Issue 1 - "What the Heck is Machine Learning and AI?"

Welcome to the first issue of this new newsletter series! Why a newsletter, you ask? Well, as AI, especially generative AI becomes easily accessible and useful for anyone, that means a lot of developers needs to reskill and upskill.

In software development, we talk about the rise of the AI Engineer, a new role that combines software engineering with machine learning. We'll discuss this and much more in this series.

I don't want to learn machine learning, it has a lot of math, math is scary!

Don't worry, that's the whole idea behind this newsletter, to make this series enjoyable and easy to understand. I might show the occasional math, but I promise I'll do my best to add make it understandable.

"What the Heck is Machine Learning?"

I have lost count on how many times I wanted someone to explain a topic to me like I'm 5 years old. So let's do just that, most things are quite approachable if you meet people where they, focus on understandable language and analogies. Let's begin!

It's popular to think machine learning and robots, "training a machine", so let's take that analogy and run with it. :)

Imagine you have a toy robot that loves to learn new things. Machine learning is like teaching that robot how to do things on its own by showing it lots of examples.

Now, let's imagine that instead of a physical robot, it's a piece of software running on your computer. Feed said software a bunch of examples, and it starts to notice patterns in the data. These patterns can then be used to make predictions, for example telling you if a picture is of a cat or a dog.

Why is this useful? Well, this has a lot of industrial applications, for example in healthcare, finance, and transportation. It can help doctors diagnose diseases, predict stock prices, and even drive cars!

What is AI?

Ok, cool, I think I got it, but what's the difference between AI and machine learning? Ah, excellent question. I've heard some folks say AI is what PR folks use to make things sound cooler, but there's a bit more to it than that.

AI is the broader concept of creating machines that can perform tasks that typically require human intelligence. This includes reasoning, learning, problem-solving, perception, and language understanding. Machine learning, on the other hand, is a subset of AI that focuses on the development of algorithms that allow computers to learn from and make predictions or decisions based on data.

Ok, so AI is broader concept and machine learning is a subset of AI, got it!

Real-world Applications

There are many many areas where Machine learning, ML and AI are being used. In some cases, it's quite obvious, like in self-driving cars, but in some cases AI works behind the scenes, like in your email spam filter or ensuring your credit card isn't being used fraudulently or that your photos are being organized or they come out looking great.

AI and ML affects most of the industries out there, here are some more examples:

• Healthcare: AI is used for diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. For example, AI algorithms can analyze medical images to detect early signs of diseases like cancer.

• Finance: AI helps in fraud detection, credit scoring, and algorithmic trading. It can analyze large datasets to identify suspicious activities and make real-time trading decisions.

• Retail: AI powers recommendation systems that suggest products to customers based on their browsing and purchase history. This enhances the shopping experience and increases sales.

• Transportation: AI is used in autonomous vehicles to navigate and make decisions in real-time. It also optimizes routes for delivery services, reducing fuel consumption and improving efficiency.

• Customer Service: AI chatbots and virtual assistants provide 24/7 customer support, handling inquiries and resolving issues quickly. This improves customer satisfaction and reduces the workload on human agents.

As you can see, AI and ML is heavily integrated into our daily lives, and the need to understand it is becoming more and more important.

But don't worry, let's take it one step at a time. Let's talk about some basic terminology next.

Basic Terminology

Even if I said I will be sparse with showing math and equations, all areas have their own terms and technologies, so let's try to list the most common ones you'll come across:

Start here

There are some terms that you'll come across first, let's go through them:

• Algorithm: A set of rules or instructions given to an AI, which it uses to solve problems or make decisions.

• Model: A model, is what you get when you apply an algorithm to a specific dataset. It's the result of the learning process, a representation of the knowledge extracted from the data. Models are the specific instances that have been trained on data and can make predictions or decisions based on new data they haven't seen before

• Artificial Intelligence (AI): The simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, and self-correction.

• Machine Learning (ML): A subset of AI that involves the use of algorithms and statistical models to enable computers to improve their performance on a task through experience.

Bit more detailed

• Neural Network: A series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

• Supervised Learning: A type of machine learning where the model is trained on labelled data. This means the input data is paired with the correct output.

• Unsupervised Learning: A type of machine learning where the model is trained on unlabelled data and must find patterns and relationships in the data on its own.

• Deep Learning: A subset of machine learning that uses neural networks with many layers (hence "deep") to analyse various factors of data

Model specific terms

• Bias: In machine learning, bias refers to the error introduced by approximating a real-world problem, which may be complex, by a simplified model.

• Overfitting: A modelling error that occurs when a function is too closely aligned to a limited set of data points, making it less effective at predicting future observations.

• Feature Engineering: The process of using domain knowledge to extract features (characteristics, properties, or attributes) from raw data to improve the performance of machine learning models

You might be thinking at this point, "Wow, that's a lot of terms!" and you're right, but don't worry, , we have reason to go through them all a bit more in-depth in future issues.

Summary

We've taken our first steps into a new world of AI and machine learning. We've learned that machine learning is like teaching a robot to do things on its own, and AI is the broader concept. Additionally, we've explored some real-world applications and how AI and ML is used in various industries, we're surrounded by it!

Hope this gave you a good introduction to the world of AI and machine learning, and that you're keen to learn more.