Artificial Intelligence VS Machine Learning VS Data Science

 An artificial intelligence platform is one of the primary technology solutions required by digital marketers to make automated decisions based on data collection, analysis and other observations that may impact marketing trends.

What is Artificial Intelligence?

In simple terms, artificial intelligence (AI) refers to the science and intelligence of having systems and machines that mimic human intelligence that performs activities and improve themselves as per the information they collect. AI technology is not intended to replace humans, rather it enhances human capabilities and contributions.

Machine learning is a subset of artificial intelligence. People often ask, “Is deep learning artificial intelligence?” Well, it is a field of AI that allows automatic learning through the absorption of unstructured data like video, text or images.

One of the leading AI textbooks is ‘Artificial Intelligence: A Modern Approach’ by Stuart Russell and Peter Norvig. The 3 top-rated artificial intelligence movies are The Matrix (1999), Ex Machina (2015) and Blade Runner (1982).

As per the current classification, there are four types of AI: Theory of mind, reactive, self-aware and limited memory.

• Theory of mind AI – Machines can understand and remember emotions and behave based on those emotions as they interact with people.
• Reactive AI – It is the most basic type which provides a predictable output based on the received input.
• Self-aware AI – Artificial intelligence with the most advanced feature. Here the machines have consciousness and intelligence similar to that of humans. They are self-aware of their internal emotions and needs.
• Limited memory AI – It uses historical and observational information to make predictions and or perform actions.

How Does Artificial Intelligence Work in Marketing?

An artificial intelligence platform is one of the primary technology solutions required by digital marketers to make automated decisions based on data collection, analysis and other observations that may impact marketing trends. Artificial intelligence marketing solution helps augment marketing teams and perform tasks that require less human nuance.

Benefits of AI in Marketing

Artificial intelligence marketing tools jot out huge chunks of information from social media platforms, the web, email, etc. and help in bridging the gap between data and actionable solutions to enhance marketing campaigns.

• Automates decisions – Artificial intelligence marketing automation technology shows the best strategy for email marketing campaigns and customer reactions.
• Time and cost saving – An AI tool may require an initial investment but it will save your cost of hiring a full-fledged team for some work and will also deliver results much faster.
• Real-time personalized experience – Artificial intelligence provides a personalized customer experience with the help of predictive analysis of customers’ behaviour and buying habits.
• Improves ROI – AI marketing tools maximize the produced information and minimize the wastage of money on other campaigns.  

What is Machine Learning?

Artificial intelligence and machine learning engineering are interrelated to each other. Machine learning is a branch of AI that focuses to adapt new data and algorithms without human intervention. This concept is popularly used in various sectors. It derives the required insightful information from large data volumes by leveraging algorithms to identify patterns.   

People often wonder about the difference between artificial intelligence and machine learning. Both concepts are closely related to each other, but they are not the same. Machine Learning is a subset of AI. For example, if an ‘intelligent’ computer uses AI to think like a human, then machine learning depicts how it develops its intelligence.  

Deep Learning VS Machine Learning

Deep learning and machine learning are often used interchangeably. Although both are sub-fields of AI, however, deep-learning is a sub-field of machine learning. Deep learning is often referred to as ‘scalable machine learning’, which eliminates human intervention and enables the use of large data sets through automation.

On the other hand, machine learning, also known as classical or ‘non-deep’ learning is dependent on human intervention to learn. A machine learning engineer determines the set of features to identify the differences between data inputs for structured data to learn.

Machine Learning Models

With the constant rise of ubiquitous computing, big data and IoT, machine learning in artificial intelligence holds a primary position in solving problems through its various models, which are –

• Supervised Learning – It is the simplest machine learning model. It predicts outcomes accurately by training algorithms to use labelled datasets.
• Unsupervised Learning – Opposite to supervised learning, unsupervised learning trains algorithms to use unlabeled datasets to predict outcomes.
• Reinforcement Learning – The behaviour of this model is similar to human learning. It is a feedback-based learning model that takes feedback signals by interacting with the environment.

What is Data Science?

Data science is an interdisciplinary field that uses domain expertise, algorithms, programming skills and processes to examine large amounts of data and extract meaningful insights. Data science and machine learning are interrelated as data science experts apply machine learning algorithms to numbers, text, audio, video, etc. to produce AI systems.

Irrespective of the niche or size of the industry, every company realizes the importance of the data science industry, AI and machine learning. If you look towards data science as a concept used for all, then you are right, as it helps every business in increasing operational efficiency, identifying new opportunities, improving marketing and sales programs, etc. 

Python is one of the most popular programming languages, Python for data analysis is the best way out for data manipulation tasks and for building data-centric applications.     

Difference between Data Science and Business Analytics

Data science and analytics both involve data extraction, modelling and insight gathering. The difference between the two is that business analytics mostly uses structured data, while data science uses both structured and unstructured data. Also, business analytics involves the usage of data for strategic business decisions and data analytics is the process of gathering data, manipulating it and extracting useful information from it.

Unlike business analytics, data science for business industries studies every trend and pattern to gather the most relevant information with the help of coding. Some of the big data analyst industries are machine learning, e-commerce, manufacturing and finance.