This is a modest endeavour written from an engineering perspective by a nonphilosopher to set things straight if somewhat roughly: What does artificial intelligence boil down to? What are its merits and why some dangers may stem from its development in this time of confusion when, to quote Rémi Brague: “From the point of view of technology, man appears as outdated, or at least superfluous”?
We all face a wide array of different choices every day of our lives. Asst. Prof. Miłosz Kadziński explains how artificial intelligence could be used to help us make decisions.
Dr. Aleksandra Przegalinska explains why we find humanoid robots so creepy and considers whether watching machines play football is actually fun.
This paper presents a deep learning-based image texture recognition system. The methodology taken in this solution is formed in a bottom-up manner. It means we swipe a moving window through the image in order to categorize if a given region belongs to one of the classes seen in the training process. This categorization is done based on the Deep Neural Network (DNN) of fixed architecture. The training process is fully automated regarding the training data preparation, investigation of the best training algorithm, and its hyper-parameters. The only human input to the system is the definition of the categories for further recognition and generation of the samples (region markings) in the external application chosen by the user. The system is tested on road surface images where its task is to categorize image regions to a different road category (e.g. curb, road surface damage, etc.) and is featured with 90% and above accuracy.
This paper proposes a new approach to the processing and analysis of medical images. We introduce the term and methodology of medical data understanding, as a new step in the way of starting from image processing, and followed by analysis and classification (recognition). The general view of the situation of the new technology of machine perception and image understanding in the context of the more well known and classic techniques of image processing, analysis, segmentation and classification is shown below
In the paper an application of evolutionary algorithm to design and optimization of combinational digital circuits with respect to transistor count is presented. Multiple layer chromosomes increasing the algorithm efficiency are introduced. Four combinational circuits with truth tables chosen from literature are designed using proposed method. Obtained results are in many cases better than those obtained using other methods.
Background: a humidity sensor is used to sense and measure the relative humidity of air. A new composite system has been fabricated using environmental pollutants such as carbon black and low-cost zinc oxide, and it acts as a humidity sensor. Residual life of the sensor is calculated and an expert system is modelled. For properties and nature confirmation, characterization is performed, and a sensing material is fabricated. Methodology: characterization is performed on the fabricated material. Complex impedance spectroscopy (CIS), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and scanning electron microscopy (SEM) are all used to confirm the surface roughness, its composite nature as well as the morphology of the composite. The residual lifetime of the fabricated humidity sensor is calculated by means of accelerated life testing. An intelligent model is designed using artificial intelligence techniques, including the artificial neural network (ANN), fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS). Results: maximum conductivity obtained is 6.4×10⁻³ S/cm when zinc oxide is doped with 80% of carbon black. Conclusion: the solid composite obtained possesses good humidity-sensing capability in the range of 30–95%. ANFIS exhibits the maximum prediction accuracy, with an error rate of just 1.1%.
Due to fast-paced technical development, companies are forced to modernise and update
their equipment, as well as production planning methods. In the ordering process, the customer
is interested not only in product specifications, but also in the manufacturing lead
time by which the product will be completed. Therefore, companies strive towards setting
an appealing but attainable manufacturing lead date.
Manufacturing lead time depends on many different factors; therefore, it is difficult to predict.
Estimation of manufacturing lead time is usually based on previous experience. In the
following research, manufacturing lead time for tools for aluminium extrusion was estimated
with Artificial Intelligence, more precisely, with Neural Networks.
The research is based on the following input data; number of cavities, tool type, tool category,
order type, number of orders in the last 3 days and tool diameter; while the only output
data are the number of working days that are needed to manufacture the tool. An Artificial
Neural Network (feed-forward neural network) was noted as a sufficiently accurate method
and, therefore, appropriate for implementation in the company.
Artificial Intelligence, both as a hope of making a substantial progress, and a fear of the unknown and unimaginable, has its roots in human dreams. These dreams were materialized by means of rational intellectual efforts. We see beginnings of such a process in Lullus’s fancies. Many scholars and enthusiasts participated in the development of Lullus’s art, ars combinatoria. Amongst them, Athanasius Kircher was distinguished. Gottfried Leibniz ended the period in which the idea of artificial intelligence had been shaped, and started a new one when artificial intelligence could be considered a part of science, according to today’s standards.