What Is Artificial Intelligence and How Is It Related to Machine and Deep Learning?

In the constantly changing world, new technologies are gearing up for an easy and smart life. Artificial Intelligence is one such technology that has brought about a revolution in daily human life. Although there is a widespread lack of familiarity, Artificial Intelligence affects and transforms every step of human life by being deployed to various sectors. Be it finance, healthcare, transportation, or national security, Artificial Intelligence is significantly impacting the augmenting human capabilities.

What Is Artificial Intelligence and How Is It Related to Machine and Deep Learning?

You might have come across the term AI (Artificial Intelligence) a lot over the past few years along with terms like machine learning, data science, and deep learning being used interchangeably. All these technical terms signify a presence of technical support upon which human life has been built over the past few years. To make it more precise, these terms hold different meanings wherein Artificial Intelligence is a bigger umbrella tool, Machine Learning is the subset of AI, and Deep Learning is the subset of Machine Learning. Through this article, we have explained what Artificial Intelligence is and how it is related to Machine Learning and Deep Learning. Read more to learn about Artificial Intelligence and Machine Learning Certification Courses.

What is Artificial Intelligence?

Artificial Intelligence an umbrella term used to explain technologies that help machines mimic human behaviour and intelligence. It is an combination of various techniques which would eventually be used to solve real-world problems. AI deals with problem-solving by using computer science and data to enable problem-solving in machines. Machine Learning (ML) and Deep Learning (DL) are subsets of the entire AI domain. These technologies come together to make systems intelligent. Let us try to understand in greater depth, what comprises these technologies and the difference between them.

Artificial Intelligence - How is it related to DL and ML?

The problems in the artificial intelligence domain are not straightforward enough to be solved with only a single technique. One needs to leverage a host of algorithms, frameworks, and platforms to finally solve a business problem. This explains the need for multiple technologies to come together and derive business value and often solve real-world problems that are quite complex for a single person to manage. The entire domain of artificial intelligence is dynamic and you might often come across terms that eventually join streams with the bigger ocean of performing tasks like humans. Thus, ML and DL combine to power multiple AI systems and use cases. This answers the question of how is machine learning related to artificial intelligence.

Machine Learning - As a part of Artificial Intelligence

Machine Learning was originally a subfield of Artificial Intelligence. Machine learning is more or less concerned with the development of algorithms to help the computer predict models so that computers can automatically learn (predictive) models from data. Thus, Machine Learning helps to develop Artificial Intelligence, though ML is not necessarily needed for AI.

ML or Machine Learning is very close to analytics and it leverages various algorithms to solve a host of data problems. The technique is further divided into the following three major streams:

Supervised learning
Unsurprised learning
Reinforced learning

Supervised learning

Comprises use cases where the output is known. An example can be predicting house prices based on the location, size, and type.

Unsupervised learning

Comprises use cases where the output is unknown. The problems where you need to derive insights from a chunk of data are managed under this stream. An example can be discovering the type of products a target group would like based on its past purchase history.

Reinforced learning

Comprises techniques that teach systems based on a reward-punishment framework. An example can be a system used to mimic a human online gamer.

Also Read: 15+ AI Courses for Beginners available online

Deep Learning - As a part of Artificial Intelligence

Let us begin with understanding deep learning and its applications. A lot of the terms within this domain are inspired by actual human brain functioning. Words such as neural networks, pattern recognition, and analysis, are all inspired by actual human functioning. Deep Learning does have similarities in how our brain works but it is still quite different. The technology leverages the ability to train multiple layers of neural networks to train a data set. This might seem complex at first but at the very core is quite simple and intriguing. Data is used to feed a series of networks that learn to adapt patterns.

Imagine teaching a machine to identify cats. You would feed multiple images and try to train your network. The system would understand the various contours and features of a cat. The shape of its ears, whiskers, four feet, a tail and other multiple things essentially are characteristics of a cat. Eventually, after multiple iterations, your system would be smart enough to identify a cat.

Continuing with the same example, let us say you have a large data set of cats and you would like to derive multiple insights from it. Some of these insights could be the type of food each breed prefers. The climatic conditions causing problems in pet cats could be one more example or you might want to simply forecast how many cat food cans your company can sell in a particular geography in the coming months. These questions are a part of the Machine Learning (ML) domain.

Who can pursue AI Certification?

Being the host of all techniques and technologies, Artificial Intelligence is used to deal with several business problems in the fields of Finance, Marketing, HR, and Operations. These terms are “business” centric and not “IT” (Information Technology) centric. Similarly, the term “Technology” is no longer relevant to just IT professionals but to all Business Managers and industry workers.

Related: Popular Artificial Intelligence Courses and Certifications By Top Providers

Are AI certifications relevant to only IT professionals?

Here, we will get to know about how AI is not just relevant to IT Developers and Managers but also to the overall industry veterans and SMEs who want to derive meaningful business insights and solutions.

Taking an example of the manufacturing unit, a Floor Manager ensures the safety of all his workers on the shop floor. He is closely working in the manufacturing domain and ignores the host of IT solutions available to him. Now, to solve his business problem he ensures adding manual safety tags, exit routes, warning signs, alarms, and sensors are in place. He ignores all the AI solutions that are available to his disposable to augment manual installations.

When their manual installations fail, there is no plan B. The safety of these workers is at risk and solely dependent on one core line of safety. What they need is a backup or an augmented solution to be in place, which takes center stage to assist when conventional measures fail. Now, let us try to list how AI can solve this problem and act as a support system for the entire manufacturing facility.

Image recognition for workers not wearing their safety gear
Predictive models to predict equipment failure
Virtual agents/chatbots to assist in manufacturing and safety monitoring
Text analytics to scan and maintain essential safety details on manufacturing equipment

This list is suggestive and not exhaustive. Using this we realize that there are a bunch of solutions and optimisations that the manager would miss out on, just by being ignorant of AI technology. It is difficult to achieve high levels of expertise in this domain however, one must not be ignorant of the benefits AI offers. This might also open new avenues to drive value and open business verticals if carefully analyzed and contemplated.

Conclusion

Artificial Intelligence is not just a technological solution to solve use cases but can also help in creating new verticals in an organisation. Amazon Alexa, Google Analytics, Self-Driving cars, Uber Analytics, and Google Lens are all bright examples of products and solutions that opened new avenues to drive business.

To summarise the same, one must not look at AI just as a problem solver but also as a value driver. Hence, this technology should not be limited to only IT or Analytics professionals. It is relevant for all and dynamic to derive value and solve complex business problems. We hope that when this question arises on how machine learning is related to AI, then this article would have been a great help to the readers.

Frequently Asked Question (FAQs)

1. How is machine learning related to artificial intelligence?

Machine learning is a small part of artificial intelligence that automatically uses algorithms for analysing large amounts of data, learning from deep insights, and then making informed decisions.

2. Is AI machine learning and deep learning are interconnected with each other ?True or false?

This is completely false because Deep learning is a subdivision of machine learning but not the other way around. Deep learning is to be evolved directly from machine learning, a form of artificial intelligence or AI.

3. Who is known as the father of machine learning?

The father of machine learning is known by the name of Geoffrey Everest Hinton.

4. What is the link between deep learning and machine learning in the application of artificial intelligence in organisations?

Machine learning is a subset of AI that has a special focus on training machines to improve their performance on specific tasks by providing them with data and algorithms. Deep learning is a part of machine learning which makes use of neural networks which help in analysing data and learning patterns.

5. How are AI and machine learning similar?

Machine learning paves the way toward artificial intelligence. This subcategory of AI uses different kinds of algorithms that automatically help with insights and recognises patterns from data, thereby learning to make better decisions.