Welcome to Day 3 of DailyAIWizard, your 20-minute journey to mastering AI! In this beginner-friendly lesson, I’m Anastasia, your AI guide, and we’ll explore the differences between Artificial Intelligence, Machine Learning, and Deep Learning. Sophia joins us for a demo using Google’s Teachable Machine to classify cats and dogs, showing Machine Learning in action! Whether you're new to AI or building on Days 1 and 2, this series will guide you from the fundamentals to Python programming. Let’s dive in!
Task of the Day: Try Google’s Teachable Machine yourself—train it to classify something simple (like cats and dogs) and share your results in the comments!
Google Teachable Machine: https://teachablemachine.withgoogle.com/
Learn More: Visit wisdomacademy.ai for additional resources and to join our AI learning community. Subscribe for Daily Lessons: Don’t miss Day 4, where we’ll dive into How Machine Learning Works. Hit the bell to stay updated!
00:00Welcome to Day 3 of Daily AI Wizard, your 20-minute journey to mastering AI.
00:08I'm Anastasia, your AI guide, here to make learning AI simple and fun.
00:14Today, we're comparing three big concepts, AI, machine learning, and deep learning.
00:19We'll explore what they are, how they're related, and how they differ, with some exciting examples along the way.
00:26Let's get started.
00:27Before we dive in, let's recap Day 2.
00:32We explored the three types of AI—narrow, general, and superintelligent.
00:37Narrow AI is specialized and exists today, like ChatGPT, which Sophia showed us.
00:44General AI aims to be human-like but is still in research.
00:49Superintelligent AI would surpass humans, but it's a future possibility.
00:53I hope you shared which type excites you most in the comments.
00:57Now, let's move on to today's topic.
01:01Today, we'll cover a lot of ground.
01:03We'll start by defining AI, machine learning, and deep learning, then see how they're related.
01:09We'll explore their key differences with examples to make things clear.
01:14Plus, Sophia will join us for a demo showing a machine learning application in action.
01:18This lesson will help you understand the building blocks of AI, so let's begin.
01:24Let's start with AI, which we touched on in Day 1.
01:28Artificial intelligence, or AI, is when machines mimic human intelligence.
01:33It's a broad field that includes abilities like learning, reasoning, and problem-solving.
01:40Think of AI as the big picture of intelligent systems, covering everything from chatbots like me to self-driving cars.
01:48AI is the overarching goal of creating machines that can think and act like humans, and it encompasses many techniques, including the ones we'll talk about today.
02:00Now, let's zoom in on machine learning, or ML, which is a subset of AI.
02:07Machine learning is when machines learn from data without being explicitly programmed.
02:12Instead of writing rules for every scenario, we give ML systems data, and they figure out patterns on their own.
02:19For example, email spam filters learn from your emails to decide what's spam and what's not.
02:26Machine learning powers many AI applications today, making it a key part of the AI world.
02:34Machine learning comes in three main types, each suited for different tasks.
02:39First, supervised learning uses labeled data, like teaching a child with examples.
02:45Second, unsupervised learning works with unlabeled data, finding patterns on its own.
02:52Third, reinforcement learning learns through rewards, like training a dog with treats.
02:59We'll dive deeper into these types in future lessons, but for now, know that they're the building blocks of machine learning,
03:06helping AI systems learn in different ways.
03:09Next, we have deep learning, or DL, which is a subset of machine learning.
03:16Deep learning uses neural networks, structures inspired by the human brain, with many layers to process data.
03:25For example, deep learning powers image recognition, like identifying cats in photos.
03:31It's incredibly powerful, but requires lots of data and computing power to work well.
03:37Deep learning is behind many advanced AI applications today, taking machine learning to the next level.
03:45So, how are AI, machine learning, and deep learning related?
03:50Think of AI as the big umbrella.
03:53It's the broad field of creating intelligent machines.
03:57Machine learning is a subset of AI, focusing on learning from data.
04:01Deep learning is a subset of machine learning, using neural networks for more complex tasks.
04:09Each layer builds on the previous one, making AI a family of techniques that work together to create smart systems.
04:17Let's compare AI, machine learning, and deep learning by their scope.
04:23AI is the broadest.
04:24It includes all intelligent systems, from simple rules to advanced learning.
04:30Machine learning is narrower, focusing on data-driven learning without explicit programming.
04:36Deep learning is even narrower, relying on neural networks for specific, complex tasks.
04:43The scope narrows as we go deeper, but each level adds more power and complexity to what machines can do.
04:50Another key difference is data and computing needs.
04:54AI varies.
04:56It can use simple rules or learning, so its needs depend on the method.
05:00Machine learning needs data to learn and moderate computing power, like for spam filtering.
05:07Deep learning, however, requires lots of data and high computing power.
05:12Think of image recognition, where it processes millions of images to learn.
05:16The more advanced the technique, the more resources it demands, which is why deep learning often needs powerful machines.
05:25Let's look at examples to see the differences in action.
05:29AI includes complex systems like self-driving cars, which combine many techniques to work.
05:36Machine learning powers recommendation systems, like Netflix suggesting shows based on your viewing habits.
05:43Deep learning excels in tasks like voice recognition.
05:48Think of Alexa understanding your commands.
05:51Each approach shines in different areas, showing how they complement each other within the AI family.
05:57To see machine learning in action, let's bring in Sophia for a quick demo.
06:03She'll use Google's Teachable Machine, a free tool to show how machine learning can classify images, like telling cats from dogs.
06:12This will give you a hands-on look at how ML works.
06:15Over to you, Sophia.
06:17Hi, I'm Sophia, your demo guide for Daily AI Wizard.
06:22Today, I'll show you machine learning with Google's Teachable Machine.
06:27I've uploaded some images of cats and dogs to train the model.
06:31Cats in one category, dogs in another.
06:35See how the ML model learned from the data and classified a new image?
06:41That's machine learning in action.
06:44Learning patterns from data to make decisions.
06:48Back to you, Anastasia.
06:49Thanks, Sophia.
06:52That was a fantastic demo.
06:54Let's break down how Teachable Machine works.
06:57It uses supervised learning, one of the ML types we mentioned.
07:01The steps are simple.
07:03First, you provide data, like the cat and dog images Sophia uploaded.
07:09Then, you train the model, letting it learn patterns.
07:13Finally, you test it with new images, and the model classifies them.
07:17Machine learning finds patterns in the data to make decisions, which is why it's so powerful
07:23for tasks like classification.
07:26Machine learning has many real-world applications.
07:30It's used in fraud detection, like spotting suspicious transactions in banking.
07:35Predictive maintenance in factories uses ML to predict when machines might fail, saving time and money.
07:43Even personalization, like Spotify curating playlists for you, relies on machine learning.
07:50ML is practical and widely used, making our lives easier in so many ways.
07:55Deep learning, on the other hand, excels in more complex tasks.
08:01It powers image recognition, like facial recognition, to unlock your phone.
08:06Natural language processing, such as translating languages, often uses deep learning.
08:13Think of Google Translate.
08:14Autonomous driving, like Tesla's vision systems, also relies on deep learning to process camera data and make decisions.
08:22Deep learning is perfect for complex tasks that need lots of data and power.
08:28So, which should you use?
08:30AI, machine learning, or deep learning?
08:33It depends on the task.
08:36For simple tasks, traditional AI or basic machine learning might be enough.
08:41For data-driven tasks, like the teachable machine demo, use machine learning.
08:46For complex tasks, like image recognition, deep learning is the way to go.
08:52Your choice depends on the data you have and the resources available, like computing power.
08:58It's all about picking the right tool for the job.
09:02What's next for AI, machine learning, and deep learning?
09:06AI is moving towards general and super-intelligent AI, as we discussed in Day 2.
09:12Machine learning is focusing on more efficient algorithms that need less data to learn.
09:18Deep learning is improving neural networks, opening up even wider applications, like better autonomous systems.
09:26The future is exciting, with advancements that will make AI even more powerful and helpful in our lives.
09:33Let's recap what we've learned today.
09:37AI is the broad field of creating intelligent systems.
09:40Machine learning is a subset of AI, focusing on learning from data, like Sophia showed with Teachable Machine.
09:49Deep learning is a subset of machine learning, using neural networks for complex tasks like image recognition.
09:57Here's your task.
09:58Try Google's Teachable Machine yourself.
10:01Train it to classify something simple, like cats and dogs, and share your results in the comments.
10:08For more resources, visit wisdomacademy.ai to keep learning.
10:14That's it for Day 3, everyone.
10:16Thank you for joining me on this AI journey.
10:19I'm Anastasia, and I hope you enjoyed learning about AI, machine learning, and deep learning.
10:24If you found this lesson helpful, please give it a thumbs up, subscribe, and hit the bell for daily lessons.
10:31Tomorrow, we'll dive deeper into how machine learning works.
10:34Let's hear from Sophia before we go.
10:36I loved showing you Teachable Machine today, and I can't wait for more demos in this series.
10:44Day 4 is going to be amazing, so don't miss it.
10:47See you tomorrow, wizards!
10:48It's a good day.
10:50Day 5 has been happening, so Fest and the
10:53metrics are always said, for many reasons.
10:54Did you find this skill 3 hours total of companies'
