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1

W jakich obszarach i w jaki sposób sztuczna inteligencja może wspomagać lekarzy?

Tadeusiewicz Ryszard

Śląskie Wiadomości Elektryczne

|

2023

|

Nr 3 (147)

4--9

PL

Sztuczna inteligencja jest obecnie bardzo szybko i wielokierunkowo rozwijana. Towarzyszą temu liczne zastosowania tej techniki w rożnych dziedzinach. Artykuł dokonuje wstępnego przeglądu obszarów, w których sztuczna inteligencja mogłaby wspomagać lekarzy. Przegląd ten nawiązuje do opracowania, które autor przygotował do wystąpienia firmy Microsoft na Forum Ekonomicznym w Karpaczu. Poruszana problematyka jest bardzo poważna, ale dla kontrastu i zaciekawienia czytelników ilustracje wskazujące kolejne obszary zastosowań sztucznej inteligencji w medycynie przedstawiono w postaci żartobliwych rysunków. W samym artykule nie przywoływano żadnych rozwiązań szczegółowych, bo chodzi tu o spojrzenie na całość problematyki „z lotu ptaka”, ale w spisie literatury przywołano publikacje autora, ilustrujące szczegółowo zarysowane ogólnie tezy.

EN

Artificial intelligence is currently being developed very quickly and in many directions. This is accompanied by numerous applications of this technique in various fields. The article provides an initial overview of areas where artificial intelligence could support physicians. This review refers to the study prepared by the author for Microsoft’s presentation at the Economic Forum in Karpacz. The issues raised are very serious, but for the sake of contrast and interest of the readers, the illustrations showing the subsequent areas of application of artificial intelligence in medicine are presented in the form of humorous drawings. The article itself did not refer to any detailed solutions, because it is about looking at the whole problem from a “bird’s eye view”, but the list of references mentions the author’s publications, illustrating the general theses outlined in detail.

2

Ryszard Michalski - father of machine learning

Tadeusiewicz Ryszard

Polish Technical Review

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2023

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No. 3

23--25

3

Henryk Górecki - the man who made AGH, a university originally known as the Academy of Coal and Steel, famous in automation, electronics, it, telecommunications and biomedical engineering

Tadeusiewicz Ryszard

Polish Technical Review

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2023

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No. 2

29--35

4

Śmierć prof. Henryka Góreckiego, pioniera automatyki

Tadeusiewicz Ryszard

Napędy i Sterowanie

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2023

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R. 25, nr 1

65--69

PL

Wielu Czytelników miesięcznika „Napędy i Sterowanie” miało okazję korzystać z wyników prac Profesora Henryka Góreckiego, nestora polskich automatyków, wychowawcy wielu pokoleń naukowców i inżynierów, człowieka, który na AGH stworzył szereg katedr i instytutów poświęconych nowym dziedzinom techniki: automatyce, elektronice, informatyce, telekomunikacji, robotyki i inżynierii biomedycznej. Jeszcze w czerwcu 2022 roku mieliśmy okazję wręczać mu (z okazji 70-lecia wydziału elektrycznego AGH) sympatyczne wyróżnienie, jakim była rzeźba smoka ze stosownymi napisami na skrzydłach. Wcześniej Profesor nagrał swoje bardzo ciekawe wspomnienia z okazji 100-lecia AGH. Aż tu nagle dotarła do nas niezwykle smutna wiadomość, że w nocy z 11 na 12 grudnia Profesor Henryk Górecki zmarł. Mnie przypadła rola tego, który 20 grudnia na zaśnieżonym cmentarzu musiał wygłosić przemówienie, którym my, jego uczniowie i następcy, żegnaliśmy naszego Mistrza. [Wstęp]

5

Refleksje nad sztuczną inteligencją

Tadeusiewicz Ryszard

Napędy i Sterowanie

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2023

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R. 25, nr 10

62--66

PL

W artykule przytoczono i przedyskutowano wybrane opinie na temat sztucznej inteligencji. Przywołano okoliczności, które powodują duży (ostatnio) wzrost powszechnego zainteresowania sztuczną inteligencję, przy czym wspomniano o jej znaczeniu gospodarczym i społecznym, ale także ustosunkowano się do fali różnych opinii i kategorycznych (a nie zawsze uzasadnionych) sądów. Sądy takie wypowiadają czasem osoby mające duży autorytet naukowy, ale nie pracujące ściśle przy tworzeniu i stosowaniu narzędzi sztucznej inteligencji. W związku z tym sądy te często są kontrowersyjne i zbyt daleko idące. Co więcej, opinie o sztucznej inteligencji wypowiadają także ludzie zupełnie z tą dziedziną nie związani, a ich jedyna wiedza (i wielka pewność siebie w ferowaniu opinii!) bierze się stąd, że korzystali oni z usług programu Chat GPT i mają zarejestrowane jakieś tam dialogi. W artykule jest to dość kategorycznie spuentowane.

EN

The article cites and discusses selected opinions on artificial intelligence. The circumstances that cause a large (recent) increase in popular interest in artificial intelligence were recalled, its economic and social importance was mentioned, but also the wave of different opinions and categorical (and not always justified) judgments were addressed. Such judgments are sometimes expressed by people who have great scientific authority, but do not work closely in creating and applying artificial intelligence tools. Therefore, these judgments are often controversial and too farreaching. What’s more, opinions about artificial intelligence are also expressed by people who are completely unrelated to this field, and their only knowledge (and great self-confidence in expressing their opinions!) comes from the fact that they used the services of the Chat GPT program and have some recorded dialogues. This is stated quite categorically in the article.

6

Constant Q-transform-based deep learning architecture for detection of obstructive sleep apnea

Kandukuri Usha Rani, Prakash Allam Jaya, Patro Kiran Kumar, Neelapu Bala Chakravarthy, Tadeusiewicz Ryszard, Pławiak Paweł

International Journal of Applied Mathematics and Computer Science

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2023

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Vol. 33, no. 3

493--506

EN

Obstructive sleep apnea (OSA) is a long-term sleep disorder that causes temporary disruption in breathing while sleeping. Polysomnography (PSG) is the technique for monitoring different signals during the patient’s sleep cycle, including electroencephalogram (EEG), electromyography (EMG), electrocardiogram (ECG), and oxygen saturation (SpO2). Due to the high cost and inconvenience of polysomnography, the usefulness of ECG signals in detecting OSA is explored in this work, which proposes a two-dimensional convolutional neural network (2D-CNN) model for detecting OSA using ECG signals. A publicly available apnea ECG database from PhysioNet is used for experimentation. Further, a constant Q-transform (CQT) is applied for segmentation, filtering, and conversion of ECG beats into images. The proposed CNN model demonstrates an average accuracy, sensitivity and specificity of 91.34%, 90.68% and 90.70%, respectively. The findings obtained using the proposed approach are comparable to those of many other existing methods for automatic detection of OSA.

7

SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

Patro Kiran Kumar, Allam Jaya Prakash, Hammad Mohamed, Tadeusiewicz Ryszard, Pławiak Paweł

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 1

352--368

EN

Background and Objective: The global population has been heavily impacted by the COVID-19 pandemic of coronavirus. Infections are spreading quickly around the world, and new spikes (Delta, Delta Plus, and Omicron) are still being made. The real-time reverse transcription-polymerase chain reaction (RT-PCR) is the method most often used to find viral RNA in a nasopharyngeal swab. However, these diagnostic approaches require human involvement and consume more time per prediction. Moreover, the existing conventional test mainly suffers from false negatives, so there is a chance for the virus to spread quickly. Therefore, a rapid and early diagnosis of COVID-19 patients is needed to overcome these problems. Methods: Existing approaches based on deep learning for COVID detection are suffering from unbalanced datasets, poor performance, and gradient vanishing problems. A customized skip connection-based network with a feature union approach has been developed in this work to overcome some of the issues mentioned above. Gradient information from chest X-ray (CXR) images to subsequent layers is bypassed through skip connections. In the script’s title, ‘‘SCovNet” refers to a skip-connection-based feature union network for detecting COVID-19 in a short notation. The performance of the proposed model was tested with two publicly available CXR image databases, including balanced and unbalanced datasets. Results: A modified skip connection-based CNN model was suggested for a small unbalanced dataset (Kaggle) and achieved remarkable performance. In addition, the proposed model was also tested with a large GitHub database of CXR images and obtained an overall best accuracy of 98.67% with an impressive low false-negative rate of 0.0074. Conclusions: The results of the experiments show that the proposed method works better than current methods at finding early signs of COVID-19. As an additional point of interest, we must mention the innovative hierarchical classification strategy provided for this work, which considered both balanced and unbalanced datasets to get the best COVID-19 identification rate.

8

Hybrid EEG-fNIRS brain-computer interface based on the non-linear features extraction and stacking ensemble learning

Maher Asmaa, Mian Qaisar Saeed, Salankar N., Jiang Feng, Tadeusiewicz Ryszard, Pławiak Paweł, Abd El-Latif Ahmed A., Hammad Mohamed

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 2

463--475

EN

The Brain-computer interface (BCI) is used to enhance the human capabilities. The hybridBCI (hBCI) is a novel concept for subtly hybridizing multiple monitoring schemes to maximize the advantages of each while minimizing the drawbacks of individual methods. Recently, researchers have started focusing on the Electroencephalogram (EEG) and ‘‘Functional Near-Infrared Spectroscopy” (fNIRS) based hBCI. The main reason is due to the development of artificial intelligence (AI) algorithms such as machine learning approaches to better process the brain signals. An original EEG-fNIRS based hBCI system is devised by using the non-linear features mining and ensemble learning (EL) approach. We first diminish the noise and artifacts from the input EEG-fNIRS signals using digital filtering. After that, we use the signals for non-linear features mining. These features are ‘‘Fractal Dimension” (FD), ‘‘Higher Order Spectra” (HOS), ‘‘Recurrence Quantification Analysis” (RQA) features, and Entropy features. Onward, the Genetic Algorithm (GA) is employed for Features Selection (FS). Lastly, we employ a novel Machine Learning (ML) technique using several algorithms namely, the ‘‘Naïve Bayes” (NB), ‘‘Support Vector Machine” (SVM), ‘‘Random Forest” (RF), and ‘‘K-Nearest Neighbor” (KNN). These classifiers are combined as an ensemble for recognizing the intended brain activities. The applicability is tested by using a publicly available multi-subject and multiclass EEG-fNIRS dataset. Our method has reached the highest accuracy, F1-score, and sensitivity of 95.48%, 97.67% and 97.83% respectively.

9

A new super resolution Faster R-CNN model based detection and classification of urine sediments

Avci Derya, Sert Eser, Dogantekin Esin, Yildirim Ozal, Tadeusiewicz Ryszard, Plawiak Pawel

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 1

58--68

EN

The diagnosis of urinary tract infections and kidney diseases using urine microscopy images has gained significant attention of medical community in recent years. These images are usually created by physicians’ own rule of thumb manually. However, this manual urine sediment analysis is usually labor-intensive and time-consuming. In addition, even when physicians carefully examine an image, an erroneous cell recognition may occur due to some optical illusions. In order to achieve cell recognition in low-resolution urine microscopy images with a higher level of accuracy, a new super resolution Faster Region-based Convolutional Neural Network (Faster R-CNN) method is proposed. It aims to increase resolution in low-resolution urine microscopy images using self-similarity based single image super resolution which was used during the pre-processing. Denoising based Wiener filter and Discrete Wavelet Transform (DWT) are used to de-noise high resolution images, respectively, to increase the level of accuracy for image recognition. Finally, for the feature extraction and classification stages, AlexNet, VGFG16 and VGG19 based Faster R-CNN models are used for the recognition and detection of multi-class cells. The model yielded accuracy rates are 98.6%, 96.4% and 96.2% respectively.

10

Kierunki i perspektywy rozwoju sztucznej inteligencji

Tadeusiewicz Ryszard

Śląskie Wiadomości Elektryczne

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2022

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Nr 2 (140)

6--21

PL

Prezentowany tekst przedstawia zasadnicze elementy wykładu wygłoszonego podczas uroczystej sesji związanej z jubileuszem 30-lecia Akademii Inżynierskiej w Polsce. Omówiono w nim źródła i kierunki rozwoju sztucznej inteligencji, wskazano przyczyny jej rosnącej popularności oraz dokonano przeglądu ważniejszych metod informatycznego rozwiązywania problemów, zaliczanych do sztucznej inteligencji. Omówiono także kierunki rozwoju owych metod, wskazując, że nie stanowią one zwartej metodologii, lecz są kolekcją różnych rozwiązań, zmierzających do uzyskania inteligentnego działania maszyn, co zasygnalizowano mówiąc o archipelagu sztucznej inteligencji. Skupiono także uwagę na problemie zagrożeń potencjalnie niesionych przez rozwój sztucznej inteligencji i wskazano na sposoby ograniczania tych zagrożeń.

EN

The presented text shows the essential elements of the lecture delivered during the solemn session on the 30th anniversary of the Engineering Academy in Poland. It discusses the sources and directions of the development of artificial intelligence, indicates the reasons for its growing popularity and reviews the more important methods of IT problem-solving, classified as artificial intelligence. The directions of development of these methods were also discussed, indicating that they do not constitute a coherent methodology, but are a collection of various solutions aimed at achieving intelligent operation of machines, which was indicated when talking about the archipelago of artificial intelligence. Attention was also focused on the problem of threats potentially brought by the development of artificial intelligence and indicated ways of reducing these threats.

11

A hybrid approach of a deep learning technique for real-time ECG beat detection

Patro Kiran Kumar, Prakash Allam Jaya, Samantray Saunak, Pławiak Joanna, Tadeusiewicz Ryszard, Pławiak Paweł

International Journal of Applied Mathematics and Computer Science

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2022

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Vol. 32, no. 3

455--465

EN

This paper presents a new customized hybrid approach for early detection of cardiac abnormalities using an electrocardiogram (ECG). The ECG is a bio-electrical signal that helps monitor the heart’s electrical activity. It can provide health information about the normal and abnormal physiology of the heart. Early diagnosis of cardiac abnormalities is critical for cardiac patients to avoid stroke or sudden cardiac death. The main aim of this paper is to detect crucial beats that can damage the functioning of the heart. Initially, a modified Pan–Tompkins algorithm identifies the characteristic points, followed by heartbeat segmentation. Subsequently, a different hybrid deep convolutional neural network (CNN) is proposed to experiment on standard and real-time long-term ECG databases. This work successfully classifies several cardiac beat abnormalities such as supra-ventricular ectopic beats (SVE), ventricular beats (VE), intra-ventricular conduction disturbances beats (IVCD), and normal beats (N). The obtained classification results show a better accuracy of 99.28% with an F1 score of 99.24% with the MIT–BIH database and a descent accuracy of 99.12% with the real-time acquired database.

12

Signal-piloted processing metaheuristic optimization and wavelet decomposition based elucidation of arrhythmia for mobile healthcare

Qaisar Saeed Mian, Khan Sibghatullah I., Dallet Dominique, Tadeusiewicz Ryszard, Pławiak Paweł

Biocybernetics and Biomedical Engineering

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2022

|

Vol. 42, no. 2

681--694

EN

The next generation healthcare systems will be based on the cloud connected wireless biomedical wearables. The key performance indicators of such systems are the compression, computational efficiency, transmission and power effectiveness with precision. The electrocardiogram (ECG) signals processing based novel technique is presented for the diagnosis of arrhythmia. It employs a novel mix of the Level-Crossing Sampling (LCS), Enhanced Activity Selection (EAS) based QRS complex selection, multirate processing, Wavelet Decomposition (WD), Metaheuristic Optimization (MO), and machine learning. The MIT-BIH dataset is used for experimentation. Dataset contains 5 classes namely, ‘‘Atrial premature contraction”, ‘‘premature ventricular contraction”, ‘‘right bundle branch block”, ‘‘left bundle branch block” and ‘‘normal sinus”. For each class, 450 cardiac pulses are collected from 3 different subjects. The performance of Marine Predators Algorithm (MPA) and Artificial Butterfly Optimization Algorithm (ABOA) is investigated for features selection. The selected features sets are passed to classifiers that use machine learning for an automated diagnosis. The performance is tested by using multiple evaluation metrics while following the 10-fold cross validation (10-CV). The LCS and EAS results in a 4.04-times diminishing in the average count of collected samples. The multirate processing lead to a more than 7-times computational effectiveness over the conventional fix-rate counter parts. The respective dimension reduction ratios and classification accuracies, for the MPA and ABOA algorithms, are 29.59-times & 22.19-times and 98.38% & 98.86%.

13

EEG based alcoholism detection by oscillatory modes decomposition second order difference plots and machine learning

Salankar Nilima, Qaisar Saeed Mian, Pławiak Paweł, Tadeusiewicz Ryszard, Hammad Mohamed

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 1

173--186

EN

The excessive drinking of alcohol can disrupt the neural system. This can be observed by properly analysing the Electroencephalogram (EEG) signals. However, the EEG is a signal of complex nature. Therefore, an accurate categorization between alcoholic (A) and nonalcoholic (NA) subjects, while using a short time EEG recording, is a challenging task. In this paper a novel hybridization of the oscillatory modes decomposition, features mining based on the Second Order Difference Plots (SODPs) of oscillatory modes, and machine learning algorithms is devised for an effective identification of alcoholism. The Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) are used to respectively decompose the considered EEG signals in Intrinsic Mode Functions (IMFs) and Modes. Onward, the SODPs, derived from first six IMFs and Modes, are considered. Features of SODPs are mined. To reduce the dimension of features set and computational complexity of the classification model, the pertinent features selection is made on the basis of Wilcoxon statistical test. Three features with p-values (p) of < 0.05 are selected from each intended SODP and these are the Central Tendency Measure (CTM), area and mean. These features are used for the discrimination between A and NA classes. In order to determine a suitable EEG signal segment length for the intended application, experiments are performed by considering features extracted from three different length time windows. The classification is carried out by using the Least Square Support Vector Machine (LS-SVM), Multilayer perceptron neural network (MLPNN), K-Nearest Neighbour (KNN) and Random Forest (RF) algorithms. The applicability is tested by using the UCI-KDD EEG dataset. The results are noteworthy for MLPNN with 99.89% and 99.45% accuracies for EMD and VMD respectively for 8-second window.

14

BAED: A secured biometric authentication system using ECG signal based on deep learning techniques

Jaya Prakash Allam, Patro Kiran Kumar, Hammad Mohamed, Tadeusiewicz Ryszard, Pławiak Paweł

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 4

1081--1093

EN

Biometric authentication technology has become increasingly common in our daily lives as information protection and control regulation requirements have grown worldwide. A biometric system must be simple, flexible, efficient, and secure from unauthorized access. The most suitable and flexible biometric traits are the face, fingerprint, palm print, voice, electrocardiogram (ECG), and iris. ECGs are difficult to falsify among these biometric traits and are less attack-prone. However, designing biometric systems based on ECG is very challenging. The major limitations of the existing techniques are that they require a large amount of training data and that they are trained and tested on an on-person database. To cope with these issues, this work proposes a novel biometric authentication scheme based on ECG detection called BAED. The system was developed based on deep learning algorithms, including a convolutional neural network (CNN) and a long-term memory (LSTM) network with a customized activation function. The authors evaluated the proposed model with on-and off-person databases including ECG-ID, Physikalisch-Technische Bundesanstalt (PTB), Check Your Bio-signals Here Initiative (CYBHi), and the University of Toronto Database (UofTDB). In addition to the standard performance parameters, certain key supportive identification parameters such as FMR, FNMR, FAR, and FRR were computed and compared to increase the model’s credibility.The proposed BAED system outperforms prior state-of-the-art approaches.

15

A novel end-to-end deep learning approach for cancer detection based on microscopic medical images

Hammad Mohamed, Bakrey Mohamed, Bakhiet Ali, Tadeusiewicz Ryszard, Abd El-Latif Ahmed A., Pławiak Paweł

Biocybernetics and Biomedical Engineering

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2022

|

Vol. 42, no. 3

737--748

EN

As a result of late diagnosis, cancer is the second leading cause of death in most countries in the world. Usually, many cases of cancer are diagnosed at an advanced stage, which reduces the chances of recovery from the disease due to the inability to provide appropriate treatment. The earlier cancer is detected, the more effective the treatment can be, especially for incurable cancers, which can result in a shorter life expectancy due to the rapid spread of the disease. The early detection of cancer also greatly reduces the financial consequences of it, as the cost of treating it in its early stages is much lower than in its other stages. Therefore, several previous studies focus on developing computer-aided cancer diagnosis systems (CACDs) that can detect cancer in its earliest stages automatically. In this paper, a novel approach is proposed for cancer detection. The proposed approach is an end-to-end deep learning approach, where the input images are fed directly to the deep model for final decision. In this research, the accuracy of a new deep convolutional neural network (CNN) for cancer detection is explored. The microscopic medical images obtained from the cancer database were used to evaluate our study, which were labelled as normal and abnormal images. The presented model achieved an accuracy of 99.99%, which is the highest accuracy compared with other deep learning models. Finally, the proposed approach would be very useful and effective, especially in low-income countries where referral systems for patients with suspected cancer are often unavailable, resulting in delayed and fragmented care.

16

Book review “Process maturity of hospitals and the quality of medical services” by Beata Detyna

Tadeusiewicz Ryszard

Bio-Algorithms and Med-Systems

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2022

|

Vol. 18, no. 1

13--15

17

Systemy wizyjne automatu do przedsiewnego przygotowania żołędzi

Tylek Paweł, Tadeusiewicz Ryszard, Jabłoński Mirosław, Piłat Adam, Kaliniewicz Zdzisław, Adamczyk Florian, Klocek Jakub

Przegląd Elektrotechniczny

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2021

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R. 97, nr 1

188--191

PL

W przypadku żołędzi przeznaczonych do siewu w szkółkach leśnych powszechnie stosuje się skaryfikację mechaniczną, polegającą na odcięciu od strony znamienia ich końców. Ten pracochłonny zabieg przyspiesza kiełkowanie nasion oraz wyrównuje wschody. Wartością dodaną skaryfikacji jest możliwość wzrokowej oceny zmian mumifikacyjnych żołędzi i odrzucenia nasion nekrotycznych. Autorzy podjęli się opracowania urządzenia do automatyzacji ww. procesów, który został wyposażony w dwa niezależne systemy rozpoznawania i analizy obrazów.

EN

In the case of acorns intended for sowing in forest nurseries, mechanical scarification is commonly used, consisting in cutting off their ends. This laborious treatment accelerates seed germination and even out the size of the seedlings. The added value of scarification is the ability to visually assess the mummification changes of acorns and the rejection of necrotic seeds. The authors undertook to develop a device for the automation of the above-mentioned processes, which has been equipped with two independent image recognition and analysis systems

18

The Pole, whom we owe three-phase current

Tadeusiewicz Ryszard

Polish Technical Review

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2021

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No. 3

20--24

EN

The article presents the profile and achievements of Michał Doliwo-Dobrowolski, a Polish inventor born in Russia. He played an outstanding role in the history of world electrical engineering at the turn of the 19th and 20th centuries. He was a pioneer of the three-phase current technique. In 1888, he constructed a sensational, easy to use, cheap to produce and operate, the world's first three-phase squirrel-cage induction motor. The patent, filed on March 8, 1889, started a new era in electricity, which continues to this day, the era of alternating current.

PL

Artykuł przedstawia sylwetkę i dokonania Michała Doliwo-Dobrowolskiego, polskiego wynalazcy urodzonego w Rosji. Odegrał on wybitną rolę w historii światowej elektrotechniki na przełomie XIX i XX wieku. Był pionierem techniki prądu trójfazowego. W 1888 r. konstruował rewelacyjny, prosty w obsłudze, tani w produkcji i eksploatacji, pierwszy na świecie trójfazowy indukcyjny silnik klatkowy. Patent zgłoszony 8 marca 1889 r., zapoczątkował nową erę w elektryce, trwającą do dziś, epokę prądu przemiennego.

19

Jak zapewnić kontynuację rozwoju sztucznej inteligencji ?

Tadeusiewicz Ryszard

Napędy i Sterowanie

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2021

|

R. 23, nr 10

56--58

PL

Sztuczną inteligencją (oznaczaną często skrótem AI od angielskiej nazwy Artificial Intelligence) w takiej lub innej postaci zajmuje się coraz większa liczba inżynierów i naukowców. Wynika to z potrzeb, gdyż niemal każdy system techniczny jest obecnie wyposażany w komputer pełniący funkcje kontrolne, sterujące i optymalizacyjne. Zaś funkcjonowanie każdego komputera jest tym wygodniejsze, im więcej inteligencji zdołamy umieścić w jego oprogramowaniu. W efekcie mamy już inteligentne obrabiarki, inteligentne telefony, inteligentne pojazdy i inteligentne domy. Różnie się o tym mówi, ale ja postrzegam sztuczną inteligencję jako przyjazną dłoń wyciągniętą do użytkownika różnych komputerowo sterowanych urządzeń i usług, dlatego sam intensywnie pracuję nad rozwojem sztucznej inteligencji i staram się zachęcać do tego także moich współpracowników.

20

Archipelag sztucznej inteligencji. Część IV

Tadeusiewicz Ryszard

Napędy i Sterowanie

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2021

|

R. 23, nr 3

48--56

PL

Artykuł ten jest (jak wynika z tytułu) czwartym z serii artykułów poświęconych przeglądowej prezentacji poszczególnych metod sztucznej inteligencji (AI) prezentowanych jako wyspy archipelagu. Wyjaśnienie, dlaczego przyjęto taką właśnie metaforę, znaleźć można w pierwszym artykule tego cyklu, opublikowanym w numerze 12/2020 miesięcznika „Napędy i Sterowanie”. W tym samym artykule, zapoczątkowującym cały cykl, zaproponowałem zasadę, że chociaż mamy tu do czynienia z metaforami (gdy mowa o wyspach i o archipelagu), to jednak nazw tych nie będę ujmował w cudzysłów, pozostawiając właściwą interpretację domyślności Czytelnika.