Artificial Intelligence vs Human Intelligence

Introduction

Ever since the idea of artificial intelligence was introduced to society, it has been a topic of much debate. Which is better, artificial or human intelligence? Artificial intelligence is a vast subject that can’t be fully touched upon in an introductory article. But we will do our best to explain what AI is and what people are trying to accomplish with it. Before going further, If you are interested in learning more about AI or Machine Learning, don’t forget to check out the Artificial Intelligence (AI) course in Kochi.

#dontbeacreeper

Intelligence and knowledge are two separate things. We all have access to knowledge — with the internet (and libraries), if you want something then you usually just need to type it up into Google or go get a book at the library. However, intelligence means different things to different people.

Because of this, people have debated about what is the most intelligent being — for years, and continue to do so. We all know that dolphins are smarter than us, but we also know that dolphins are not humans (at least not from a biological perspective). So what is the most intelligent being, then? Is it a dolphin? What about a human or an ape (chimpanzee)?

#searchingforanswers

Let’s go back to the question of intelligence. People have different definitions of intelligence, and because of this we can’t just say that dolphins are smarter than humans. This is because some people may value intelligence differently; for example, if you asked someone who was a smart businessman, he may tell you that he is smart. But if you ask someone who is good at math, they would likely say that they are smart — in their field. Therefore, we can’t compare one person’s definition of intelligence with another’s and claim which one is better.

#biosof2011

So what do we do? Why do we even bother asking about the intelligence of dolphins and apes, when it comes down to it — does it really matter? Why should a human being care how smart a dolphin or an ape is, when they are not even human. At least in this case, we know that dolphins and apes cannot talk.

We also realize that there isn’t really any way to prove how intelligent/smarter an animal is. Therefore, we cannot judge which animals are smarter — it is just a pointless argument. If you compare intelligence to math, then you could say that dolphins or apes might be better at math than humans.

We know that dolphins and apes are not human — they are just different kinds of animals. It is an animal that can walk on 2 legs and use tools, therefore we know that they are at least a little bit more intelligent than a rock (not that rocks are stupid). But the question remains: How do we create artificial intelligence?

#turningintohumansform

There have been many attempts over the years to make intelligent machines/robots. They all start out as some kind of framework for creating intelligent systems. This framework is like a building that can be used to create intelligent systems. The main components that are needed are:Introduction

Ever since the idea of artificial intelligence was introduced to society, it has been a topic of much debate. Which is better, artificial or human intelligence? Artificial intelligence is a vast subject that can’t be fully touched upon in an introductory article. But we will do our best to explain what AI is and what people are trying to accomplish with it. Before going further, If you are interested in learning more about AI or Machine Learning, don’t forget to check out the Artificial Intelligence (AI) course in Kochi.

#dontbeacreeper

Intelligence and knowledge are two separate things. We all have access to knowledge — with the internet (and libraries), if you want something then you usually just need to type it up into Google or go get a book at the library. However, intelligence means different things to different people.

Because of this, people have debated about what is the most intelligent being — for years, and continue to do so. We all know that dolphins are smarter than us, but we also know that dolphins are not humans (at least not from a biological perspective). So what is the most intelligent being, then? Is it a dolphin? What about a human or an ape (chimpanzee)?

#searchingforanswers

Let’s go back to the question of intelligence. People have different definitions of intelligence, and because of this we can’t just say that dolphins are smarter than humans. This is because some people may value intelligence differently; for example, if you asked someone who was a smart businessman, he may tell you that he is smart. But if you ask someone who is good at math, they would likely say that they are smart — in their field. Therefore, we can’t compare one person’s definition of intelligence with another’s and claim which one is better.

#biosof2011

So what do we do? Why do we even bother asking about the intelligence of dolphins and apes, when it comes down to it — does it really matter? Why should a human being care how smart a dolphin or an ape is, when they are not even human. At least in this case, we know that dolphins and apes cannot talk.

We also realize that there isn’t really any way to prove how intelligent/smarter an animal is. Therefore, we cannot judge which animals are smarter — it is just a pointless argument. If you compare intelligence to math, then you could say that dolphins or apes might be better at math than humans.

We know that dolphins and apes are not human — they are just different kinds of animals. It is an animal that can walk on 2 legs and use tools, therefore we know that they are at least a little bit more intelligent than a rock (not that rocks are stupid). But the question remains: How do we create artificial intelligence?

#turningintohumansform

There have been many attempts over the years to make intelligent machines/robots. They all start out as some kind of framework for creating intelligent systems. This framework is like a building that can be used to create intelligent systems. The main components that are needed are:

Data — information about the world (observations)

Knowledge — what should a system know and be able to do. A system will always want to know more than it currently knows and can do (and predict what it may think in the future). This means that it needs knowledge and code through which this information is passed so that the system can use it. For example, if you have a robot or car, then you would pass information from sensors on the roads through your robot’s brain into its head so that it could drive better and not crash.

Reasoning — the ability to think and process information to find a solution. This can be done with both logic and heuristics. Heuristics are simpler rules of thumb that help a machine think quicker (these are found in different programming languages). Logic is more complex because heuristics require prescriptive knowledge, while logic uses deductive knowledge, which it uses to make inductive conclusions. These types of reasoning can be thought of as similar to human psychology.

Action — the ability to take a decision and then execute it. Artificial intelligence uses what is called “subsumption architecture” that is similar to how an animal’s brain works. The difference is that an animal’s brain has hardwired processes while artificial intelligence has soft-wired processes, which means that the AI could be programmed to do something different in the same way (as opposed to hard-wiring it).

The above 4 components are all needed for artificial intelligence; it would be no good if you had data, but no knowledge or reasoning, etc. For us humans, we were born with a brain that has almost all the knowledge and reasoning needed. But there is one thing missing: data.

#butwhydon’thumanshaveaheadsupdisplay

Most of the data that a human brain has comes from outside sources; our parents and society. The more we interact with the world and people, the more knowledge we gain. However, there are some things that we can’t know without prior knowledge. For example, if you asked a baby to teach an adult how to tie shoes, they would not know how. The reason for this is that the baby doesn’t have any prior experience tying shoes — it was never exposed to this information by its parents or anyone else. Therefore, a baby needs its parents to teach it how to do different things (like walking or talking) since this is something that it could not have learned by itself.

#thefutureofintelligentmachines

As of 2015, there have been many different attempts at making artificial intelligence. Artificial Intelligence has become popular in the past decade, especially because of the internet. The internet has allowed artificial intelligence to grow in ways that were not so easy before; this is because now we have access to more “data” and more “knowledge”. Now there are many different types of AI, all based around a different kind of architecture or framework for thinking.

The most common type of AI today is called Machine Learning: these are systems that can learn from their data (such as the internet) how to respond or behave accordingly. There are many new artificial systems that work using Machine Learning, such as Google’s AlphaGo, that learns how to play Go by playing games against itself. The AI is able to create a hypothesis (for example, “I should never attack the white stones” or “I should always move my king”), and then test it out in the game in order to see if it works. This is called supervised learning and also known as a reinforcement learning. These artificial systems do not require prior knowledge either; they are able to learn from their own data, thus making them natural learners.

Many other AIs have used what is known as Supervised Learning. Because supervised learning requires prior knowledge and experience; these systems are not natural learners (they are a bit like a child that needs its parents to teach it). An example of supervised learning is IBM’s Watson, which uses prior knowledge in the form of heuristics to make conclusions based on more data (which it can process very fast due to the speed of computers). Watson would study very large amounts of data to form hypotheses, which would then be tested against more data. For example, if you were stuck on a question in an exam, you could type in your answer and Watson could probably tell you whether you got it right

Conclusion

In this article, we have discussed human intelligence vs artificial intelligence and real world scenarios. People have different definitions of intelligence, and because of this, we can’t just say that dolphins are smarter than humans. The difference is that an animal’s brain has hardwired processes while artificial intelligence has soft-wired processes, which means that the AI could be programmed to do something different in the same way (as opposed to hard-wiring it). Data, Knowledge, Reasoning, Action these 4 components are all needed for artificial intelligence; it would be no good if you had data, but no knowledge or reasoning, etc. For us humans, we were born with a brain that has almost all the knowledge and reasoning needed. Many companies in the industry use Python to implement AI and machine learning. We will discuss how to implement AI with Python and more in future posts. If you want to learn more about Artificial Intelligence or Machine Learning, you can check out AI machine learning training institute in Kochi.

Data — information about the world (observations)

Knowledge — what should a system know and be able to do. A system will always want to know more than it currently knows and can do (and predict what it may think in the future). This means that it needs knowledge and code through which this information is passed so that the system can use it. For example, if you have a robot or car, then you would pass information from sensors on the roads through your robot’s brain into its head so that it could drive better and not crash.

Reasoning — the ability to think and process information to find a solution. This can be done with both logic and heuristics. Heuristics are simpler rules of thumb that help a machine think quicker (these are found in different programming languages). Logic is more complex because heuristics require prescriptive knowledge, while logic uses deductive knowledge, which it uses to make inductive conclusions. These types of reasoning can be thought of as similar to human psychology.

Action — the ability to take a decision and then execute it. Artificial intelligence uses what is called “subsumption architecture” that is similar to how an animal’s brain works. The difference is that an animal’s brain has hardwired processes while artificial intelligence has soft-wired processes, which means that the AI could be programmed to do something different in the same way (as opposed to hard-wiring it).

The above 4 components are all needed for artificial intelligence; it would be no good if you had data, but no knowledge or reasoning, etc. For us humans, we were born with a brain that has almost all the knowledge and reasoning needed. But there is one thing missing: data.

#butwhydon’thumanshaveaheadsupdisplay

Most of the data that a human brain has comes from outside sources; our parents and society. The more we interact with the world and people, the more knowledge we gain. However, there are some things that we can’t know without prior knowledge. For example, if you asked a baby to teach an adult how to tie shoes, they would not know how. The reason for this is that the baby doesn’t have any prior experience tying shoes — it was never exposed to this information by its parents or anyone else. Therefore, a baby needs its parents to teach it how to do different things (like walking or talking) since this is something that it could not have learned by itself.

#thefutureofintelligentmachines

As of 2015, there have been many different attempts at making artificial intelligence. Artificial Intelligence has become popular in the past decade, especially because of the internet. The internet has allowed artificial intelligence to grow in ways that were not so easy before; this is because now we have access to more “data” and more “knowledge”. Now there are many different types of AI, all based around a different kind of architecture or framework for thinking.

The most common type of AI today is called Machine Learning: these are systems that can learn from their data (such as the internet) how to respond or behave accordingly. There are many new artificial systems that work using Machine Learning, such as Google’s AlphaGo, that learns how to play Go by playing games against itself. The AI is able to create a hypothesis (for example, “I should never attack the white stones” or “I should always move my king”), and then test it out in the game in order to see if it works. This is called supervised learning and also known as a reinforcement learning. These artificial systems do not require prior knowledge either; they are able to learn from their own data, thus making them natural learners.

Many other AIs have used what is known as Supervised Learning. Because supervised learning requires prior knowledge and experience; these systems are not natural learners (they are a bit like a child that needs its parents to teach it). An example of supervised learning is IBM’s Watson, which uses prior knowledge in the form of heuristics to make conclusions based on more data (which it can process very fast due to the speed of computers). Watson would study very large amounts of data to form hypotheses, which would then be tested against more data. For example, if you were stuck on a question in an exam, you could type in your answer and Watson could probably tell you whether you got it right

Conclusion

In this article, we have discussed human intelligence vs artificial intelligence and real world scenarios. People have different definitions of intelligence, and because of this, we can’t just say that dolphins are smarter than humans. The difference is that an animal’s brain has hardwired processes while artificial intelligence has soft-wired processes, which means that the AI could be programmed to do something different in the same way (as opposed to hard-wiring it). Data, Knowledge, Reasoning, Action these 4 components are all needed for artificial intelligence; it would be no good if you had data, but no knowledge or reasoning, etc. For us humans, we were born with a brain that has almost all the knowledge and reasoning needed. Many companies in the industry use Python to implement AI and machine learning. We will discuss how to implement AI with Python and more in future posts. If you want to learn more about Artificial Intelligence or Machine Learning, you can check out AI machine learning training institute in Kochi.

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