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Register a new userCan Computers overcome Humans? Consciousness interaction and its implications
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Camilo Miguel Signorelli
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Can computers overcome human capabilities? This is a paradoxical and controversial question, particularly because there are many hidden assumptions. This article focuses on that issue putting on evidence some misconception related with future generations of machines and the understanding of the brain. It will be discussed to what extent computers might reach human capabilities, and how it could be possible only if the computer is a conscious machine. However, it will be shown that if the computer is conscious, an interference process due to consciousness would affect the information processing of the system. Therefore, it might be possible to make conscious machines to overcome human capabilities, which will have limitations as well as humans. In other words, trying to overcome human capabilities with computers implies the paradoxical conclusion that a computer will never overcome human capabilities at all, or if the computer does, it should not be considered as a computer anymore.
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This work summarizes part of current knowledge on High-level Cognitive process and its relation with biological hardware. Thus, it is possible to identify some paradoxes which could impact the development of future technologies and artificial intelligence: we may make a High-level Cognitive Machine, sacrificing the principal attribute of a machine, its accuracy.
The emergence of powerful artificial intelligence is defining new research directions in neuroscience. To date, this research has focused largely on deep neural networks trained using supervised learning, in tasks such as image classification. However, there is another area of recent AI work which has so far received less attention from neuroscientists, but which may have profound neuroscientific implications: deep reinforcement learning. Deep RL offers a comprehensive framework for studying the interplay among learning, representation and decision-making, offering to the brain sciences a new set of research tools and a wide range of novel hypotheses. In the present review, we provide a high-level introduction to deep RL, discuss some of its initial applications to neuroscience, and survey its wider implications for research on brain and behavior, concluding with a list of opportunities for next-stage research.
Physical symbol systems are needed for open-ended cognition. A good way to understand physical symbol systems is by comparison of thought to chemistry. Both have systematicity, productivity and compositionality. The state of the art in cognitive architectures for open-ended cognition is critically assessed. I conclude that a cognitive architecture that evolves symbol structures in the brain is a promising candidate to explain open-ended cognition. Part 2 of the paper presents such a cognitive architecture.
CAPTCHAs are employed as a security measure to differentiate human users from bots. A new sound-based CAPTCHA is proposed in this paper, which exploits the gaps between human voice and synthetic voice rather than relays on the auditory perception of human. The user is required to read out a given sentence, which is selected randomly from a specified book. The generated audio file will be analyzed automatically to judge whether the user is a human or not. In this paper, the design of the new CAPTCHA, the analysis of the audio files, and the choice of the audio frame window function are described in detail. And also, some experiments are conducted to fix the critical threshold and the coefficients of three indicators to ensure the security. The proposed audio CAPTCHA is proved accessible to users. The user study has shown that the human success rate reaches approximately 97% and the pass rate of attack software using Microsoft SDK 5.1 is only 4%. The experiments also indicated that it could be solved by most human users in less than 14 seconds and the average time is only 7.8 seconds.
Objectives: Functional connectivity triggered by naturalistic stimulus (e.g., movies) and machine learning techniques provide a great insight in exploring the brain functions such as fluid intelligence. However, functional connectivity are considered to be multi-layered, while traditional machine learning based on individual models not only are limited in performance, but also fail to extract multi-dimensional and multi-layered information from brain network. Methods: In this study, inspired by multi-layer brain network structure, we propose a new method namely Weighted Ensemble-model and Network Analysis, which combines the machine learning and graph theory for improved fluid intelligence prediction. Firstly, functional connectivity analysis and graphical theory were jointly employed. The functional connectivity and graphical indices computed using the preprocessed fMRI data were then all fed into auto-encoder parallelly for feature extraction to predict the fluid intelligence. In order to improve the performance, tree regression and ridge regression model were automatically stacked and fused with weighted values. Finally, layers of auto-encoder were visualized to better illustrate the connectome patterns, followed by the evaluation of the performance to justify the mechanism of brain functions. Results: Our proposed methods achieved best performance with 3.85 mean absolute deviation, 0.66 correlation coefficient and 0.42 R-squared coefficient, outperformed other state-of-the-art methods. It is also worth noting that, the optimization of the biological pattern extraction was automated though the auto-encoder algorithm. Conclusion: The proposed method not only outperforming the state-of-the-art reports, but also able to effectively capturing the biological patterns from functional connectivity during naturalistic movies state for potential clinical explorations.
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