Artificial Intelligence: When people talk about Artificial Intelligence (AI), Machine Learning (ML), Deep learning (DL), and Artificial Neural Networks or Neural Networks, they use the words in different ways but most of the time interchangeably. However, each term actually has its own definition, meaning, purpose, and connection to the others. Let’s understand the idea of AI as described below.

Artificial Intelligence (AI)

Artificial intelligence is like a popular kid in computer science, and it wants to make machines as smart as humans. AI is a widely known and researched concept that is used in modern businesses and in various areas of our daily lives. When people use artificial intelligence, the science of how things move, and taking pictures together, they can make better pictures, come up with new ways of taking them, and make the process of taking pictures overall better.

The collaboration among sciences like Artificial intelligence, Physics and Photography  helps to improve how pictures are processed, how computers see things, how lenses work, and all Photography  technology. Machines that are very clever and have been taught well can do things that people usually do with their minds, like understanding things, thinking logically, learning new things, and deciding what to do. AI helps machines become really smart by using tools and strategies. This tells us everything we need to make smart systems, both in theory and practice.

AI and Theoretical Physics, have a lot in common in things like creating simulations, looking at data, finding ways to improve processes, using quantum computers, and exploring how new patterns and behaviors emerge. AI and theoretical physics can work together to improve both areas and help us learn more about how the universe works.

Artificial Intelligence and Machine Learning

Artificial intelligence is a bigger subject that covers more than just letting machines learn. AI includes many things, and one of them is machine learning. Machine learning is a very important part of artificial intelligence.

This means using rules that help computers get smarter based on decisions made by computer programs that act like humans. AI is more than just ML, and it’s important to understand that. AI is not just about learning from data; it also includes various methods to solve problems.

Artificial Intelligence makes machines smarter with its powerful algorithms, so computers can do similar tasks as humans. It can do many things, like understand, talk, and think like a person. Machine learning makes machines more capable, but AI has other tools that help them too. AI can learn in various ways, and there are machines that can process information like humans for which ML doesn’t work.

Artificial intelligence is a new kind of technology that changes things a lot. It changes many businesses, too. AI can handle a lot of information, understand patterns, and make smart choices. This is changing how businesses work. This article explains what AI is and how it can be useful for businesses.

AI Not Strictly Machine Learning

There are many other fields of artificial intelligence that are not technically categorized as machine learning. AI is a vast and diverse discipline that includes a variety of strategies and approaches for achieving intelligent behavior in computers. Here are few examples:

  1. Expert Systems – Computer programs that can make decisions like human capabilities and expertise in certain fields These systems use rules, ideas, and algorithms to help users solve complex problems and inference engines to reason and provide expert-level advice or solutions.
  2. Symbolic Logic – Part of AI that uses mathematical logic or logic programming to show information and do automated thinking. It is about playing with symbols and using rules to solve problems or to draw conclusions or solve problems conclusions.
  3. Reasoning – In the amazing world of AI, we build smart computer programs to help them understand things better. It’s all about teaching computers to think about information. Imagine planning a fun event like a wild party. Ontologies, semantic networks, and frames are like the awesome party planners who can help machines think and reason intelligently, just like Einstein, but instead of using neurons, they use circuits. They’re the important part that makes machines work smartly and confidently. When you see a machine thinking, remember it capture and structure knowledge to facilitate intelligent reasoning and clever organizing methods.
  4. Natural Language Processing (NLP) – A powerful tool for computers to understand human language. It helps computers learn how to decipher our language. It’s like teaching them to speak our language quickly, understand what we are saying, and speak their own sentences. NLP is an expert at understanding and processing human language. It can recognize our voice, understand what we say or write, figure out our emotions through our words, translate languages, and answer all our questions. The next time you talk to a computer and it responds in a clever and charming way, it’s all thanks to something called NLP, which is a special way to help computers and people understand each other better.
  5. Planning and Scheduling – AI applications that uses algorithms and systems that can autonomously plan sequences of actions to achieve specific goals or solve problems in dynamic environments like a good party planners who are great at organizing and making schedules. It’s like having someone really smart who can handle lots of things at once and make sure the party runs smoothly. AI uses its intelligent technology to create exciting sequences towards achieving specific goals and winning problem-solving challenges. Imagine a machine that can plan events, manage resources, and schedule everything perfectly. It’s like having a really good event planner.

Above mentioned examples are just few cases where AI used without data and incline towards pure AI and not machine learning. In Rule-based machine learning (RBML) computer science intended to encompass any machine learning method that identifies, learns, or evolves ‘rules’ to store, manipulate or apply succinctly.

Books Referred & Other material referred

  • Self-Learning through : Live Webinars, Conferences, Lectures, Seminars, AI Talkshows, Open Internet Research, News Portals and White Papers Reading.
  • Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.

Points to Note:

it’s time to figure out when to use what AI, ML or DL, tricky decision that can really only be tackled with a combination of experience and the type of problem in hand. So if you think you’ve got the right answer, take a bow and collect your credits! And don’t worry if you don’t get it right; this next post will walk us through neural networks’ “neural network architecture” in detail.

Feedback & Further Question

Do you need more details or have any questions on topics such as technology (including conventional architecture, machine learning, and deep learning), advanced data analysis (such as data science or big data), blockchain, theoretical physics, or photography? Please feel free to ask your question either by leaving a comment or by sending us an  via email. I will do my utmost to offer a response that meets your needs and expectations.


Conclusion : Artificial Intelligence is no longer just in movies; it’s now in the real world and giving companies a lot of benefits. We have discussed amazing analysis of information, tasks being done automatically, customers feeling like they have a personal helper, and criminals being scared. With the help of AI, businesses can expand their abilities and improve in ways they never could before. They will find new opportunities, become more efficient, and stand out among others. This is about making AI easier to understand and showing how it can help businesses become more innovative, grow, and succeed.

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Posted by V Sharma

A Technology Specialist boasting 22+ years of exposure to Fintech, Insuretech, and Investtech with proficiency in Data Science, Advanced Analytics, AI (Machine Learning, Neural Networks, Deep Learning), and Blockchain (Trust Assessment, Tokenization, Digital Assets). Demonstrated effectiveness in Mobile Financial Services (Cross Border Remittances, Mobile Money, Mobile Banking, Payments), IT Service Management, Software Engineering, and Mobile Telecom (Mobile Data, Billing, Prepaid Charging Services). Proven success in launching start-ups and new business units - domestically and internationally - with hands-on exposure to engineering and business strategy. "A fervent Physics enthusiast with a self-proclaimed avocation for photography" in my spare time.

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