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Hello everyone! Today, I’m going to delve into the fascinating world of Artificial Intelligence (AI). AI is designed to replicate human abilities but aims to execute tasks more efficiently. In this blog, I’ll simplify some complex terms and focus primarily on machine learning.

What is AI?

AI is essentially a computer program that makes decisions based on data. You can think of it as a cycle: input data, process it, and then output results. The better the data and algorithms, the more effective the AI system will be.

Deep Learning vs Machine Learning

Deep learning is a subset of machine learning and represents a more refined version of AI. While both are intricate subjects, today’s discussion will focus on machine learning, where a program adjusts its responses based on the information it receives.

Practical Applications

AI has vast applications, from assisting in surgical procedures to sorting through vast data sets. It’s not as simple as feeding the AI data and expecting perfect outcomes. The AI system learns iteratively, improving based on prior mistakes and successes.

How AI Evolves

Creating a fully-fledged AI system isn’t a straightforward task. Programmers start with fundamental units, known as nodes, in a neural network. These nodes connect to form a more complex structure, enhancing the AI’s capabilities.

Imagine wanting to develop an AI that can distinguish between horses and bees. The process starts with a ‘builder bot’ creating a first-generation AI. This initial AI is basic and not highly effective. It’s then ‘tested’ by a ‘teacher bot’. The successful models go through further rounds of improvement and testing until the AI can reliably tell a horse from a bee.

Complexity of AI Systems

By the end of this iterative process, the neural network within the AI system becomes so complex that even the developers may not fully understand its inner workings. It’s a mystery that not even the AI itself can solve.

Reward Systems in AI

There are also reinforcement learning methods where the AI gets ‘rewarded’ for performing tasks correctly. This positive reinforcement allows the AI to adjust and improve its neural network, leading to a more accurate and effective system over time.

And that’s it for now! I’ll be covering more about this topic in future blogs. Please feel free to share your thoughts in the comments. See you later!