We’ve all heard of the possibilities of artificial intelligence, machine learning, and deep learning. There have been many situations where artificial intelligence has made a measurable impact on an organization, and there have also been situations where organizations have wasted millions of dollars on seemingly innovative technologies with no direct output.
So what is the difference between machine learning, deep learning, and artificial intelligence?
AI is a bigger concept to create intelligent machines that can simulate human thinking capability and behavior, whereas, machine learning & deep learning is an application or subset of AI that allows machines to learn from data without being programmed explicitly.
The three technologies tie together like a set of Russian Dolls – one nested within the next.
Deep learning is a subset of machine learning, which is a subset of AI.
What is Artificial Intelligence?
Artificial intelligence is purely math and scientific exercise, but when it became computational it started to solve man-like problems formalized into a subset of computer science. Artificial intelligence has changed the original computational statistics paradigm to the modern idea that machines could mimic actual manlike capabilities, such as decision-making and performing more human tasks.
Artificial intelligence is a technology using which we can create intelligent systems that can simulate human intelligence.
Modern AI is into two categories
- General AI – Planning, decision making, facial recognition applications, recognizing sounds, customer service chatbots.
- Applied AI – AI analytics, Autonomous cars, machine smartly trade stocks, or Application management.
What is Machine Learning?
Instead of engineers teaching or programming computers to have what they need to carry out tasks that perhaps computers could teach themselves – learn something without being explicitly programmed to do so. ML is a form of AI where based on more data, and it can change actions and responses, which will make it more efficient, adaptable, and scalable. e.g., navigation apps and recommendation engines.
Machine learning is a subfield of artificial intelligence, which enables machines to learn from past data or experiences without being explicitly programmed.
Machine Learning can be classified into 4 Parts
- Reinforcement learning
What is Deep Learning?
It is a technique for implementing ML.
ML provides the desired output from a given input, but DL reads the input and applies it to other data. In ML, we can easily classify the flower based on the attribute. Suppose you want a machine to look at an image and determine what it represents to the human eye, whether a face, tree, landscape, car, book, etc.
Machine learning is not sufficient for this task because machine learning can only produce an output from a given data set – whether according to a known algorithm or based on the inherent structure of the data. You might be able to use machine learning to determine whether an image was of an “X” – a book, say – and it would learn and get more accurate. But that output is binary (1/0) and is dependent on the algorithm, not the data. In the image recognition case, the outcome is not binary and is not affected by the algorithm.
The neural network performs MICRO calculations with computational on many layers of neural network. Neural networks
also support weighting data for ‘confidence. These results in a probabilistic, vs. deterministic, and
can handle tasks that we think of as requiring more ‘human-type’ judgment.
Key difference between machine learning, deep learning, and artificial intelligence
|Artificial Intelligence(AI)||Machine Learning(ML)||Deep Learning(DL)|
|AI stands for Artificial Intelligence and is basically the study/process which enables machines to mimic human behavior through a particular algorithms.||ML stands for Machine Learning and is the study that uses statistical methods to enable machines to improve with experience.||DL stands for Deep Learning and is the study that makes use of Neural Networks(similar to neurons present in the human brain) to imitate functionality just like a human brain.|
|AI is the broader family consisting of ML and DL as its components.||ML is the subset of AI.||DL is the subset of ML.|
|The Efficiency Of AI is basically the efficiency provided by ML and DL respectively.||Less efficient than DL as it can’t work for longer dimensions or a higher amounts of data.||More powerful than ML as it can easily work for larger sets of data.|
|AI can perform a wide range of tasks, from recognizing images and speech to making decisions and translations.||ML algorithms are primarily used for tasks such as image and speech recognition, predictive analytics, and data mining.||DL is primarily used for applications that require large amounts of data, such as image and speech recognition, natural language processing, and self-driving cars.|
|AI algorithms can be developed using a range of techniques, including rule-based systems, expert systems, and decision trees.||ML algorithms are developed using statistical models, such as neural networks, decision trees, and logistic regression.||DL algorithms use artificial neural networks that consist of multiple layers of artificial neurons.|
|AI requires large amounts of data to perform effectively.||ML requires comparatively fewer data||However, DL algorithms require significantly larger amounts of data than ML algorithms due to the complex nature of artificial neural networks.|
|AI systems can function independently.||ML algorithms require some degree of human intervention.||DL algorithms require humans to provide data to train the algorithm, evaluate the algorithm’s performance, and tweak the algorithm to improve its accuracy.|
|AI is the broader concept that encompasses both ML and DL.||ML is a subset of AI that focuses on developing algorithms that can learn from data.||DL is a subset of ML that focuses on developing artificial neural networks that can learn and improve on their own.|
|Examples of AI: Google’s AI-Powered Predictions, Ridesharing Apps Like Uber and Lyft, Commercial Flights Use an AI Autopilot, etc.||Examples of ML: Virtual Personal Assistants: Siri, Alexa, Google, etc., Email Spam and Malware Filtering||Examples of DL: Sentiment-based news aggregation, Image analysis, and caption generation, etc.|
In summary, AI, ML, and DL are all important concepts in the tech industry, but they have distinct differences. AI is a broader concept that includes both ML and DL. ML is a subset of AI that focuses on developing algorithms that can learn from data, while DL is a subset of ML that focuses on developing artificial neural networks that can learn and improve on their own. Understanding the differences between these concepts is crucial for businesses and organizations that want to leverage these technologies effectively.
- Data science vs Data Analysis Explained
- Data Science Vs Machine Learning
- Classification in Machine Learning
- Exploring Machine Learning Datasets
- Machine Learning Applications
- Machine Learning Features
- Natural Language Processing(NLP) with Machine Learning
- Machine Learning in Healthcare
- Machine Learning Tools
- Machine Learning in Finance
- Machine Learning Pipeline
- Machine Learning Frameworks
- Difference Between Linear Regression and Logistic Regression
- Difference Between Linear Regression and Polynomial Regression
- Entropy In Machine Learning
- Gradient Descent In Machine Learning
- Hyperparameter Tuning In Machine Learning
- Machine Learning Introduction
- Machine Learning Life Cycle