- Mark Cuban
Many misconceptions are present that are related to the words Machine Learning (ML), deep learning, and Artificial Intelligence (AI), most people think all these are the same, well these words are very closely related to each other. But there are few differences too.
In this article, let’s understand the basic differences between deep learning, AI, and ML.
Artificial intelligence is the process of transmitting data, information to machines; so that the machines can function the same way as human intelligence. Its core objective is to develop self-reliant machines, which can think and act like humans. These machines can imitate human behavior and perform tasks by learning and problem-solving. Most of the systems simulate natural intelligence to solve complex issues.
AI focuses on performing 3 cognitive skills just like a human — learning, reasoning, and self-correction. It can be classified into 2 broad categories. They are:
Type-1: Based on Capabilities
- Artificial narrow intelligence: This is also called ‘Weak AI’ that can program the machines to perform specific tasks, but in a much better way than a human.
- Artificial general intelligence: The AI that can perform an array of intellectual/intelligent tasks with the same accuracy level as a human.
- Artificial super intelligence: This is the most advanced form, also called ‘Active AI’. It can outperform humans in specific tasks with better accuracy and speed in very little time.
Type-2: Based on the functionality
These are of 4-types that are based on the working principle of machines.
· Reactive machines: These are the systems that solely react. These systems don’t form memories, and they don’t use any past experiences for making new decisions.
- Limited memory: These systems reference the past, and information is added over a period of time. The referenced information is short-lived.
- Theory of mind: This covers systems that can understand human emotions and how they affect decision-making. They are trained to adjust their behavior accordingly.
- Self-awareness: These systems are designed and created to be aware of themselves. They have the ability to understand their own internal states, predict other people’s feelings, as well as act appropriately.
Currently, AI is been used in various ways. A few of them include:
- Chatbots which answer questions based on user input
- Machine translation such as Google Translate
- Self-driving vehicles such as Google’s Waymo
- AI Robots such as Sophia and Aibo
- Speech Recognition applications like Apple’s Siri, Google Assistant, Alexa, and Cortana
- Various facial recognition systems
The AI and ML are very closely related to each other, as the latter is a subset of the former. ML is a discipline of computer science, which uses computer algorithms and analytics to build predictive models or take decisions from past data or experiences without being explicitly programmed, and is helpful for solving business problems. ML uses a huge amount of structured and semi-structured data so that the ML model can generate appropriate results or allow predictions based on the data. The ML is highly used in the following places:
- Sales forecasting for different products
- Fraud analysis in banking
- Product recommendations
- Stock price prediction
Deep learning is a subset of ML, which deals with algorithms inspired by the structure and function of the human brain. The deep learning algorithms can work with a huge amount of both structured and unstructured data. Its core concept lies in Artificial Neural Networks (ANN) that enables machines to make decisions.
The major difference between deep learning and ML is the way data is presented to the machine. ML algorithms need structured data, whereas deep learning networks work on multiple layers of ANN. The concept of deep learning is mainly used in the following places:
- Captionbot for captioning an image
- Cancer tumor detection
- Music generation
- Image coloring
- Object detection
A lot of AI systems are powered by ML and deep learning algorithms. The final objective of all the three is the same, to make machines smarter. Knowledge of these three and understanding their differences will help an individual to come up with better results.