Human-Like Intelligence and Algorithms (original) (raw)
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Human Cognition and Artificial Intelligence - A Plea for Science
scip Labs, 2018
Cognition comprises mental process of the human brain. Artificial Intelligence tries to mimic these processes to solve complex problems and handle massive amounts of data. Serial vs. parallel and controlled vs. automated processes are the basis of cognitive sciences. Logic, probability theory and heuristics represent the pillars of theory formation for the human-machine comparison. Artificial neural networks (mimicking the human brain) are popular, because great advancements in specific fields were achieved. Systemic problem solving is still visionary, but a number of research projects are promising. Reciprocal, positive influences across disciplines might lead to rapid transformation. Polemics, as well idealism are out of place. Analysis must adhere to scientific and ethical standards with long-term orientation for the public good. After the physiological review, the next chapter will focus on consciousness (as a part of what gives life to physiology).
The book starts with some background on why previous attempts at understanding intelligence and building intelligent machines have failed. Then the core idea of the theory (the memory-prediction framework) is developed. There is an extensive discussion of how the highest level in the brain (the cortex) works; this is the heart of the book since our cortex is the primary region of human intelligence. There is a provocative section on the social and other implications of the theory. The book ends with a discussion of intelligent machineshow we can build them and what the future will be like. Hawkins' basic idea is that the brain is a mechanism to predict the future, specifically, hierarchical regions of the brain predict their near future input sequences. As such, the brain is a feed-forward hierarchical state machine, with features enabling it to learn. The state machine actually controls the behavior of the organism. The machine responds to future events predicted from past data. Neurologically, the focus is on the cortical column. Hawkins places particular emphasis on the role of the interconnections from peer columns, and the activation of columns as a whole. He strongly implies that a column is the cortex's physical representation of a state in a state machine.
Computational Intelligence in a Human Brain Model
2016
This paper focuses on the current trends in brain research domain and the current stage of development of research for software and hardware solutions, communication capabilities between: human beings and machines, new technologies, nano-science and Internet of Things (IoT) devices. The proposed model for Human Brain assumes main similitude between human intelligence and the chess game thinking process. Tactical & strategic reasoning and the need to follow the rules of the chess game, all are very similar with the activities of the human brain. The main objective for a living being and the chess game player are the same: securing a position, surviving and eliminating the adversaries. The brain resolves these goals, and more, the being movement, actions and speech are sustained by the vital five senses and equilibrium. The chess game strategy helps us understand the human brain better and easier replicate in the proposed ‘Software and Hardware’ SAH Model.
AI and the Human Brain, 2023
It's quite conceivable that human thinking can be completely taken over by devices. In any case, there is a stage conceivable in the future when our thinking and doings will be completely regulated by machines. Or by those who are behind this development, or those who have programmed those devices. Just as the stupid have ended up in prisons and institutions, so too it can easily be arranged that people with an intelligence much less than AI are going to be completely monitored and controlled. At the moment, this is still a matter of speculation, but experiments in that direction have already been set in motion. However, what is "stupid," I wonder. Measuring the risk of clash and crash using my algorithm is quite different from an IQ-test. My message is: • Most scientific research is superfluous because they are impracticable and unsuitable in a non-technical field.
Brain and Artificial Intelligence
2017
From ancient times, the history of human beings has developed through a succession of steps and sometimes jumps, until reaching at the relative sophistication of the modern brain and culture. Researchers are attempting to create systems that mimic human thinking, understand speech, or beat the best human chess player. Understanding the mechanisms of intelligence, and creating intelligent artefacts are the twin goals of Artificial Intelligence (AI). Great mathematical minds have played a key role in AI in recent years; to name only a few, Janos Neumann (also known as John von Neumann), Konrad Zuse, Norbert Wiener, Claude E. Shannon, Alan M. Turing, Grigore Moisil, Lofti A. Zadeh, Ronald R. Yager, Michio Sugeno, Solomon Marcus, or Lászlo A. Barabási. Introducing the study of AI is not merely useful because of its capability to solve difficult problems, but also because of its Mathematical nature. It prepares us to understand the current world, enabling us to act on the challenges of t...
The Art of Balance - Problem-Solving vs. Pattern-Recognition
2015
The dual-process theory of human cognition proposes the existence of two systems for decision-making: a slower, deliberative, “problem-solving” system and a quicker, reactive, “pattern-recognition” system. The aim of this work is to explore the effect on agent performance of altering the balance of these systems in an environment of varying complexity. This is an important question, both in the realm of explanations of expert behaviour and to AI in general. To achieve this, we implement three distinct types of agent, embodying different balances of their problem-solving and pattern-recognition systems, using a novel, hybrid, humanlike cognitive architecture. These agents are then situated in the virtual, stochastic, multi-agent “Tileworld” domain, whose intrinsic and extrinsic environmental complexity can be precisely controlled and widely varied. This domain provides an adequate test-bed to analyse the research question posed. A number of computational simulations are run. Our resu...
The Human Brain and Artificial Learning: A Convergence of Information Processing Systems
Zenodo, 2024
It has all been a bit too exciting for seriousness and too intense for art, as scientists and engineers alike have been dazzled by the remarkable similarities between human brain function and artificial learning systems. The integration of biological and artificial information processing is one of the most fascinating chapters of our view of intelligence. In this work, we further our previous investigations of neural networks and learning mechanisms by studying the striking correspondence between biological and artificial learning systems. Think about a library in which books are constantly being moved about and reorganised according to such properties as frequency of use and their mutual dependencies. The essence of both human memory and artificial learning systems is that they are dynamic, adaptive networks that constantly refine their organisation according to experience and usage patterns - this is captured in this analogy. Just as the classification system of a library allows one to retrieve books efficiently, both biological and artificial systems compute elaborate methods for storing and accessing information.
Becoming aware of our cognitive dynamics
2023
Today, humanity has an enormous amount of knowledge at its disposal. This fact does not correspond to its social behaviour. The human capacity to understand nature and develop new technologies contrasts with the inability to cope with global challenges (poverty, global warming, etc.). It seems that society lacks the cognitive resources to use this knowledge. Thanks to our current communication systems, we are part of a network that connects our brains with other people's brains. A network at a higher level. Different individual and social behavioural patterns emerge from human interaction. To understand these patterns in depth, a basic knowledge of dynamic tools from the fields of feedback control and self-organised systems is required. This approach is not new. Towards the middle of the last century, a group of eminent scientists shaped what came to be called cybernetics. It was the first rigorous interdisciplinary approach to the study of human brain dynamic functions. In recent decades, interdisciplinary work in the study of brain functions has enabled us to make progress in understanding different aspects of cognition. From the perspective of feedback and selforganised systems, the cognitive cycle involved in various processes such as learning, memory, problem solving, etc. can be easily and naturally understood. This systemic view of the learning process gives people more tools to use their knowledge and also encourages them to engage in a continuous learning process (learning to learn) and to become aware of the effects of their actions in society (network propagation). The purpose of this paper is to develop an integrated vision of the cognitive mechanisms of individual and social learning and the possible implications of the promotion of social acquisition of a dynamic systemic vision of cognition, i.e. to enhance our social agency.