Let\u2019s dive in.<\/p>\n
Quick Takeaways<\/h2>\n\n- The release of OpenAI\u2019s GPT tools has powered huge growth in AI-generated content marketing.<\/li>\n
- Google considers AI-generated content to be in violation of its Webmaster Guidelines and thus categorizes it as spam.<\/li>\n
- AI-generated content tools can generate content that sounds human, but they cannot capture unique brand personalities or write with empathy for the customer.<\/li>\n
- GPT and similar tools are powerful for enhancing content creation and strategies, but are not suitable to replace human writers.<\/li>\n<\/ul>\n
AI-generated content: definition and brief history<\/h2>\nWhat is AI-generated content?<\/h3>\n
As its name suggests, AI-generated content is content created by AI-powered tools. Founded in natural language generation (NLG) technology, AI-generated content mimics human writing with its ability to scan the internet for data (existing content) and summarize it the way a human writer would.<\/p>\n
For a long time, we heard about the pending emergence of AI-generated content marketing<\/a>, but it was slow to take. OpenAI\u2019s release of its Generative Pre-trained Transformer (GPT) tool in 2018 changed that. With only a small amount of input, the GPT tool can generate large volumes of sophisticated AI-generated text.<\/p>\nEvery version of the GPT tool brings new capabilities and higher levels of intelligence. The biggest jump occurred with the release of GPT-3, when the tool achieved its most significant jump in skill and accuracy.<\/p>\n
![\"The](\"https:\/\/marketinginsidergroup.com\/wp-content\/uploads\/2022\/07\/GPT-3-Parameter-Size-1-1.png\")
<\/p>\n
Image Source: SEMRush<\/em><\/p>\nThe evolution of OpenAI\u2019s GPT tools<\/h3>\n
GPT-1 proved that AI-generated content could be created with zero-shot performance \u2014 in other words, the tool could classify data it hadn\u2019t seen before in training models, then use it to carry out tasks. GPT-2 expanded on this with a larger dataset and more parameters to make its language processing capabilities (like translation and summarization) even stronger.<\/p>\n
What made the GPT-3 model so much more sophisticated than those that preceded it is its massive size \u2014 with more than 175 billion parameters<\/a>, it was 100X larger than GPT-2. This gave it an unprecedented ability to generate unique content that reads like a human wrote it.<\/p>\nHere\u2019s a visual of how that number compares to other NLG tools:<\/p>\n
![\"GPT-3](\"https:\/\/marketinginsidergroup.com\/wp-content\/uploads\/2022\/07\/GPT-3-Parameters.png\")
<\/p>\n
Image Source: TechTarget<\/em><\/p>\nNow, to give you an idea of how much more advanced GPT-4 is expected to be, it will have 100 trillion parameters. It hasn’t been released yet, but is expected to be here some time in 2022.<\/p>\n
So what does this all mean to us regular folks without technical knowledge of AI?<\/p>\n
Basically, each GPT tool has expanded the knowledge base on which it operates \u2014 the datasets it has stored and the variables (parameters) by which it draws conclusions and \u201clearns,\u201d \u2014 and thus the sophistication level of the content it produces has gone up with each new version.<\/p>\n
The question remains then: at what point will it get to the same sophistication level as actual humans?<\/p>\n
AI-generated content: definition and brief history<\/h2>\nWhat is AI-generated content?<\/h3>\n
As its name suggests, AI-generated content is content created by AI-powered tools. Founded in natural language generation (NLG) technology, AI-generated content mimics human writing with its ability to scan the internet for data (existing content) and summarize it the way a human writer would.<\/p>\n
For a long time, we heard about the pending emergence of AI-generated content marketing<\/a>, but it was slow to take. OpenAI\u2019s release of its Generative Pre-trained Transformer (GPT) tool in 2018 changed that. With only a small amount of input, the GPT tool can generate large volumes of sophisticated AI-generated text.<\/p>\n Every version of the GPT tool brings new capabilities and higher levels of intelligence. The biggest jump occurred with the release of GPT-3, when the tool achieved its most significant jump in skill and accuracy.<\/p>\n Image Source: SEMRush<\/em><\/p>\n GPT-1 proved that AI-generated content could be created with zero-shot performance \u2014 in other words, the tool could classify data it hadn\u2019t seen before in training models, then use it to carry out tasks. GPT-2 expanded on this with a larger dataset and more parameters to make its language processing capabilities (like translation and summarization) even stronger.<\/p>\n What made the GPT-3 model so much more sophisticated than those that preceded it is its massive size \u2014 with more than 175 billion parameters<\/a>, it was 100X larger than GPT-2. This gave it an unprecedented ability to generate unique content that reads like a human wrote it.<\/p>\n Here\u2019s a visual of how that number compares to other NLG tools:<\/p>\n Image Source: TechTarget<\/em><\/p>\n Now, to give you an idea of how much more advanced GPT-4 is expected to be, it will have 100 trillion parameters. It hasn’t been released yet, but is expected to be here some time in 2022.<\/p>\n So what does this all mean to us regular folks without technical knowledge of AI?<\/p>\n Basically, each GPT tool has expanded the knowledge base on which it operates \u2014 the datasets it has stored and the variables (parameters) by which it draws conclusions and \u201clearns,\u201d \u2014 and thus the sophistication level of the content it produces has gone up with each new version.<\/p>\n The question remains then: at what point will it get to the same sophistication level as actual humans?<\/p>\n<\/p>\nThe evolution of OpenAI\u2019s GPT tools<\/h3>\n
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