Over the last few years, Artificial Intelligence (AI) and Machine Learning (ML) have had a major impact on a wide range of industries.
AI's Neural Networks (NN) already rival human intelligence in 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. Scientists and researchers wanted computers to be able to mimic cognitive functions like perception, problem-solving and decision-making.
To sum up, AI is the development of algorithms for machines to perform tasks that normally require human intelligence.
A great example is the 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 AI and one of the main pillars behind AI's success. ML analyzes vast amounts of data so systems can make accurate predictions and decisions. This leads to helping programs learn and improve based on experience.
Within ML subfields we can find 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!
For instance, Google Maps uses AI 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.
It's also 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.
Here, Natural Language Processing (NLP) allows users to further harness GenAI tools like ChatGPT and Sora.
AI and Machine Learning in the Workplace
AI and Machine Learning have also helped workers in plenty of industries.
Suites like Microsoft Copilot are a great example for team members to automate daily tasks.
By streamlining processes with a ChatGPT-like chat, users can generate reports, handle spreadsheets, design slides and so on.
The benefits go far from Word, Excel and PowerPoint: Copilot also involves Teams and Outlook.
Teams to schedule meetings, manage emails and collaborate on documents in real time.
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.
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 to build systems that can make sense of it.
The process of independently making decisions or predictions starts by collecting the data. This data of 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. 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, teams assess its responses to check if it achieved the desired results. If it's supposed to work on anomaly detection, the accuracy would be measured by its ability to flag anomalies.
What about Machine Learning algorithms? They are a crucial part of AI, helping systems learn from the data they've been given.
Since there are a few types of ML, it can use different algorithms based on the specific task and the data nature.
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.
Outcome: The algorithm learns optimal driving policies, allowing the car to make safe and efficient decisions on the road.
What is Deep Learning?
AI also has a lot to do with Deep Learning! DL algorithms use Neural Networks (NNs), 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.
Likewise, the output layer makes decisions or predictions based on what the algorithm learned.
Other advanced DL techniques, such as Large Language Models (LLMs), can achieve stellar performance in Natural Language Processing.
This makes them key to the success of Generative Pre-trained Transformers (GPTs).
Why are AI and Machine Learning Important?
AI and ML have proven to be extremely useful for tasks like predictive maintenance, data entry and product recommendations. Yet, their power goes beyond repetitive tasks!
Financial institutions use AI and Machine Learning for risk assessment and investment portfolio management. As a result, tech helps companies can provide better financial advice and enhance Customer Experiences! JP Morgan Chase and Capital One are great examples of institutions using AI.
In healthcare, doctors work alongside intelligent machines to assist in medical diagnosis and personalized treatments. 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 ML have helped us reduce the average time needed to complete many of our most common tasks. These two fields hold a huge potential to continue to shape how most businesses approach work. We cannot wait to see how these fields revolutionize the way businesses build and deliver products!
