SIZ Approved Course | Introduction to Artificial Intelligence | Lecture 02 | AI Technologies, AI Professionals, Logic

Hello everyone! I hope all of you are doing well. Today, I am going to cover the topics of Week 2 of my approved course "Introduction to Artificial Intelligence". The topics which I will be covering today are:

• Trends, Technologies, and different AI Tools
• AI Professionals
• Introduction to Logic
• Role of Logic in AI

So, I am starting the lecture by first discussing how AI has evolved over the years and what new technologies and tools it has provided us.

TRENDS, TECHNOLOGIES, AND DIFFERENT AI TOOLS
Earlier, when people were not familiar with AI technologies and only knew that they exist, it was fine. But now, since AI has evolved massively and has been of utmost importance in almost every sector, it is necessary for people to understand its importance and how it works. Some different trends and technologies are discussed below:

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• Good-Old Fashioned AI: These were static systems, meaning they weren't able to learn by themselves, there was no automation in terms of the software improving themselves. For a given set of inputs, they always would give the same output, and to improve them, they had to be manually reprogrammed.

• Expert Systems: They are also known as Knowledge-Bases Systems, and they are composed of two parts; one represents the knowledge it has been programmed with and the other infers or concludes new information. They are coded and programmed with IF-THEN rules and give output based on different conditions and inputs.

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• Decision Trees: Consider their structure reverse of a tree, a problem or an input keeps dividing into branches and then sub-branches by applying different comparisons. It is a supervised machine learning algorithm and each attribute or input can be represented with a boolean function. There are different levels in decision trees, and so it is also necessary to identify the root node of each level, so it is easier to make decisions.

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• Semantic Graphs: Also known as Knowledge Graphs, they are used to represent knowledge with the help of graphs with different interconnected nodes, with each note representing different objects, ideas, topics of the world, and their relationship with each other. They can be easily implemented and are easily understood because they are more expressive than the representation of knowledge using logic.

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MODERN TECHNOLOGIES

• Neural Networks: They are also called Artificial Neural Networks (ANNs) and are programmed and designed to mimic the working of the human brain, to make intelligent decisions by recognizing different patterns. The name itself shows that it has been inspired by our human brain, in which there are millions of neurons linked together and sending and receiving information to one another. Similarly, in ANNs there are different layers of nodes, a few hidden layers, and then finally an output layer. Neurons are present in each layer with a certain threshold, and are linked with neurons of other layers, and are capable of sending the data to the layers if their output level is above their threshold.

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• Internet of Things: IoT refers to the devices in our homes, offices, cities that can communicate with each other without the need for human supervision. These physical or virtual devices are connected through the web or the internet and have different sensors and chips embedded within them, all of the devices are transmitting data to an IoT platform, which is capable enough to know which information is useful, and which is useless. These devices are intelligent and so they can make decisions with the data received by other connected devices.

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• Machine Learning: Machines and software of AI which can make predictions and infer new information by themselves. They can make new decisions on some prerequisite information without the need to manually program them. By using different methods, they are trained on data and to make predictions from a large set of data which in turn helps us to make important and useful decisions regarding loss or profit of an organization, and many more things which these machines can deduce.

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Apart from these, there are many technologies and tools which AI has put forth for us, such as:

• Deep Learning
• Blockchain Technology
• Augmented Reality
• Natural Language Processing
• Robotics
• Vehicle Automation

ROLE OF AI PROFESSIONALS
With the growth of AI, the demand for people who can handle AI and work with it also kept growing. Organizations and businesses look for such individuals to help them achieve their target, goals, and to stand out from other organizations. There are different roles for AI, technical as well as non-technical, and it also might be possible that in some organizations the title might be different as compared to different organizations, but what matters is that you know what you are doing and what are your skills.

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TECHNICAL Data Scientist Data Engineer Machine Learning Engineer AI Engineer Research Scientists	NON-TECHNICAL Consultant Product Manager Interface Designer Technology Translator

Whether technical or non-technical roles, both of them are crucial to an organization as the non-technical ones can help communicate tasks better, they can guide the technical workers on how to get things done within the specified time. AI is not only popular in businesses and organizations but they are also being used in:

Autonomous Robots Gaming Industry Cloud Computing Cyber Security	Big Data Healthcare Sectors Automated Transport Cognitive Computing

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INTRODUCTION TO LOGIC AND ITS ROLE IN AI
It is a basic component of computer science and is concerned with reasoning and thinking processes regarding different situations. A set of inputs and different premises and bases on those inputs, we arrive at a conclusion which is called a valid deduction. Logic must also define Semantics which refers to the meaning of sentences and their truth value. The inputs can be assigned a truth value and any statement can be either True or False, if all results in True, the conclusion is also said to be True.

Agent in AI: An agent is said to replicate human behavior and thinking, it has different sensors based on which it perceives its environment and then acts upon that environment by making different decisions. The agent makes those decisions using a reasoning mechanism and they are called Rational Agents if they can:

• Make decisions according to the environment and situations.
• Apply reasoning mechanism.
• And has the mental capability and knowledge to make those decisions.

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We expect our agents to work with logic, to use somewhat similar reasoning techniques as humans do, and make smart decisions. We also want them to behave normally and give reasons why they had selected a particular decision, they should be able to give justification that on which bases they came to a conclusion. Logic in AI is most importantly used for representing knowledge in a way that is easily understood. While representing knowledge in AI, it is important to represent the static part of that knowledge separately from the reasoning part, if they are combined, the reasoning takes more time and requires a lot of effort.

MYCIN Example: It was one of the earliest expert systems and was developed at Stanford University and it had the ability to reason quite well about bacterial infections. It was also tested by comparing its results with those of human physicians, both of them were given the same data and information regarding a patient, and MYCIN turned out to be on top. The power and reasoning of the system was great but later it was realized that the knowledge of those infections and the reasoning part of the system were mixed, causing it to consume more time and effort and also making it difficult to modify a part without affecting the other one.

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CONCLUSION
This was my lecture of week 02 for the course "Introduction to Artificial Intelligence". If there was something I missed or forgot to add, you are more than welcome to tell me about me. I hope you got to know more about AI and its technologies, thank you for giving it a read!

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