AI, ML, or DL – Learn what it Means
In this new era of the world, it is essential to know about AI, ML, and DL. All the companies and developers around the globe are talking about the same. All these acronyms are part of the acronym AI.
In this article, you will know all about AI, ML, and DL. The similarities and differences between them.
The full form of these acronyms is:
AI: Artificial Intelligence
ML: Machine Learning
DL: Deep Learning
AI is a large bowl in which ML is fitted. Similarly, ML is a large bowl in which DL is included. Or it can also be assumed as AI is a large set and ML is the subset of AI. Similarly, DL is the subset of ML. AI covers the topic which is related to making machines more intelligent. AI works in creating the machines self-reliant. ML is commonly used with AI. The systems which are getting smarter day by day without human intervention are using ML. DL is the subset of ML which is applied to large data sets. Mainly, the AI work involves ML concepts because intelligence requires appreciable knowledge.
Artificial Intelligence (AI)
When we hear about AI, the first thing that comes to our mind is robots. Humans are obsessed with the thought that machines should do everything. Everything should be automatic since the development of technology. AI interference enables this process to happen. AI allows machines to think just like human beings. Devices are enabled to think and act without human intervention. Machines can take their own decisions with AI interventions. It is a very broad area of computer science that makes machines think they have human intelligence.
Types of Artificial Intelligence
AI systems are classified into different types by their ability to duplicate human behavior. They also depend upon the hardware used and their applications in the real-life world. All the artificial intelligence systems fall into the below three types:
ANI: Artificial Narrow Intelligence
ANI is the weak type of AI. This is the one type of AI that exists in our world today. ANI is oriented toward the goal. ANI performs a single task at a time. It is programmed in such a way that it is intelligent in completing a particular task. Some examples include Siri, Alexa, chatbots, self-driving cars, the Autopilot in an airplane, etc.
ASI: Artificial Super Intelligence
ASI is a narrow AI that has more capabilities than human beings. ASI is the imaginary AI with intellectual powers further than the brightest humans. In the type of AI like this, besides having multifaceted intelligence more than humans, they also have the more fantastic problem-solving techniques. They also have more decision-making capabilities than human beings. This type of AI will race with human beings. They can also lead to the extinction of human beings!
AGI: Artificial General Intelligence
AGI is the strong AI’s or complete AI’s. These can perform general intelligent actions. AGI is the capability of the machine to understand or learn intellectual tasks. These tasks are understandable by humans. Artificial general intelligence could perform any task a human could do. Over time this could take over all the roles which can be performed by humans. The AGI should have a sensory perception, fine motor skills, and Natural language understanding. It should have problem-solving techniques, navigation, social and emotional engagement, creativity, etc.
Machine learning is the branch of AI that focuses on data usage and algorithm. This is used to imitate human beings. ML is gradually increasing its ability to imitate humans accurately. Machine learning is an important component in the field of data science which is growing rapidly. With the usage of statistical methods, algorithms are trained in such a way that they can perform classifications or predictions. The insights into these concepts help in making decisions. This includes business projects and applications related to them. This would help in the critical growth of that field. ML solves business problems using predictive and computer models. The work of a machine learning engineer is found in stock price prediction, sales forecasting, banking fraud analysis, etc. The recommendation services in the music and video streaming applications are examples of machine learning.
The machine learning algorithms are subdivided into three major types.
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
In supervised learning, we have the input and output variables x and y. We use the algorithm to understand the mapping between input and output variables.
Unsupervised learning is utilized when we do not have labeled data. Its main theme is to learn about data by inferring patterns in the dataset.
In simple words, reinforcement learning can be expressed as gaining knowledge by interacting with the environment continuously. Users can learn and develop by trial and error methods. Performing the actions and collecting the data to use it for further experiments. Reinforcement learning uses punishment and reward. If the user performs the correct action, then he is rewarded. He receives penalties for incorrect actions.
Human intelligence is compared with artificial intelligence. In this, we assume that the neurons inside our brain as deep learning. Deep learning works in the same manner as the brain. It is further complex than machine learning because it uses deep neural networks.
The machines use several layers of techniques to learn and execute. These networks comprise an input layer that accepts the data input from the user. The hidden layer is used to find the hidden features of deep learning. Finally, the output layer consists of the final information the user requires.
In other words, deep learning uses the technique called sequence learning. All industries use deep learning techniques to construct innovative ideas and products.
Deep learning techniques are used to develop self-driving cars (which do not require a driver).
Most deep learning techniques use neural network architectures, so they are also called deep neural networks.
Deep learning architecture is classified into three types of neural networks.
- Convolutional Neural Networks
- Recurrent Neural Networks
- Recursive Neural Networks