AI and ML for non-Technical

AI vs. ML: Simplified to understand:

Artificial Intelligence (AI) and Machine Learning (ML) are often used interchangeably, but are they the same.

(AI):

Definition: AI is a broad field of computer science focused on creating systems that can perform tasks that typically require human intelligence. This includes things like understanding language, recognizing objects, making decisions, and learning from experience.

(ML): A SUBSET of AI

Definition: ML is a specific type of AI that allows a system to learn from data and improve over time without being explicitly programmed for each task. Essentially, it’s a way to TEACH machines to MAKE predictions or decisions based on data.

Examples:

- AI: A smart home assistant like Alexa can control your lights, answer questions, and set reminders. It uses AI to process natural (your voice) language and understand what you’re asking it to do

- ML: Netflix's recommendation engine is driven by ML. The more you watch, the better it gets at suggesting what you might enjoy next.

More examples to help differentiate:

1: Online Shopping

AI: Imagine you’re shopping online for a new laptop. The website shows you personalized deals, recommends laptops based on your previous searches, and even provides a chatbot to answer your questions. The chatbot understands your queries and guides you through the process, thanks to AI.

ML: The website's recommendation system suggests a particular laptop because it has learned from your past purchases and browsing history. Over time, it gets better at showing you products you’re likely to buy as per your previous search pattens. This recommendation engine is driven by ML.

2. Email Spam Filtering

AI: Your email service automatically organizes your inbox, flags important messages, and filters out spam. It understands the content of your emails and sorts them accordingly. This task requires AI to manage various aspects of your inbox.

ML: The spam filter learns from the emails you mark as spam. Over time, it improves its ability to detect unwanted emails based on the patterns it recognizes. This learning process is ML at work.

3. Autonomous Vehicles

AI: Self-driving cars are a great example of AI in action. The car navigates the streets, avoids obstacles, follows traffic rules, and makes decisions in real-time—all without human input. This broad decision-making ability is powered by AI.

ML: The car's system learns from thousands of hours of driving data. It gets better at recognizing pedestrians, road signs, and other vehicles by analysing this data. The ability to improve its driving skills over time is due to ML.

4. Healthcare Diagnostics

AI: AI-powered systems in hospitals can analyse patient data, suggest diagnoses, and even predict potential health issues. These systems assist doctors in making better-informed decisions by providing comprehensive insights.

ML: A specific ML model might learn to identify tumours in medical images by studying thousands of examples. Over time, it becomes increasingly accurate in detecting early signs of disease. This ability to improve with more data is a hallmark of ML.

5. Voice Assistants

AI: When you talk to a voice assistant like Siri or Google Assistant, it can perform tasks, answer questions, and control smart devices in your home. The assistant’s ability to understand and respond to a wide range of requests involves AI.

ML: The voice assistant becomes better at understanding your specific accent, word choices, and preferences over time. This improvement in recognizing and responding to your voice is driven by ML.

6. Predictive Text and Autocorrect

AI: When you’re typing on your phone, AI is at work when it suggests entire sentences, emojis, or quick responses based on the context of your conversation.

ML: The autocorrect feature learns from the way you type and adjusts its suggestions based on your unique writing style. Over time, it gets better at predicting what you want to say. This learning is powered by ML.

7. Social Media Feeds

AI: Social media platforms use AI to decide which posts to show you on your feed. It determines what content is most relevant to you based on various factors like the time of day, trending topics, and your past interactions.

ML: The more you like, share, or comment on certain types of posts, the better the platform gets at showing you similar content. The feed becomes more personalized as it learns from your behaviour, which is an example of ML

Summary:

AI is the broad concept of machines doing various tasks that require intelligence, it is like the brain that controls and decides what to do in different situations, while ML is a specific approach within AI where machines learn from data to improve their performance on a particular task.

ML is like the memory that gets better over time, improving performance based on experience and data. It learns from what it sees and adjusts accordingly.

The above scenarios illustrate how AI and ML work together in everyday applications, with AI providing the overall intelligence and decision-making, while ML focuses on learning and improving specific tasks.