PM learning AI – Week 1 👋

Before I talk about the Week -1, want to spend next 3-4 lines talking about why I am doing this!

AI is here to stay. I am amazed at how much it’s already in my life today and intrigued by the possibilities of AI. So, few days back, I decided its time to dwell deeper into a new area, build a new skillset. Being able to just say a few fancy things in AI is not the plan. Plan is eventually to build fancy things that others talk about.

“Alright, let’s get this AI journey started!”

So with that, I want to start this blog sharing my weekly updates. Firstly, I want to seriously build some readership here and secondly, keep myself accountable.

Now coming back to the topic, Week -1 of AI. Well it started with things like what is Artificial Intelligence, why a simple calculator is not an example of AI but a chatbot is! And yes, to make it more interesting, I am pulling some data from chatGPT and Google Gemini. You can’t talk about AI, ML and what not, without using it. Seems unfair to skip that part. 😉

Here is a gist of what I learnt and then a Mindmap? Yes, I am building one along with my journey here. A series of mind maps.

First the mindmap and then the theory 🙂

1. What is Artificial Intelligence? It is the science of making intelligent machines, especially intelligent computer programs. Using these intelligent machines, aim is to enable the machines to learn, reason, perceive, understand and make decisions.
2. Its typically classified in three types
   • Weak AI (Narrow) – Designed to perform specific tasks
   • Strong AI (Generalised) – Capable of understanding, learning, and applying knowledge across a wide range of tasks, just like a human.
   • Super AI (Conscious) – Hypothetical intelligence that surpasses human intelligence in every aspect
3. What is Machine Learning?
   • It enabled machines to learn and improve from data without being explicitly programmed. So basically there is an algorithm that is used to analyse data.
4. Machine Learning can be further categorised. The main types you would hear about is :
   • Supervised Learning: Here, algorithms are trained on a labeled dataset. This means that each data point in the dataset has a corresponding label or output value. The algorithm’s goal is to learn the relationship between the input data and the output labels, so it can make accurate predictions for new, unseen data.
   • Unsupervised Learning: Models learn the patterns from unlabelled data. There is no predefined output or target variable in the learning data set. Goal is to discover hidden structures, groupings.
   • Reinforcement Learning:
   • Reinforcement Learning : Algorithm learns to make decisions by interacting with environment and gets feedback in the form of rewards/penalties. The aim is to maximize the cumulative rewards over time.
5. Within Supervised Learning there are further types:
   • Regression:The goal is to predict a continuous numerical value. Eg. forecasting the sales for a product
   • Neural Networks: It is inspired by the human brain that is composed of interconnected nodes called neurons. The neurons process information in layers, with each layer learning specific features from the input data. Similar is a functioning of Neural Networks. Some very common examples of usage will be Speech to text conversion, medical image analysis
   • Classification: Goal is to assign input data to one of several predefined categories. Some good examples will be Email spam detection, image classification., etc.

And with that let’s meet next week!!! Ciao!!!