Connectionist Artificial Intelligence Research Articles

Audiovisual Speech Recognition Method Based on Connectionism

Audio-visual speech recognition technology has greatly improved the performance of pure speech recognition by combining visual speech information and acoustic speech information, but there are problems such as large data demand, audio and video data alignment, and noise robustness. Scholars have proposed many solutions to these problems. Among them, deep learning algorithms, as representatives of connectionist artificial intelligence technology, have good generalization ability and portability, and are easier to migrate to different tasks and fields. They are becoming one of the mainstream technologies for audio-visual speech recognition. This paper mainly studies and analyzes the application of deep learning technology in the field of audio-visual speech recognition, especially the audio-visual speech recognition model of the end-to-end framework. Through experimental comparative analysis, relevant data sets and evaluation methods are summarized, and finally hot issues that need to be further studied and solved are proposed.

A Hybrid Driving Decision-Making System Integrating Markov Logic Networks and Connectionist AI

Connectionist artificial intelligence (AI) can power many critical tasks for connected and autonomous vehicles (CAVs). However, connectionist AI lacks interpretability and usually needs large amount of data for learning. A Markov logic network (MLN), which combines first-order logic (FOL) with statistical learning, learns weighted FOL formulas for inference. MLNs can incorporate domain expert knowledge in the form of FOL formulas to achieve data-efficient learning and transparent decision process. In this paper, we propose a hybrid driving decision-making system, which integrates a MLN module and a deep Q-network (DQN) for enhanced driving safety. The MLN module evaluates the safety of ranked actions from DQN to reduce potential collisions. A collective MLN (Co-MLN) learning algorithm is proposed and it enables CAVs collectively learn a global MLN model for safe state transitions, given distributed small amount of noisy data. A hybrid DQN-MLN learning algorithm is also developed for CAVs to collectively learn to drive in new driving environments. Simulations performed using a highway driving simulator show that the proposed Co-MLN algorithm is highly data-efficient and the learned hybrid driving system can effectively reduce collisions. In addition, the learned MLN module provides transparency for safety-critical driving decisions.

Artificial intelligence in the practice of work

The article proposes an analytical perspective on artificial intelligence (AI) that can be fruitful in the sociology of work. The practical logic of new forms of AI (connectionist AI) is described as an interplay of social and technical processes of opening and closing possibilities of knowledge and action. In order to develop this argument, it is first shown in which sense AI can be understood as a contingency-generating technology in socio-technical contexts. The architecture based on neural networks is elaborated as a decisive feature of connectionist AI that not only opens up technical possibilities but can also shape social processes and structures by ‘selectivity’. However, this shaping does not take place solely on the part of the AI, but only becomes apparent in the interplay with specific restrictions that lie both in the social context of use and in the algorithmic architecture of the AI itself. For research in the sociology of work, this means that contingency theory approaches must be linked with approaches that emphasise the limits of (‘intelligent’) digitalisation. The yield of such a perspective is outlined in relation to the control of work with AI.

PROBLEMS AND OPPORTUNITIES OF ARTIFICIAL INTELLIGENCE

This article reviews Artificial Intelligence (AI)’s challenges and opportunities and discusses where AI might be headed. In the first part of the article, it was tried to reveal the differences between Symbolic AI and Deep Learning approaches, then long promises but short deliveries of AI were mentioned. When we review the problems of AI in general terms, it is a problem that the media has high expectations about AI and keeps the problems and restrictions it creates low. Today, while AI is stuck with issues such as deepfake applications and carbon footprints that create moral and climatologic problems; on the other hand, it is struggling with problems such as deep learning models requiring huge amounts of data. Another problem with deep learning is that deep learning models are a black-box and not open to improvements because it is not known where mistakes were made. Among the new paths ahead of AI are Hierarchical Temporal Memory (HTM) models and hybrid models that generally try to bridge the gap between Symbolic AI and Connectionist AI. If we consider that the most important leaps in AI have been made with the features of the brain that AI can imitate, then the developed HTM models may also be a new opportunity for AI.

Edge Intelligence for Object Detection in Blockchain-Based Internet of Vehicles: Convergence of Symbolic and Connectionist AI

Reliable detection of objects is a crucial requirement to improve the safety of the visual Internet of Vehicles (V-IoVs). The resemblance of objects to each other or the background makes the detection of objects difficult. The traditional connectionist artificial intelligence (All-based object detection model is a potential approach to increase detection performance, but it has poor interpretability and high computational complexity. Edge intelligence has demonstrated a considerably good balance between efficiency and computation complexity, integrating multi-access edge computing and AI. In this article, an edge AI framework is designed to perform the object detection task, and a novel abductive learning algorithm is proposed to realize the interpretability and robustness of AI in the V-IoV system. Based on the you only look once (YOLO) classifier, the abductive model is used to learn from the data, which combines symbolic and connectionist AI. Moreover, blockchain is developed for model sharing. Compared to the state of the art, simulation results show the high accuracy of the proposed algorithm. Moreover, the proposed approach has interpretability and strong robustness.

Vulnerabilities of Connectionist AI Applications: Evaluation and Defense.

This article deals with the IT security of connectionist artificial intelligence (AI) applications, focusing on threats to integrity, one of the three IT security goals. Such threats are for instance most relevant in prominent AI computer vision applications. In order to present a holistic view on the IT security goal integrity, many additional aspects, such as interpretability, robustness and documentation are taken into account. A comprehensive list of threats and possible mitigations is presented by reviewing the state-of-the-art literature. AI-specific vulnerabilities, such as adversarial attacks and poisoning attacks are discussed in detail, together with key factors underlying them. Additionally and in contrast to former reviews, the whole AI life cycle is analyzed with respect to vulnerabilities, including the planning, data acquisition, training, evaluation and operation phases. The discussion of mitigations is likewise not restricted to the level of the AI system itself but rather advocates viewing AI systems in the context of their life cycles and their embeddings in larger IT infrastructures and hardware devices. Based on this and the observation that adaptive attackers may circumvent any single published AI-specific defense to date, the article concludes that single protective measures are not sufficient but rather multiple measures on different levels have to be combined to achieve a minimum level of IT security for AI applications.

Semantics in the Deep: Semantic Analytics for Big Data

One cannot help but classify the continuous birth and demise of Artificial Intelligence (AI) trends into the everlasting theme of the battle between connectionist and symbolic AI [...]

Habermas' Communicative Rationality and Connectionist AI

Habermas' universal pragmatics continues to draw significant attention from sociologists seeking a viable balance between poststructuralism and traditional critical theory, while at the same time becoming increasingly recognised within formal political circles worldwide. A number of social theorists and philosophers, however, have taken Habermas to task with respect to how much his ‘theory of communicative rationality’, the driving force behind universal pragmatics, in fact actually steps away from epistemological foundationalism as Habermas intends it to do. This paper explores parallels between Habermas' particular notion of human reason and rationality (i.e., communicative rationality) and that expressed within connectionism, today's dominant paradigm in the discipline of artificial intelligence (AI), created as an alternative to the classical AI view of ‘mind as computer’. Given the homology, I argue, the practical shortcomings of connectionism may indeed lend unique and compelling weight to those claims that Habermas' system of thought is foundationalist, despite Habermas' efforts.

Logic and Artificial Intelligence: Divorced, Still Married, Separated...?

Though it‘s difficult to agree on the exact date of their union, logic and artificial intelligence (AI) were married by the late 1950s, and, at least during their honeymoon, were happily united. What connubial permutation do logic and AI find themselves in now? Are they still (happily) married? Are they divorced? Or are they only separated, both still keeping alive the promise of a future in which the old magic is rekindled? This paper is an attempt to answer these questions via a review of six books. Encapsulated, our answer is that (i) logic and AI, despite tabloidish reports to the contrary, still enjoy matrimonial bliss, and (ii) only their future robotic offspring (as opposed to the children of connectionist AI) will mark real progress in the attempt to understand cognition.

Connectionism, eliminativism, and the semantic view of theories

Recently some philosophers have urged that connectionist artificial intelligence is (potentially) eliminative for the propositional attitudes of folk psychology. At the same time, however, these philosophers have also insisted that since philosophy of science has failed to provide criteria distinguishing ontologically retentive from eliminative theory changes, the resulting eliminativism is not “principled”. Application of some resources developed within the semantic view of scientific theories, particularly recent formal work on the theory reduction relation, reveals these philosophers to be wrong in this second contention, yet by and large correct in the first.

Connectionist ideas and algorithms

In our quest to build intelligent machines, we have but one naturally occurring model: the human brain. It follows that one natural idea for artificial intelligence (AI) is to simulate the functioning of the brain directly on a computer. Indeed, the idea of building an intelligent machine out of artificial neurons has been around for quite some time. Some early results on brain-line mechanisms were achieved by [18], and other researchers pursued this notion through the next two decades, e.g., [1, 4, 19, 21, 24]. Research in neural networks came to a virtual halt in the 1970s, however, when the networks under study were shown to be very weak computationally. Recently, there has been a resurgence of interest in neural networks. There are several reasons for this, including the appearance of faster digital computers on which to simulate larger networks, interest in building massively parallel computers, and most importantly, the discovery of powerful network learning algorithms. The new neural network architectures have been dubbed connectionist architectures. For the most part, these architectures are not meant to duplicate the operation of the human brain, but rather receive inspiration from known facts about how the brain works. They are characterized by Large numbers of very simple neuron-like processing elements; Large numbers of weighted connections between the elements—the weights on the connections encode the knowledge of a network; Highly parallel, distributed control; and Emphasis on learning internal representations automatically. Connectionist researchers conjecture that thinking about computation in terms of the brain metaphor rather than the digital computer metaphor will lead to insights into the nature of intelligent behavior. Computers are capable of amazing feats. They can effortlessly store vast quantities of information. Their circuits operate in nanoseconds. They can perform extensive arithmetic calculations without error. Humans cannot approach these capabilities. On the other hand, humans routinely perform simple tasks such as walking, talking, and commonsense reasoning. Current AI systems cannot do any of these things better than humans. Why not? Perhaps the structure of the brain is somehow suited to these tasks, and not suited to tasks like high-speed arithmetic calculation. Working under constraints suggested by the brain may make traditional computation more difficult, but it may lead to solutions to AI problems that would otherwise be overlooked. What constraints, then, does the brain offer us? First of all, individual neurons are extremely slow devices when compared to their counterparts in digital computers. Neurons operate in the millisecond range, an eternity to a VLSI designer. Yet, humans can perform extremely complex tasks, like interpreting a visual scene or understanding a sentence, in just a tenth of a second. In other words, we do in about a hundred steps what current computers cannot do in ten million steps. How can this be possible? Unlike a conventional computer, the brain contains a huge number of processing elements that act in parallel. This suggests that in our search for solutions, we look for massively parallel algorithms that require no more than 100 processing steps [9]. Also, neurons are failure-prone devices. They are constantly dying (you have certainly lost a few since you began reading this article), and their firing patterns are irregular. Components in digital computers, on the other hand, must operate perfectly. Why? Such components store bits of information that are available nowhere else in the computer: the failure of one component means a loss of information. Suppose that we built AI programs that were not sensitive to the failure of a few components, perhaps by using redundancy and distributing information across a wide range of components? This would open the possibility of very large-scale implementations. With current technology, it is far easier to build a billion-component integrated circuit in which 95 percent of the components work correctly than it is to build a perfectly functioning million-component machine [8]. Another thing people seem to be able to do better than computers is handle fuzzy situations. We have very large memories of visual, auditory, and problem-solving episodes, and one key operation in solving new problems is finding closest matches to old situations. Inexact matching is something brain-style models seem to be good at, because of the diffuse and fluid way in which knowledge is represented. The idea behind connectionism, then, is that we may see significant advances in AI if we approach problems from the point of view of brain-style computation rather than rule-based symbol manipulation. At the end of this article, we will look more closely at the relationship between connectionist and symbolic AI.