I revel in unlocking creative possibilities, dedicating most of my life to harnessing technology for crafting inventive marvels akin to magic. As a passionate Design Scientist and technologist, I am wholeheartedly optimistic about the transformative impact of Generative AI on the future of art and design.
As artificial intelligence (Ai) (AKA: Cognitive Computing) continues to grow and integrate into our lives, the human-obsessed cognitive experience designers (user interface designers making Ai accessible) behind these advancements continue to work tirelessly to improve and enhance the user experience that is enabling builders to expand Ai's use cases at a rate and pace we have not seen in human history.
The Ai revolution is not in some far off future. It's right now.
According to a 2020 McKinsey Global Survey on AI adoption, about 50% of the surveyed companies had adopted AI in at least one business function. This number has exponentially increased since then. As of Jan 2023 ChatGPT has over 100 million users and the adoption of Generative Ai continues to rise as the interfaces become easier and easier to use.
Fear, Loathing and Blame: Ai is not to blame for the world's problem's, people are.
It's not AI that lies at the heart of the world's challenges; instead, the primary driving forces are the choices and actions made by people.
Despite the media hype and alarmist claims by some 'experts' forecasting humanity's downfall, Artificial Intelligence—through its various applications and data models—is actively contributing to our collective pursuit of a brighter future for everyone. AI is not the root cause of global issues; rather, human actions and decisions are primarily responsible.
Generative Ai freeing creator's to think & solve more complex problems
Generative Ai, a branch of artificial intelligence that creates new content based on data inputs, is transforming the way we interact with digital interfaces. From text generation and image manipulation to music composition and design, generative AI offers a wide range of possibilities to create more intuitive, personalized, and engaging user experiences. In this article, we will explore how generative AI is impacting interface design and the future of user experience (UX).
The Generative Ai Landscape is constantly evolving.
The following diagram illustrates the underlying platform layer driving each category, along with examples of potential applications that can be developed based on this foundation.
Several trends have emerged in the generative AI landscape, showcasing the rapid advancement and increasing potential of this technology.
- GPT-4 and large-scale language models: The release of OpenAI's GPT-3 and other large-scale language models has marked a significant milestone in natural language processing. These models have shown remarkable capabilities in generating human-like text, answering questions, and even coding, leading to a growing number of applications in content creation, customer support, and more.
- Ai-generated art and design: Generative AI has made significant strides in the world of art and design, with AI-powered tools being used for image synthesis, style transfer, and even creating entirely new artworks. Platforms like RunwayML and DALL-E demonstrate the potential for AI-generated visual content, opening up new possibilities for artists, designers, and creative professionals.
- Music and audio generation: AI models like OpenAI's Jukebox and MuseNet have shown the ability to compose original music, generate accompanying instruments, and even mimic specific styles or artists. This has led to growing interest in AI-generated music for background scores, advertising, and other creative applications.
- Video synthesis and deepfakes: Generative AI has also made significant progress in video synthesis, with tools like DeepArt.io and First Order Motion Model being used for generating realistic animations and deepfake videos. While this technology has raised ethical concerns, Generative Ai and video technologies offer a variety of positive applications. e.g. Education & Training, Film and Animation, Advertising and Marketing, Video summarization, Accessibility, Restoration and enhancement
As the first and third party data models get bigger and bigger, they begin to deliver human-level, and then superhuman results. With the platform layer solidifying, models continuing to get better/faster/cheaper, and model access trending to free and open source, the application layer is ripe for an explosion of creativity.
Anatomy of a Generative AI Application
Today, Generative AI apps largely exist as plugins in existing software ecosystems. Code completions happen in your IDE; image generations happen in Figma or Photoshop; even Discord bots are the vessel to inject generative AI into digital/social communities. The anatomy of a generative AI application typically consists of several key components that work together to create new content based on input data. These components include:
- Data: Generative AI applications require a substantial amount of data to function effectively. This data, which can be in the form of text, images, audio, or video, serves as the basis for training the AI model and influences the generated output.
- AI Model: The core of any generative AI application is the AI model itself, which is typically based on deep learning techniques such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), or Transformer architectures. These models learn patterns and relationships within the input data and use this knowledge to generate new content.
- Training Process: The AI model needs to be trained on the input data to learn the underlying patterns and features. This training process usually involves optimizing the model's parameters through techniques such as gradient descent and backpropagation. The quality and quantity of the training data play a crucial role in the model's performance.
- Evaluation Metrics: To assess the quality and effectiveness of the generative AI model, evaluation metrics are employed. These metrics, such as the Inception Score (IS) for GANs or the Bilingual Evaluation Understudy (BLEU) score for text generation, help gauge the model's success in generating realistic and coherent outputs.
- User Interface: A simple human interface is crucial for generative AI applications, as it allows users to interact with the AI model and provide input data, customize settings, and obtain generated content or concepts.
Top 2023 Generative Ai Applications
There are several generative AI products and tools that cater specifically to creation here are a few that the team at Sensory6.ai has trialed.
Music: Make Music with Ai
Generative music applications are software tools that use algorithms, artificial intelligence, and user input to create original music compositions or modify existing ones. These applications can cater to various use cases, from providing inspiration for musicians to creating background music for different scenarios. Some popular generative music applications include:
- Ecrett Music
- OpenAI – MuseNet
- Amadeus Code
- Dance Diffusion
- Microsoft’s Museformer
- OpenAI’s Jukebox
- Amper Music
- Google's Magenta Studio
Design: Design Products, Interfaces and Customer Experiences with Ai
Generative design applications are software tools that leverage artificial intelligence, algorithms, and computational methods to automate and optimize the design process. These applications assist designers and engineers in creating more efficient, innovative, and optimized solutions by rapidly exploring numerous design variations based on specific constraints and goals. Some popular generative design applications include
- ChatGPT: Empathy map.
- ChatGPT: Customer Journey Map
- ChatGPT: Storyboards
- ChatGPT:User flows
- ChatGPT: Information architecture (IA)
- ChatGPT: Metrics with calculation formulas
- ChatGPT: Sitemap
- Promethean AI
- Autodesk Fusion 360: Fusion 360
- Altair Inspire
- Siemens NX
Writing: Write with Ai
Generative writing applications use artificial intelligence, particularly natural language processing and generation, to assist users in creating written content. These applications can help with tasks ranging from generating ideas and writing prompts to drafting full-length articles. Some of the best generative writing applications include: