Understanding Knowledge Representation in Artificial Intelligence

Humans are good at interpreting knowledge, understanding, and reasoning and with this knowledge, they are able to perform various actions in the real world. But the question is how do machines perform the same? In this blog, we will learn more about Knowledge Representation in artificial intelligence and how it helps the machines perform.

Knowledge Representation is a study of how the beliefs, judgments, and intentions of an intelligent agent will be expressed suitably for automated reasoning. The main purposes of Knowledge Representation include modeling intelligent behavior for an agent.

Under artificial intelligence services , Knowledge Representation translates the information from the real world for a machine to understand and then use this knowledge to solve complex problems like communicating with a human. It is not just about storing the data, it also allows a machine to learn from the knowledge and behave like a human.

There are different kinds of knowledge that used to represent the knowledge in AI are Objects, Events, Performance, Facts, and Knowledge-base.

Types of Knowledge:- There are 5 types of Knowledge are as follow:-

1. Declarative Knowledge – Declarative Knowledge includes concepts, facts, and objects and shows in a declarative sentence.

2. Structural Knowledge – It’s a basic problem-solving knowledge that describes the relationship between objects and concepts.

3. Procedural Knowledge – Procedural Knowledge is responsible for knowing how to do something and also includes strategies, procedures, rules, etc.

4. Meta Knowledge – It defines knowledge about other types of Knowledge.

5. Heuristic Knowledge – Heuristic Knowledge represents the expert knowledge in the field or subject.

The cycle of Knowledge Representation:- Artificial Intelligent Systems usually consist of many components to shows their intelligent behavior. This is the example of the different components and how it works:

The Relation between Knowledge & Intelligence:- In the real world, knowledge plays an important role in intelligence as well as creating AI. It demonstrates the intelligent behavior in AI machines. It is possible for a machine to act accurately on some input only when it has the knowledge or experience about the input. However, cloud machine learning solutions rely on historical data to drive actionable insights and value from the given system.

There is one decision-maker in this example whose actions are justified by sensing the environment and using the knowledge, so if we remove this part then it will not be able to show any intelligent behavior.

Techniques of Knowledge Representation:- There are 4 techniques of representing knowledge such as:

1. Logical Representation:- Logical representation is a language with some definite rules which deal with propositions and has no ambiguity in representation. It represents a conclusion that supported varied conditions and lays down some important communication rules. Also, it consists of precisely defined syntax and semantics that supports the sound inference. Every sentence can be translated into logics using syntax and semantics.

2. Semantic Network Representation:- Semantic networks work as another of predicate logic for knowledge representation. In Semantic networks, you will represent your knowledge in the form of graphical networks. This network consists of nodes representing objects and arcs that describe the connection between those objects. Also, it categorizes the object in several forms and links those objects.

3. Frame Representation:- A frame could be a record like structure that consists of a group of attributes and values to describe an entity in the world. These are the Artificial Intelligence data structure that divides data into substructures by representing stereotypes situations. Basically, it consists of a group of slots and slot values of any kind and size.

4. Production Rules:- In production rules, agent checks for the condition and if the condition exists then production rule fires and corresponding action is distributed. The condition a part of the rule determines that rule could also be applied to a problem. The action part brings out the associated problem-solving steps. This whole method is called a recognize-act cycle.

Representation Requirements:- A good knowledge representation system must have properties such as:

1. Representational Accuracy: It should represent every kind of needed knowledge.

2. Inferential Adequacy: It should be able to manipulate the representational structures to supply new knowledge corresponding to the existing structure.

3. Inferential Efficiency: The power to direct the inferential knowledge mechanism into the foremost productive directions by storing acceptable guides.

4. Acquisitional efficiency: The power to acquire new knowledge simply using automatic methods.

Approaches to Knowledge Representation:- There are different approaches to knowledge representation such as:

1. Simple Relational Knowledge:- It’s the only method of storing facts that uses the relational method. Here, all the facts a couple of set of the object are set out systematically in columns. Also, this approach is famous in knowledge systems wherever the connection between completely different entities is represented. Thus, there is very little chance for inference.

2. Inheritable Knowledge:- In this approach all data must be stored into a hierarchy of classes and should be organized in a generalized form. Also, this approach contains inheritable knowledge that shows a relation between instance and class, and it’s referred to as instance relation.

3. Inferential Knowledge:- The inferential knowledge approach represents knowledge within the kind of logic. Thus, it can be used to derive additional facts. Also, it guarantees correctness.

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Start a Career in Machine Learning and Artificial Intelligence

Artificial Intelligence (AI) made headlines recently when people started reporting that Alexa was laughing unexpectedly. Those news reports led to the standard jokes about computers taking up the planet.

The AI Career Landscape
AI is returning more traction lately due to recent innovations that have made headlines, Alexa’s unexpected laughing notwithstanding. But AI has been a sound career choice for a short time now due to the growing adoption of the technology across industries and therefore the need for trained professionals to try to to the roles created by this growth.

AI and Machine Learning Explained
If you’re new to the sector, you would possibly be wondering, just what’s AI then? AI is how we make intelligent machines. It’s software that learns almost like how humans learn, mimicking human learning so it can take over a number of our jobs for us and do other jobs better and faster than we humans ever could. Machine learning may be a subset of AI, so sometimes when we’re describing AI, we’re describing machine learning join online machine learning course, which is that the process by which learn Artificial Intelligence course now!

The Three Main Stages of AI
AI is rapidly evolving, which is one reason why a career in AI offers such a lot potential. As technology evolves, learning improves. Van Loon described the three stages of AI and machine learning development as follow:

Stage one is machine learning - Machine learning consists of intelligent systems using algorithms to find out from experience.
Stage two is machine intelligence - Which is where our current AI technology resides now. during this stage, machines learn from experience supported false algorithms. it’s a more evolved sort of machine learning, with improved cognitive abilities.
Stage three is machine consciousness - this is often when systems can do self-learning from experience with none external data. Siri is an example of machine consciousness.

Subsets of Machine Learning

Neural Networks
Natural Language Processing (NLP)
Deep Learning

How to start in AI?
If you’re intrigued by this career field and wondering the way to start , Van Loon described the training paths for 3 differing types of professionals; those new the sector , programmers, and people already working in data science. He also points out that various industries require different skill sets, but all working in AI should have excellent communication skills before addressing the maths and computing skills needed.

Specific Jobs in AI

• Machine Learning Researchers
• AI Engineer
• Data Mining and Analysis
• Machine Learning Engineer
• Data Scientist
• Business Intelligence (BI) Developer

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As the demand for AI and machine learning has increased, organizations require professionals with in-and-out knowledge of those growing technologies and hands-on experience.If you would like to be one among those professionals, get certified, because the earlier you get your training started, the earlier you’ll be working during this exciting and rapidly changing field.CETPA provides Graduate program will assist you substitute the gang and grow your career in thriving fields like AI , Machine Learning, and Deep Learning.

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7 Ways Artificial Intelligence Can Help Make Your Time at Home

Artificial Intelligence enhances the speed, precision, and effectiveness of human efforts. In financial institutions, AI techniques are often wont to identify which transactions are likely to be fraudulent, adopt fast and accurate credit scoring, also as automate manually intense data management tasks.

AI would have a coffee error rate compared to humans if coded properly. they might have incredible precision, accuracy, and speed. they will not be suffering from hostile environments, thus ready to complete dangerous tasks, explore in space, and endure problems that might injure or kill us.

1. AI goes to show your kitchen into a Michelin star restaurant
Smart kitchen appliances and smart speakers are making their way into kitchens all around the world. you’ll even have one now. Whether it is a coffee machine or an oven, these tools are evolving, learning your schedules and patterns so that they will provide you with warm food, coffee, etc. However, this is often just the start.

Your new smart fridge could also be ready to track when food is low and place orders for you when food is low. Or, better yet, AI might be wont to assist you to create the right meal with just the ingredients you’ve got within the refrigerator. Utilizing AI technologies with gastronomical learning, companies like Plant Jammer and Chefling are helping people create delicious food with the ingredients they need available. Currently, Facebook has developed an image-to-recipe generation system that permits users to reverse engineer a recipe by only taking an image of the dish.

2. The way you experience entertainment will change

Google Assistant, Cortana, and Alexa have already infiltrated your home, impacting the way you interact together with your TV and streaming services, allowing you to voice control almost everything; slowly making remotes obsolete. almost like the kitchen example, these devices are learning your watching habits, eventually directing you on what to observe. However, it’s getting to go much further.

3.You’re getting to have tons more fun together with your games

You may be proud of the gaming industry, or perhaps you wish to ascertain some major changes. Though a touch slower on the buyer side, there’s a change coming to the gaming industry, change driven by AI. Developers are using AI to make more immersive and realistic experiences, even within a fantasy world.
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4. You’ll have your own Alfred soon

Maybe you usually wanted to possess a Jarvis AI system like Tony Stark? Or, perhaps you would like to travel the more traditional route and obtain yourself a loyal butler-like Alfred. Whichever the case, AI could make this possible via robotics. the world of robot personal assistants is an industry growing rapidly. Though some would simply dub the present models as just smart speakers with wheels, many of those current robotic personal assistants offer tons of impressive features. Soon, you would possibly have something that appears tons less like Wall-E and more just like the robots in iRobot

Robots like Jib are a little example of the approaching future. The social robot looks around, learning about you and your home. He even has an “expressive face.” He can even take pictures of you and share them on social media.

5. Enhanced health and fitness reception
Being able to watch patient’s reception with real-time data remotely, effectively, might be revolutionary. Going far beyond the Apple watch that you simply wear your wrist immediately, healthcare professionals could tap into the predictive powers of AI to work outpatients who are potentially in danger of disease or injury. This can give doctors tons more power but could alleviate a number of the pressure placed on the healthcare systems during flu season, saving lives. Companies like Gyant, Medopad, and Chonisense Medical are utilizing current AI technologies to seem after the elderly and chronic patients.

6. Your home will become more environmentally friendly
As humans, there’s no denying it; we will be wasteful, especially in our homes. However, having more control and knowledge of our waste and energy consumption could help us become more environmentally friendly, saving you money within the long-term. Though already available in some places across the planet, with products to get, AI energy-saving systems have yet to be fully adopted.

7. Your home is going to be ready to fix itself

The idea isn’t too far away. And, let’s agree home projects aren’t always the foremost exciting. Even more so, when something breaks in your home, you would like to repair it as soon as possible. a bit like a sensible medical device, homes are going to be ready to run self-diagnostics predicting potential issues before they occur, contacting the acceptable repairman, who may very well be a robot.

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How Artificial Intelligence Is Reshaping the IT Industry

Artificial Intelligence has powerfully penetrated the way we live. It doesn’t only change the way we work but also reshaped how we used to live. Speaking of AI, it is one of the most interesting technologies that we’ve ever encountered.

Without a doubt, AI is contributing a lot in boosting business and IT productivity. Therefore, in this blog, I will highlight important insights on how AI is reshaping IT. Before digging deeper into details, let’s start with some basics on AI and how it works.

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Knowledge and Reasoning in Artificial Intelligence

A Knowledge Based Agent in Artificial Intelligence has two levels: Knowledge Base (KB) and Inference Engine.

1. Knowledge Base- It is the base level of an agent, which consist of domain specific content. In this level agent has facts or information about the surrounding environment in which they are working. It does not consider the actual implementation.
2. Implementation level- It consists of domain independent algorithms. At this level, agents can recognize the data structures used in the knowledge base and algorithms which use them. For example, propositional logic and resolution. Knowledge based agents are crucial to use in partially observable environments. Before choosing any action, knowledge based agents make use of the existing knowledge along with the current inputs from the environment in order to infer hidden aspects of the current state.

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