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Intro to AI and Machine Learning

Intro to AI and Machine Learning

Artificial Intelligence (AI) and Machine Learning are two of the most popular fields these days. Over the last few years, they've had a major impact on a wide range of industries. AI's Neural Networks (NN) that mimic the human brain already rival human intelligence in multiple areas like math, writing, analysis, and problem-solving.

AI and Machine Learning are shaping our lives to a large extent, and that seems to be far from over. With a Compound Annual Growth (CAGR) of over 20%, AI's market size is projected to reach USD 2,740.46 billion by 2032. As it happens, Google CEO Sundar Pichai claimed AI will be more transformative for us than electricity and fire. Some may claim that AI is far from perfect, yet studies show that it can increase productivity by 40% or even more.

What Is AI And Machine Learning?

AI and Machine Learning are two of the most revolutionizing fields in Computer Science. You may think these are relatively new concepts, but the truth is that scientists have been working on them for a while. AI's origins date back to the mid-20th century, with the work laid by Alan Turing and the discussions at the Dartmouth College Conference.

From the beginning, the goal was to build computer systems that could replicate human intelligence. Inspired by the human brain, scientists and researchers wanted computers to be able to mimic our cognitive functions. That includes learning, perception, problem-solving, decision-making, and so on.

To sum up, we can define AI as the creation of algorithms that can perform tasks that normally require human intelligence. It largely seeks to make computers smart so they can help us save time and be more productive. AI was promising from the start, but it took quite a few decades to see the first breakthroughs.

Some examples include autonomous decision-making thanks to Reinforcement Learning. An example is Deep Blue, which defeated Gary Kasparov in a Chess match in the 90s. On the other hand, Andrew Ng founded the Google Brain project, which merged with Google Deepmind's AlphaGo. AlphaGo was so successful that it defeated the world's Go champion, Lee Sedol. These days, AI can assist in very complex tasks such as speech recognition, fraud detection, customer service, and image recognition.

On the other hand, Machine Learning is a subfield of AI. In fact, you could think of it as one of the main pillars behind AI's success. Machine Learning focuses on analyzing vast amounts of data so that systems can make accurate predictions and decisions. It helps computer programs learn and improve performance based on experience. There are three main types of Machine Learning methods you should be aware of are Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

AI and Machine Learning in Everyday Life

We've had access to several products that leverage AI to give us a seamless experience for quite a while. One of the most common examples is Google Maps, which uses AI to find the best route to reach our destination. It also uses AI to predict traffic if we're driving. On the other hand, we have popular voice assistants like Siri, Alexa, and Google Assistant.

AI and Machine Learning give these tools an impressive ability to understand human language and respond to user queries. Plus, tools like Shopify and Netflix use AI to find songs, series, and films we may like based on past interactions.

Amazon also uses AI in its recommendation system to suggest products users may be interested in. Thanks to Natural Language Processing (NLP), plenty of users also use AI to summarize text, draft emails, and search for information.

Generative AI tools like ChatGPT and Gemini (Google Bard) are great examples of this type of Artificial Intelligence technology; they can also help users understand complex topics better and come up with ideas for marketing content. Plus, AI-based systems have become proficient at translations, image and code generation, spam detection, and even video creation with tools like Sora.

AI and Machine Learning in the Workplace

AI has helped workers in plenty of industries become more productive. Any office job can now use tools like Microsoft Copilot to automate a wide range of tasks. Microsoft Copilot involves all Office tools, allowing users to streamline processes by interacting with a ChatGPT-like chat.

Users can use the chat to import information, generate reports, create graphics, handle spreadsheets, design slides, and so on. It's like having a smart assistant integrated into the Office tools that so many workers use on a daily basis. That includes Microsoft Word, Excel, and PowerPoint.

On the other hand, Microsoft Copilot also involves Teams and Outlook, allowing users to schedule meetings, manage emails, and collaborate on documents in real time.

One of the most popular examples of AI in the workplace is the use of tools like GitHub Copilot to help automate Software Development. GitHub Copilot can access your code repository and help with code suggestions, debugging, and testing. Let's review an example use case.

Think of a Software Developer who is working on a complex algorithm Data Science app. Despite the fact they understand the algorithm they need, they’re unsure about the implementation details. GitHub Copilot can understand the context and provide useful suggestions, which can help save valuable time. It’s worth noting that suggestions are not always 100% accurate and developers will likely have to edit and refine them. However, they’re proven to be very helpful. Those who are on Linux or MacOS can also use Warp as terminal for automatic command generation and reusable workflows.

How Do AI And Machine Learning Work?

AI is all about using complex algorithms that analyze vast amounts of data. The goal is to build systems that can make sense of that data so they can make decisions or predictions independently. The process starts by collecting the data, which is normally in the form of text, images, videos, or audio.

Then, Data Scientists must categorize the data and define the criteria for preprocessing it. This process also involves defining the purpose of the AI model. In other words, what the desired outcome is. The learning process focuses on pattern recognition, which will help the AI model work without human intervention.

Once the model has learned from the data, Data Scientists must assess the model's predictions or responses. The goal is to check if the model achieved the desired outcomes. For example, if it's supposed to work on anomaly detection, the model's accuracy would be measured by its ability to identify and flag anomalies.

The team behind AI must work to minimize false positives and negatives. What about Machine Learning algorithms? They represent a crucial part of AI, helping systems learn from the data they've been given. As mentioned, there are a few types of Machine Learning. That means we can use different algorithms based on the specific task at hand and the nature of the data.

Let's explore some use cases for each type of Machine Learning model.

Supervised Learning

Practical Use Case: Email Spam Detection
: An email service provider wants to classify incoming emails as spam or non-spam.
: They have a dataset of emails labeled as spam or non-spam.
: They use a Supervised Learning algorithm like Support Vector Machines (SVM) or Naive Bayes.
: The algorithm learns from the labeled dataset, extracting features such as keywords, sender information, and email structure.
: After training, the model predicts whether new emails are spam or not with high accuracy.

Unsupervised Learning

Practical Use Case: Customer Segmentation for Marketing
: A retail company wants to understand its customer base better for targeted marketing campaigns.
: They collect data on customer purchases but without any predefined categories.
: They employ an Unsupervised Learning algorithm like k-means clustering.
: The algorithm groups customers based on purchasing behavior, creating segments such as high-spending customers, bargain hunters, etc.
This segmentation allows the company to tailor marketing strategies to different customer groups, improving customer engagement and sales.

Reinforcement Learning

Practical Use Case: Autonomous Driving
: A tech company is developing self-driving cars that need to navigate real-world environments safely.
: The car collects data from sensors like cameras, LiDAR, and radar during driving.
: They use Reinforcement Learning algorithms like Deep Q-Networks (DQN) or Proximal Policy Optimization (PPO).
: The algorithm learns from the data by interacting with the environment and receiving rewards (or penalties) based on its actions (e.g., staying in lanes, avoiding collisions).
: Over time, the AI learns optimal driving policies, allowing the car to make safe and efficient decisions on the road. That includes lane-keeping, overtaking, and responding to traffic signals.

What Is Deep Learning?

It's worth noting that the power of modern AI has a lot to do with Deep Learning, which is a subfield of Machine Learning. Deep Learning algorithms use NNs to analyze raw data and find complex patterns. Pioneered by Warren McCulloch and Walter Pitts, Neural Networks involve a collection of layers that process that data. First, the input layer receives the raw data and propagates it through hidden layers that extract complex features from it. Then, the output layer receives the data and makes decisions or predictions based on what the algorithm learned.

Advanced Deep Learning techniques, such as Large Language Models (LLMs), can achieve stellar performance in Natural Language Processing (NLP) and predictive analytics. They also have a lot to do with the success of Generative Pre-trained Transformers (GPT).

"The main advantage of Deep Learning techniques over the classic Machine Learning algorithms, such as the ones mentioned above, is that they can handle large datasets and improve their accuracy. Classic Machine Learning methods have a limit on data dimensionality. Also, unlike Machine Learning algorithms, Deep Learning algorithms can improve their performance when they receive more data. In fact, the most successful AI models are Deep Learning models trained for different tasks such as Computer Vision and Language Processing." — Julio Estrada, Lead Data Scientist at Capicua.

Why are AI and Machine Learning Important?

AI and Machine Learning have proven to be extremely useful for automating a vast range of routine tasks. Some examples include predictive maintenance, data entry, product recommendations, facial recognition, and so on. Yet, their power goes beyond repetitive tasks. Financial institutions use AI and Machine Learning for risk assessment and investment portfolio management.

This way, they help practitioners provide better financial advice, which can lead to more customer satisfaction. JP Morgan Chase and Capital One are great examples of financial institutions using AI to provide better employee and Customer Experience (CX).

In healthcare, doctors use intelligent machines to assist in medical diagnosis and personalized treatments. CaDet is one of the best examples of an expert system that helps doctors identify cancer at its early stages.

On the other hand, there have also been huge improvements in the gaming industry thanks to AI. For example, the game “Alien: Isolation” uses advanced AI to make the system learn from users’ behavior, helping provide a unique experience. Other games like “Red Dead Redemption” and “The Last of Us Part II” use AI to Non-Player Characters (NPCs) behavior. It's also worth mentioning that AI has made great contributions to expert systems, extending the capabilities of human workers.


Advances in Machine Learning and Artificial Intelligence have helped us reduce the average time needed to complete many of our most common tasks. They have brought us a large range of benefits, making our lives a lot simpler. These two fields hold a huge potential to continue to shape how most businesses approach work. They have revolutionized the way businesses build and deliver products. 

“Nowadays, AI is democratized, which means that more and more people have access to foundation models to train and fine-tune for new tasks. That includes software developers, scientists, technicians, and other practitioners. As a result, the access and usage of AI isn’t limited to large corporations. Thanks to the AI community, we have open-source foundation models that rival big tech companies’ models, including Open AI. Open-source foundation models allow us to use AI and develop applications more freely, opening up a wide range of opportunities for small businesses.” — Julio Estrada, Lead Data Scientist at Capicua.