Exploring The Differences Between Artificial Intelligence And Machine Learning

Welcome to this blog post exploring the differences between Artificial Intelligence (AI) and Machine Learning (ML). Here, we will go into detail about what AI and ML are, how they differ from each other, where they are applied, and ultimately how these two concepts connect in modern technology. By the end of this post, you will have a better understanding of AI and ML, their applications in the real world, and how the two intersect. Let’s get started!

Introduction

The digital world has ushered in a new era of technology with Artificial Intelligence (AI) and Machine Learning (ML). These two terms are often used interchangeably, but they have distinct differences that make them unique. In this blog post, we'll explore the differences between AI and ML, their use cases, and their potential applications in our everyday lives.

What Is Artificial Intelligence?

Artificial Intelligence (AI) is a broad field of computer science focused on creating intelligent machines capable of performing tasks that would normally require human intelligence. AI has been around since the 1950s and while it has made great progress in the past few decades, its ultimate goal is to create technology that can think, reason, and act independently just like humans. AI-based systems are able to learn from their experiences, analyze data and make decisions on their own without any human intervention. The ability to learn and make decisions gives machines an advantage over manual processes allowing for faster problem-solving, more efficient decision-making, and predictive insights into scenarios where humans might not be able to identify potential problems.

Please read also What is AI content writing?

What Is Machine Learning?

Machine Learning is a branch of Artificial Intelligence that uses algorithms to find patterns in data and use them to make predictions or decisions. These algorithms are trained with large datasets and allow machines to automate tasks that were previously impossible. The main focus of Machine Learning is on creating algorithms that can learn from data and improve themselves over time, enabling machines to become better at solving complex problems.

Differences Between Ai And Ml

Artificial Intelligence (AI) and Machine Learning (ML) are two growing fields in technology, but there are differences between the two.

AI is a broad set of techniques used to create intelligent behavior from machines such as algorithms, neural networks, and robotics.

ML takes this one step further by using data-driven techniques to identify patterns and make decisions based on these insights.

AI typically requires explicit programming, while ML utilizes datasets to measure progress toward goals.

While AI can provide a wide variety of benefits when applied correctly, ML can offer even greater potential by adapting automatically over time with more data input into the system. Ultimately, learning AI and ML processes together can create powerful new technologies that drive innovation across many industries today.

We will keep the boring formal definitions of AI/ML for last. Let's start with an interesting story a person named Deep Blue (introduced by IBM) who became famous in 1997 when he defeated the Chess champion - Gary Kasparov. For each 3-min move, Deep Blue could analyze 50 billion positions & take a decision based on pre-programmed software rules. This was an example of AI without ML. Two decades passed by and the spotlight shifted to Seoul in 2016 where an even more interesting character named AlphaGo created by Google defeated the Go world champion - Lee Sedol. This was an example of AI with ML. No rule was pre-programmed into Alphago! Not even AlphaGo's development team would be able to pinpoint exactly what set of final rules are used by AlphaGo to make its moves and why!

AI without ML - Humans provide the rules to the machines

AI with ML - Humans provide only the data. The machines learn the rules themselves.

You may have seen nice circular graphs where ML is shown to be a subset of AI and DL is shown to be a subset of ML. Well, the fact is in recent times, ML is hijacking nearly all AI space and there is very little non-ML AI advancement happening. So you can say that most of the AI systems today run using ML.

AI began in 1953 when Claude Shanon at Bell labs hired two assistants named Marvin Minsky and John McCarthy setting in motion a chain of events that was to have wide-ranging implications for humankind. They had a common interest in a quaint scientific field of those times called 'thinking machines'. Turing had a couple of years back proposed his now-famous Turing test - a computer can be said to be intelligent if a human judge can't tell whether he is interacting with a human or a machine and it was a hot subject in those days. Anyway in 1958, the three of them came up with an interesting proposal requesting a break from regular work for 8 weeks and for funding of a 'series of brain-storming sessions' to discuss this new field which they formally titled 'Artificial Intelligence'. While I am sure that in the modern day this kind of proposal would raise eyebrows, it did get approved and the rest is history!

While John McCarthy gave the general definition of AI as “the science & engineering of making intelligent machines or machines that think the way humans think”, Arthur Samuel in 1959 defined Machine Learning (ML) as - “a field of study that gives computers the ability to learn without being explicitly programmed”.

In those days and for several decades afterward, ML was one of the (several) techniques by which AI (“making intelligent machines”) could be achieved. Starting in the late 90’s the face of AI changed as never before! The Internet era threw in an abundance of DATA. This fueled up ML like never before because as we discussed in ML systems what happens is - Humans provide only the data. The machines learn the rules themselves. They don't need explicit programming.

So most of the AI systems today are based on ML. As to what DL is, it is a subset of ML inspired by biological systems like the human brain which use multiple layers to progressively extract higher-level features from the raw input. For e.g. when we see something, data is passed from our eyes to the brain to be interpreted. The brain identifies the object thru’ several layers of processing..at first, it will identify the edges and corners, while subsequent layers extract higher-level features and finally we see whole features like digits or letters, or faces. The adjective "deep" in deep learning comes from the use of multiple layers in the network.

Applications Of Ai And Ml

Artificial Intelligence (AI) and Machine Learning (ML) are two important and related fields of study that continue to grow in relevance in the modern world.

Both offer solutions to complex problems that can benefit businesses, create smarter consumer experiences, and improve everyday life. AI is the broader concept and encompasses ML—which focuses on replicating human intelligence through machines and data analysis.

While both AI and ML have numerous applications, some of their most prominent uses include natural language processing, computer vision applications such as facial recognition, self-driving cars, virtual assistants, text analysis for sentiment analysis or predictive analytics, autonomous robots, drug discovery for healthcare purposes, and more. As understanding of the technology deepens and taking advantage of its potential continues to become easier with each passing day, so do the opportunities for using AI & ML in our lives increase.

Conclusion

In conclusion, Artificial Intelligence and Machine Learning are two distinct but related fields of technology. Artificial Intelligence is an umbrella term that concerns the ability of computers to understand and interact with their environment autonomously, while Machine Learning is a subset of AI that focuses on teaching machines to make decisions based on patterns found in data sets. Understanding the differences between these two concepts can help businesses take full advantage of the potential benefits each technology has to offer.