Summarize the key points of this article on the history of artificial intelligence: 

The history of artificial intelligence (AI) spans decades, with roots in ancient philosophical inquiries about the nature of intelligence and the possibility of creating thinking machines. Early work in AI focused on developing algorithms for problem-solving and decision-making, using techniques like game playing and theorem proving. The 1950s saw the emergence of the first AI programs, such as the Logic Theorist and the General Problem Solver. The 1960s witnessed significant advances in natural language processing and computer vision. However, the field faced challenges in addressing limitations in computational power and data availability. The 1980s saw the rise of expert systems, which used knowledge representation and reasoning to solve specific problems. The 1990s brought about the development of machine learning algorithms, enabling computers to learn from data without explicit programming. This led to significant progress in areas like speech recognition and image classification. The 21st century has witnessed a resurgence of interest in AI, driven by advancements in computing power, data availability, and algorithmic development. This has resulted in the development of sophisticated AI systems capable of performing tasks that were once considered the exclusive domain of humans, such as driving cars, playing games, and generating creative content.
Summarize the key points of this article on the history of artificial intelligence: 

The history of artificial intelligence (AI) spans decades, with roots in ancient philosophical inquiries about the nature of intelligence and the possibility of creating thinking machines. Early work in AI focused on developing algorithms for problem-solving and decision-making, using techniques like game playing and theorem proving. The 1950s saw the emergence of the first AI programs, such as the Logic Theorist and the General Problem Solver. The 1960s witnessed significant advances in natural language processing and computer vision. However, the field faced challenges in addressing limitations in computational power and data availability. The 1980s saw the rise of expert systems, which used knowledge representation and reasoning to solve specific problems. The 1990s brought about the development of machine learning algorithms, enabling computers to learn from data without explicit programming. This led to significant progress in areas like speech recognition and image classification. The 21st century has witnessed a resurgence of interest in AI, driven by advancements in computing power, data availability, and algorithmic development. This has resulted in the development of sophisticated AI systems capable of performing tasks that were once considered the exclusive domain of humans, such as driving cars, playing games, and generating creative content.
user
The history of AI traces back to early inquiries about creating thinking machines. Early work focused on algorithms for problem-solving and decision-making. The field has evolved through key periods, including the development of the first AI programs in the 1950s, advances in natural language processing and computer vision in the 1960s, the rise of expert systems in the 1980s, and the emergence of machine learning algorithms in the 1990s. The 21st century has seen a resurgence of interest in AI, leading to advanced systems capable of performing complex tasks.
assistant
Generate a series of (instruction, response) pairs that are similar in context and structure to the example provided below. Each pair should consist of a concise instruction followed by an appropriate, detailed response. The instruction should pose a clear task or question, while the response should provide a comprehensive answer or solution that could be understood by someone with a basic understanding of the subject.

Example pair: 
Instruction: Please read the below paragraph and provide a summary

12. Increase engagement with calls to action

Sometimes simply asking for engagement is the best way to get your videos noticed, especially when you consider the immediacy and connection that video content can create.

Not everyone who enjoys one of your videos is going to remember to drop a like or become your subscriber, so it’s common to put these reminders in the description or in the video itself.

Especially if you’re an up-and-coming channel, there’s no shame in asking for some love directly. In fact, asking visitors a question to answer in the comments suggestion or to check out another video are perfect ways to keep the engagement rolling. You can likewise link to other videos or drop an external link to your site to serve as a sort of CTA.
Response: Of course. Here is a summary:

A call-to-action (CTA) reminds viewers to like and subscribe at the end of a video or in the description. You can also include external links as part of your CTA.

Your task is to generate more pairs that maintain this level of clarity and detail. The topic is Summarization. Write a long text of instruction by yourself, then summarize the given instruction in a response. Ensure that the responses are informative and accurate, suitable for an educational context. 

Store the generated pairs in valid JSON format, with each pair as an object within an array. Each object should have two key-value pairs: "instruction" and "response". For instance: 

{"contents": [{"instruction": "text", "response": "text"}, {"instruction": "text", "response": "text"}, ...]} 

Remember to maintain consistency in the format and ensure the generated pairs are diverse and cover a broad range of subjects. You must return the response in the asked format and you must not add any additional text in your response.
