Ai VS machine learning: What are the biggest differences?

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Artificial intelligence and machine learning are hot terms that have caused a number of stir in the tech world. The two terms are often used interchangeably but are not quite the same. Ai and machine learning are very different in terms of methods, algorithms and logical thinking.

Let’s take a look at the performance and development space of AI and machine learning in the future global market through data. According to The Motley Fool, The market for ARTIFICIAL intelligence will grow to $5.05 billion by 2020. This astonishing figure once again demonstrates the powerful potential of artificial intelligence technology.

According to statistics, this data has opened the eyes not only of businesses, but also of machine learning developers spreading job opportunities in further learning.

But ordinary people, business owners and even developers often confuse AI with machine learning. They also often don’t understand the potential of ARTIFICIAL intelligence and machine learning. To address these issues, let’s look at the differences between AI and machine learning.

Here’s the difference between artificial intelligence and machine learning.

Before getting to the core of this article, let’s briefly outline the basic concepts of artificial intelligence and machine learning.

Artificial intelligence:

The term artificial intelligence is made up of two parts: artificial intelligence and intelligence, which, as the name implies, is the ability of artificial intelligence to think. One of the biggest misconceptions about AI is that it is just a system, but AI is not a system. It can operate inside a system, giving machines the logical ability to perform tasks.

In summary, ARTIFICIAL intelligence can be defined as the field of computer science that aims to create intelligent machines that work and react like humans.

Machine learning:

Machine learning can be defined as a branch of artificial intelligence or a specific application of artificial intelligence. In machine learning, machines have the ability to learn independently, without explicit programming.

This allows the application to adjust itself to the data in the live scenario.

With the basic concepts of ARTIFICIAL intelligence and machine learning behind you, let’s move on.

The following table shows how AI differs from machine learning:

In addition to these differences, some tools can also be used with AI and machine learning, or ai and machine learning can work better on these interconnected platforms.

1. Tensorflow:

Tensorflow is an open source software library that uses data flow diagrams to perform numerical calculations. Engineers and researchers on Google’s Brain team first noticed the tool. TensorFlow’s flexible architecture allows users to deploy computing to multiple Gpus and cpus in server/mobile/desktop/using a single API.

So if you want to integrate TensorFlow, hire a professional TensorFlow developer!

2. IBM Watson:

IBM also has a high reputation in the field of artificial intelligence, and has long been committed to the development and research of related technologies. IBM has its own in-house AI platform, including AI tools for business users and developers. IBM Watson is an open set of APT apis through which users can access a wide range of smarter toolkits and sample code, as well as generate virtual agents and cognitive search engines. IBM Watson is also a chatbot platform for beginners that requires less machine learning syntax code.

3.Torch:

The open source machine learning library has been adopted by ai research teams at major tech giants Yandex, IBM, and Facebook. Torch is also a scientific computing framework and scripting language based on the Lua programming language. After successfully running on the web, the Torch’s app has also been expanded to Android and Ios.

One thing is certain about ARTIFICIAL intelligence: the journey of human intelligence to build modern machines is endless. But it is far too far to expect systematic manipulation to completely replace human thought. Programming applications of artificial intelligence also need further development. In terms of machine learning, you can start by trying to work with small data sets that are used to filter and adopt the initial task. Machine learning as a branch of artificial intelligence, its development and application also have a long way to go.