Technology, technology & technology, Technology is everywhere now. It has made rapid progression from the last few decades and it continues to bring up several innovations in the world. From the discoveries and new intelligence, we are getting innovative products. Artificial Intelligence, Data Science and Machine Learning have taken new wings and continuously researchers or scientists are exploring the new out of these. Talking about all of the three terms, all are inter-related. People are talking about these things but they are still confused regarding the differentiation between them. In this blog we have tried to resolve this confusion by stating some relevant points.
Artificial Intelligence Vs Machine Learning Vs Data Science
Overview
The process of “making machines intelligent” so as they can develop their thinking ability to make decisions according to the situations is known as Artificial Intelligence.
The process in which “the pathway of making the machine intelligent” is performed is known as Machine Learning.
The process of “utilizing machine learning algorithms for analyzing data and making predictions” is called Data Science.
Artificial Intelligence Vs Machine Learning Vs Data Science
Definition
Artificial intelligence: The process of making computer thinking just like humans.
Machine Learning: It is a subset of AI in which systems learn automatically from past experiences.
Data science: The process of extracting knowledge from structured and unstructured data.
Skills Required
Artificial intelligence: Algorithms, Probability, Statistics, Python/R/Java, UNIX Tools.
Machine Learning: Computer Fundamentals, Data Modeling & Evaluation, ML algorithms, Software Engineering, Problem Solving.
Data science: Programming skills, Machine Learning, Statistics, Probability, Data Visualization, and Data Wrangling.
Programming language
Artificial intelligence: Python, C++, Java, Lisp, and Prolog
Machine Learning: Python, C++, Java, Lisp and Prolog, Scala, Shell, R, and TypeScript
Data science: Python, R, Scala, SQL, Scala, and Julia
Hardware requirements
Artificial intelligence: CPUs(Intel Scalable Processor), Intel 17 bit Qubit, superconducting chip, Intel FPGAs and Special purpose built-in silicon.
Machine Learning: TPU, GPU- NVidia TitanX Pascal, Processor, Motherboard, and Stormtrooper cabinet
Data science: CPU — 2 Intel Xeon SP Gold 5217’s, 8 core / 16 thread each @ 3.0Ghz.
Tools Used
Artificial Intelligence: TensorFlow, Scikit Learn, Keras, and Open NN
Machine Learning: TensorFlow, Scikit Learn, Weka and KNIME
Data Science: SAAS, Apache Spark, BigML and MATLAB
Types
Artificial Intelligence: Reactive Machine, Theory of mind, Self-awareness and limited memory.
Machine learning: Supervised, Unsupervised and reinforcement learning
Data science: Supervised, Unsupervised and reinforcement learning
Type of data used
Artificial Intelligence: Standardised, Data in terms of embeddings and vector
Machine learning: Structured, Unstructured, Continuous and Discrete
Data Science: Structured and Unstructured
Working Process
Artificial Intelligence: Data->Fast processing->Algorithms->Learn automatic->Give result.
Machine Learning: Data->Select Model->Train->Evaluate->Make predictions.
Data Science: Data->Analyse->Clean->Validate->apply algorithms->Get output.
Career Choices
Artificial Intelligence: Algorithms specialists, Computer Engineers, Computer scientists, Surgical Technicians, and Research Scientists
Machine Learning: Machine Learning Engineer, Data scientist, Cloud Architects, Cyber and Security analyst
Data Science: Data scientist, Application Architect, Business Intelligence Developer, and Data Analyst.
Packages offered
Artificial Intelligence: Artificial intelligence scientists or computer scientist in this category takes around $100,000 and rise to $150,000 annually.
Machine Learning: Machine learning engineer offers a salary of $114122 approx and it can vary depending on the role.
Data Science: Data scientists are making handsome salaries ranging from $91,4711 to $130,000 annually.
Market Demand
Artificial Intelligence: The global enterprise AI market size was valued at $4.69 billion in 2018, and is probable to reach $53.06 billion by 2026, recording a CAGR of 35.5% from 2019 to 2026.
Machine Learning: According to statistics the global market size was $4.99 billion in the year 2018 and will take a value of $35.35 billion by year 2025.– The total funding allocated to machine learning globally during the onset of 2019 was almost $28.4 billion.
Data Science: Demand is increasing day by day, according to reports by great learning approx. 1.5 lakh job openings will be published in the year 2020 and 71% of jobs post in this area only belongs to data scientists having experience less than 5 years.
Examples
Artificial Intelligence: Google’s AI-powered prediction, spams, plagiarism, Robo-readers Machine Learning: Virtual personal assistants, Video Surveillance, E-mail Spam and malware detection
Data Science: Oncora medical, Targeted advertisement and Website recommendations
Artificial Intelligence, machine learning, and data science play a vital role in every aspect of technologies these days. We have briefly learned Artificial Intelligence vs Machine Learning vs Data Science. We also learned the features which differentiate these from one another. The race for investigation of AI, data science and machine learning is still exploring to another level and will continue to explore better and innovative.
