Generative AI: The Next Frontier for Businesses
Generative AI has been the talk of the town for most of 2023, with platforms such as ChatGPT and Midjourney opening up possibilities around quick and easy content creation. But in this article, I want to take a closer look at what generative AI is, what it can do beyond creating pictures of dogs riding surfboards in space and see if Generative AI has a place in the world of business… or if it’s just a lot of hype and wishful thinking.
So, What is Generative AI?
At its core, Generative AI is a specific field of artificial intelligence that pertains to machine learning models and algorithms that can create new content, whether text, images, sounds, or other forms of data. While AI has traditionally been developed for tasks such as classification or prediction, generative AI models aim to generate new data instances that weren’t in their training set but are stylistically or structurally similar. It’s not that these models can think for themselves, more that they can identify patterns and produce a piece of content that follows and immitates those patterns.
What Can It Do?
From crafting intricate art pieces to synthesising lifelike human speech, the applications of generative AI are vast and varied. Key capabilities include:
- Text Generation: Producing articles, poetry, and even scripts.
- Image and Video Generation: From creating detailed digital art to generating video frames.
- Audio Synthesis: Crafting new songs or generating human-like voice narrations.
- Data Augmentation: For businesses with limited data, generative AI can produce additional synthetic data to boost the training of other models.
With these capabilities, we can integrate them into numerous areas of business life, including ChatBots and service agents, personalized prompts for phone interactions… right through to marketing content, and creating product documentation and guides (text and video).
How Does It Work?
At a high level, generative AI uses various machine learning techniques, such as GANs (generative adversarial network), VAE (Variational Autoencoders) or LLMs (Large Language Models) to generate new content based on patterns learned from the training data used to build the models. Because modern foundation models (the models built using the various techniques referenced above) are trained on billions of pieces of data (websites, documents, books, images) the models do a good job of matching the patterns of human-generated content and can produce content of a similar nature.
The Surge of AI Complexity: What Changed?
- Data Availability: The proliferation of digital data has provided vast training sets for models to learn from.
- Hardware Advancements: Modern GPUs and TPUs have provided the computational power to train and deploy large models.
- Model Architectures: Novel architectures, like Transformers, have enabled more efficient and sophisticated model designs.
- Cloud Infrastructure: Platforms like AWS have democratized access to powerful computing resources, allowing a broader community to innovate and iterate on model designs.
Benefits and Risks of Using Generative AI
Like any new technology, Generative AI comes with both some pro’s and con’s and it’s important to understand both sides of the situation prior to implementing it in a business setting.
- Automation: Businesses can automate content generation, whether marketing material, design drafts, or reports.
- Cost Efficiency: Generative AI can reduce the need for manual input, leading to long-term savings.
- Innovation: AI can lead to novel solutions and experiences from product design to customer interaction.
- Bias: AI models can perpetuate biases present in their training data. For businesses, this can lead to PR nightmares or even legal issues.
- Hallucinations: Generative models sometimes produce nonsensical or factually incorrect outputs, termed ‘hallucinations’.
- Cost: While AI can be cost-effective in the long run, the initial setup, including training and fine-tuning, can be expensive. Plus, running large models requires significant computational resources.
- Content Ownership: Most models are trained on publicly available data which may or may not be copyrighted, which raises ethical considerations when talking about derived works.
Getting Started with Generative AI on AWS
So, you are excited about giving generative AI a go… how do you get started? For businesses keen on exploring generative AI, AWS offers a plethora of tools and services:
- Start with SageMaker: AWS SageMaker provides tools to build, train, and deploy machine learning models, including generative ones.
- Access Pre-trained Models: Instead of starting from scratch, businesses can leverage pre-trained models available in the AWS SageMaker Jump Start, modifying them and augmenting their datasets according to specific needs.
- Educate Your Team: AWS offers a range of educational resources, from online courses to detailed documentation, that can help your team get up to speed with generative AI concepts and best practices.
Generative AI is opening up a world of new opportunities for businesses to take advantage of, from automating mundane tasks to fostering innovation. However, companies must be aware of the risks and challenges, ensuring their foray into generative AI is well-informed and strategic. In the coming weeks, we’ll be publishing a series of guides on how to get started with Generative AI on AWS and walk through a couple of examples of use cases. In the meantime, if you’d like to discuss implementing generative AI in your company, please feel free to reach out using the contact information at the bottom of the page.