Dear Doctor
"There are several key differences between human brains and neural networks.
- human brains are far more complex and sophisticated than artificial neural networks.
- human brains are able to learn and adapt much more quickly than neural networks.
- human brains are able to generate new ideas and concepts, while neural networks are limited to the data they are given.
ANNs, neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks, while having nothing in common. NNs are digital stochastic networks. And they are rather numerical than neural.
ANNs are Stochastic Numeral Networks (SNNs), using stochastic models, as regression models and Markov-chain models or Monte Carlo simulation, in terms of probability theory.
Stochastic modeling of random phenomena implies predicting a set of possible outcomes, while deterministic modeling - predicting a single outcome.
Human language activities, be it writing an article or giving a free speech or just talking, are looking as stochastic and random, what could make our actions original and creative and innovative.
You might never know what word, phrase or sentence could come next. But as an intelligent agent, you have a comprehension of rules, syntactic, semantic, pragmatic, logical and ontological, regulating the information entropy of your communication, to make it less stochastic/random and more sensible/intelligible.
Ontologically, the world is an infinite stochastic but knowable environment, with all sorts of random phenomena and processes described by a global network of stochastic variables or random data.
Its deterministic certainty comes from causal patterns or lawful/objective regularities providing accurate predictability in a seemingly chaotic world."
Brain has neural network but brain is the powerhouse of all neural action. Brain and mind are different whereas mind has no neural network. Simply call mind is the outcome of brain.
Dear Sir, Psychology is belive in mind and behaviour, resulted mind is overt and working as directed by the brain signals as received by the brain. Ofcourse structure wise lot of difference between brain structure and neural networks. The responses areare mostly combined effects of both.
One could ask if the objectives of AI are really so different from those of the neurosciences that to necessitate completely dissimilar approaches of study. If the fields are not dissimilar, there is much to be gained by combining theory and experiment with modeling to create a complete science of the brain.
I would content that the fundamental objectives of AI and the neurosciences are entirely akin but have become masked by erroneous conclusions.
Machine learning, in its current incarnation, is not the same process as human intelligence inasmuch as the latter is not simply the rate of learning, the number of trials to acquisition of a skill, or even the ability to perform on intelligence tests, but rather a general mental ability for reasoning, problem-solving, and learning. Intelligence integrates cognitive functions such as perception, attention, memory, language, and planning. With this understanding, intelligence can be reliably measured by standardized tests. Structural and functional neuroimaging studies have generally supported a fronto-parietal network relevant to intelligence. This same network has also been found to underlie cognitive functions related to perception, short-term memory storage, and language. The distributed nature of this network and its involvement in a wide range of cognitive functions fits well with the integrative nature of human intelligence and poorly with the non-integrative nature of machine intelligence.
Artificial intelligence as a consequence is precisely that, artificial and not representative of biological processes, but in and of itself not detracting from self-learning but not necessarily “intelligently.”`
It cannot be compared. I human neurons chances constantly depending on the neurotrnamitters traveling through the axons
- Badges
- Science topic