Artificial Intelligence And Machine Learning Differences - AIBridge ML Pvt Ltd
  • by Gopi Krishna

Machine Learning and Artificial Intelligence are two terms that are increasingly emerging when it comes to technology. Apparently similar and closely connected to each other, they are not synonyms but have characteristics that differentiate them, making them two distinct and different concepts.

So how do these technologies work? What are they used for and what do they have in common?

Let's see complete details in this content.

What is Artificial Intelligence?

Artificial Intelligence is the ability of a machine, computer, or system to imitate the cognitive functions of the human being, relying on mathematics and logic to stimulate their thinking.

It is therefore an innovative technology with which to perform numerous operations previously reserved only for the human intellect, such as learning, solving a problem, understanding language, planning events, and recognizing visual and sound stimuli.

A potentially unlimited resource with which innovative goals can be achieved in numerous sectors.

Thinking about the future, four types of Artificial Intelligence can be outlined:

  • Reactive Machines: Basic AI systems are defined as reactive and do not rely on previous experience to make decisions. They react to present triggers without relying on any type of memory and can only perform the actions for which they were programmed.
  • Machines with limited memory: They act based on the memory of past events and data already stored.
  • The theory of the mind: They are the prototype of the machines of the future, capable of understanding their surroundings and of maturing thoughts and emotions. They will be able to act consciously, taking into account external factors of any nature, such as - for example - feelings, intentions, and ethics.
  • Self-Conscious Machines: Machines will have full-fledged consciousness and will be able to represent themselves. It is the highest level of development that can be aspired to.

To date, Artificial Intelligence has already reached important milestones and includes Machine Learning, Deep Learning, and neural networks.

what is Machine Learning?

Machine Learning is an area of ​​Artificial Intelligence that includes methods and techniques that have developed over the years in the fields of mathematics, statistics, and information technology.

In the IT field, Machine Learning can be defined as a sort of variant of traditional programming with which systems learn autonomously without explicit instructions and predetermined rules in the code. It is used when it is not possible to solve problems by designing and programming explicit algorithms and to work it requires large amounts of data and reliable, easily scalable, and resource-intensive infrastructures.

Machine Learning is therefore a new way of exploiting algorithms to extract data, learn from them and extract useful information, for example, to predict trends or anticipate possible scenarios.

This technology not only learns from data but interprets them. Thanks to special algorithms with which it translates and re-processes the information acquired, performing predictive analyzes, automatically optimizing information systems, and creating new content based on what has been learned.

A powerful tool for companies that must compete today in increasingly aggressive global markets.

Machine Learning Vs Artificial Intelligence

So what are the differences between these two technologies?

First of all, it must be held in mind that Machine Learning is a subset of Artificial Intelligence: it is therefore also part of the AI ​​family but has different characteristics that have developed over time.

Machine Learning was born at a later time, to educate machines to understand data independently. It is used to solve even more complex problems and makes machines even more intelligent.

How does it manage to perform such advanced actions?To work it is based on data and the more data to draw from, the greater the level of autonomy that this technology can achieve: by acquiring new information and storing new stimulants it can learn to perform more and more operations, minimizing the need. human intervention.

In this way, machine learning becomes a better version of itself capable of performing sophisticated operations previously unimaginable for a computer.

Machine Learning can therefore be defined as the algorithm that allows machines to evolve, giving Artificial Intelligence, that is the necessary basic architecture, the right input to get started and function like a human brain.

How Machine Learning and Artificial Intelligence work together:

Machine Learning and Artificial Intelligence, together, allow machines to perform extremely sophisticated activities with a high level of autonomy that reduces human intervention to a minimum.

The sectors in which these technologies can be applied to obtain great advantages are numerous and very different from each other.

Here are some of the main examples:

  • Image recognition: The mix of the two technologies it is possible to outline the identity of a person based on a photo or a frame.
  • Speech Recognition: This is the fastest and most convenient way to interact with a computer. From this technology, for example, extended applications such as virtual assistants - Siri, Alexa, and Google Assistant - are born, which perform tasks or services for an individual based on commands or questions.
  • Automated cars: Among the most popular results of using Artificial Intelligence are autonomous driving and self-driving cars.
  • Google, for example, has already started a self-driving car service for commercial vehicles called Waymo One. A technology that, with the support of 5G, will revolutionize the way we travel in the future.
  • Consumer finance: the prospect is to have the possibility, thanks to AI, to outline an effective investment plan based on the amount available.
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