AI vs ML vs DL vs DS - The quad gladiators of modern computer Science
These terms Artificial intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Data Science (DS) have been the buzzwords in any tech-related meet or activity. With multiple trends and research gaining the better part of Tech Funding, we can only assume that these will grow to a much larger field. Even if you are not directly involved in Tech, these terms pop out in the form of different futuristic implementations.
So, Should you even know about their meaning and significance? And more, why should you care about one of these or more? In this post, you will have a general understanding of these technical terms and the clarification of their coverage in Information technology. Let's get started from the individual topics then.
About Artificial Intelligence
Artificial Intelligence or simply AI refers to the simulation of a human brain function to the highest degree by machines. In simpler terms, it means incorporating human intelligence into computers. Broadly, it is the ability which if imparted with the computers can enable them (machines) to understand the existing data (or to read real-time data), learn from the acquired data, and make logical/intelligent decisions based on the patterns that might be hidden in the data. Moreover, AI research and development are done in such a way that an ideal AI would resolve problems and learn better than humans can. Although the development and implementation of AI in much different product might seem new, its concepts goes back to 1956. It dealt more with power and storage management then. The '60s saw the use of AI in problem-solving and more. The first personal assistant arrived in the early 2000s. Siri, Cortona, Alexa are relevantly new and to some extent define Artificial Intelligence for the general public. Movies might show you different robots with AI taking over the world (Transformers and such). To be real, the modern level of AI is neither that smart nor terrifying.
With the present level and improvement in AI, It deals with the following issues:
- General Intelligence
- Knowledge representation
- Motion and Manipulation
- Natural Language Processing
- Social Intelligence
- Reasoning and Problem Solving
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About Machine Learning
If you ask Siri or Alexa or Cortana "What is Machine Learning", the response might simply be -' It's Machine learning for themselves and improving from its experience.’ And yes, it is as simple as that. It's simply empowering the computer systems or machines with the ability to learn. Technically, it is just a method of training algorithms in such a way that they learn to make decisions. Machine learning is a technique for realizing AI, hence sometimes ML can be said to be the subset of AI.
But How Does a Machine learn? It's the first question to arise. Well, there are different methods by which we can make the machines learn. Generally, these methods can characteristically be separated into three types:-
Supervised learning - The aim here is to learn a function. This function is an approximation such that the given set of data maps the desired output using that function. It simply maps inputs to outputs.
Unsupervised learning - Its sole goal is to infer the structure (natural) that might be present within a set of the data point. It doesn't need any labeled output to do so. It learns by finding the inherent patterns in data.
Semi Supervised (reinforcement) Learning - The aim of the machine here to label some unlabeled data points. It does so with the assistance of knowledge gained from a small number of known data points. Hence, it learns both with the unlabeled and labeled data points.
Machine learning in itself might have some major problems and shortcomings. However, there are some proven issues to have solved or improved with Machine Learning -
- MANUAL DATA ENTRY
- DETECTING SPAM
- FINANCIAL ANALYSIS
- PRODUCT RECOMMENDATION
- PREDICTIVE MAINTENANCE (Industries)
- MEDICAL DIAGNOSIS
- IMAGE RECOGNITION (COMPUTER VISION)
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Deep Learning can be taken as the next level or the evolution of Machine learning. It simply arises in situations where machine learning might leave a lot to be desired. The main idea behind Deep Learning is the possibility to make the Machine be able to learn in a way that we 'human beings' learn i.e., with our brains. It is inspired roughly by the information-processing patterns as found in the human brain. Here's how the normal brain works:-
When a brain receives new information, it deciphers the information and breaks it into smaller components (items). It labels and assigns these small items into various categories and in the process of labeling it. In this way, the brain can now compare this new information with the known item and makes a sense out of it. This is, in general, the same concept that Deep Learning (DL) algorithms use. These algorithms are then used to accomplish the same tasks for the machine. Deep learning uses Multi Neural Network Architecture created to achieve the mimicry of Human Brain
The three general techniques of deep learning are with:-
- Artificial Neural Network (ANN) including Deep Neural network (DNN)
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
Deep Learning also requires tons of research and advancements to reach its full potential. It is often considered to be the game changer in AI and its development as well as implementation. With the level of DL we have it can deal with the following issues:-
- Object detection and Classification (with high accuracy)
- Image Inpainting/reconstruction
- Virtual Customer Assistance
- Augmented intelligence in voice assistants
- Complex Predictive Systems
- Adaptive imitation
Data Science steps out of the subset-like field that we were discussing right from AI. However, it is not very far from it as you will learn. Data science is a multidisciplinary term defining a whole set of techniques and tools of data inference and algorithm development for the purpose of solving complex analytical problems. Data Science is used to solve complex data problems. These results after solving can be used to bring out the correlation and insight relevant to different businesses or organizations.
Data Science plays an important role in the design/implementation of major Data areas such as:
- Data architecture
- In data acquisition
- Data Analysis and
- Data archiving
Data Science is one of the most widely used and approved techniques in AI and ML. It also assists in the improvement of the large data required for deep learning.
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The Obvious Differences
Although these four terminologies are conventionally used interchangeably, they do not quite refer to the same things.
- Data Science is the first different thing as it takes and implements different parts of AI, DL, and ML for data-related problems with the help of other major statistical tools. It is not generally taken under the wing of AI but rather is like an important ally to AI.
- AI is the board term consisting of so many other fields in it, Machine learning being one of the most significantly developed fields. Deep Learning is an evolution of Machine learning designed for specifically enabling the Machine to learn from the human brain.
- A huge data is generally required for all AI, ML, and DL and Data Science can help in building a better model resulting in effective results in the other fields respectively.
- Deep Learning can discover the features automatically that is used for classification, Machine Learning requires the manual input of the features.
- Deep learning might require high-end machines in comparison to other different constituents of our topic.
Here we have briefly and in the most surficial level studied Artificial intelligence (AI), Machine Learning (ML), Deep Learning (DL) and Data Science (DS). For the broader concepts and courses you can explore the professional help from TechAxis. Take the first Step in having a successful IT career in Nepal with TechAxis.