelligence vs. Artificial Intelligence: Issues and Challenges in Computer Assisted Language Learning." International Journal of English Linguistics 8, no. 5 (2018): 259. http://dx.doi.org/10.5539/ijel.v8n5p259.
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In this study, the researcher has advocated the importance of human intelligence in language learning since software or any Learning Management System (LMS) cannot be programmed to understand the human context as well as all the linguistic structures contextually. This study examined the extent to which language learning is perilous to machine learning and its programs such as Artificial Intelligence (AI), Pattern Recognition, and Image Analysis used in much assistive learning techniques such as voice detection, face detection and recognition, personalized assistants, besides language learning programs. The researchers argue that language learning is closely associated with human intelligence, human neural networks and no computers or software can claim to replace or replicate those functions of human brain. This study thus posed a challenge to natural language processing (NLP) techniques that claimed having taught a computer how to understand the way humans learn, to understand text without any clue or calculation, to realize the ambiguity in human languages in terms of the juxtaposition between the context and the meaning, and also to automate the language learning process between computers and humans. The study cites evidence of deficiencies in such machine learning software and gadgets to prove that in spite of all technological advancements there remain areas of human brain and human intelligence where a computer or its software cannot enter. These deficiencies highlight the limitations of AI and super intelligence systems of machines to prove that human intelligence would always remain superior.
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Anki, Prasnurzaki, and Alhadi Bustamam. "Measuring the accuracy of LSTM and BiLSTM models in the application of artificial intelligence by applying chatbot programme." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (2021): 197. http://dx.doi.org/10.11591/ijeecs.v23.i1.pp197-205.
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Python programme contains a question and answer system that derived from data sets that have used and implemented the chatbot in this modern era. where the data collected is in the form of corpuses containing extensive metadata-rich fictional conversations derived from extracted film scripts, commonly called cornell movie dialogue corpus. The various models have been used chatbots in python programmes, and LSTM and BiLSTM models were specifically used in this study. Where the form of accuracy will be reported as a result of the implementation of LSTM and BiLSTM models in the chatbot programme. The programme performance will be influenced by the data from the model selection, because the level of accuracy is determined by the target programme being taken. So this is the main factor that determines which model to choose. Based on considerations required for choosing the programme model, in the end the LSTM and the BiLSTM models are chosen and will be applied to the programme. Based on the LSTM and BiLSTM chatbot programmes that have been tested, it can be concluded that the best parameters come from a pair of BiLSTM chatbots using the BiLTSM model with an average accuracy value of 0.995217.
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Qin, Zhi Zhen, Tasneem Naheyan, Morten Ruhwald, et al. "A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers." Tuberculosis 127 (March 2021): 102049. http://dx.doi.org/10.1016/j.tube.2020.102049.
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Horn, Berthold K. P., David Marr, John Hollerbach, et al. "Research in Progress at the Massachusetts Institute of Technology Artificial Intelligence Laboratory." AI Magazine 1, no. 1 (2017): 21. http://dx.doi.org/10.1609/aimag.v1i1.87.
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The MIT AI Laboratory has a long tradition of research in most aspects of Artificial Intelligence. Currently, the major foci include computer vision, manipulation, learning, English-language understanding, VLSI design, expert engineering problem solving, common-sense reasoning, computer architecture, distributed problem solving, models of human memory, programmer apprentices, and human education.
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Pettersen, Lene. "Why Artificial Intelligence Will Not Outsmart Complex Knowledge Work." Work, Employment and Society 33, no. 6 (2018): 1058–67. http://dx.doi.org/10.1177/0950017018817489.
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The potential role of artificial intelligence in improving organisations’ performance and productivity has been promoted regularly and vociferously since the 1960s. Artificial intelligence is today reborn out of big business, similar to the occurrences surrounding big data in the 1990s, and expectations are high regarding AI’s potential role in businesses. This article discusses different aspects of knowledge work that tend to be ignored in the debate about whether or not artificial intelligence systems are a threat to jobs. A great deal of knowledge work concerns highly complex problem solving and must be understood in contextual, social and relational terms. These aspects have no generic nor universal rules and solutions and, thus, cannot be easily replaced by artificial intelligence or programmed into computer systems, nor are they constructed based on models of the rational brain. In this respect, this article draws on philosopher Herbert Dreyfus’ thesis regarding artificial intelligence.
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Olson, Eric T. "The Ontological Basis of Strong Artificial Life." Artificial Life 3, no. 1 (1997): 29–39. http://dx.doi.org/10.1162/artl.1997.3.1.29.
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This article concerns the claim that it is possible to create living organisms, not merely models that represent organisms, simply by programming computers (“virtual” strong alife). I ask what sort of things these computer-generated organisms are supposed to be (where are they, and what are they made of?). I consider four possible answers to this question: (a) The organisms are abstract complexes of pure information; (b) they are material objects made of bits of computer hardware; (c) they are physical processes going on inside the computer; and (d) they are denizens of an entire artificial world, different from our own, that the programmer creates. I argue that (a) could not be right, that (c) collapses into (b), and that (d) would make strong alife either absurd or uninteresting. Thus, “virtual” strong alife amounts to the claim that, by programming a computer, one can literally bring bits of its hardware to life.
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Kuganesan, Anchana. "Artificial Intelligence Can Improve the Healthcare System." Sciential - McMaster Undergraduate Science Journal, no. 2 (March 29, 2019): 37–39. http://dx.doi.org/10.15173/sciential.v1i2.2097.
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Artificial intelligence (AI) is a computer system used to model human cognitive functions, intelligence, and behaviour. Components include both, a virtual and a physical aspect. Virtual aspects of AI include algorithms and neural networks instilled within the system to execute its assignments. Physical components include the entity in conjunction with a code. 1 AI is currently being developed by Nvidia Corporation, Alphabet, Twilio, Amazon, Micron Technology, Microsoft Corp., Baidu, Intel Corp., Facebook, and Tencent. 2 Expanding AI into the health care system can be beneficial for preventative care, patient safety, and reducing treatment costs for families. AI has proven to be useful in machine learning, thus, it can be programmed to complete specific tasks. By performing tasks such as data interpretation, the amount of time that it takes for a physician to consult patients regarding their results will be reduced. In addition, AI is capable of analyzing medical images to identify tumours and it has previously been used in various other branches of medicine such as neurology and cardiology. Overall, AI has great potential to improve the health care industry in North America and worldwide. However, potential violations while utilizing personal patient data must be addressed whilst modifying this technology.
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Abdulllah, Shahino Mah. "Artificial Intelligence (AI) and Its Associated Ethical Issues." ICR Journal 10, no. 1 (2019): 124–26. http://dx.doi.org/10.52282/icr.v10i1.78.
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The adoption of AI in many industries has been regarded by some as a threat to low- and middle-skill workers, as it will drastically cut down reliance on the human workforce. Besides unemployment, there are also concerns about rising economic inequality caused by AI-driven companies. With fewer employees, these companies could gain a disproportionate advantage over conventional companies that still depend on normal, shift-based systems. There is also the issue that some AI bots have achieved the capability to interact with humans and build relationships through conversations. This influential communication could eventually enable these bots to affect human behaviour and possibly trigger certain actions. Significantly, therefore, such intelligent machines are not immune to mistakes and confusion since not all possible examples of real world interaction are covered during their training; this weakness could be manipulated to fulfil certain ends. Also, Al cannot be expected to be entirely fair and neutral, since it is dependent on human programmers, who have their own interests and whims.
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ASTLEY, SUE, IAN HUTT, PETER MILLER, et al. "AUTOMATION IN MAMMOGRAPHY: COMPUTER VISION AND HUMAN PERCEPTION." International Journal of Pattern Recognition and Artificial Intelligence 07, no. 06 (1993): 1313–38. http://dx.doi.org/10.1142/s0218001493000649.
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Mammographic screening programmes generate large numbers of highly variable, complex images, most of which are unequivocally normal. When present, abnormalities may be small or subtle. Two processes critical to the success of screening programmes are the perception of potential abnormalities and the subsequent analy-sis of each detected lesion to determine its clinical significance. The consequences of errors are costly, and in many screening centres, films are read by two radiologists in an attempt to reduce errors. The prime objective of our research is to improve the accuracy of the detection and analysis of breast lesions by providing radiologists with computer-aided digital image analysis tools. In this paper we focus on the detection and analysis of mammographic microcalcifications. We describe a philosophy of research aimed at generating useful computer-based aids for radiologists. Firstly, it is necessary to accurately identify specific tasks which are difficult for the human observer. Having correctly identified a problem, appropriate computer vision methods must be developed and their performance evaluated. It is then important to determine effective ways of using such methods to aid radiologists, and it is essential to prove that the effect on radiologists’ performance is entirely beneficial. We present results of experiments to determine factors affecting radiologists’ perception of microcalcifications, and to investigate the effects of attention-cueing on detection performance. Our results show that radiologists’ performance can be significantly improved with the use of prompts generated from automatically-detected microcalcification clusters. We describe a new method for the delineation of mammographic abnormalities based on the analysis of multiple high quality X-ray projections of excised lesions. Biopsy specimens are secured inside a rigid tetrahedron, the edges of which provide a reference frame to which the locations of features can be related. A three-dimensional representation of an abnormality can be formed and rotated to resemble its appearance in the original mammogram.
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Qiu, Jane. "Research and development of artificial intelligence in China." National Science Review 3, no. 4 (2016): 538–41. http://dx.doi.org/10.1093/nsr/nww076.
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Abstract This year saw several milestones in the development of artificial intelligence. In March, AlphaGo, a computer algorithm developed by Google's London-based company, DeepMind, beat the world champion Lee Sedol at Go, an ancient Chinese board game. In October, the same company unveiled in the journal Nature its latest technique that allows a machine to solve tasks that require logic and reasoning, such as finding its way around the London Underground using a map it has never seen before. Such progress in recent years has provided significant impetus to developing cutting-edge learning machines around the world, including China. In 2015, the Chinese Academy of Sciences (CAS) set up the Centre for Excellence in Brain Science and Intelligence Technology—a consortium of laboratories from more than 20 CAS institutes and universities. Early this year, China rolled out the China Brain Project, a fifteen-year programme that will focus on brain mapping, neurological diseases and brain-inspired artificial intelligence. In a forum chaired by National Science Review's Executive Associative Editor, Mu-ming Poo, who also leads the CAS centre for excellence and the China Brain Project, several researchers discussed China's latest initiatives and progress in artificial intelligence, where the future lies and what the main challenges are. Yunji Chen Institute of Computing Technology, Chinese Academy of Sciences, Beijing Tieniu Tan Institute of Automation, Deputy President of Chinese Academy of Sciences, Beijing Yi Zeng Institute of Automation, Chinese Academy of Sciences, Beijing Hongbin Zha Director of Key Lab of Machine Perception (MOE), Peking University, Beijing Mu-ming Poo (Chair) Director of Institute of Neuroscience, Chinese Academy of Sciences, Shanghai
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Kotiv, B. N., Igor A. Budko, Igor A. Ivanov, and Igor U. Trosko. "Artificial intelligence using for medical diagnosis via implementation of expert systems." Bulletin of the Russian Military Medical Academy 23, no. 1 (2021): 215–24. http://dx.doi.org/10.17816/brmma63657.
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Modern biomedical technologies development affords to provide the doctor with colossal amount of information about patients organism condition. However, the opportunity of using this data for medical diagnosis fully now is a distantive perspective only. The reason is a humans limited ability in assessment and interpretation this data arrays. The solution seems in artificial intelligence and expert systems wide introduction to medicine. Currently, almost all authors consider various options for constructing artificial neural networks as a way to implement artificial intelligence. This approach, which goes back to the fundamental theorem of A.N. Kolmogorov, the works of V.I. Arnold and Hecht-Nielsen [3], demonstrates excellent capabilities in a number of pattern recognition problems, which are reduced to revealing hidden details against the background of input noises. Much less often is mentioned such a method of modeling formal thinking as expert systems, which arose in the 1960s and then went into the shadows. Since the inception of cybernetics, computer programmers have tried to reproduce the mechanism of human thinking, that is, the task was to teach the computer to "think". The first known results in the field of creating and using intelligent systems were laid by the work of Norbert Wiener and G.S. Altshuller. At the same time, the creation of intelligent systems was reduced to the development of programs that solve problems using a variety of heuristic methods based on the property of human thinking to generalize.
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Hägglund, Sture. "The Linköping approach to technology transfer in knowledge engineering." Knowledge Engineering Review 2, no. 3 (1987): 153–58. http://dx.doi.org/10.1017/s0269888900000898.
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AbstractA technology transfer programme, where people from industry have been educated and trained in knowledge engineering on a project basis, has been in operation since 1984 in the Computer and Information Science Department at Linköping University. This review presents the background for the programme, its organization, examples of training projects, educational activities and plans for the future development.
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Apatova, N. V., A. I. Gaponov, and O. Yu Smirnova. "POSSIBILITIES OF ARTIFICIAL INTELLIGENCE IN TEACHING HIGHER MATHEMATICS." Scientific Notes of V.I. Vernadsky Crimean Federal University. Sociology. Pedagogy. Psychology 7 (73), no. 2 (2021): 27–39. http://dx.doi.org/10.37279/2413-1709-2021-7-2-27-39.
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The example of application of methods of computer representation of knowledge to the «Matrix» of the higher mathematics course, the authors examine the possibility of using elements of artificial intelligence, but do not propose an exhaustive algorithm for the construction of relevant systems, and based on the known thesis: «intellectual tasks no formal solution algorithm». Moreover, we implicitly proceed from the concept that when solving problems that require the participation of human intelligence, artificial intelligence can be only an auxiliary tool. Analysis of Russian and English sources has led to the conclusion that automated the process of proving mathematical theorems with the use of artificial intelligence (including neural networks) leads first-year students does not lead first-year students to the natural goal of teaching higher mathematics – » to teach logical reasoning. Usually the result comes down to «programmed» using the keyboard that, in certain circumstances, of course, is a very useful skill. But, unfortunately, the task «to teach the student to think, to reason logically and think outside the box», based on modern level of development of artificial intelligence (machine learning), according to the authors, is not yet resolved. Using the same knowledge base in an adequate format, and semantic networks allow at least allow you to at least get closer to the possibility of using artificial intelligence in the proof of theorems of higher mathematics. Because properly constructed semantic network will greatly assist as a teacher is in presenting the proof of the theorem, and demonstrate to the students a well-known logical connections and, we hope, will interest them in creating a new one. The above arguments determine the purpose of the proposed study is to build semantic networks for proof of mathematical theorems, and to solve the so-called «proof-based» tasks. In our opinion, the results of this study will not only contribute to streamlining the structure by the teacher and better understanding by students of the relevant sections of mathematics, but few will bring the solution of the problem of formation of artificial intelligence, based on the decision of «evidence-based» tasks of higher mathematics.
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Rey-López, Marta, Ana Fernández-Vilas, Rebeca P. Díaz-Redondo, et al. "Enhancing TV programmes with additional contents using MPEG-7 segmentation information." Expert Systems with Applications 37, no. 2 (2010): 1124–33. http://dx.doi.org/10.1016/j.eswa.2009.06.053.
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Goksel Canbek, Nil, and Mehmet Emin Mutlu. "On the track of Artificial Intelligence: Learning with Intelligent Personal Assistants." International Journal of Human Sciences 13, no. 1 (2016): 592. http://dx.doi.org/10.14687/ijhs.v13i1.3549.
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<p>In a technology dominated world, useful and timely information can be accessed quickly via Intelligent Personal Assistants (IPAs). By the use of these assistants built into mobile operating systems, daily electronic tasks of a user can be accomplished 24/7. Such tasks like taking dictation, getting turn-by-turn directions, vocalizing email messages, reminding daily appointments, setting reminders, responding any factual questions and invoking apps can be completed by IPAs such as Apple’s <a href="http://searchconsumerization.techtarget.com/definition/Siri" target="_blank">Siri</a>, <a href="http://whatis.techtarget.com/definition/Google-Now" target="_blank">Google Now</a> and Microsoft Cortana. The mentioned assistants programmed within Artificial Intelligence (AI) do create an interaction between human and computer through a natural language used in digital communication. In this regard, the overall purpose of this study is to examine the potential use of IPAs that use advanced cognitive computing technologies and Natural Language Processing (NLP) for learning. To achieve this purpose, the working system of IPAs is reviewed briefly within the scope of AI that has recently become smarter to predict, comprehend and carry out multi-step and complex requests of users.</p>
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Liu, Zhonghui, and Gongyi Jiang. "Optimization of intelligent heating ventilation air conditioning system in urban building based on BIM and artificial intelligence technology." Computer Science and Information Systems, no. 00 (2021): 27. http://dx.doi.org/10.2298/csis200901027l.
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The study aims to effectively reduce building energy consumption, improve the utilization efficiency of building resources, reduce the emission of pollutants and greenhouse gases, and protect the ecological environment. A prediction model of heating ventilation air conditioning (HVAC) energy consumption is established by using back propagation neural network (BPNN) and adapted boosting (Adaboost) algorithm. Then, the HVAC system is optimized by building information modeling (BIM). Finally, the effectiveness of the urban intelligent HVAC optimization prediction model based on BIM and artificial intelligence (AI) is further verified by simulation experiments. The research shows that the error of the prediction model is reduced, the accuracy is higher after the Adaboost algorithm is added to BPNN, and the average prediction accuracy is 86%. When the BIM is combined with the prediction model, the HVAC programme of hybrid cooling beam + variable air volume reheating is taken as the optimal programme of HVAC system. The power consumption and gas consumption of the programme are the least, and the CO2 emission is also the lowest. Programme 1 is compared with programme 3, and the cost is saved by 37% and 15%, respectively. Through the combination of BIM technology and AI technology, the energy consumption of HVAC is effectively reduced, and the resource utilization rate is significantly improved, which can provide theoretical basis for the research of energy-saving equipment.
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Punchoo, Rivak, Sachin Bhoora, and Nelishia Pillay. "Applications of machine learning in the chemical pathology laboratory." Journal of Clinical Pathology 74, no. 7 (2021): 435–42. http://dx.doi.org/10.1136/jclinpath-2021-207393.
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Machine learning (ML) is an area of artificial intelligence that provides computer programmes with the capacity to autodidact and learn new skills from experience, without continued human programming. ML algorithms can analyse large data sets quickly and accurately, by supervised and unsupervised learning techniques, to provide classification and prediction value outputs. The application of ML to chemical pathology can potentially enhance efficiency at all phases of the laboratory’s total testing process. Our review will broadly discuss the theoretical foundation of ML in laboratory medicine. Furthermore, we will explore the current applications of ML to diverse chemical pathology laboratory processes, for example, clinical decision support, error detection in the preanalytical phase, and ML applications in gel-based image analysis and biomarker discovery. ML currently demonstrates exploratory applications in chemical pathology with promising advancements, which have the potential to improve all phases of the chemical pathology total testing pathway.
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Nelson, Chris R., Jessica Ekberg, and Kent Fridell. "Prostate Cancer Detection in Screening Using Magnetic Resonance Imaging and Artificial Intelligence." Open Artificial Intelligence Journal 6, no. 1 (2020): 1–11. http://dx.doi.org/10.2174/1874061802006010001.
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Background: Prostate cancer is a leading cause of death among men who do not participate in a screening programme. MRI forms a possible alternative for prostate analysis of a higher level of sensitivity than the PSA test or biopsy. Magnetic resonance is a non-invasive method and magnetic resonance tomography produces a large amount of data. If a screening programme were implemented, a dramatic increase in radiologist workload and patient waiting time will follow. Computer Aided-Diagnose (CAD) could assist radiologists to decrease reading times and cost, and increase diagnostic effectiveness. CAD mimics radiologist and imaging guidelines to detect prostate cancer. Aim: The purpose of this study was to analyse and describe current research in MRI prostate examination with the aid of CAD. The aim was to determine if CAD systems form a reliable method for use in prostate screening. Methods: This study was conducted as a systematic literature review of current scientific articles. Selection of articles was carried out using the “Preferred Reporting Items for Systematic Reviews and for Meta-Analysis” (PRISMA). Summaries were created from reviewed articles and were then categorised into relevant data for results. Results: CAD has shown that its capability concerning sensitivity or specificity is higher than a radiologist. A CAD system can reach a peak sensitivity of 100% and two CAD systems showed a specificity of 100%. CAD systems are highly specialised and chiefly focus on the peripheral zone, which could mean missing cancer in the transition zone. CAD systems can segment the prostate with the same effectiveness as a radiologist. Conclusion: When CAD analysed clinically-significant tumours with a Gleason score greater than 6, CAD outperformed radiologists. However, their focus on the peripheral zone would require the use of more than one CAD system to analyse the entire prostate.
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Hanczyc, Martin M., Juan M. Parrilla, Arwen Nicholson, Kliment Yanev, and Kasper Stoy. "Creating and Maintaining Chemical Artificial Life by Robotic Symbiosis." Artificial Life 21, no. 1 (2015): 47–54. http://dx.doi.org/10.1162/artl_a_00151.
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We present a robotic platform based on the open source RepRap 3D printer that can print and maintain chemical artificial life in the form of a dynamic, chemical droplet. The robot uses computer vision, a self-organizing map, and a learning program to automatically categorize the behavior of the droplet that it creates. The robot can then use this categorization to autonomously detect the current state of the droplet and respond. The robot is programmed to visually track the droplet and either inject more chemical fuel to sustain a motile state or introduce a new chemical component that results in a state change (e.g., division). Coupling inexpensive open source hardware with sensing and feedback allows for replicable real-time manipulation and monitoring of nonequilibrium systems that would be otherwise tedious, expensive, and error-prone. This system is a first step towards the practical confluence of chemical, artificial intelligence, and robotic approaches to artificial life.
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Banyasad, Omid, and Philip T. Cox. "Visual Programming of Subsumption-Based Reactive Behaviour." International Journal of Advanced Robotic Systems 5, no. 4 (2008): 42. http://dx.doi.org/10.5772/6226.
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General purpose visual programming languages (VPLs) promote the construction of programs that are more comprehensible, robust, and maintainable by enabling programmers to directly observe and manipulate algorithms and data. However, they usually do not exploit the visual representation of entities in the problem domain, even if those entities and their interactions have obvious visual representations, as is the case in the robot control domain. We present a formal control model for autonomous robots, based on subsumption, and use it as the basis for a VPL in which reactive behaviour is programmed via interactions with a simulation.
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MCGAUGHRAN, DANIEL, and MENGJIE ZHANG. "EVOLVING MORE REPRESENTATIVE PROGRAMS WITH GENETIC PROGRAMMING." International Journal of Software Engineering and Knowledge Engineering 19, no. 01 (2009): 1–22. http://dx.doi.org/10.1142/s021819400900409x.
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This paper describes a new representation of tree-based genetic programs in Genetic Programming, an approach of artificial intelligence and knowledge engineering, in order to adopt a form more conducive to imperative functions as developed by human programmers. This representation incorporates the Abstract Syntax Tree form into a larger tree structure based on a Control Flow Graph, thereby causing statements to be chained together sequentially and allowing genetic programs to be output as (non-object-oriented) C++ code fragments. Maintaining or improving the evolutionary performance has been a key priority in this development. These prompt additional genetic operators to be defined to better preserve chains of statements than the traditional Mutation and Crossover operators, thereby encouraging a more efficient evolution of genetic programs. Experimental results suggest that adopting a chained approach can make a significant improvement in evolutionary performance over using ProgN functions that evaluate their children sequentially. The introduction of additional operators can improve the evolutionary performance even further. This approach can automatically generate computer programs for a particular problem using artificial intelligence and knowledge engineering approaches. In particular, the newly developed operators in the chained approach have great potential for generating human competitive programs in commonly used imperative programming languages such as C++.
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Mott, D. H., J. Cunningham, G. Kelleher, and J. A. Gadsden. "Constraint-based reasoning for generating naval flying programmes." Expert Systems 5, no. 3 (1988): 226–46. http://dx.doi.org/10.1111/j.1468-0394.1988.tb00066.x.
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Jakiela, M. J., and P. Y. Papalambros. "Design and Implementation of a Prototype ‘Intelligent’ CAD System." Journal of Mechanisms, Transmissions, and Automation in Design 111, no. 2 (1989): 252–58. http://dx.doi.org/10.1115/1.3258991.
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System requirements and system design for integrating a production rule program and a computer aided design system are presented. An implementation using a commercially available graphics modeling system is described. A “suggestive mode” interface is programmed as an example with application to design for automated assembly. Initial use of the implementation indicates that encoding production rules is more difficult than with conventional text-only knowledge-based system, but that this system is a more effective way to use artificial intelligence techniques in design. The system is intended for use with knowledge domains that are not well represented by usual analytical means.
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Burget, Radek, and Pavel Smrz. "Extracting Visually Presented Element Relationships from Web Documents." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 2 (2013): 13–29. http://dx.doi.org/10.4018/ijcini.2013040102.
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Many documents in the World Wide Web present structured information that consists of multiple pieces of data with certain relationships among them. Although it is usually not difficult to identify the individual data values in the document text, their relationships are often not explicitly described in the document content. They are expressed by visual presentation of the document content that is expected to be interpreted by a human reader. In this paper, the authors propose a formal generic model of logical relationships in a document based on an interpretation of visual presentation patterns in the documents. The model describes the visually expressed relationships between individual parts of the contents independently of the document format and the particular way of presentation. Therefore, it can be used as an appropriate document model in many information retrieval or extraction applications. The authors formally define the model, the authors introduce a method of extracting the relationships between the content parts based on the visual presentation analysis and the authors discuss the expected applications. The authors also present a new dataset consisting of programmes of conferences and other scientific events and the authors discuss its suitability for the task in hand. Finally, the authors use the dataset to evaluate results of the implemented system.
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Huemer, Florian, Michael Leisch, Roland Geisberger, et al. "Combination Strategies for Immune-Checkpoint Blockade and Response Prediction by Artificial Intelligence." International Journal of Molecular Sciences 21, no. 8 (2020): 2856. http://dx.doi.org/10.3390/ijms21082856.
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The therapeutic concept of unleashing a pre-existing immune response against the tumor by the application of immune-checkpoint inhibitors (ICI) has resulted in long-term survival in advanced cancer patient subgroups. However, the majority of patients do not benefit from single-agent ICI and therefore new combination strategies are eagerly necessitated. In addition to conventional chemotherapy, kinase inhibitors as well as tumor-specific vaccinations are extensively investigated in combination with ICI to augment therapy responses. An unprecedented clinical outcome with chimeric antigen receptor (CAR-)T cell therapy has led to the approval for relapsed/refractory diffuse large B cell lymphoma and B cell acute lymphoblastic leukemia whereas response rates in solid tumors are unsatisfactory. Immune-checkpoints negatively impact CAR-T cell therapy in hematologic and solid malignancies and as a consequence provide a therapeutic target to overcome resistance. Established biomarkers such as programmed death ligand 1 (PD-L1) and tumor mutational burden (TMB) help to select patients who will benefit most from ICI, however, biomarker negativity does not exclude responses. Investigating alterations in the antigen presenting pathway as well as radiomics have the potential to determine tumor immunogenicity and response to ICI. Within this review we summarize the literature about specific combination partners for ICI and the applicability of artificial intelligence to predict ICI therapy responses.
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Kim, Jeong-Min, Youngsik Kim, Shin-Dug Kim, Tack-Don Han, and Sung-Bong Yang. "An Adaptive Parallel Computer Vision System." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 03 (1998): 311–34. http://dx.doi.org/10.1142/s021800149800021x.
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An approach for designing a hybrid parallel system that can perform different levels of parallelism adaptively is presented. An adaptive parallel computer vision system (APVIS) is proposed to attain this goal. The APVIS is constructed by integrating two different types of parallel architectures, i.e. a multiprocessor based system (MBS) and a memory based processor array (MPA), tightly into a single machine. One important feature in the APVIS is that the programming interface to execute data parallel code onto the MPA is the same as the usual subroutine calling mechanism. Thus the existence of the MPA is transparent to the programmers. This research is to design an underlying base architecture that can be optimally executed for a broad range of vision tasks. A performance model is provided to show the effectiveness of the APVIS. It turns out that the proposed APVIS can provide significant performance improvement and cost effectiveness for highly parallel applications having a mixed set of parallelisms. Also an example application composed of a series of vision algorithms, from low-level and medium-level processing steps, is mapped onto the MPA. Consequently, the APVIS with a few or tens of MPA modules can perform the chosen example application in real time when multiple images are incoming successively with a few seconds inter-arrival time.
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Rath, Mamata, Joel J. P. C. Rodrigues, and George S. Oreku. "Applications of Cognitive Intelligence in the Information Retrieval Process and Associated Challenges." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 1 (2021): 26–38. http://dx.doi.org/10.4018/ijcini.2021010103.
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Information retrieval refers to a noteworthy system of identifying relevant information and recovering it through specific procedures from stored system. These technique is used in many differentiated applications that deal with subjective intelligence. Applications based on information retrieval are identified with various issues, for example, in technology domain, the sudden size changes of the objectives as they approach the sensor. If not taken care of appropriately, the altered changes can present substantial issues in information affiliation and position estimation. Under such a system, the meaning of the objective state is the fundamental advance for programmed comprehension of dynamic scenes. This is the reason of requirement of cognitive models for information retrieval. The existent models move around the connection between data list terms and records.
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M., Supriya, and Vijay Kumar Chattu. "A Review of Artificial Intelligence, Big Data, and Blockchain Technology Applications in Medicine and Global Health." Big Data and Cognitive Computing 5, no. 3 (2021): 41. http://dx.doi.org/10.3390/bdcc5030041.
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Artificial intelligence (AI) programs are applied to methods such as diagnostic procedures, treatment protocol development, patient monitoring, drug development, personalized medicine in healthcare, and outbreak predictions in global health, as in the case of the current COVID-19 pandemic. Machine learning (ML) is a field of AI that allows computers to learn and improve without being explicitly programmed. ML algorithms can also analyze large amounts of data called Big data through electronic health records for disease prevention and diagnosis. Wearable medical devices are used to continuously monitor an individual’s health status and store it in cloud computing. In the context of a newly published study, the potential benefits of sophisticated data analytics and machine learning are discussed in this review. We have conducted a literature search in all the popular databases such as Web of Science, Scopus, MEDLINE/PubMed and Google Scholar search engines. This paper describes the utilization of concepts underlying ML, big data, blockchain technology and their importance in medicine, healthcare, public health surveillance, case estimations in COVID-19 pandemic and other epidemics. The review also goes through the possible consequences and difficulties for medical practitioners and health technologists in designing futuristic models to improve the quality and well-being of human lives.
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Amrullah, Amrullah, and Mutasar Mutasar. "Membangun Framework Repository Source Code Algoritma Komputer pada Platform Web." JURNAL TIKA 5, no. 3 (2021): 105–9. http://dx.doi.org/10.51179/tika.v5i3.87.
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Era Revolusi Industri 4.0 programmer dihadirkan platform CMS, Web builder yang mempercepat pembuatan program seperti create templete responsive/dinamis, CRUD dan grafik namun belum adanya framework untuk membangun source code pada platform web yang memecahkan alur proses pada sebuah algoritma sehingga akan membantu programmer/peneliti dalam menyelesaikan masalah aritmatika/logical proses pada sebuah algoritma/metode jika di-implementasikan kedalam source code platform web HTML, PHP, CSS. Urgensi permasalahan ini harus adanya sistem repository yang menyediakan source code algoritma komputer meliputi (expert system, data mining, decision system, artificial intelligence, kriptografi/security computer, system information, searching, image processing). Adanya repository ini akan memberikan kemudahan dalam menghasilkan source code algoritma/metode komputer untuk dapat digunakan sebagai, modify,combine, comparation, example, trying, testing , implementation, sehingga akan mudah dalam penyelesaian program penelitian dan membantu mempercepat laju skill programmer/peneliti di perguruan tinggi.
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Juniper, Dean. "A creative renewal programme." AI & Society 7, no. 1 (1993): 78–85. http://dx.doi.org/10.1007/bf01891451.
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ElMaraghy, H. A., and J. M. Rondeau. "Automated planning and programming environments for robots." Robotica 10, no. 1 (1992): 75–82. http://dx.doi.org/10.1017/s0263574700007098.
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SummaryTraditionally, most industrial robots are programmed by teaching. Automatic planning of robotic tasks has many potential benefits for flexible automation. It allows the user to describe a task to the robot programming system in a formal and natural manner, and reduces the time required to generate and update robot programs. Two main levels of abstraction in describing robot tasks can be identified. Robot-level programming is based on robot movements and actions, as detailed by the programmer. Object-level or task-level programming allows the user to describe assembly tasks in terms of operations performed on objects being manipulated instead of specifying the individual motions of the robot end-effector. However, commercially available robot-level programming languages still fall short of the robot user's need to programme complex tasks and consequently are not widely used in industry. There is an increasing need for integrating sensors feedback into the robot system to provide better perception and for improving the capacity of the robot to reason and make decisions intelligently in real-time. Task-level programming represents the highest level of abstraction and is the most attractive, as it uses reasoning capabilities provided by Artificial Intelligence. To date, no system of this class has been completely implemented in industry. This paper reviews the progress made in robot programming and task planning systems in the last twenty years, and discusses the current research trends.
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Bench-Capon, TJM. "Humpty dumpty, private languages and logic programmers." AI & Society 2, no. 3 (1988): 271–72. http://dx.doi.org/10.1007/bf01908553.
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Iseda, Tetsuji. "Scientific rationality and the ?even stronger programme?" AI & Society 13, no. 1-2 (1999): 156–63. http://dx.doi.org/10.1007/bf01205264.
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Nanglae, Lalida, Natthakan Iam-On, Tossapon Boongoen, Komkrit Kaewchay, and James Mullaney. "Determining patterns of student graduation using a bi-level learning framework." Bulletin of Electrical Engineering and Informatics 10, no. 4 (2021): 2201–11. http://dx.doi.org/10.11591/eei.v10i4.2502.
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The practice of data science, artificial intelligence (AI) in general, has expanded greatly in terms of both theoretical and application domains. Many existing and new problems have been tackled using different reasoning and learning methods. These include the research subject, generally referred to as education data mining (or EDM). Among many issues that have been studied in this EMD community, student performance and achievement provide an interesting, yet useful result to shaping effective learning style and academic consultation. Specific to this work at Mae Fah Luang University, the pattern of students’ graduation is determined based on their profile of performance in different categories of courses. This course-group approach is picked up to generalize the framework for various undergraduation programmes. In that, a bi-level learning method is proposed in order to predict the length of study before graduation. At the first tier, clustering is applied to derive major types of performance profiles, for which classification models can be developed to refine the prediction further. With the experiments on a real data collection, this framework usually provides accurate predictive outcomes, using several conventional classification techniques.
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Donepudi, Praveen Kumar. "Impact of Machine Learning in Neurosurgery: A Systematic Review of Related Literature." Malaysian Journal of Medical and Biological Research 8, no. 1 (2020): 13–20. http://dx.doi.org/10.18034/mjmbr.v8i1.520.
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Machine learning is a domain within artificial intelligence that allows for computer algorithms to be learned from experience without them having being programmed. The objective of this study is to summarize the neurosurgical applications of machine learning when compared to clinical expertise. This study uses a systematic search to review articles from the PubMed and Embase databases in comparing various machine learning studies approaches to that of the clinical experts. For this study, 23 studies were identified which used machine learning algorithms for the diagnosis, pre-surgical planning, and outcome prediction. In conclusion, this study identifies that machine learning models can augment decision-making capacity for the surgeons and clinicians in neurosurgical applications. Despite this, there still exist hurdles that involve creation, validation, and the deployment of the machine learning techniques in clinical settings.
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Ennals, Richard. "Sabanci University launches Industrial PhD programme in Action Research." AI & SOCIETY 35, no. 3 (2020): 773. http://dx.doi.org/10.1007/s00146-019-00935-6.
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Yulianto, Budi, Harjanto Prabowo, Raymond Kosala, and Manik Hapsara. "Novice Programmer = (Sourcecode) (Pseudocode) Algorithm." Journal of Computer Science 14, no. 4 (2018): 477–84. http://dx.doi.org/10.3844/jcssp.2018.477.484.
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Peshin, Rajinder, Rajinder Kalra, A. K. Dhawan, and Tripat Kumar. "Evaluation of insecticide resistance management based integrated pest management programme." AI & SOCIETY 21, no. 3 (2007): 357–81. http://dx.doi.org/10.1007/s00146-006-0078-x.
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Bogatyreva, Olga, and Alexandr Shillerov. "Robot Swarms in an Uncertain World: Controllable Adaptability." International Journal of Advanced Robotic Systems 2, no. 3 (2005): 19. http://dx.doi.org/10.5772/5788.
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There is a belief that complexity and chaos are essential for adaptability. But life deals with complexity every moment, without the chaos that engineers fear so, by invoking goal-directed behaviour. Goals can be programmed. That is why living organisms give us hope to achieve adaptability in robots. In this paper a method for the description of a goal-directed, or programmed, behaviour, interacting with uncertainty of environment, is described. We suggest reducing the structural (goals, intentions) and stochastic components (probability to realise the goal) of individual behaviour to random variables with nominal values to apply probabilistic approach. This allowed us to use a Normalized Entropy Index to detect the system state by estimating the contribution of each agent to the group behaviour. The number of possible group states is 27. We argue that adaptation has a limited number of possible paths between these 27 states. Paths and states can be programmed so that after adjustment to any particular case of task and conditions, adaptability will never involve chaos. We suggest the application of the model to operation of robots or other devices in remote and/or dangerous places.
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Pyatin, V., A. Kolsanov, N. Romanchuk, et al. "Bioinformatics and Artificial Intelligence: Gerontological and Geriatric Components Medical and Social Support for Active Healthy Longevity." Bulletin of Science and Practice 6, no. 12 (2020): 155–75. http://dx.doi.org/10.33619/2414-2948/61/16.
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The “cognitive reserve” construct is a set of variables, including intelligence, education, and mental stimulation, that presumably allows the brain to adapt to underlying pathologies, supporting cognitive function despite underlying neural changes. Brain Homo Sapiens also points to resistance to neuropathological damage and can be defined as the ability to optimize or maximize performance through an effective set of neural networks and/or alternative cognitive strategies. Learning in childhood, the level of education and activities for adults — all this independently contributes to the formation of a cognitive reserve. The introduction of biocomputer nanoplatforms and modules consisting of small molecules, polymers, nucleic acids or proteins/peptides, nanoplatforms are programmed to detect and process external stimuli, such as magnetic fields or light, or internal stimuli, such as nucleic acids, enzymes or pH, using three different mechanisms: system assembly, system disassembly or system transformation. Current biocomputer nanoplatforms are invaluable for many applications, including medical diagnostics, biomedical imaging, environmental monitoring, and delivery of therapeutic drugs to target cell populations. The future implementation of systems biology and systems neurophysiology paradigms based on complex analysis of large and deep heterogeneous data sources will be crucial to achieve a deeper understanding of the pathophysiology of Alzheimer’s disease, using current brain-computer and artificial intelligence interface technologies, in order to increase information that can be extracted from preclinical and clinical indicators. Integration of different sources of information will allow researchers to obtain a new holistic picture of the pathophysiological process of the disease, which will cover from molecular changes to cognitive manifestations. The new competencies of psychoneuroimmunoendocrinology and psychoneuroimmunology play a strategic role in interdisciplinary science and interdisciplinary planning and decision-making. The introduction of multi-vector neurotechnologies of artificial intelligence and the principles of digital health care will contribute to the development of modern neuroscience and neuromarketing. Medical and social support for active healthy longevity is possible when synchronizing information systems of medical organizations and social institutions, introducing a single neurophysiological circuit and modern neurointerfaces, a combined and hybrid cluster in the diagnosis, treatment, prevention and rehabilitation of cognitive disorders and cognitive disorders. A key factor in medical and social support is the participation of interdisciplinary business employees and data processing specialists (their support, monitoring), as well as the availability of sufficient staff literacy in data management.
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Vidal, René Victor Valqui. "Rural development within the EU LEADER+ programme: new tools and technologies." AI & SOCIETY 23, no. 4 (2007): 575–602. http://dx.doi.org/10.1007/s00146-007-0178-2.
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Dellen, Barbara, Frank Maurer, Jürgen Münch, and Martin Verlage. "Enriching Software Process Support by Knowledge-Based Techniques." International Journal of Software Engineering and Knowledge Engineering 07, no. 02 (1997): 185–215. http://dx.doi.org/10.1142/s0218194097000102.
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Representations of activities dealing with the development or maintenance of software are called software process models. Process models allow for communication, reasoning, guidance, improvement, and automation. Two approaches for modeling processes and instantiating and managing the process models, namely CoMo-Kit and MVP-E, are combined to build a more powerful one. CoMo-Kit is based on AI/KE technology; it is a support tool system for general complex design processes, and was not been developed specifically with software development processes in mind. MVP-E is a process-sensitive software engineering environment for modeling and analyzing software development processes, and guides software developers. Additionally, it provides services to establish and run measurement programmes in software organizations. Because both approaches were developed independently from one another, major integration efforts had to be made to combine both their advantages. This article concentrates on the resulting language concepts, and their operationalization necessary for building automated process support.
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Moon, Ajung, Maneezhay Hashmi, H. F. Machiel Van Der Loos, Elizabeth A. Croft, and Aude Billard. "Design of Hesitation Gestures for Nonverbal Human-Robot Negotiation of Conflicts." ACM Transactions on Human-Robot Interaction 10, no. 3 (2021): 1–25. http://dx.doi.org/10.1145/3418302.
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When the question of who should get access to a communal resource first is uncertain, people often negotiate via nonverbal communication to resolve the conflict. What should a robot be programmed to do when such conflicts arise in Human-Robot Interaction? The answer to this question varies depending on the context of the situation. Learning from how humans use hesitation gestures to negotiate a solution in such conflict situations, we present a human-inspired design of nonverbal hesitation gestures that can be used for Human-Robot Negotiation. We extracted characteristic features of such negotiative hesitations humans use, and subsequently designed a trajectory generator (Negotiative Hesitation Generator) that can re-create the features in robot responses to conflicts. Our human-subjects experiment demonstrates the efficacy of the designed robot behaviour against non-negotiative stopping behaviour of a robot. With positive results from our human-robot interaction experiment, we provide a validated trajectory generator with which one can explore the dynamics of human-robot nonverbal negotiation of resource conflicts.
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Wu, Zhefu, Jianan Li, Chenbo Fu, Qi Xuan, and Yun Xiang. "Network-Based Ranking for Open Source Software Developer Prediction." International Journal of Software Engineering and Knowledge Engineering 28, no. 06 (2018): 845–68. http://dx.doi.org/10.1142/s0218194018500250.
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Open source software (OSS) projects and communities are becoming increasingly popular and influential recently. Communications and collaborations are essential for the success of projects. Usually, the most active and productive programmers are awarded with promotion to developers. To more effectively manage and progress the projects, it is important and beneficial to rank the programmers and thus, predict the developer candidates. In this work, we propose to combine machine learning techniques with existing complex network node ranking algorithms to improve the prediction results. Specifically, we have made the following contributions: (1), we have designed a novel machine learning-based classifier with significantly improved prediction performance; (2), we have constructed and tested various networks built based on the programmer email communication information; and (3), we have used real-world project data to compare different techniques and validate our methods. Experimental results demonstrate that our technique reduces the error rate by 25% compared with the second best. Moreover, we discover that the [Formula: see text] nearest neighbor (KNN)-based machine learning algorithm and non-directional temporal network with a time window of 1–3 months give the best prediction results.
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Sharif. "Open Source Programmers' Information Seeking During Software Maintenance." Journal of Computer Science 7, no. 7 (2011): 1060–71. http://dx.doi.org/10.3844/jcssp.2011.1060.1071.
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Singh, Naresh. "Reidar Dale: ‘People’s Development Through People’s Institutions: The Social Mobilisation Programme Hambantota, Sri Lanka’." AI & SOCIETY 21, no. 3 (2006): 383–85. http://dx.doi.org/10.1007/s00146-006-0055-4.
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Konov, Evgenii Aleksandrovich, and Petr Sergeevich Sorokoumov. "The available methods of computer modeling of biofilms and their development trends." Программные системы и вычислительные методы, no. 4 (April 2020): 53–68. http://dx.doi.org/10.7256/2454-0714.2020.4.34615.
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The object of this research is the available means of computer modeling of bacterial communities-biofilms. Such communities include the majority (95-99%) of bacteria and are ubiquitous. Biofilms are much more resistant than single bacteria to antibiotics and other antibacterial means due to weak permeability of intercellular environment and moderation of metabolism of some specimen of the community. Examination of influence techniques on biofilms is an important problem of biology; its solution requires various computer modeling tools that allow achieving significant scientific results. The subject of this research is the mathematical models used in modern techniques of biofilm modeling, and as well as the software for their implementation. The conclusion is made that the available methods of biofilm modeling are capable of successful reproduction of multiple behavioral aspects of these bacteria communities, including growth, destruction, and self-regulation; however, certain relevant problems are yet to be resolved. This is associated with the fact that the development of modern software for two-dimensional and three-dimensional agent modeling of biofilms &ndash; BSim, iDynoMiCS, CellModeller, require competent programmers for describing the interactions between simulated objects. Most promising direction in further development of this software implies a more active usage of tools for describing behavior and interaction of agents applied in the sphere of artificial intelligence, for example, fin-state automaton or production rules systems, with mandatory preservation of biological content of the models.
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Cresswell, Kathrin, Margaret Callaghan, Sheraz Khan, Zakariya Sheikh, Hajar Mozaffar, and Aziz Sheikh. "Investigating the use of data-driven artificial intelligence in computerised decision support systems for health and social care: A systematic review." Health Informatics Journal 26, no. 3 (2020): 2138–47. http://dx.doi.org/10.1177/1460458219900452.
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There is growing interest in the potential of artificial intelligence to support decision-making in health and social care settings. There is, however, currently limited evidence of the effectiveness of these systems. The aim of this study was to investigate the effectiveness of artificial intelligence-based computerised decision support systems in health and social care settings. We conducted a systematic literature review to identify relevant randomised controlled trials conducted between 2013 and 2018. We searched the following databases: MEDLINE, EMBASE, CINAHL, PsycINFO, Web of Science, Cochrane Library, ASSIA, Emerald, Health Business Fulltext Elite, ProQuest Public Health, Social Care Online, and grey literature sources. Search terms were conceptualised into three groups: artificial intelligence-related terms, computerised decision support -related terms, and terms relating to health and social care. Terms within groups were combined using the Boolean operator OR, and groups were combined using the Boolean operator AND. Two reviewers independently screened studies against the eligibility criteria and two independent reviewers extracted data on eligible studies onto a customised sheet. We assessed the quality of studies through the Critical Appraisal Skills Programme checklist for randomised controlled trials. We then conducted a narrative synthesis. We identified 68 hits of which five studies satisfied the inclusion criteria. These studies varied substantially in relation to quality, settings, outcomes, and technologies. None of the studies was conducted in social care settings, and three randomised controlled trials showed no difference in patient outcomes. Of these, one investigated the use of Bayesian triage algorithms on forced expiratory volume in 1 second (FEV1) and health-related quality of life in lung transplant patients. Another investigated the effect of image pattern recognition on neonatal development outcomes in pregnant women, and another investigated the effect of the Kalman filter technique for warfarin dosing suggestions on time in therapeutic range. The remaining two randomised controlled trials, investigating computer vision and neural networks on medication adherence and the impact of learning algorithms on assessment time of patients with gestational diabetes, showed statistically significant and clinically important differences to the control groups receiving standard care. However, these studies tended to be of low quality lacking detailed descriptions of methods and only one study used a double-blind design. Although the evidence of effectiveness of data-driven artificial intelligence to support decision-making in health and social care settings is limited, this work provides important insights on how a meaningful evidence base in this emerging field needs to be developed going forward. It is unlikely that any single overall message surrounding effectiveness will emerge - rather effectiveness of interventions is likely to be context-specific and calls for inclusion of a range of study designs to investigate mechanisms of action.
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Hollingshaus, Wade. "A Literary (Techno)science." Nordic Theatre Studies 31, no. 1 (2019): 58–72. http://dx.doi.org/10.7146/nts.v31i1.113001.
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Beginning in the late 1970s, Finland’s Erkki Kurenniemi (1941-2017) actively labored to archive every possible aspect of his life. He took photos, made videos, and collected his tram tickets, receipts, body hairs, etc. Kurenniemi believed that within the next forty years, computer technoscience will have advanced sufficiently that it could be programmed to interpret the data of his archive and—on his 107th birthday, 10 July 2018—resurrect his consciousness. For Kurenniemi, this project was an experiment in the realms of neuroscience and artificial intelligence. However, it can also be seen as an experiment in aesthetics, or in what Jacques Rancière calls the “aesthetic regime” of art—an aesthetic-political historical framework imbued with the dynamics of democracy, where “everything speaks.” This article reframes Kurenniemi’s work within the aesthetic regime of art to draw attention to the “silent speech” and “aestheticunconscious” (Rancière) of the work and what is the literary nature of Kurenniemi’s experiments with (techno)science.