Formalization of the meaning. Part 3. Formation of contexts

The previous parts of this article have attempted to begin describing an approach to building strong artificial intelli-gence based on sense of information. A model was proposed in which concepts were described through related points of view. The point of view was set as a context that changes the original description to its interpretation. It was shown that the meaningfulness of the interpretation can be judged by its adequacy to the memory of previous experience. The space of contexts is described, which defines a system of concepts that cover a certain subject area. In this part of the article, an algorithm is described that allows you to create an initial system of concepts based on the observable signs of phe-nomena, and move from it to the contexts corresponding to these concepts. For the space of contexts, a method for cre-ating concept codes is proposed, which allows concept codes to convey the system of their internal proximity, a com-parison with convolutional networks is made. Explanations of the proposed approach are considered on the example of training the visual cortex.

Teachers’ Roles in Online Environments: How AI Based Techniques Can Ease the Shift Challenges from Face-to-Face to Distance Learning

The Covid-19 emergency has brought a mandatory shift to online systems in the education sector worldwide. This document gives an overview about the online teaching challenges encountered from the teachers’ point view, restitutes how the teacher’s role in online settings can be determining in the successfulness of the learning experience and more importantly provides insights into Artificial Intelli-gence techniques that can solve the equation of transferring the role of teachers in face-to-face settings to distance learning environments.

Research on Synchronization Mechanism of Artificial Intelli-gence Model Based on Belief Measure and Its Application in Pumped Storage Power Station

For the security monitoring of pumped storage power station, a model synchroniza-tion mechanism for cloud edge cooperation framework is proposed. The method uses the belief function to describe the threshold and uses the ping-pong operation strategy to update the model alternately, which solves the problem of artificial intelligence model synchronization and update of edge equipment. The cloud is based on Baidu BML platform, the edge uses customized servers, and the average model update cycle is about three months.

Bodily Processing: What Progress Has Been Made in Understanding the Embodiment of Computing Systems?

" In this article I will address the issue of the embodiment of computing sys-tems from the point of view distinctive of the so-called Unconventional Computation, focusing on the paradigm known as Mor-phological Computation. As a first step, I will contextualize Morphological Computa-tion within the disciplinary field of Embod-ied Artificial Intelligence: broadly con-ceived, Embodied Artificial Intelligence may be characterized as embracing both conventional and unconventional ap-proaches to the artificial emulation of natu-ral intelligence. Morphological Computa-tion stands out from other paradigms of unconventional Embodied Artificial Intelli-gence in that it discloses a new, closer kind of connection between embodiment and computation. I will further my investigation by briefly reviewing the state-of-the-art in Morphological Computation: attention will be given to a very recent trend, whose core concept is that of “organic reconfigu-rability”. In this direction, as a final step, two advanced cases of study of organic or living morphological computers will be pre-sented and discussed. The prospect is to shed some light on our title question: what progress has been made in understanding the embodiment of computing systems? Keywords: Embodied Artificial Intelligence; Morphological Computation; Reservoir Compu-ting Systems; Organic Reconfigurability; 3D Bio-Printed Synthetic Corneas; Xenobots "

Artificial Intelligence, the Need of the Hour

Fourth Industrial revolution is currently sweeping the high-income countries (HIC) with Artificial Intelli- gence (AI) based automation affecting virtually every aspect of life. The term AI was first coined by McCar- thy in 1956. It was not until 2000s that AI began to thrive. The evolution of AI into the current status occurred in the last decade owing to the enhanced computing power using Graphic Processing Units (GPU), development of high-powered computer languages, and the emergence of the Big Data. The latter is generated through wireless communication between ‘Smart’ sensors/devices and self-learning machines. The word ‘smart’ is applied to any device that has memory and is able to connect with data networks such as the internet and the processors. In the last few years, there has been exponential growth in AI applications. This can be judged by the projec- tion that the AI field will add $ 15 Trillion to global economy, by the year 2030, up from $ 600 Million in 2016. This will occur mostly in the HIC. The adoption of AI by low- and middle-income countries (LMIC) lags far behind that of HICs. The LMICs would miss out in the economic benefits, further widening the global inequalities. Machine Learning and Deep Learning are branches of AI that are beginning to form the basis of the automation of financial and business decisions, and are the tools of self-driving cars, industrial produc- tion, data analytics, quality improvement and health- care processes to name a few. In healthcare, some of the AI applications have shown to enhance patient care, reduce medical errors, support clinical and administrative decision making, automate equipment maintenance and help reduce operational cost. For instance, AI led cost reductions achieved up to 25 percent drop in the length of hospital stay and up to 91 per cent reduction in admissions to step down facili- ties. In the United States alone, by the year 2026, AI in healthcare is estimated to realize $150 billion in annual cost savings.

Fifth generation communication networks: on the way to networks 2030

In 2030 networks, it is assumed that criteria such as security, confidentiality, and high data transfer rate with ultra-dense networks will be the key characteristics of 2030 networks, and it should be given special attention from the research community in the field of wireless technologies. The networks 2030 are de-signed to provide terabits per second, which are expected to be achieved using a number of advanced technologies, such as MEC, FoG, mmWave, new radio, Software-defined networking and Artificial intelli-gence in networks. It is necessary to solve several important aspects in order to ensure the quality of service for the new services, first, to ensure the density of network coverage even in sparsely populates areas. The article analyzes the development of firth generation communication networks 5G/IMT-2020 and the main fundamental changes in the development of communication networks 2030.

Sequence to Sequence Model Performance for Education Chatbot

Chatbot for education has great potential to complement human educators and education administrators. For example, it can be around the clock tutor to answer and clarify any questions from students who may have missed class. A chatbot can be implemented either by ruled based or artificial intel-ligence based. However, unlike the ruled-based chatbots, artificial intelli-gence based chatbots can learn and become smarter overtime and is more scalable and has become the popular choice for chatbot researchers recently. Recurrent Neural Network based Sequence-to-sequence (Seq2Seq) model is one of the most commonly researched model to implement artificial intelli-gence chatbot and has shown great progress since its introduction in 2014. However, it is still in infancy and has not been applied widely in educational chatbot development. Introduced originally for neural machine translation, the Seq2Seq model has been adapted for conversation modelling including question-answering chatbots. However, in-depth research and analysis of op-timal settings of the various components of Seq2Seq model for natural an-swer generation problem is very limited. Additionally, there has been no ex-periments and analysis conducted to understand how Seq2Seq model handles variations is questions posed to it to generate correct answers. Our experi-ments add to the empirical evaluations on Seq2Seq literature and provides insights to these questions. Additionally, we provide insights on how a cu-rated dataset can be developed and questions designed to train and test the performance of a Seq2Seq based question-answer model.

IA confiable del Instituto de Ingeniería Eléctrica y Electrónica | Reliable AI of the Institute of Electrical and Electronic Engineering

“AI will be such a program which in an arbitrary world will cope not worse than a human” (Dobrev 2004, 2); “Artificial intelli-gence is the enterprise of constructing a symbol sys-tem that can reliably pass the Turing test” (Ginsberg 2012, 9); See Figure 1.1 Russell and Norvig (1995 page 5). “Artificial intelli-gence is a field of com-puter science concerne dwith the computational understanding of what is commonly called intelli-gent behavior and with the creation of artifacts that exhibit such behav-ior. This definition may e examined more closely by considering the field from three points of view: computational psychology (the goal of which is to understand human intelli-gent behvaior by creating computer programs that behave in the same way that people do), computa-tional philosopy (the goal of which is to form a com-putational understanding of human-level intelligent behavior, without being resticted to the algorithms and data structures that the human mind actually does use), and machine intelligence (the goal of which is to expand the fronteir of what we know how to program” (Reilly 2004, 40-41).

Automation Accuracy Is Good, but High Controllability May Be Better

When automating tasks using some form of artificial intelli- gence, some inaccuracy in the result is virtually unavoidable. In many cases, the user must decide whether to try the auto- mated method again, or fix it themselves using the available user interface. We argue this decision is influenced by both perceived automation accuracy and degree of task “control- lability” (how easily and to what extent an automated result can be manually modified). This relationship between accu- racy and controllability is investigated in a 750-participant crowdsourced experiment using a controlled, gamified task. With high controllability, self-reported satisfaction remained constant even under very low accuracy conditions, and over- all, a strong preference was observed for using manual con- trol rather than automation, despite much slower perfor- mance and regardless of very poor controllability.

Smart E bicycle: an efficient and effective way to greener future

This paper presents and proposes a smart electric bicycle(SeB) leveraging the power of wireless technologies, artificial intelli- gence and cloud computing in order make its user’s experience smooth, safe and enjoyable hence encouraging the user to choose SeB over other modes of transportation. The proposed system introduces an Electric Bicycle connected with a smartphone in one variant or with “smartphone and cloud” in another variant for smart decision-making and efficiency and other related tips for the user. The range of bicycle is predicted based upon the user profile (weight, age etc.), route details (inclinations, distances of al- ternative routes), State of Charge(Soc) and State of Health(SoH) of the battery used. Multiple user profiles and minute details of the route (slope, speed breakers etc.) are captured using sensor like accelerometer and basis on these data smart decisions for pow- er saving and range extensions are made. Also, safety critical and predictive maintenance features are presented.