Penetration of AI in Content Creation

Content creation has been a source of entertainment, fruitful knowledge and also serves a medium of contribution to society in terms of spreading awareness.The industry of content creation has not started in recent times but has been there since the very starting, from spreading information as a word of mouth to television and now social media.

After the penetration of the internet around the globe the power of visual content has increased exponentially . The consumption of visual content from social media platforms like facebook ,instagram , youtube etc such that there are over 200 million content creators in the world today.

The phrase "creator economy" refers to the emerging trend of content producers, curators, and community builders generating income from their online works. Everything from podcasts and YouTube videos to online courses created and sold, blogging, social media influencer marketing, and blogging can be included. Other companies that assist the producers are also included, including analytics systems, video hosting services, and advertising agencies. With visual material, such as photographs, movies, and GIFs, you may inform your audience, create relationships with them on an emotional level, and expand your brand through visual content marketing. Contrary to common opinion, distributing attractive images and infographics is not the main purpose of visual content marketing.

It involves comprehending how the brain interprets visuals and using that knowledge to strengthen your content marketing efforts.

A few figures on content creation

• The average time it takes a content producer to make their first dollar is six and a half months.
• Only 10% of influencers have an annual income of $100K or more.
• In 2022, it is anticipated that the market for influencers would grow to $16.4 billion.
• In 2020 alone, European corporations spent €1.3 billion on creative marketing.
• 85% of full-time content producers claim to appreciate what they do

Size of the Maker Economy Market and Data

• The market for digital video content is anticipated to reach $204 billion USD by 2022.
• 12% of full-time creators make over $50,000 annually.
• 9% of niche content creators make over $100k annually.
• 68% of creators identify as part-time creators.
• On Instagram, short-form video is the most interesting content.
• By 2025, the market for online education is anticipated to reach $305 billion.
• 17% of users pay a creator's membership website a visit.
• 67% of consumers discovered a new product from creator videos.
• Throughout 2021, YouTube paid creators more than $15 billion.
• Ad revenue fell by 31% for creators during the pandemic.
• More than 21K YouTube producers now have over 1 million subscribers
• Enrolments in online learning courses have increased by 425%

Media Industry now vs then

The Internet has given a completely new democratic dimension to communication, in addition to speeding up and expanding the amount of information at our fingertips. It may take many years of unsuccessful work to publishedAt a physical book, yet all it takes is the click of a button to published online content. Everyone can contribute ideas to the web because of social media, including blogs, social networking sites, wikis, and video-sharing websites. Social media provides several benefits, including the immediate dissemination of news, the capacity to engage with individuals around the world, and a number of various perspectives on a single event. Mass-media executives believe newspapers will adapt to the times despite long-standing predictions from some industry analysts that the Internet will make print media useless. Newspaper experts will need to rethink their content delivery strategies in the age of the Internet, much as the radio business had to do when TV began to gain popularity.

Artificial Intelligence in content creation

The development of content is already being significantly impacted by artificial intelligence, and this trend is only expected to continue. Following are some examples of how AI is currently applied to the production of content.  Many Automatic content creation: Tools that use AI to fuel content creation can automatically produce blog posts, product descriptions, social media updates, and other sorts of material. Softwares like Dalle 2 and Stable Diffusion are flushing the content creation platform by creating amazing content with just some basic inputs and a few clicks.

AI can assist content producers in the curation of content from a variety of sources. AI-enabled systems can recognise and extract pertinent facts from vast volumes of data, including blog postings, news articles, and social media posts.Automatic production of photos and movies is possible with Runaway ML.

Stability AI

A text-to-image conversion model called Stability AI enables billions of users to instantly create outstanding masterpieces.

To represent the model with text descriptions, this model employs a rigid CLIP ViT-L/14 text encoder. Stable Diffusion weighs only 860M for UNet and 123M for the text encoder and requires a GPU with at least 10GB of VRAM.

While Stable Diffusion, a sort of diffusion model (DM) that was originally introduced in 2015, is trained with the intention of removing successive applications of Gaussian noise to training images, it may be thought of as a sequence of denoising autoencoders.

Stable diffusion allows for the creation of detailed images based on written descriptions. Additionally, it may be used to make image-to-image translations guided by text cues and do other tasks like inpainting, outpainting, and both.

A forward process, also known as a diffusion process, slowly contaminates a datum (typically an image), while a reverse process, also known as a reverse diffusion process, transforms the noise back into a sample from the target distribution.The forward process's sample chain transitions can be updatedAt to conditional Gaussians when the noise level is low enough.

Another OpenAI model named CLIP learns the relationship between textual semantics and their visual representations in DALL-E 2.

In order to determine how much a given text passage connects to an image, LIP is trained on hundreds of millions of photographs and the captions that go along with them. In other words, CLIP just learns how closely connected any given caption is to a particular image rather than attempting to predict a caption given an image. CLIP can understand the relationship between textual and visual representations of the same abstract item thanks to its contrastive rather than predictive purpose.

CLIP Training

The fundamental principles of training CLIP are quite simple:

1.     First, all images and their associated captions are passed through their respective encoders, mapping all objects into an m-dimensional space.

2.     Then, the cosine similarity of each (image, text) pair is computed.

3.     The training objective is to simultaneously maximise the cosine similarity between N correct encoded image/caption pairs and minimise the cosine similarity between N2 - N incorrect encoded image/caption pairs. 

GLIDE

By adding more textual data to the training process and generating text-conditional images as a result, GLIDE expands the fundamental idea of Diffusion Models.

GLIDE is crucial to DALL-E 2 because by conditioning on image encodings in the representation space rather than text, it made it simple for the authors to transfer GLIDE's text-conditional photorealistic image generation capabilities to DALL-E 2. In light of CLIP image encodings, DALL-E 2's updatedAt GLIDE develops the ability to produce semantically consistent images. Another crucial point is that since the reverse-Diffusion process is stochastic, it is simple to produce variations by repeatedly passing the identical image encoding vectors through the updatedAt GLIDE model.

Conclusion

AI is capable of analysing user interaction and behaviour data to optimise content for optimum impact. AI-powered solutions can offer insights into how to improve content to better engage consumers by examining data like click-through rates, time spent on page, and bounce rates.We may anticipate seeing even more complex uses of AI in content creation as the technology develops. It's crucial to remember that while AI can automate some aspects of content creation, it can't take the place of human content creators' originality and nuance. After all, uniqueness and originality is what we call the “X Factor “ in this industry. AI needs to be viewed as a tool to support and supplement the work of human content producers and not as a replacement.