AI vs. Machine Learning correlation: Simple Terms Explained for Beginners

Photo source: pexels.com

In this article, we'll know about the correlation and the comparable features of machine learning and artificial intelligence. And also explore how artificial intelligence compliments machine learning. Now, the fundamental objective of artificial intelligence is to build intelligent machines that are capable of reacting like humans. And artificial intelligence builds such intelligent machines by providing knowledge, storage, and represent data for interrogation and investigation to manage information like the human brain. Artificial intelligence provides the capabilities of building applications that are capable of recognizing speech using natural language processing. And it also provides the capability of processing text like humans do, along with the capability of answering questions. 

Artificial intelligence also provides the capability of planning, scheduling, and automating tasks. And it finally extends the capability of building self-learning machines and algorithms to machine learning. And as such, machine learning is considered as the subset of artificial intelligence.

Comparing Machine Learning and AI:

Now, differentiating artificial intelligence with machine learning is rather complicated due to the cross-cutting boundaries of both of these streams of studies.

Similarities: Uses learning algorithms with prime objective of automation. 

Differences: Machine learning not inspired by human intelligence while AI is influenced by human intelligence. 


As per various schools of thought, both artificial intelligence and machine learning are similar. As they both use learning algorithms with the primary objective of automating tasks and reducing human intervention, or without any human intervention. Now machine learning or artificial intelligence are dissimilar from the perspective of their respective derivative inspirations. While artificial intelligence is primarily influenced by the human brain and intelligence, whereas machine learning is algorithmic in nature.


Machine Learning Lifecycle:

Machine learning has a typical lifecycle which may differ depending on the practices that are adopted by different domains and industries. When working with machine learning, the lifecycle always begins with a business problem that helps identify or define the goals or objectives. Now after we've identified or defined the goals or objectives, we have to also identify the process and data involved.

We need to identify the data and analyze the data with the identified assumptions of the processes to be applied. And after deriving the processes and analyzing the data analysis, we need to identify the problem criteria so we can select or choose the right algorithm that is to be applied. Which in turn is largely dependent on the nature of the available data and the problem categorization like classification, regression, cluster, and others.


Following which, we'll need to ensure that we're identifying and using the right metrics when calculating for data accuracy. After all these are done, the next objective is to build training sets. And test the training sets to build and validate the model. Finally, we can apply the model within applications and model accuracy. The lifecycle, along with the phases or stages involved, are perfectly illustrated in the sample figure. Where the phases or stages involved begin with the business problem. Move into analyzing and preparing the data, building the model, evaluating and refining with the collected feedbacks, and finally deploying the model.

Machine learning lifecycle


In the above image, there is a flowchart depicting the machine learning lifecycle as follows: Business problem with datasets is interconnected with analyze data through double-sided arrows. The analyze data phase is connected to prepare data, which is further connected to model, and finally to the evaluate phase. The phase deploy, is connected to the evaluate phase via gather feedback/refine. 

Now let's conclude the features and capabilities of machine learning and artificial intelligence.

Conclusion:

The primary focus of machine learning is acquisition of knowledge, or skill for the systems. While artificial intelligence goes a step beyond to apply the acquired knowledge. Machine learning focuses on increased accuracy. Whereas artificial intelligence focuses on increased chances of success to make them applicable. Machine learning is data-oriented. And it learns from the data of various tasks, with the sole objective of maximizing the machines involved with the tasks. 

While in the case of artificial intelligence, it uses simulations of the capabilities of natural intelligence to solve complex problems. So in short, while machine learning leads to knowledge acquisition, artificial intelligence, on the other hand, leads to wisdom and intelligence to apply acquired knowledge.

Comments

Post a Comment

Popular posts from this blog

Introduction to the fine tuning in Large Language Models

Image
Fine tuning in LLM In this article, we're going to delve into the techniques used to adapt a generic pre-trained model for a specific task or application. Now, pre-trained language models can do really impressive stuff just immediately off the shelf, including things like text generation, summarization, and even providing levels of code. But LLMs are not one-size-fits-all as you can imagine and maybe I want to specialize it even further after the model has been trained. I'd like to make it really good at answering questions based on certain topics such as medicine or travel or code or something. This is what the idea of fine-tuning comes in.  Methods used in Fine tuning: Fine-tuning a large language model refers to the process of taking a pre-trained language model or a model that's already been trained on a large dataset and further training it on a smaller task-specific dataset. Now this process will allow the model to adapt and specialize its capability. And if we take t...
Read more

The basic difference between machine learning and deep learning

Image
Photo source: pexels.com In this article, we'll explore the critical features of machine learning. Along with the comparable features of machine learning and deep learning. We can utilize the capabilities afforded by machine learning to resolve various categories of problems. Now, machine learning utilizes various approaches in order to resolve and provide solutions. Some of the approaches adopted by machine learning to resolve problems includes Classification, Clustering, Forecasting, Simulation, Regression, and Optimization. With the Classification approach, the application learns from the input data. And then utilizes what it has learned to classify newly identified observations. We can utilize Clustering algorithms to classify each data point into a specific group, depending on the similarity of the identified attributes. Now the objective of the forecasting algorithms provided in machine learning is to achieve accuracy of data against time. Simulation provides a view of simula...
Read more

Understanding machine learning for data analytics in simple terms

Image
Photo source: pexels.com In this article, we'll explore the core concept to machine learning and elaborate the essential features and implementation of machine learning. As the data continues to grow along with the subsequent analytical requirement, depending on the human brain to cater to the growing requirement and synthesize the data, becomes all the more difficult and inconvenient. Now, machine learning is an algorithmic approach of making machines intelligent by learning from the data and derived training models by utilizing the data and generalized decisions. Machine learning is a branch of artificial intelligence which utilizes intuitive algorithms to minimize human intervention and build smart systems capable of deriving decision and solutions.  Machine learning is proven effective when working with datasets that are large, diversified, and rapidly changing, where managing such datasets with human interventions can be tedious. Machine Learning Technologies.  Here, we n...
Read more