Intro to AI and Machine Learning

Over the last few years, Artificial Intelligence (AI) and Machine Learning (ML) have had a major impact on a wide range of industries. For instance, AI's Neural Networks (NN), which mimic the human brain, already rival human intelligence in multiple areas like math, writing, analysis, and problem-solving! 

With a Compound Annual Growth (CAGR) of over 20%, AI's market size is projected to reach USD 2,740.46 billion by 2032. Further, 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. 

As you can see, AI and Machine Learning are shaping our lives to a large extent, but how? 

What is AI And Machine Learning?

You may think AI and Machine Learning are relatively new concepts, but scientists have been working on them for a while. Within Computer Science, 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 like learning, perception, problem-solving, and decision-making.

To sum up, Artificial Intelligence is the development of algorithms for machines to perform tasks that normally require human intelligence, like Reinforcement Learning-based autonomous decision-making or the Deep Blue that defeated Gary Kasparov in a Chess match in the '90s. There is also the Google Brain project, founded by Andrew Ng, which later merged with Google Deepmind's AlphaGo. AlphaGo was so successful that it defeated the world's Go champion, Lee Sedol. 

Likewise, Machine Learning is a subfield of Artificial Intelligence, one of the main pillars behind AI's success. ML focuses on analyzing vast amounts of data so systems can make accurate predictions and decisions, helping programs learn and improve based on experience. Within ML subfields, there are three main edges: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

AI and Machine Learning in Everyday Life

Several daily-used products leverage AI and Machine Learning to provide seamless experiences! A great example is Google Maps, which uses Artificial Intelligence to find the best route to reach a destination and predict traffic while driving. 

We can also see AI in popular voice assistants like Siri, Alexa, and Google Assistant and in the interaction-based recommendation systems we see in platforms like Shopify and Netflix. AI and Machine Learning give these tools an impressive ability to understand human language and respond to user queries, with Natural Language Processing (NLP) allowing users to further harness Generative AI tools like ChatGPT and Gemini (Google Bard) and even video creation with tools like Sora.

AI and Machine Learning in the Workplace

AI and Machine Learning have also helped workers in plenty of industries, with suites like Microsoft Copilot as a great example for team members to automate daily tasks in the well-known Office products. By 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. 

However, the benefits go far from Word, Excel, and PowerPoint: Copilot also involves Teams and Outlook for teams to schedule meetings, manage emails, and collaborate on documents in real time. As you may know, this suite also includes GitHub Copilot to help automate Software Development. 

Think of it this way: a Software Developer is working on a complex algorithm Data Science app, and despite the fact they understand the algorithm they need, they’re unsure about the implementation details. GitHub Copilot can help them understand the context while providing useful suggestions that can help them save valuable time! Yet, it’s worth noting that suggestions are not always 100% accurate, and devs will likely have to edit and refine them. 

How Do AI And Machine Learning Work?

AI uses complex algorithms that analyze vast amounts of data with the goal of building systems that can make sense of said data. The process of independently making decisions or predictions starts by collecting the data, normally in the form of text, images, videos, or audio.

After collecting, Data scientists categorize it and define the criteria for its preprocessing. This step also involves defining the purpose and the desired outcome of the AI model. Here, the learning process focuses on pattern recognition so that the AI model can work without human intervention. Once the model has learned from the data, Data Scientists assess the model’s predictions or responses to check if it achieved the desired results. 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.

What about Machine Learning algorithms? They are a crucial part of AI, helping systems learn from the data they've been given. As mentioned, since there are a few types of Machine Learning, it can use different algorithms based on both the specific task and the nature of the data. Let’s explore some use cases for each subfield of Machine Learning! 

Supervised Learning  Use Case

Case: Email Spam Detection.
Scenario: Email provider wanting to classify incoming emails as spam or non-spam.
Data: A dataset of emails labeled as either spam or non-spam.
Algorithm: Supervised Learning algorithms like Support Vector Machines (SVM) or Naive Bayes.
Training: The algorithm learns from the labeled dataset, extracting features such as keywords, sender information, and email structure.
Outcome: The model predicts whether new emails are spam or not with high accuracy.

Unsupervised Learning Use Case

Case: Marketing Customer Segmentation.
Scenario: Retail company wanting to understand its customer base better for targeted campaigns.
Data: Customer purchase-based data collection with no predefined categories.
Algorithm: Unsupervised Learning algorithm like k-means clustering.
Training: The algorithm groups customers based on purchasing behavior, creating segments such as high-spending customers or bargain hunters.
Outcome: The segmentation allows campaigns tailoring to different groups, improving customer engagement and sales.

Reinforcement Learning Use Case

Case: Autonomous Driving
Scenario: Tech company developing self-driving cars that navigate real-world environments safely.
Data: Data is collected from sensors like cameras, LiDAR, and radar during driving.
Algorithm: Reinforcement Learning algorithms like Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO).
Training: The algorithm learns from data by interacting with the environment and receiving rewards or penalties based on its actions (e.g., staying in lanes, avoiding collisions, etc.).
Outcome: The algorithm learns optimal driving policies, allowing the car to make safe and efficient decisions on the road, like lane-keeping and acknowledging traffic signals.

What is Deep Learning?

AI also has a lot to do with Deep Learning, the subfield of Machine Learning! For instance, Deep Learning algorithms use Neural Networks (NNs), pioneered by Warren McCulloch and Walter Pitts, which involve layer collections that process data. The input layer receives raw data and propagates it through hidden layers that extract complex features from it, and the output layer makes decisions or predictions based on what the algorithm learned.

Other advanced Deep Learning techniques, such as Large Language Models (LLMs), can achieve stellar performance in Natural Language Processing (NLP) and are key to the success of Generative Pre-trained Transformers (GPT).

Why are AI and Machine Learning Important?

AI and Machine Learning have proven to be extremely useful for automating tasks like predictive maintenance, data entry, product recommendations, and facial recognition. Yet, their power goes beyond repetitive tasks! 

Financial institutions use AI and Machine Learning for risk assessment and investment portfolio management. Tech helps companies provide better financial advice and enhance Customer Experiences, with JP Morgan Chase and Capital One being great examples of institutions using AI.

In healthcare, doctors work alongside intelligent machines to assist in medical diagnosis and personalized treatments, and a great example is CaDet, an expert system that helps doctors identify cancer at its early stages.

Lastly, the gaming industry also saw huge improvements thanks to AI and Machine Learning. For example, “Alien: Isolation” uses advanced AI to learn from users’ behavior and provide a unique experience. Other games like “Red Dead Redemption” and “The Last of Us Part II” use AI to shape Non-Player Characters' (NPCs) behavior. 

Conclusion

Advances in AI and Machine Learning have helped us reduce the average time needed to complete many of our most common tasks, bringing a large range of benefits. These two fields hold a huge potential to continue to shape how most businesses approach work, revolutionizing the way businesses build and deliver products!