The difference between AI, machine learning and data science?

Modern technologies like artificial intelligence, machine learning, data science have become popular but no one completely understands it. They appear to be extremely complicated to a layman. All these popular terms sound like a business executive or a student from a non-technical background. People often get confused by words like AI, ML and data science. In this blog, we clarify these technologies in basic words so you can easily distinguish them and how they are being used in business. Let us discuss Artificial Intelligence vs Machine Learning vs Data Science.

What’s Artificial Intelligence?

The main purpose for artificial intelligence is to impart human intelligence to machines. Artificial intelligence can relate to anything – from applications for playing chess to speech recognition systems. Just like the Amazon Alexa voice assistant, which recognizes speech and answers questions. Artificial intelligence focuses on making smart devices that think and act like people. These devices are trained to solve issues and learn in a superior manner than humans do.

AI application examples include:

• Game-playing algorithms (like Deep Blue)

• Robotics and control theory (motion planning, walking a robot)

• Optimization (like Google Maps creating a route)

• Natural language processing

• Reinforcement learning

Best example of AI implementation is self-driving cars and robots. What’s more, here’s the manner by which Amazon utilizes brilliant robots. Amazon Prime used to be fueled by individuals whose occupations rotated around getting items from distribution centers to clients’ doorsteps. Artificial intelligence specialists work with AI frameworks like Pytorch and Torch, TensorFlow, Caffe, Chainer, and lots of others.

What is Machine Learning?

Machine learning is one of the areas of artificial intelligence. It’s the science of getting computers to learn and also act like people do and improve their learning after some time in an autonomous fashion. Rather than writing code, you feed information to the generic algorithm, and it builds its logic based on that information. Basically, in ML, computers learn to program themselves. ML makes programming more scalable and helps us to deliver better results in a shorter time.

How companies use machine learning?

Netflix takes benefit of predictive analytics to improve recommendations to site visitors. That is the means by which the platform involves them in more active use of their service. Using machine learning algorithms all suggestions are given to site visitors. These suggestions analyze users’ preferences and ‘understand’ which films they like most. ML experts are responsible for applying the logical technique to business scenarios, cleaning, and preparing data for statistical and ML modeling. They work with analytical algorithms to build models that better explain data relationships, predict scenarios, and translate data insights into business value.

Specialists who work with ML should have:

• Hands-on experience with MALLET

• Knowledge of Apache Tomcat/Open Source

• Experience with C++, Python

• Experience with GraphLab Create, scikit-learn, scipy, NetworkX, Spacy, NLTK

What is Data Science?

The main focus of data science is getting new results from data. It is  based on strict analytical evidence and works with structured and unstructured data. Everything connected with data selecting, preparation, and analysis relates with data science. Data science allows you to find the significance and required data from huge volumes of data. As there are huge amounts of raw data stored in data warehouses, there’s a large scope to learn by processing it.

Uses of Data science-

• Strategic optimization (improving marketing campaigns, business processes)

• Predicted analytics (prediction of demand and events)

• Recommendation systems (like those of Amazon, Netflix)

• Automatic decision-making systems (like face recognition or drones)

• Social research (processing of questionnaires)

Netflix uses its data mines to search for viewing patterns. This helps you to understand users’ interests better and make decisions on what Netflix series they should make next.

Who’s answerable for DS usage?

Data scientists who can well understand data insights and sees the figures.

Data scientists should be capable of:

• Understanding of SAS and other analysis tools

• Skills in programming (R, Python, SQL, RapidMiner)

• Ability to process data

• Skills in statistical analysis

DS specialists should be expert in domains like simulations and quality control, computational finance, industrial engineering, and number theory.

Artificial Intelligence vs Machine Learning vs Data Science-

Artificial intelligence is an extremely wide term with applications ranging from robotics to text analysis. It is a technology under evolution and there are arguments of whether we should be aiming for high level AI or not. Machine learning is a subset of AI that focuses on a less activities. It is the only real artificial intelligence with some applications in real world problems.

Data science isn’t actually a subset of Machine learning however it uses ML to analyze data and make predictions about future. It combines Machine learning with different controls like big data analytics and cloud computing. Data science is an application of machine learning with a focus on solving real-world problems.

Relation Between Data Science and Machine Learning-

Machine learning and statistics are parts of data science. So there’s a lot of relations between data science and machine learning. The ML algorithms train on data delivered by data science to become smarter and more informed in giving back business predictions. In this manner, ML algorithms rely upon the data; they won’t learn without using it as a training set. In DS, data could possibly come from a machine or mechanical process.

The main difference is that data science covers the whole spectrum of data processing. It’s not limited to the algorithmic or statistical aspects. Fields that data science covers are- data integration, distributed architecture, data visualization, data engineering, deployment in production mode, data-driven decisions. So while ML specialists are busy with building useful algorithms all through the project lifecycle, data scientists must be more flexible switching between different data roles as per the project requirements.

Also know- Data Science vs Machine Learning : Know the difference.

Artificial Intelligence vs Data Science-

Data science is more of a tech field of data management. It uses AI to interpret data, recognize patterns in the current, and make forecasts. For this situation, AI and ML help data scientists to gather data about their competitors as insights.

Data science includes analysis, visualization, and prediction. It uses different statistical techniques. While AI executes models to predict future events and uses algorithms. With the help of data science, we create models that use statistical insights. Artificial intelligence works with models that make machines act like people.

Artificial Intelligence vs Machine Learning?

Basic difference between AI and ML:

• Artificial intelligence means that the computer, somehow copies human behavior.

• Machine learning is a subset of AI which consists of methods that allow computers to make conclusions from data and provide them to AI applications.

AI is a wide scientific field that works on automating business processes and making machines work like humans. You can better understand the difference between Machine learning and AI through their use cases. Today, AI is mostly associated with Human- AI interaction gadgets like Google Home, Siri, and Alexa. While we consider video and audio prediction systems like Netflix, Amazon, Spotify, and YouTube to be ML-powered. Regardless of the difference between ML and AI, they can work together to automate customer services and vehicles (such as self-driving cars). These technologies help companies to make huge cost savings by avoiding human workers from these tasks and allowing them to move to more urgent ones.

Final words-

We can’t simply pick one of the technologies like data science and ML. Data science and machine learning are connected: machines can’t learn without data, and data science is better done with ML. Also we can’t use ML for self-learning or adaptive systems skipping AI. Artificial intelligence makes devices that show human-like intelligence, ML – allows algorithms to learn from data.