These tech terms are often mixed up, but what makes each one special?
Over the past few decades, tech has become more and more a part of our daily lives. Companies use machine learning to meet customer needs faster. Think about how social media spots things in photos or how we chat with Alexa or Siri. It is quite interesting, isn’t it?
AI, machine learning, deep learning, and neural networks are all connected. However, people use the terms like they mean the same thing. This can make it confusing for many to understand what each one does.
Keep reading the article, here, we’ll discuss AI vs ML vs DL. Furthermore, you’ll see how they’re connected and what makes each one different.
What is Artificial Intelligence?
AI is tech that can do tasks like humans or even better, especially when combined with other tech like sensors or robots. Things like digital helpers, GPS navigation, self-driving cars, and smart tools like ChatGPT from OpenAI show how AI is part of our lives.
In computer science, AI includes machine learning and deep learning. These fields create algorithms that learn from data and make better predictions over time, mimicking how our brains work.
AI has had ups and downs in popularity, but breakthroughs like ChatGPT show big progress, especially in understanding and processing human language. Now, AI can learn and create not just words but also images, videos, code, and even chemical structures.
Here are some examples of AI in action:
1. Personalized Recommendations: Websites like Amazon and Netflix use AI to suggest products or shows based on what you’ve liked before.
2. Virtual Personal Assistants (VPAs): Siri and Alexa can understand and respond to your voice commands, like playing music or answering questions.
3. Fraud Detection: Banks use AI to spot unusual spending or transactions that might be fraud.
What is Machine Learning (ML)?
ML is about machines learning from data without being directly programmed. ML algorithms use stats to find patterns and make predictions based on past data. It’s part of AI, focusing on learning from data and improving over time.
ML came about to make AI better by using data for learning. It’s great at tasks like recognizing images and speech, understanding language, making recommendations, and more.
However, ML can lose accuracy when dealing with lots of data. Humans also have to figure out what data features are important, and ML struggles with complex tasks or detailed patterns. That’s why Deep Learning (DL) became important.
Here are some examples of Machine Learning:
1. Speech Recognition: ML helps systems like Siri and Alexa understand and transcribe speech in call centres and virtual assistants.
2. Natural Language Processing (NLP): ML is used in chatbots and virtual assistants to understand and generate human language for better interactions.
3. Sentiment Analysis: ML can classify text or speech as positive, negative, or neutral, used in social media monitoring and similar tasks.
What is Deep Learning (DL)?
DL is a special part of AI that’s super powerful and advanced. It teaches computers to learn like humans do, using lots of data.
DL uses deep neural networks with many layers to understand data in a detailed way. It figures out important features on its own, without humans having to do it. DL is great at handling big tasks and lots of data, especially in areas like seeing, understanding language, and recognizing speech.
However, working with huge datasets that need constant labelling can be slow and costly. DL models can also be hard to understand or tweak because they’re so complex. There’s also a risk of attacks that can trick DL models into making mistakes, which is a concern for their reliability and safety in real-world use. These challenges led to the rise of Generative AI, a part of DL that focuses on creating new data.
Here are some examples of Deep Learning:
1. Generative Models: Deep learning creates new content based on existing data, used in generating images, text, and more.
2. Autonomous Vehicles: Deep learning algorithms analyze sensor data for self-driving cars to make decisions about speed, direction, and safety.
3. Game-playing AI: Deep Learning powers AI that can play games at a superhuman level, like AlphaGo defeating the world champion in Go.
AI vs ML vs DL: Differences & Application
Let’s break down the important things about AI vs ML vs DL, so it’s easier to understand how they’re different and what they’re used for:
| Category | Description |
| Artificial Intelligence | – Study/process enabling machines to mimic human behaviour through specific algorithms. |
| – Broad family including Machine Learning (ML) and Deep Learning (DL). | |
| Machine Learning | – Uses statistical methods to enable machines to improve with experience. |
| – Subset of Artificial Intelligence. | |
| Deep Learning | – Utilizes Neural Networks (similar to human brain neurons) to imitate human brain functionality. |
| – Subset of Machine Learning. | |
| AI vs ML vs DL | – AI encompasses ML and DL. |
| – ML uses data to improve system performance. | |
| – DL employs deep neural networks for data analysis. | |
| AI Applications | – Google’s AI-Powered Predictions. |
| – Ridesharing Apps Like Uber and Lyft. | |
| ML Applications | – Virtual Personal Assistants: Siri, Alexa, Google, etc. |
| – Email Spam and Malware Filtering. | |
| DL Applications | – Sentiment-based news aggregation. |
| – Image analysis and caption generation. | |
| AI Types | – Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI). |
| ML Types | – Supervised Learning, Unsupervised Learning, and Reinforcement Learning. |
| DL Types | – Unsupervised Pre-trained Networks, Convolutional Neural Networks, Recurrent Neural Networks, Recursive Neural Networks. |
| Efficiency | – AI efficiency relies on ML and DL. |
| – DL is more powerful with larger sets of data. | |
| AI Subfields | – Robotics, Natural Language Processing, Computer Vision, Expert Systems, etc. |
| ML Algorithms | – Supervised learning with labeled data, Unsupervised learning with unlabeled data, Reinforcement learning with trial and error. |
| DL Inspiration | – Inspired by human brain structure and function. |
| AI Systems | – Rule-based, Knowledge-based, Data-driven. |
| DL Networks | – Consists of multiple layers of interconnected neurons. |
Conclusion
At last, in the world of AI, there are different parts like AI, ML, DL, and Generative AI, each doing unique things to make smart systems better. AI covers a wide range, ML learns patterns, DL uses deep networks for complex patterns, and Generative AI creates new stuff.
What’s cool is how these parts work together, making AI better for different industries. They’re not perfect, but together they push AI forward, breaking limits and making new things possible. It’s important to know that while Generative AI is part of AI, not all AI is Generative AI. The same goes for DL and ML, they’re related but not the same.