Generative AI – The Future of Creative Work

Are you ready for the future of Generative AI? This exciting new technology has the potential to revolutionize a wide range of industries, from social media to gaming, advertising to architecture, and much more.

Generative AI models are used in many different application areas, from art and music to computer vision and robotics. There is no clear limit for generative AI yet, and it is still just a baby. However, the potential for this technology is vast, and we are only just beginning to scratch the surface.

In the future, machines will become increasingly capable of writing, coding, drawing, and creating with credible, sometimes superhuman results. This will have dramatic and unforeseen implications for content ownership and intellectual property protection.

If you’re curious about the future of Generative AI and how it will change creative work, then this blog post is for you.

Read on to learn more about this exciting new technology and what it could mean for the future.

What is Generative AI?

Generative AI is a term that sums up all models that are built to enable computers to create new content using previously created content as input. 

This can include all creative work like: 

• Text
• Audio
• Video
• Images
• Computer Code

The aim is to create authentic-looking artifacts that are completely original.

Generative AI models are used in many different application areas, from art and music to computer vision and robotics. There is now clear limit for generative AI yet, and it is still just a baby.

Generative Artificial Intelligence models may know how to generate images that look like faces, dogs or cars given the parameters and the datasets they were trained on.

By using generative AI, computers can generate new content by abstracting the underlying patterns associated with input data.

In the near future, machines will become increasingly capable of writing, coding, drawing and creating with credible, sometimes superhuman results, because of a new class of large language models.

LLMs like GPT-3 have made headlines for writing full articles. However, as the field has evolved, it has become evident that generative models are unreliable when left on their own. 

Many scientists agree that current deep learning models – no matter how large they are – lack some of the basic components of intelligence, but this will soon change.

In 2022 is the year when generative AI really has increased its pace, and there is no stopping this train now. We have seen the rise of generative models like:

• Dall-E 2
• Stable Diffusion
• Midjourney
• GPT-3 based application
• ++ many more

The State of Generative AI 22/23

The current state of Generative AI in late 2022 is booming. 2022 has for the first time really brought Generative AI on everyone’s lip, and there is no going back now.

The speed of development in text-to-image models like Dall-E 2 and Stable Diffusion has been mind blowing. And this visual component has really taken the internet by storm. But hidden in the shadows of these visual prompt based technologies lies perhaps a bigger disruptor, large langues models like GPT-3 or the next GPT-4.

This Generative AI technology has, according to the experts, the biggest potential to disrupt almost all we know about communication and language. Time will tell if this will happen, but I think I probably will have to update this section again in early 2023, because the speed of development in this field is extremely fast.

What is the definition of Generative AI?

Generative AI refers to the use of artificial intelligence to generate new content from existing data. This approach allows computers to learn the fundamental patterns relevant to input, which is then used to manufacture similar content. Generative AI can be used to create new images, video, audio, text code or other creative work.

The History of Generative AI 

Google was one of the earliest companies to use generative AI, using it to create AdWords. In the early 2000s, generative AI was also used to create the first online chatbots. More recently, generative AI has been used to create new types of music, art, and even fashion.

The History of Large Language Models

Large language models were first introduced in 2015 by Google with their release of the BERT model. This model revolutionized NLP by training models to jointly learn language representation and task-specific prediction. 

Two years later, OpenAI released the large language model GPT, which extends BERT’s pre-training by using a Transformer-based architecture and learning from much larger amounts of data. 

The original paper on generative pre-training (GPT) was published in 2018 and showed how a generative model can acquire world knowledge and process long-range dependencies. GPT-2, the successor to GPT, was released in 2019. However, due to concerns over potential misuse, only a limited demonstrative version was initially made public. 

The full version of GPT-2 was released in November 2019. 

GPT-3, the successor to GPT-2, was first described in May 2020. It is a much larger model, with 175 billion parameters. 

OpenAI has not released the full version of GPT-3 to the public, but is instead making it available through a paid cloud API.

The History of Stable Diffusion

The idea for Stable Diffusion came about in 2014, but the model wasn’t released until August 22 in 2022 by a collaboration of Stability AI, CompVis LMU, and Runway. The team behind Stable Diffusion is also working on training transformers to generate images. This project is open source, which means that anyone can use it and build off of it.

In October 2022, Stability AI raised $101 million USD in a round led by Lightspeed Venture Partners and Coatue Management.

Thr History of GANs

The history of GANs can be traced back to 2010, when machine learning researcher Goodfellow first studied noise-contrastive estimation. This led to the development of the first GAN, which was a generative model that used stochasticity in the generator. 

Since then, GANs have been used for various purposes, including image enhancement, video generation, and melody generation. In May 2019, researchers at Samsung demonstrated a GAN-based system that produces videos of a person speaking, given only a single photo of that person. 

In May 2020, Nvidia researchers taught an AI system (termed “GameGAN”) to recreate the game of Pac-Man simply by watching it being played.

Generative AI in Gaming

The future of gaming is about to change, and it’s all thanks to generative AI. With this new technology, small teams or even individuals will be able to create games that are on par with the AAA titles of today. 

So what is generative AI, and how will it change the gaming industry?

Generative AI Will Change the Future of Gaming

In the near future, Generative AI will change the gaming industry as we know it. With this new technology, small teams or even individuals will be able to create games that are on par with the AAA titles of today. 

All you’ll need is a good idea and creative direction. What’s more, Generative AI will allow for personalized experiences, meaning each player will be able to have a unique experience. The possibilities are truly endless.

Generative AI will create new game genres

This technology has the potential to change the gaming industry forever by creating new game genres that were not possible before. For example, a new game genre called “generative games” has the potential to be truly unique and endlessly replayable.

Generative AI will allow game developers to create personalized experiences for each player, rather than the same experience for everyone.

This could mean that the player can shape the game and story, including how characters in the game looks, behaves, sounds and even how the world evolves. 

Generative AI will lower the barrier to entry for new game developers

Generative AI will lower the barrier to entry for new game developers by making it easier to create unique and interesting content for their games. This technology will open up a whole new world of possibilities for indie game developers and we are only just beginning to scratch the surface. Generative AI is going to change the landscape of game development and the indie game scene is poised to benefit greatly from this new technology.

Generative AI Use Cases and Examples

Generative AI has a range of applications that can be used to create realistic images, videos, and 3D models. Some of the most popular use cases include video generation, 3D modeling, and voice synthesis. 

Generative AI is a rapidly evolving field with new use cases and applications being developed all the time. 

Let’s have a look at some great use cases:

Generative AI Images

Dall-E 2

Open AI has developed a new AI system, DALL-E 2, that can generate realistic images from a text-based description. The system uses natural language processing and a series of neural network layers to generate images. 

It works best when the text input is specific, such as a given art style. The system can generate four images at a time, giving variations based on the input.

Stable Diffusion

Stable Diffusion is a text-to-image AI model that was released in 2022. It is considered to be much better than previous text-to-image AI models, and is especially good at generating faces and realistic 3D scenes. 

The code is open source, meaning anyone can use it and build off of it. It requires a modest GPU with at least 8GB VRAM to run.

Watch a YouTube video about Stable Diffusion here:

Midjourney

Midjourney is a new text-to-image generator that is quickly taking over the internet.

Midjourney uses a freemium business model, with a limited free tier and paid tiers that offer faster access, greater capacity, and additional features.Users create artwork with Midjourney using Discord bot commands.

The new V4 model from Midjourney is aiming to improve in a lot of areas when it comes to producing more precise and fulfilling the Midjourney prompt author’s imagination.

Watch a YouTube video about Midjourney here:

Generative AI Text

GPT-3

OpenAI`s GPT-3 is the largest neural network ever produced and is better than any prior model for producing text that is convincing enough to seem like a human could have written it. 

GPT-3 can create anything that has a language structure, which means it can answer questions, write essays, summarize long texts, translate languages, take memos, and even create computer code.

Watch a YouTube video about GPT-3 here:

GPT-4

GPT-4 will be a text-only large language model with vastly better performance on a similar size as GPT-3. 

It will also be more aligned with human commands and values. GPT-4 will be used for various language applications such as code generation, text summarization, language translation, classification, chatbot, and grammar correction. 

The new version of the model will be more secure, less biased, more accurate, and more aligned. It will also be cost-efficient and robust.

More to come in 2023

Hugging Face BLOOM

The BLOOM model is a large language model (LLM) that can generate text in 46 languages and 13 programming languages. It is trained on vast amounts of text data using industrial-scale computational resources. 

The model has been proposed with its various versions through the BigScience Workshop. BigScience is inspired by other open science initiatives where researchers have pooled their time and resources to collectively achieve a higher impact.

DeepMind Goper

DeepMind’s new Gopher AI model outperforms existing models on many natural language processing tasks, despite being smaller than some of them. 

The researchers say that Gopher is particularly good at answering questions about specialized subjects, and that it could be helpful in detecting bias or misinformation.

According to the DeepMind team, Gopher is part of their effort to explore the strengths and weaknesses of large language models.

Generative AI Coding

OpenAI CODEX

OpenAI has created an improved version of its AI system, Codex, which translates natural language into code. The new version of Codex is now available through an API in private beta. 

With Codex, businesses and developers can build on top of OpenAI’s technology to create natural language interfaces for existing applications.

Watch a YouTube video about CODEX here:

Github Co-Pilot

GitHub Copilot is a new AI system that provides autocomplete-style suggestions as you code. It is powered by OpenAI Codex and is trained on all languages that appear in public repositories. 

With GitHub Copilot, developers can get AI-based coding suggestions that match a project’s context and style conventions.

Generative AI Voice

Sonatic

Sonantic has developed a voice platform that allows professionals to create realistic voice performances using AI. This technology is poised to revolutionize game and film production by speeding up creative workflows and scaling storytelling. 

The company’s founders are dedicated to providing the best entertainment solutions now and in the future.

Sonantic was acquired by Spotify in summer 2022.

Watch a YouTube video about AI Voice here:

Azure Neural Voice

Azure Neural Text to Speech is a powerful speech synthesis tool that enables developers to convert text to lifelike speech using AI. 

The Azure Neural TTS product team is continuously working on bringing new voice styles and emotions to the US market and beyond, making it an ideal tool for video game characters, chatbots, content readers, and more.

Generative AI Video

META – Make a video

Meta’s new AI-powered video generator, Make-A-Video, can create novel video content from text or image prompts, similar to existing image synthesis tools. It can also make variations of existing videos. The system learns what the world looks like from paired text-image data and how the world moves from video footage with no associated text. With just a few words or lines of text, Make-A-Video can bring imagination to life and create one-of-a-kind videos.

Microsoft X-Clip

The X-CLIP model is trained in a contrastive way on (video, text) pairs, allowing it to be used for tasks like zero-shot, few-shot or fully supervised video classification and video-text retrieval. 

Extensive experiments demonstrate that X-CLIP is effective and can be generalized to different video recognition scenarios.

More to come in 2023

Generative AI 3D Modeling

Nvidia GET3D

NVIDIA`s  GET3D, a project that uses AI to populate virtual worlds. GET3D can generate 3D models with fine details, like small wheels on office chairs or wireframe on the tires of motorcycles. 

The main advantage of GET3D is that it can generate 3D models with textures that are ready to use.

More to come in 2023

DreamFusion

Google’s DreamFusion AI tool can generate 3D models of text inputs without any prior training. The system uses 2D images of an object generated by the Imagen text-to-image diffusion model to understand different perspectives of the model it is trying to generate. 

More to come in 2023

The Benefits of Generative AI

There are many huge benefits of using Generative AI both for personal use and for business. Let’s have a look at the 6 biggest benefits of Generative AI:

1.Create high-quality content.

Generative AI can create high-quality content because it can identify the underlying pattern of input and generate similar outputs. Additionally, the upgraded variety of generative AI can leverage mathematical emulation and function unknown patterns revealed through it. 

This technology can be used to create avatars to protect the identity of the people being interviewed, generate high-quality images and videos, create new inventions, and boost productivity.

Watch a YouTube about Generative AI content here:

2. Help protect identities and privacy

Generative AI can be used to create avatars, which can help protect identities by Concealing the real appearance of people who are not comfortable disclosing their identities for any reason while being interviewed or working online.

Generative AI can also be used to create synthetic data sets, which can act as data privacy barriers and help protect the personal information of individuals.

Generative AI can also be used to create synthetic data sets, which can act as data privacy barriers by resembling real data but without any personal identifying information.

3. Help create new inventions

Generative AI can help create new inventions by leveraging mathematical emulation and function unknown patterns revealed through it. 

Generative AI can help create new inventions by leveraging mathematical emulation and function unknown patterns revealed through it. For example, a sentence could be generated from information found on Wikipedia and thousands of other websites. In this way, the system can learn to use complex grammar rules without being programmed beforehand

Organizations in various industries can benefit from adopting generative AI. In healthcare, it can be used in early detection of brain tumors. In manufacturing, it can be used to produce synthetic chemical materials. In media and entertainment, it can be used to improve image processing and film restoration. In banking, it can be used to create synthetic data sets.

Overall, it is a promising technology that can help us solve many complex problems.

4. Can increase productivity

Generative AI is a powerful tool that can be used to boost productivity. By cutting time and effort needed for tasks, generative AI can help creative people be more productive. 

In addition, by synthesizing new ideas and inventions, generative AI can help people be more innovative. The benefits of generative AI are many and varied, and it is clear that this technology can help us achieve greater things.

5. Improve health

Generative AI techniques are helpful in early detection of brain tumors, as well as in the synthesis of new chemicals and substances that could lead to new medical breakthroughs. 

Additionally, generative AI can be used to create avatars which can conceal the real appearance of people who are not comfortable disclosing their identities. In this way, generative AI can help improve both the quality of life and the overall health of a population.

6. Improve risk management

Generative AI can help businesses and individuals reduce financial and reputational risks. 

One of the benefits of Generative AI is that it can help improve risk management. For example, generative AI can be used to create multiple versions of a design for a product or structure. 

This can help identify which design is the best and which one is the most likely to fail. Additionally, generative AI can be used to create synthetic data sets. These data sets can act as privacy barriers between real data sets and individuals. This can help reduce the risk of data breaches and protect the privacy of individuals.

Disadvantages of Generative AI

With great benefits comes also some disadvantages. We have listed what we think are the 4 biggest disadvantages of Generative AI at the moment, but this could change fast.

1. Limited control and predictability

Generative AI systems are hard to control and predict. This is because they are based on machine learning models which are difficult to change. The outputs of these models are often hard to interpret and may not meet the expectations of users. 

2. High setup cost

The initial setup of AI systems can be high at the moment, although this is expected to come down in the future. The cost of Generative AI adoption can be a challenge for some businesses. 

However, the long-term benefits of Generative AI should be taken into account when making a decision about whether or not to adopt the technology.

3. Lack of originality and creativity

Generative AI creates new content based on existing data, but it does not have the ability to come up with new ideas on its own. This lack of originality and creativity can be a problem in some applications. 

For instance, deep-fake videos that show politicians or celebrities saying things that they did not actually say can be used to create misinformation or to discredit someone. 

Additionally, apps like deep-nude that use generative AI to create nude images of people without their consent have caused a lot of controversy.

4. Moral and ethical considerations

Generative AI technologies have the potential to create fake content that convincingly looks real. This has raised ethical considerations about how the technology could be used to create deep fakes that could damage someone’s reputation or even incite violence. 

There is also concern that Generative AI could be used to create fake news stories that could influence the outcome of elections.

How is Generative AI Changing Creative Work?

Artificial intelligence is drastically changing the way we view creative work. The application of generative AI is broad, and its potential is only beginning to be realized. 

The aim of generative AI is to create authentic-looking artifacts that are completely original. This can include all creative work like: text, audio, video, images, computer code, etc.

The development of such capabilities would have dramatic and unforeseen implications for content ownership and intellectual property protection. But they are also likely to revolutionize knowledge and creative work. 

Assuming that these AI models continue to progress as they have in the short time they have existed, we can hardly imagine all of the opportunities and implications that they may engender.

In the near future, machines will become increasingly capable of writing, coding, drawing and creating with credible, sometimes superhuman results, because of a new class of large language models.

LLMs like GPT-3 have made headlines for writing full articles. However, as the field has evolved, it has become evident that generative models are unreliable when left on their own.

Many scientists agree that current deep learning models – no matter how large they are – lack some of the basic components of intelligence. 

But this will soon change. In 2022 is the year when generative AI really has increased its pace, and there is no stopping this train now. We have seen the rise of generative models like: Dall-E 2, Stable Diffusion, Midjourney, GPT-3 based application, etc.

Generative AI is a term that sums up all models that are built to enable computers to create new content using previously created content as input. The aim is to create authentic-looking artifacts that are completely original. 

Generative AI models are used in many different application areas, from art and music to computer vision and robotics.

The application of generative AI is broad, and its potential is only beginning to be realized. In the future, generative AI will drastically change the way we view creative work.

Generative AI and Business

Generative AI is well on the way to becoming not just faster and cheaper, but better in some cases than what humans create by hand. 

Every industry that requires humans to create original work – from social media to gaming, advertising to architecture, coding to graphic design, product design to law, marketing to sales – is up for reinvention. 

Certain functions may be completely replaced by generative AI, while others are more likely to thrive from a tight iterative creative cycle between human and machine – but generative AI should unlock better, faster and cheaper creation across a wide range of end markets. 

The dream is that generative AI brings the marginal cost of creation and knowledge work down towards zero, generating vast labor productivity and economic value – and commensurate market cap.

Generative AI is a hot topic in business circles thanks to its potential to revolutionize a wide range of industries. 

While some businesses may be worried about the impact of generative AI on jobs, the reality is that this technology can make workers more efficient and creative. As such, generative AI has the potential to generate trillions of dollars of economic value.

The Future of Generative AI

The potential applications of generative AI are limitless, and we are only just beginning to scratch the surface. In the future, we will see more and more businesses and organizations using generative AI to drive innovation and create new value.

Generative AI is still in the early days. There is no one-size-fits-all answer as the conclusions of generative AI will vary depending on the specific application or domain being considered. 

However, some general insights that can be gleaned from the current state:

• The potential for generative AI to enable the creation of new, previously unimaginable things, such as completely new types of text, art or music.

• The potential for generative AI to help us better understand and model complex systems, such as the brain or the climate.

• The need for careful consideration of ethical and societal implications when deploying generative AI systems, as they have the potential to cause significant disruption

Conclusion

As we continue to develop new generative AI models, the potential applications for this technology become limitless. 

Generative AI has the ability to revolutionize a wide range of industries, from social media to gaming, advertising to architecture, and much more.

As we enter into a new era of AI-driven innovation, it’s important to consider the ethical and societal implications of these powerful new tools. Generative AI has the ability to create new and previously unimaginable things, and it’s important that we deploy these systems responsibly.

Looking to the future, we can be excited about all the possibilities that generative AI brings. This technology has the potential to change the way we work, play, and even think. We are only just beginning to scratch the surface of what’s possible with generative AI, and the future promises to be even more exciting.