ChatGPT, Bard, Jasper: The age of generative AI begins

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The future is now: ChatGPT continues to dominate the headlines. The technology of the open AI all-purpose weapon belongs to an emerging branch of artificial intelligence, generative artificial intelligence. These are algorithms for generating text, audio, images, videos, simulations, and computer codes. How does the technology everyone is talking about work and what are the competing products on the market?

Welcome to the age of machine text, images, photos, music, and videos. At least now with the hype and availability of ChatGPT, we have access to a wealth of digital content created by artificial intelligence. From automated text generators to artificial intelligence capable of generating realistic images, music, and videos, there is a multitude of technologies that expand our creative potential. It’s an exciting time where the lines between what’s being made by humans and what’s made by machines are slowly blurring.

ChatGPT and the synthetic content

Generative AI uses artificial intelligence techniques known as machine learning and Deep Learning to generate content based on statistical predictions of words, sounds, images, and videos. a controversial topic” to the development of computer code.

The most important output of generative AI is the so-called synthetic data or content generated by machines (applications) in the digital world. This is text, image, audio, and video content that is generated from existing data using statistical methods and patterns learned from the input data. A generative AI application may have been trained to read thousands of articles about politics to be able to ask or answer new questions on the subject. Another application can create impressionist paintings by associating pairs of data with text and images after being trained on hundreds of images by impressionist painters. Ideally, the result of generating synthetic content is so good that, depending on the maturity of the application, we cannot tell whether it was produced by machines or by humans.

Generative AI and its areas of application

All of this makes generative AI an ultimate tool that can be used in a wide variety of scenarios. It can produce different content, such as:

Text: articles, poems, news, scripts, presentations, translations, computer code

Pictures: landscapes, faces, paintings, avatars, virtual environments, videos

Audio: music, sound effects, voiceover, video or audio-to-text conversion

Commands via prompts

The exciting thing about this is the fact that Generative Artificial Intelligence products provide answers to questions that the user puts at a computer prompt, i.e. Users can ask applications like ChatGPT to write a product text, or DALL-E to generate a suitable image.

ChatGPT, could you write an article on a controversial topic?

DALL-E, could you do an impressionist painting about the environment?

problem areas

ChatGPT, Open AI’s newly released chatbot, has garnered immense popularity since its inception but shows where major problems still lie with the technology. While the chatbot can perform various tasks like writing texts, answering questions, writing essays or translating texts, etc. However, just like any other application, ChatGPT has some limitations. Those limitations certainly do not apply to every application of generative AI.

1. ChatGPT cannot access the internet

ChatGPT cannot connect to the internet. For example, when users ask ChatGPT for the weather forecast or the current price of gold, it cannot provide accurate data. This special AI is therefore currently not a substitute for the major search engines.

2. It can produce nonsensical data

ChatGPT can interact like a human but often makes glaring mistakes. The chatbot only answers questions that are direct and as defined in the system. Sometimes the answer will be irrelevant and nonsensical. So users must be prepared for inaccurate results when using the chatbot.

3. It does not provide detailed information

While ChatGPT can answer almost any question, it has been observed that it often provides generic rather than detailed information. There are short answers or a summary of the topic.

4. There is a lack of expressions

ChatGPT can answer questions, but often not as expressively as a literate person would. The chatbot is a machine and has no expressive language.

5. Deepfake-Videos

Another example of using generative AI is the creation of deep fake videos. ChatGPT currently has very little to do with this, other software has been busy here for a long time. These deep fake videos are also created using generative AI and can be used to slander people or spread inaccurate information. There is a risk that this technology will be misused to spread political disinformation and other harmful content.

How does this happen? For this purpose, generative AI models, such as GANs (Generative Adversarial Networks), are created for deep fake video production. The process of creating a deep fake video usually starts with selecting data to use as training data for the GAN model. This data can come from a variety of sources, including existing videos and photos of the data subjects. The GAN model is then trained to analyze this data and generate new images or videos that look similar to the people. The generator creates new images or videos, while the discriminator checks these images or videos to see if they look realistic enough. The model is trained until the discriminator can no longer differentiate between the generated images or videos and real recordings. Deepfake videos have raised concern in the past as they can be used to harm people.

What are the most common Generative AI tools?

AI products are currently springing up in droves. In addition to the big Big Tech companies, other companies are also jumping on the bandwagon and bringing their products onto the market, mostly based on the GPT interface. presents some of them:


ChatGPT-4 is the improved version of the well-known chatbot system ChatGPT, based on the groundbreaking GPT-4 model. The GPT-4 model is an evolution of the GPT-3 model and offers higher accuracy and better performance in the area of ​​natural language processing.

ChatGPT-4 is an advanced machine learning and artificial intelligence-based system capable of human-like conversations. It can do a variety of tasks, such as answering questions, making recommendations, making small talk, and even performing complex tasks like translation and speech recognition.

ChatGPT-4 improvements include improved contextualization, increased ability to understand speech, and improved ability to respond to input. These improvements make ChatGPT-4 one of the most advanced chatbot systems out there.

ChatGPT-4 also offers better personalization and customization to the user. It can respond to individual preferences and interests and thus achieve even more natural conversational behavior. Also, it can cover a wider range of topics and conversations due to its improved model and higher performance.

Google Bard

Bard was introduced by Sundar Pichai, CEO of Google and Alphabet on February 6th. The AI ​​chat service is based on Google’s language model for dialog applications (LaMDA), which was introduced two years ago. LaMDA, in turn, is based on Transformer, Google’s neural network architecture that the company developed and released in 2017.

The first release of Bard uses a lightweight model version of LaMDA as it requires less computing power and can be scaled to accommodate more users

Google’s Bard got off to a rocky start after a demo revealed inaccurate information about the James Webb Space Telescope (JWST).

Google Bard uses information from the web to provide the latest answers. This gives it an advantage over chat GPT, which only has data up to 2021.

Users can sign up now for early access to Google Bard AI in select countries. Google has yet to comment on its plans to integrate Bard into the company’s search engine, but the company plans to bring new AI-powered features to Google Search.


Chatsonic is one of the newest and fairly far-reaching ChatGPT alternatives to make the rounds lately. It was built on top of ChatGPT and therefore inherits its great potential. However, this AI chatbot has more features and knowledge because it can access the internet – something ChatGPT cannot do yet.

The ability to issue replies based on internet results gives Chatsonic the ability to disseminate correct information, making it a little less error-prone. The AI ​​chatbot also remembers conversations and uses them to keep the conversation flowing. 

Jasper Chat

Jasper has been in the AI ​​content creation space for some time and has been well-received by users. However, alongside content creation and other service features, Jasper also offers a relatively new chatbot. This ChatGPT alternative called Jasper Chat is also based on GPT, has more language models, and has OpenAI as a partner.

The website itself is minimally packaged and easy to use. The tool offers ChatGPT-like functionality, including the ability to have conversations and provide simple to nuanced responses. However, unlike ChatGPT, Perplexity advertises the sources it uses to answer your questions.

AI image generators

Generative image AIs are another exciting development in the field of artificial intelligence. This technology allows computers to create complex images and visual content that ideally would be almost indistinguishable from human designers.

At its core, Generative Image AI also uses a so-called neural network based on machine learning. This network can learn from a variety of image data and recognize patterns to reproduce and expand independently.

One of the most important application areas of Generative Image AI is the creation of works of art. Artists and designers use this technology to create complex and beautiful images designed by the AI ​​itself. Generative image AI is also increasingly being used in the field of media production. Here, media companies can use AI systems to create images and graphics that are of higher quality and complexity than manual designs. In addition, companies can also use Generative Image AI to develop automated image-generation solutions that allow them to create large volumes of images in a short amount of time.

Which AI image generators are there?

What works with words and text also works with pictures. Although many AI image generators are still immature, they can also achieve attractive results with the right commands.

These are the most popular AI image generators:


MidJourney is a well-known Generative Image AI generator developed by digital artist Mike Tyka. This technology uses what is known as a conditional GAN, which can generate images based on certain parameters and conditions. MidJourney is known for his ability to create abstract and surrealistic images that often have a strong narrative component. The technology also uses a special technique called “deep dreaming” that allows images to be generated from a variety of sources and influences. MidJourney has been featured in numerous art exhibitions and events and has also found application in the world of fashion and advertising. For the application, users need allergens discord.


DALL-E is one of the most famous Generative Image AI developed by OpenAI. Unlike other image generators, however, DALL-E can not only generate images but also convert text into images. DALL-E uses a GAN network that learns from textual descriptions and is then able to create images that reflect those descriptions.

DreamStudio (Stable Diffusion)

This means that anyone with the necessary technical knowledge can download it and run it locally on their computer. It also means that users can train and fine-tune the model for specific purposes. Almost all services that Artificial intelligence to create artistic portraits, historical portraits, architectural renderings, and more, use Stable Diffusion in this way.

However, Stable Diffusion is also available in a powerful public application called DreamStudio, developed by the developers of Stability AI.

DreamStudio gives tremendous control over the various aspects of image creation with AI. diffusion model takes, and how many images are produced.

A look into the future

Generative artificial intelligence has made amazing progress in recent years and will continue to play an increasingly important role in the future. We can expect that in the coming years, generative AI technologies will continue to improve to be even more human-like. This includes better control over the output of generative models to ensure the results generated are safe, fair, and ethical.

Another important advance will be the ability of generative AI to combine different types of data such as text, audio, and images to create even more powerful models. These models will be able to handle even more complex tasks, such as creating realistic virtual worlds. The competition between the big tech companies fighting for dominance in the development of generative artificial intelligence will certainly also contribute to this. It can be expected that companies like Google and Microsoft will continue to invest heavily in research and development to create more and more advanced models. At the same time, there will also be room for smaller companies that specialize in niche applications and develop their own innovative solutions.

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