Introduction

Welcome! In this module, you will get acquainted with the history of artificial intelligence in the 2000s. In the first part, you can read a short story about the “new generation AI” and then you get some explanations about the name of this period.

Those who complete this module will be able to:

• understand the reasons why we call this age new generation AI,
• get to know why the projects were not performed as expected.

During the lesson you must read the written explanations and follow the given instructions at interactive elements. You can assess your understanding by taking the quizzes. In the second part of the module, you will need your smartphone, as you have to download and recognize songs with the help of an application called Shazam.

Why is the period 2000s called the new generation AI?

In this module, we familiarize ourselves with the fourth stage of AI development. 

The 2000s are commonly referred to as the “modern AI” or “new generation AI” era in the field of artificial intelligence (AI). This period was characterized by significant developments and breakthroughs that contributed to the widespread adoption and application of AI across various industries and in our daily lives. It brought substantial advancements in machine learning, deep learning, big data analysis, and other key areas, fundamentally transforming the field of AI.

The 2000s brought numerous significant developments and breakthroughs in the field of artificial intelligence (AI). Some key developments and events were:

Spread of Machine Learning and Deep Learning: In the 2000s, significant advancements occurred not only in machine learning techniques such as support vector machines and decision trees but also in the field of deep learning. The widespread adoption of deep neural networks and convolutional neural networks (CNN) was a crucial milestone in the 2000s.

Big Data and Data Analysis: In the 2000s, technologies for handling and analysing large datasets, such as Hadoop and data mining techniques, advanced. These developments facilitated more effective exploitation of data and informed business decision-making.

Development of Robotics: In the 2000s, significant advancements occurred in the field of robotics, enabling the widespread application of smart and interactive robots, such as service robots and industrial robots.

Semantic Web: In the 2000s, the development of the semantic web enabled the storage and analysis of data in a more structured and interpretable form, which was crucial for the automatic processing and understanding of content.

Advancements in Natural Language Processing and Speech Recognition: In the 2000s, significant developments occurred in the fields of natural language processing and speech recognition, allowing for the automatic processing and interpretation of human language by computers.

These developments and breakthroughs contributed to the rapid progress of the field of artificial intelligence in the 2000s, laying the foundation for the technologies and principles that form the basis of current AI applications.

Click on the link and watch the video!

What were the major ‘breakthroughs’ in the development of AI during the 2000s?

In the next section, you can read about the ‘breakthroughs’ and explore them in detail.

Spread of Machine Learning and Deep Learning: In 2012, Google introduced deep learning-based neural networks, which were utilized, for example, in Google Image Search to identify objects and entities in images more efficiently and accurately.

Availability of Big Datasets: For example, Hadoop, emerging in 2005, is an open-source framework for distributed processing of large datasets. Widely used by enterprises and organizations such as Yahoo and Facebook, Hadoop enables efficient management and analysis of large datasets.

Advancements in Robotics: For example, the Roomba robot vacuum, introduced by iRobot in 2002, was one of the first popular home robotics applications. Roomba autonomously cleans households and has the ability to sense and avoid obstacles.

Development of the Semantic Web: For example, the Resource Description Framework (RDF), developed in the 2000s, is a framework for the development of the semantic web. RDF allows the description of structured data that is easily interpretable by both computers and humans.

Progress in Natural Language Processing and Speech Recognition: In the 2000s, speech recognition systems developed by SpeechWorks International enabled broader applications of speech recognition in business and consumer applications. The technology developed by SpeechWorks was successfully applied in speech-based interactions by various clients, including banks and call centers.

You can learn more about the Natural Language Processing and Speech Recognition from the following videos. Click on the links!

Let’s check your knowledge! Click on the link and take the quiz!

https://view.genial.ly/65321f83553a840011acf023/interactive-content-the-fourth-age-of-ai-quiz

https://www.youtube.com/watch?v=uSNUmJffK4chttps:/

/www.youtube.com/watch?v=CMrHM8a3hqw/

https://view.genial.ly/65321f83553a840011acf023/interactive-content-the-fourth-age-of-ai-quiz

Creative task

In this task, you will learn to use a useful application. This app is Shazam, which is used for recognizing songs after listening them.

All the information you need to complete the task can be found in the video below. Click on the link and watch the video now!

https://www.youtube.com/watch?v=yV0ZEzwvk48

Your task is to install the Shazam app, turn on the radio and identify the currently playing song with the help of it. It is possible that Shazam may not be able to recognize every song, especially music from smaller, local bands, but due to the continuous improvement of AI, the success rate of recognitions increasing.

Use your phone to recognize several songs!

Learning outcomes: 

In this module, we have acquainted with the fourth chapter of the history of artificial intelligence development, along with its key characteristics. During this era, explosive progress was initiated, laying the foundational prerequisites for today’s applied technologies.

Main takeaways:

• In the 2000s, the development of artificial intelligence (AI) showed remarkable growth. 
• The reasons behind this included:
  • the increase in computational power, 
  • the availability of large datasets, 
  • the advancement of algorithms, 
  • the growing interest in open-source communities and larger investments. 

This development facilitated the emergence of newer and more efficient AI technologies, such as deep learning and convolutional neural networks.

Brief introduction of the next block: 

In the following module, we will learn about the last decade, history of AI since 2013.