Scientists plan to implement aspects like pattern recognising

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In summary, the conversation discusses the challenges and limitations of implementing aspects like pattern recognition, spontaneity, and creativity in Artificial Intelligence (AI). While recognition and classification algorithms are quite good, recognizing objects in images can be difficult due to lack of invariance under projection and illumination change. AI deals with logical reasoning rather than interpretation of sensory information. So far, AI has not been able to replicate true intelligence, creativity, or emotion. The possibility of teaching a computer common sense and learning like a human is also discussed, with the conclusion that it would require advanced technology to replicate the human brain.
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chhitiz
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i was reading this article on AI and i have a question:
how do scientists plan to implement aspects like pattern recognising(and not just simple pattern recognition, but even linking images from two totally unrelated fields, like we sometimes link mental images, i hope u get it), or spontaneity and creativity?
how far have they gone?
 
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Recognition and classification algorithms are quite good if features can be extracted that are invariant under the desired transformations. In the case of recognition in images, this is quite difficult because nothing is really invariant under projection and illumination change, so even recognizing identical objects can be extremely difficult. However, we have some stuff that works OK.

Artificial Intelligence is separate, and deals with the logical aspects of reasoning rather than interpretation of sensory information to recognize patterns etc. It is assumed that some other "hardware" has already processed this input and parsed it into more meaningful information tokens which can then be dealt with in a logic framework.

So far AI has yet to live up to its name. There is nothing that resembles true intelligence, creativity, or emotion -- actually, it is yet to be shown that this is even possible, as it may be nothing more than an illusion in humans. We do have machines that can solve simple planning problems, but only if those problems can be posed into a very formalized in rigid framework -- and we don't really have a good way of transforming observations into this logic framework. Because we don't have machines that can actually think, we can't even begin to work on adding things like spontaneity or creavity..although if we did have machines that could think, this would probably be a trivial addition. Instead what you may see are toy problems where randomness is used to give the cursory illusion of human-like qualities.
 
  • #3


what about common sense? the amount of common axioms that we take for granted is so huge that it don't think that can be stored in a small memory. is it even possible for us to make a computer learn, like we make a baby learn?
 
  • #4


chhitiz said:
what about common sense? the amount of common axioms that we take for granted is so huge that it don't think that can be stored in a small memory. is it even possible for us to make a computer learn, like we make a baby learn?

A simple program that records all its input in memory could be considered "learning" the data. A simple one pole moving average filter could be considered to learn the average of a stationary series. A neural network can "learn" arbitrary associations through back propagation or other update schemes. Hebbian learning can be used as well.

Basically, learning is trivial...but it is only meaningful if you have an intelligence that can use that learned information.

Common sense information (with exception to natural instincts) is learned through experience in humans, and any good AI system would also need a system for learning such as this that would allow it to develop without needing a bunch of axioms being manually programmed in. Common sense rules are not strictly "if then" statements, either..everything is relative and has a degree of uncertainty.
 
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Unless the computer replicates the human brain to a very high resolution, it is not likely that it will ever seem completely human to a trained observer. But there are scientists currently trying to replicate the human brain, cell by cell, in a computer. And if nanotechnology ever does become advanced enough, then maybe someday the contents of ones brain could be downloaded into a computer.
 

What is pattern recognition and why is it important in scientific research?

Pattern recognition is the ability to identify and interpret recurring patterns or trends in data. In scientific research, it is important because it allows scientists to make sense of complex data, identify relationships between variables, and make predictions about future outcomes.

How do scientists plan to implement pattern recognition in their research?

Scientists plan to implement pattern recognition in their research by utilizing advanced computer algorithms and machine learning techniques. These tools can analyze large amounts of data and identify patterns that would be difficult or impossible for humans to detect.

What are the potential benefits of incorporating pattern recognition in scientific research?

The potential benefits of incorporating pattern recognition in scientific research include improved accuracy and efficiency in data analysis, identification of new patterns and relationships that may lead to new discoveries, and the ability to make more accurate predictions and informed decisions based on data.

Are there any ethical concerns surrounding the use of pattern recognition in scientific research?

There could be ethical concerns surrounding the use of pattern recognition in scientific research, particularly in areas such as data privacy and potential biases in the algorithms used. It is important for scientists to carefully consider and address these concerns in their research.

How can the general public benefit from the implementation of pattern recognition in scientific research?

The general public can benefit from the implementation of pattern recognition in scientific research through improved understanding of complex data, more accurate and informed decisions made by scientists, and potential advancements and discoveries in various fields that may have a positive impact on society.

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