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    Creating art and innovation through code

    Generative AI

    Generative AI has the ability to generate new content, such as images, music, text, and even entire virtual environments, by learning patterns and rules from large amounts of data. It has significant applications in fields such as art, music and design, and is rapidly advancing the frontiers of creativity and innovation.
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    What is it?
    Understanding generative AI
    Generative AI is a subset of artificial intelligence that uses machine learning algorithms to create new and original content. It is based on the idea of learning patterns and rules from data, and using that knowledge to generate new content in the form of images, music, text, video, and even virtual environments. Generative AI has the potential to revolutionize many fields, such as art, design, and entertainment, by enabling new levels of creativity and innovation.
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    What can you do?
    How can you use generative AI?
    Document generation
    Create legal, engineering or product documents that can be enhanced by humans in the loop, automating the work and improving speed and efficiencies.
    Code generation
    Write or test your code automatically with code automatically generated following your requirements.
    Content generation
    Generate new content for websites or social media. You can train a generative model to write articles, stories, or captions for images.
    AutoGPT
    Create agents that can carry on complex tasks by writing instructions and executing them. Integrate this into your workflows.
    Product design
    Generate designs for products like furniture, jewelry, or clothing. You can train a generative model to create a range of designs for a new product, and then select the best one to produce.
    Avatar generation
    Create intelligent chatbots, virtual assistants or avatars that can interact with users and answer their questions. These can be used in gaming, customer support, education and more.
    Applications
    Engineering
    Create code or agents capable of executing complex tasks. Find optimizations that would normally require an experienced engineering team. Automate tasks such as orchestrating infrastructure with AutoGPT.
    Fashion
    Create unique designs for clothes and accessories. It can also be used to optimize production processes, such as pattern-making and sizing, based on customer preferences and historical sales data.
    Advertising
    Generative AI can be used to create personalized and targeted advertisements. Advertisers can use generative AI to create ads that are tailored to individual customers based on their preferences and buying habits.
    Healthcare
    AI can be used to generate synthetic data for medical research, simulate and predict disease outcomes, and optimize treatment plans. It can also be used to create personalized healthcare plans based on individual patient data, and even assist in drug discovery and clinical trials.
    Legal
    Write legal documents that can be complemented by a human in the loop, automating a large part of the work.
    Architecture
    Create building designs based on factors such as location, materials, and function. For instance, an architect could use generative AI to generate various building designs based on a client's requirements.
    Education
    Generative AI can be used to create personalized learning materials, such as quizzes, practice exams, and even textbooks. It can also be used to generate automated feedback on student performance and optimize learning outcomes.
    Entertainment
    Generate game content, characters, and storylines for gaming and entertainment companies. It can create unique and engaging experiences for players, increasing user engagement and loyalty.
    Generate images, text, videos, audio & 3D objects
    The data used to train a generative AI model is crucial to its ability to generate realistic and relevant output. A large and diverse dataset is required to teach the model patterns and relationships present in real-world scenarios. These, correctly used, can output images such as the Corgi dog driving this car.
    Produce not only realistic but creative work while gaining control of the output to reflect any changes requested. Significantly reduce costs and increase speed by using these powerful models. Avoid any licensing or privacy issues by having your own models that can run in your cloud.
    Generate images, text, videos, audio & 3D objects
    The data used to train a generative AI model is crucial to its ability to generate realistic and relevant output. A large and diverse dataset is required to teach the model patterns and relationships present in real-world scenarios. These, correctly used, can output images such as the Corgi dog driving this car.
    Produce not only realistic but creative work while gaining control of the output to reflect any changes requested. Significantly reduce costs and increase speed by using these powerful models. Avoid any licensing or privacy issues by having your own models that can run in your cloud.
    Generative AI in production
    Production ready applications
    Generative AI has the potential to revolutionize the way we approach production and manufacturing. By automating the design and production processes, it can reduce human error, increase efficiency, and optimize resource allocation. Additionally, generative AI can help companies innovate by generating new and original designs that may not have been considered by human designers. This can lead to products with improved functionality, aesthetics, and usability.
    Should I use this?
    Generative AI has revolutionized the way businesses approach tasks such as image and speech recognition, language translation, and content creation. Depending on your specific needs and objectives, you should consider implementing generative AI. It can help automate repetitive and time-consuming tasks, significantly improve accuracy, and speed up the creative content generation process. Additionally, generative AI is highly versatile, making it a valuable asset in a wide range of industries, including fashion, gaming, art, and music. It can be utilized to create new designs, images, and sounds, providing businesses with a significant competitive advantage.
    What do I need to use them?
    To use generative AI, you will typically need the following:
    • Data: Generative AI systems learn from existing data, so you will need a large dataset to train your AI model.
    • Computing resources: Generative AI models can require significant computing resources, especially if you are training a complex model.
    • Domain expertise: Depending on the industry and application, you may need domain expertise to develop a generative AI model that produces useful and relevant output.
    • Marvik: As a team of experts we can help you define and execute an action plan, leveraging state of the art technology to your benefit.
    Generative AI + Computer vision
    Stable diffusion XL
    The latest version of Stable Diffusion, a model that generates photorealistic images given a textual description. This new version achieves better-looking images with less prompt engineering.
    For example, we now see a women with a yellow dress. The prompts used were: Wooden sculpture of a tree, with intricate branches, textured bark, and a strong trunk, highly detailed, natural lighting, grounding presence.
    Stable diffusion XL
    The latest version of Stable Diffusion, a model that generates photorealistic images given a textual description. This new version achieves better-looking images with less prompt engineering.
    For example, we now see a women with a yellow dress. The prompts used were: Wooden sculpture of a tree, with intricate branches, textured bark, and a strong trunk, highly detailed, natural lighting, grounding presence.
    ControlNet
    Stable Diffusion has incorporated conditioning options that enable users to exercise more precise control over the algorithm's output. This feature is particularly useful for tasks like architectural visualization, where simply providing a written prompt such as "modern design bedroom" may not yield the desired results.
    Instead, users can input a reference image of the specific room they wish to redesign, allowing for greater granularity in the generated renderings. As an illustration, consider the accompanying image of a room rendered using this approach.
    ControlNet
    Stable Diffusion has incorporated conditioning options that enable users to exercise more precise control over the algorithm's output. This feature is particularly useful for tasks like architectural visualization, where simply providing a written prompt such as "modern design bedroom" may not yield the desired results.
    Instead, users can input a reference image of the specific room they wish to redesign, allowing for greater granularity in the generated renderings. As an illustration, consider the accompanying image of a room rendered using this approach.
    Gen-2
    Gen-2 is capable of generating videos using a single prompt and is able to maintain a high level of temporal consistency, which was a challenging task in earlier iterations.
    To illustrate, we have included an image depicting a man, and the accompanying video that was generated using Gen-2, demonstrating its ability to create smooth and consistent video sequences from a single prompt.
    Gen-2
    Gen-2 is capable of generating videos using a single prompt and is able to maintain a high level of temporal consistency, which was a challenging task in earlier iterations.
    To illustrate, we have included an image depicting a man, and the accompanying video that was generated using Gen-2, demonstrating its ability to create smooth and consistent video sequences from a single prompt.
    ChatGPT and GPT4
    ChatGPT is a language model developed by OpenAI that can understand and generate human-like responses to text-based inputs. GPT-4 is OpenAI’s most advanced system, producing safer and more useful responses, it can solve difficult problems with greater accuracy, thanks to its broader general knowledge and problem solving abilities.
    Autonomous conversational Agents
    All the advancements in conversational agents lead to the development of autonomous agents that can interact with the world using language and APIs, for example, do market research or find a local restaurant and order a pizza.
    Retrieval Augmented Models
    What if you want to make a ChatGPT for your own business? Retrieval Augmented models allow LLMs to use external knowledge bases, even at a very large scale. When you ask a question, these models search for relevant information in a large database and generate an accurate response.
    Open Source LLM
    The release of ChatGPT sparked a lot of movement in the open-source community, leading to the development of many models such as LLaMA, Bloom, Flan-T5 and Alpaca. These models have very similar performance to OpenAI’s ChatGPT while being more open, and also being able to fine-tune them with custom datasets.
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