Deep Dive: How AI content generators work

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Artificial intelligence (AI) has been steadily influencing business processes, automating repetitive and mundane tasks even for complex industries like construction and medicine. 

While AI applications often work beneath the surface, AI-based content generators are front and center as businesses try to keep up with the increased demand for original content. However, creating content takes time, and producing high-quality material regularly can be difficult. For that reason, AI continues to find its way into creative business processes like content marketing to alleviate such problems. 

AI can effectively personalize content marketing to the audience it is aimed at, according to David Schubmehl, research vice president for conversational AI and intelligent knowledge discovery at IDC. 

“Using pre-existing data, AI algorithms are used to make sure that the content fits the interests and desires of the person it is being targeted to,” Schubmehl said. “Such AI can also be used to provide recommendations on what the person might be most interested in engaging with, whether it is a product, information or experience.” 


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AI can not only aid in responding to your audience’s questions but can also help connect with consumers, generate leads, build connections and, in turn, gain consumer trust. These advantages are now being made possible, in part, with the use of AI content generator tools. 

“AI-supported and AI-augmented content creation capabilities have begun to blossom over the past 18 months and are approaching an inflection point where they are transforming content creation and content-scaling,” said Rowan Curran, an analyst at Forrester.

How AI content generators work

AI content generators work by generating text through natural language processing (NLP) and natural language generation (NLG) methods. This form of content generation is beneficial in supplying enterprise data, customizing material to user behavior and delivering personalized product descriptions.

Algorithms organize and create NLG-based content. Such text generation models are generally trained through unsupervised pre-training, where a language transformer model learns and captures myriads of valuable information from massive datasets. Training on such vast amounts of data allows the language model to dynamically generate more accurate vector representations and probabilities of words, phrases, sentences and paragraphs with contextual information. 

Transformers are rapidly becoming the dominant architecture for NLG. Traditional recurrent neural network (RNN) deep learning models struggle with long-term modeling contexts due to the vanishing gradient issue. The issue occurs when vanishing gradient occurs when a deep multilayer feed-forward network or recurrent neural network cannot propagate information from the model’s output end back to the layers near the model’s input end. The outcome is a general failure of models with multiple layers to train on a given dataset or to prematurely settle for a suboptimal solution.

Transformers overcome this issue as the language model expands with data and architecture size, transformers enable parallel training and capture longer sequence features, making way for much more comprehensive and effective language models. 

Today, AI systems like GPT-3 are designed to generate text similar to human creativity and writing style that most humans cannot generally distinguish. Such AI models are also known as generative artificial intelligence, i.e., algorithms that can create novel digital media content and synthetic data for a wide range of use cases. Generative AI works by generating many variations of an object and screening results to select the ones that have helpful target features. 

AI content generation use cases

There are various ways AI is assisting enterprises in creating great content, some of which are the following:

  • Voice Assistants: With the assistance of NLG, AI content generation tools can be used to build voice assistants ready to answer our queries. Alexa and Siri are examples of how companies can use the technology in real-life applications. 
  • User-based personalization: AI is adept at targeting each client by leveraging customer data to develop customized content. This is currently being improved by obtaining data from multiple sources, such as social media platforms and smart gadgets in the home, to learn further about the customer’s requirements and desires.
  • Chatbots: Chatbots are one of the most used services in the market since they can answer most requests in a few seconds. These AI-powered bots employ a speech generator to generate pre-programmed information based on realistic human conversations. 
  • Extensive content creation: Currently, content generation is mainly confined to short to medium copy, such as newsletter subject lines, marketing copy and product descriptions. However, in the future, AI content production is expected to write lengthy chapters, if not whole novels.

Top content generation tools

The following is a list of widely used content generators — compiled with information from reviews by Search Engine Journal, G2, Marketing AI Institute and others: 

  • Writesonic: Writesonic is built on GPT-3 and claims the machine is trained on the content that the brands using the tool produce. The generator is based on facilitating marketing copy, blog articles and product descriptions. The generator can also provide content ideas and outlines and has a full suite of templates for different types of content. 
  • MarketMuse: MarketMuse assists in developing content marketing strategies by using AI and ML. The tool shows you which keywords to target to compete in specific topic areas. It also highlights themes one may need to target if you wish to own particular topics. AI-powered SEO tips and insights of this caliber can guide your whole content development team throughout the entire process.
  • Copy AI: Contains over 70 AI templates for various purposes. Its AI creates high-quality material and provides limitless usage alternatives. Copy AI offers templates for various content categories, including blogs, advertisements, sales, websites and social media. The generator can also translate into 25 different languages.
  • Frase IO: Frase builds outline briefings on various search queries using AI and ML. It also includes an AI-powered response chatbot that uses material from your website to answer user inquiries. The chatbot understands user inquiries using natural language processing (NLP) and then brings up content on your site that provides suitable replies. The outlines can help you speed up content development by automatically summarizing articles and gathering relevant statistics. One may also utilize the user questions compiled by the response bot to help you decide what to write about next.
  • Jasper AI: Jasper is an AI writing assistant that can write high-quality content, blog articles, social media posts, marketing emails and more. Jasper knows more than 25 languages, the content is built word-by-word from scratch. Jasper has been taught over 50 skills based on real-world examples and frameworks to aid writing tasks such as writing email subject lines to fictional stories.

Pros and cons of AI content generation

Businesses can establish an effective content marketing strategy using AI content generator tools. A study by Fortune Business Insights predicts that the AI-based content technology market to reach $267 billion by 2027. According to the data, organizations that use these systems receive more traffic and have more excellent conversion rates than those that do not. 

AI content technologies have shown to be far more valuable to businesses than human resources because they are far less expensive and time-consuming to invest in. AI content generation is significantly faster because computers can handle enormous volumes of data in much less time than humans can. These AI content generators can also generate infinite pieces with little input, making them ideal for enterprises that require consistent, new material.

Curran noted that the industry is just beginning to see what these tools and techniques can do in terms of content creation, but fundamentally it’s still going to be about humans being enhanced by AI. 

“Over the next few years, we’ll likely see a Cambrian explosion of different applications, use-cases and approaches for AI-supported content generation as the technology gets into the hands of a wider array of enthusiastic users,” Curran said.

However, there are also some drawbacks associated with using an AI content generator. First, setting the generator to hit the right tone for your content can be challenging. The generator may produce AI text that is not particularly well-written or appropriate, as AI sometimes lacks the judgment to give an opinion and cannot provide a definitive answer. While AI is smart, writing depends on the context and triggering the correct emotions, and humans are still superior at both. 

“AI can be a powerful tool for generating large quantities of text, but the output can sometimes lack emotion and common sense,” Schubmehl said. “This happens because an AI writer cannot read between the lines like human writers and may use words that are not necessarily what was meant by the author.” 

Schubmehl also noted that AI-based content generators (NLG programs) do not really understand the text that is being generated, as the created text is only based on a series of algorithms.

“While natural language-generated text can provide increasingly accurate summaries, there are still areas of preference such as brand voice, tone, empathy, etc. that are difficult to program into AI algorithms and will continue to require human intervention in the content creation process,” he said. “Over time, we expect that large language models, based on billions of lines of text, will use unsupervised machine learning to do a better job of creating AI-based content.”

Machine-generated content cannot be subjective, no matter how great the ML training using structured data is. Human writing reflects our richness of topic knowledge and has an expressive aspect that a machine cannot equal. 

Only a human content expert can address such gray areas. Therefore, developing an AI tool that can completely replace a person while matching human authors will take time.

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Top 3 text-to-image generators: How DALL-E 2, GLIDE and Imagen stand out

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The text-to-image generator revolution is in full swing with tools such as OpenAI’s DALL-E 2 and GLIDE, as well as Google’s Imagen, gaining massive popularity – even in beta – since each was introduced over the past year. 

These three tools are all examples of a trend in intelligence systems: Text-to-image synthesis or a generative model extended on image captions to produce novel visual scenes. 

Intelligent systems that can create images and videos have a wide range of applications, from entertainment to education, with the potential to be used as accessible solutions for those with physical disabilities. Digital graphic design tools are widely used in the creation and editing of many modern cultural and artistic works. Yet, their complexity can make them inaccessible to anyone without the necessary technical knowledge or infrastructure.

That’s why systems that can follow text-based instructions and then perform a corresponding image-editing task are game-changing when it comes to accessibility. These benefits can also be easily extended to other domains of image generation, such as gaming, animation and creating visual teaching material. 

The rise of text-to-image AI generators 

AI has advanced over the past decade because of three significant factors – the rise of big data, the emergence of powerful GPUs and the re-emergence of deep learning. Generator AI systems are helping the tech sector realize its vision of the future of ambient computing — the idea that people will one day be able to use computers intuitively without needing to be knowledgeable about particular systems or coding. 

AI text-to-image generators are now slowly transforming from generating dreamlike images to producing realistic portraits. Some even speculate that AI art will overtake human creations. Many of today’s text-to-image generation systems focus on learning to iteratively generate images based on continual linguistic input, just as a human artist can. 

This process is known as a generative neural visual, a core process for transformers, inspired by the process of gradually transforming a blank canvas into a scene. Systems trained to perform this task can leverage text-conditioned single-image generation advances.

How 3 text-to-image AI tools stand out

AI tools that mimic human-like communication and creativity have always been buzzworthy. For the past four years, big tech giants have prioritized creating tools to produce automated images. 

There have been several noteworthy releases in the past few months – a few were immediate phenomenons as soon as they were released, even though they were only available to a relatively small group for testing. 

Let’s examine the technology of three of the most talked-about text-to-image generators released recently – and what makes each of them stand out. 

OpenAI’s DALL-E 2: Diffusion creates state-of-the-art images

Released in April, DALL-E 2 is OpenAI’s newest text-to-image generator and successor to DALL-E, a generative language model that takes sentences and creates original images. 

A diffusion model is at the heart of DALL-E 2, which can instantly add and remove elements while considering shadows, reflections and textures. Current research shows that diffusion models have emerged as a promising generative modeling framework, pushing the state-of-the-art image and video generation tasks. To achieve the best results, the diffusion model in DALL-E 2 uses a guidance method for optimizing sample fidelity (for photorealism) at the price of sample diversity.

DALL-E 2 learns the relationship between images and text through “diffusion,” which begins with a pattern of random dots, gradually altering towards an image where it recognizes specific aspects of the picture. Sized at 3.5 billion parameters, DALL-E 2 is a large model but, interestingly, isn’t nearly as large as GPT-3 and is smaller than its DALL-E predecessor (which was 12 billion). Despite its size, DALL-E 2 generates resolution that is four times better than DALL-E and it’s preferred by human judges more than 70% of the time both in caption matching and photorealism. 

Image source: Open AI

The versatile model can go beyond sentence-to-image generations and using robust embeddings from CLIP, a computer vision system by OpenAI for relating text-to-image, it can create several variations of outputs for a given input, preserving semantic information and stylistic elements. Furthermore, compared to other image representation models, CLIP embeds images and text in the same latent space, allowing language-guided image manipulations.

Although conditioning image generation on CLIP embeddings improves diversity, a specific con is that it comes with certain limitations. For example, unCLIP, which generates images by inverting the CLIP image decoder, is worse at binding attributes to objects than a corresponding GLIDE model. This is because the CLIP embedding itself does not explicitly bind characteristics to objects, and it was found that the reconstructions from the decoder often mix up attributes and objects. At the higher guidance scales used to generate photorealistic images, unCLIP yields greater diversity for comparable photorealism and caption similarity.

GLIDE by OpenAI: Realistic edits to existing images

OpenAI’s Guided Language-to-Image Diffusion for Generation and Editing, also known as GLIDE, was released in December 2021. GLIDE can automatically create photorealistic pictures from natural language prompts, allowing users to create visual material through simpler iterative refinement and fine-grained management of the created images. 

This diffusion model achieves performance comparable to DALL-E, despite utilizing only one-third of the parameters (3.5 billion compared to DALL-E’s 12 billion parameters). GLIDE can also convert basic line drawings into photorealistic photos through its powerful zero-sample production and repair capabilities for complicated circumstances. In addition, GLIDE utilizes minor sampling delay and does not require CLIP reordering. 

Most notably, the model can also perform image inpainting, or making realistic edits to existing images through natural language prompts. This makes it equal in function to editors such as Adobe Photoshop, but easier to use. 

Modifications produced by the model match the style and lighting of the surrounding context, including convincing shadows and reflections. These models can potentially aid humans in creating compelling custom images with unprecedented speed and ease, while significantly reducing the production of effective disinformation or Deepfakes. To safeguard against these use cases while aiding future research, OpenAI’s team also released a smaller diffusion model and a noised CLIP model trained on filtered datasets.

Image source: Open AI

Imagen by Google: Increased understanding of text-based inputs

Announced in June, Imagen is a text-to-image generator created by Google Research’s Brain Team. It is similar to, yet different from, DALL-E 2 and GLIDE. 

Google’s Brain Team aimed to generate images with greater accuracy and fidelity by utilizing the short and descriptive sentence method. The model analyzes each sentence section as a digestible chunk of information and attempts to produce an image that is as close to that sentence as possible. 

Imagen builds on the prowess of large transformer language models for syntactic understanding, while drawing the strength of diffusion models for high-fidelity image generation. In contrast to prior work that used only image-text data for model training, Google’s fundamental discovery was that text embeddings from large language models, when pretrained on text-only corpora (large and structured sets of texts), are remarkably effective for text-to-image synthesis. Furthermore, through the increased size of the language model, Imagen boosts both sample fidelity and image text alignment much more than increasing the size of the image diffusion model. 

Image source: Google

Instead of using an image-text dataset for training Imagen, the Google team simply used an “off-the-shelf” text encoder, T5, to convert input text into embeddings. The frozen T5-XXL encoder maps input text into a sequence of embeddings and a 64×64 image diffusion model, followed by two super-resolution diffusion models for generating 256×256 and 1024×1024 images. The diffusion models are conditioned on the text embedding sequence and use classifier-free guidance, relying on new sampling techniques to use large guidance weights without sample quality degradation. 

Imagen achieved a state-of-the-art FID score of 7.27 on the COCO dataset without ever being trained on COCO. When assessed on DrawBench with current methods including VQ-GAN+CLIP, Latent Diffusion Models, GLIDE and DALL-E 2, Imagen was found to deliver better both in terms of sample quality and image-text alignment. 

Future text-to-image opportunities and challenges

There is no doubt that quickly advancing text-to-image AI generator technology is paving the way for unprecedented opportunities for instant editing and generated creative output. 

There are also many challenges ahead, ranging from questions about ethics and bias (though the creators have implemented safeguards within the models designed to restrict potentially destructive applications) to issues around copyright and ownership. The sheer amount of computational power required to train text-to-image models through massive amounts of data also restricts work to only significant and well-resourced players. 

But there is also no question that each of these three text-to-image AI models stands on its own as a way for creative professionals to let their imaginations run wild. 

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