AI vs ML vs DL vs Data Science — Difference Explained
Hey everyone, welcome here, where you learn something new every day. Today’s post will compare and contrast Artificial Intelligence, Deep Learning, Machine Learning, and Data Science.
Before moving on, let me ask you two interesting queries. Which among the following is not a branch of artificial intelligence? Data Analysis, Machine Learning, Deep Learning, Neural Networks. And the second query is, What is the main difference between machine learning and deep learning? Please leave your answer in the comments section below, and stay tuned to get the answer.
First, we will unwrap deep learning. Deep learning was first introduced in the 1940s. Deep learning did not develop suddenly. It developed slowly and steadily over seven decades. Many theses and discoveries were made on deep learning from the 1940s to 2000. Thanks to companies like Facebook and Google, the term deep learning has gained popularity and may give the perception that it is a relatively new concept.
Deep learning can be considered as a type of machine learning and artificial intelligence, or AI, that imitates how humans gain certain types of knowledge. Deep learning includes statistics and predictive modeling. Deep learning makes processes quicker and simpler, which is advantageous to data scientists to gather, analyze, and interpret massive amounts of data.
Having the fundamentals discussed, let’s move into the different types of deep learning. Neural networks are the main component of deep learning, but neural networks comprise three main types, which contain artificial neural networks, or ANN, neural Convolution Neural Networks, or CNN, and Recurrent Neural Networks, or RNN.
Artificial neural networks are inspired biologically by the animal brain. Convolutional neural networks surpass other neural networks when given inputs, such as images, voice, or audio. It analyzes images by processing data. Recurrent neural networks uses sequential data or series of data. Convolutional neural networks and recurrent neural networks are used in natural language processes, speech recognition, image recognition, and many more.
Generate Images In Open Source AI-Powered Image
Download All the Models from Hugging Face
Machine Learning The evolution of ML started with the mathematical modeling of neural networks that served as the basis for the invention of machine learning. In 1943, Neuroscientist Warren McCulloch and logician Walter Pitts attempted to quantitatively map out how humans make decisions and carry out thinking processes.
Therefore, the term machine learning is not new. Machine learning is a branch of artificial intelligence and computer science that uses data and algorithms to imitate how humans learn, gradually increasing the system’s accuracy. There are three types of machine learning, which include supervised learning.
What is supervised learning? Well, here, machines are trained using labeled data. Machines predict output based on this data. Now, coming to unsupervised learning, models are not supervised using a training dataset. It is comparable to the learning process that occurs in the human brain while learning something new.
And the third type of machine learning is reinforcement learning. Here, the agent learns from feedback. It learns to behave in a given environment based on actions and the result of the action. This feature can be observed in robotics. Now, coming to the evolution of AI. The potential of artificial intelligence wasn’t explored until the 1950s, although the idea has been known for centuries.
The term artificial intelligence has been around for a decade. Still, it wasn’t until British polymath Alan Turing posed the question of why machines couldn’t use knowledge like humans do to solve problems and make decisions. We can define artificial intelligence as a technique of turning a computer based robot to work and act like humans.
Now let’s have a glance at the types of artificial intelligence. Weak AI performs only specific tasks, like Apple’s Siri, Google Assistant, and Amazon’s Alexa. You might have used all of these technologies, but the types I am mentioning after this are under experiment. General AI can also be addressed as Artificial General Intelligence.
It is equivalent to human intelligence. Hence, an AGI system is capable of carrying out any task that a human can. Strong AI aspires to build machines that are indistinguishable from the human mind. Both general and strong AI are hypothetical right now. Rigorous research is going on on this matter. There are many branches of artificial intelligence, which include machine learning, deep learning, natural language processing, robotics, expert systems, fuzzy logic.
Therefore, the correct answer for which is not a branch of artificial intelligence is option A, data analysis. Now that we have covered deep learning, machine learning, and artificial intelligence, the final topic is data science. Concepts like deep learning, machine learning, and artificial intelligence can be considered a subset of data science.
Let us cover the evolution of data science. The phrase data science was coined in the early 1960s to characterize a new profession that would enable the comprehension and analysis of the massive volumes of data being gathered at the time. Since its beginnings, Data science has expanded to incorporate ideas and methods from other fields, including artificial intelligence, machine learning, deep learning, and so forth.
Data science can be defined as the domain of study that handles vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Therefore, data science comprises machine learning, artificial intelligence, and deep learning. I hope this was helpful.
Thank you for reading, and happy learning!
