AIHUB

Introduction In the data age, machines have grown more adept at making forecasts, spotting trends, and making choices. At the heart of this smart action is a very strong branch of artificial intelligence called supervised learning. From detecting junk mail to anticipating stock prices and diagnosing diseases, supervised learning is behind many real-world applications of AI. It allows machines to learn from labeled examples (i.e., each item in the dataset has a correct response) so they can make correct predictions on new data. One of the most popular techniques in machine learning, supervised learning is a must for anyone who wants to get started with intelligent systems. In this blog, we will inspect what is supervised learning. How does it work? What are the different types of it? The common algorithms and applications, and how to build your models using this approach. If you're new to AI, check out our Introduction to Machine Learning before diving into supervised learning. Tra...

Introduction to Machine Learning Machine Learning (ML) is perhaps the most revolutionary technology within Artificial Intelligence (AI). Systems can learn automatically, adapt, and make intelligent decisions without explicit programming. From recommendation systems on YouTube and Netflix and anti-fraud detection in banking, machine learning is behind much of the digital world we engage with today. What is Machine Learning? Machine Learning is one of the AI subsets that deals with creating algorithms that can learn patterns from data and make decisions or predictions from that learning. Rather than hard-coding rules, machine learning systems are trained on data. They identify patterns and modify their actions based on them, which is why they find extensive applications and data-intensive situations. Learn more about the history of Artificial Intelligence and how it led to the rise of Machine Learning in our detailed post on the History of AI . Machine Learning Clustering Types of Machin...

Introduction Artificial Intelligence (AI) is getting machines to think and behave smartly, and much of this smartness is all about problem-solving. From finding one’s way around a city to defeating an opponent at chess to solving the Rubik’s Cube, a smart system needs to make choices given a list of options and limitations. Search algorithms enter the picture here. Search in AI is navigating a problem space to reach a goal state. It can be imagined as moving from one node to another within a tree or graph in which the nodes are states and the actions are edges. Selecting the right path is crucial for effective problem-solving, particularly when navigating complex environments, limited resources, or time constraints. Search procedures are generally classed into two categories: Uninformed search: These searches the search space without using any knowledge of the domain regarding how close a node is to the goal. They are straightforward and systematic but usually inef...

  Introduction to Logic Programming Logic programming is a rich paradigm for artificial intelligence (AI) and knowledge representation. With this paradigm at its center is PROLOG (Programming in Logic), a language in which problems are solved by stating facts, rules, and queries. In contrast to imperative languages such as Python or Java, PROLOG programming is all about what you would like to accomplish and not how it has to be done. Learn more about the basics of logical reasoning in AI by exploring our detailed guide on Propositional Logic. What is PROLOG? PROLOG is a declarative language that is extensively used in expert systems, computational linguistics, and AI. It enables the developers to specify logic with facts and rules. Popular PROLOG Implementations: The following are some of the popular PROLOG implementations: SWI-PROLOG:  This type of PROLOG is free, open-source, and ideal for beginners. GNU PROLOG: This type of PROLOG has constraint-solving...

 Logic is the underpinnings of early artificial intelligence (AI) and gives systems the ability to reason, infer facts, and draw conclusions from formal rules. Logic has made numerous contributions to AI, but it has severe limitations that disqualify it from scaling, adapting, and coping with the complexity of the natural world. The following are the major limitations of logic in AI: The Search Space Problem Decidability and Incompleteness The Flying Penguin Problem Modeling Uncertainty Learn more about how logical systems shape AI decision-making in our Logic in AI articles. The Search Space Problem One of the most significant challenges with logical systems in AI is the exponential growth of the search space. When attempting to reason conclusions out of a large set of facts and rules, the number of potential combinations or routes the AI will have to analyze can increase exponentially. Example Consider a chess program attempting to search through all possible mov...