In recent years, Artificial Intelligence, Machine Learning, and Deep Learning have emerged as popular topics of discussion. However, they are often used interchangeably and many have difficulties in differentiating between them. These terms are for sure interconnected, but, they have different focuses and methodologies. In this article, I will explain their meanings in Layman’s terms to provide a clearer understanding of each concept.
“Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise, you’re going to be a dinosaur within 3 years.”
– Mark Cuban, American entrepreneur, and television personality.
In simple terms, Artificial Intelligence is the capability of computer systems and machines to perform tasks that typically require human intelligence. The main goal of AI is to develop self-reliant machines that can think like humans, act like humans, think rationally, and act rationally.
To develop a fully functioning AI program or a machine with Artificially intelligent abilities, various techniques, algorithms, and methodologies are utilized to enable them to perceive insights, acquire knowledge, make decisions, and engage in reasoning. Basically, the AI concept is about mimicking the thinking patterns(cognitive function) of humans.
Scopes of Artificial Intelligence
- Virtual Assistants: Powers Siri, Alexa, and Google Assistant
- Autonomous Vehicles: For instance; Tesla, Google’s Waymo, and Audi e-tron
- Computer Vision: Image and video recognition, object detection, and facial recognition.
- Expert systems: In medical diagnosis, financial advisory systems, etc.
- Robotics: Such as Sophia, NAO, Cozmo
Machine learning is a sub-part of artificial intelligence that focuses on using algorithms to teach machines to improve their performance by learning from data. Unlike traditional programming, where developers have to hand-code each task every time, machine learning enables machines to learn, make predictions, and make decisions autonomously based on patterns of data and previous mistakes.
One of the key aspects of machine learning is its ability to improve its performance over time. Initially, the algorithms are given large sets of data to look for patterns and trends. They learn from these patterns and gain knowledge. Then, they use this knowledge to make predictions and decisions when faced with new and unfamiliar data.
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Scopes of Machine Learning
- Predictive Analytics: Analyze historical data and make predictions about future outcomes of any domain
- Financial Modeling: Stock market prediction, credit scoring, and risk assessment
- Recommendation Systems: Suggest personalized products or content to users
- Fraud Detection: Identify fraudulent activities in finance, cybersecurity
- Natural Language Processing: For example, Google Translate and ChatGPT.
Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers to understand and learn from complex patterns in data. It mimics the biological neural networks in the brain. Deep learning has greatly advanced artificial intelligence by allowing computers to learn and make sense of information in a more sophisticated way.
Artificial neural networks have unique capabilities that enable deep learning models to solve tasks that machine learning models could never solve. The models can learn directly from raw data, making them more self-sufficient compared to traditional machine-learning approaches. Hence, it achieves its success by being able to handle vast amounts of data and take advantage of parallel computing capabilities.
Scopes of Deep Learning
- Autonomous Vehicles: Interpret sensor data from cameras, LIDAR, and other sensors
- Image and Speech Recognition: Identify objects, classify images, and perform tasks like facial recognition and object detection
- Natural Language Processing: Understand and generate human language with high accuracy and fluency.
- Generative Models: Generate new and practical content, such as images, music, and text.
Artificial Intelligence v/s Machine Learning v/s Deep Learning: Conclusion
Before machine learning and deep learning algorithms were even thought of, Artificially intelligent machines needed to be hand-coded to provide specific instructions for how they should respond to limited inputs.
AI, ML, and DL have made significant changes in our environment, revolutionizing industries and improving productivity. Although they come with potential threats, their beneficial aspects, such as increased efficiency and enhanced decision-making, cannot be overlooked.
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