Artificial Intelligence Attributes

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Despite the wide use (and misuse) of terms such as intelligent systems, there is no widely agreed-upon scientific definition of intelligence. It is then useful to think of intelligence in terms of an open collection of attributes. There is a list of attributes that are seen as the general characteristics of intelligence and a few examples of these are Communication, adaptation, and reasoning. AI systems do not come anywhere close to exhibiting any of these characteristics, except for in narrow areas.
People would probably agree that trademark of intelligence is certainly not the ability to display any of these attributes but the ability to do this in a wide range of areas. It is also known that different systems can display different attributes …show more content…

Well, we can observe artificial intelligence in our everyday lives, for example, Alexa which is amazon’s google search based machine, fingerprint / Facial recognition for our cell phones, and even the self-driving cars that were created by Tesla. Each of these listed is some forms of artificial intelligence (AI) and but as stated before, they are only narrow but they are showing us that there are advances being made at an astonishing rate. AI is often viewed as machines that express human characteristics. Today’s day in age, Artificial intelligence is only used in narrow forms, for example, facial recognition, voice-activated online searches, and even driving a car as seen in the Tesla. The end goal that most researchers have for AI is that they wish that humans will be outperformed by machines in nearly every cognitive …show more content…

This is the focus on what society is doing now. In the 1970’s scientists found that computers could be programmed to do a set of instructions but were unable to provide a code for feelings or even the ability to give meaning to their life (Honavar). However in today’s society, AI is generally made out of a discipline called Machine learning, and this often explores the construction and the algorithms that can be learned. In machine learning, there are three subgroups, Supervised learning, unsupervised learning and reinforcement learning. Each of these having a significant impact on the world of

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