In a nutshell
- Generative AI is a type of artificial intelligence (AI) that can produce various forms of content, including text, code, images and audio.
- Large language models (LLMs) such as ChatGPT are a type of generative AI. They refer to models that mimic human intelligence to analyse vast volumes of data and find patterns.
- Basic AI/LLM applications are available for companies to use via application program interfaces (APIs). However, AI is data and model dependent and needs to be tailored to the individual needs and goals of the business.
- We bring your AI strategy to life with an end-to-end approach, using proven tools and accelerators and can put you on the fast track to controlled AI solutions.
What is generative AI?
Generative AI can generate new content based on the content it was trained on. Training can be based on data in multiple formats, including text, voice or images. Generative AI can consume vast amounts of data to find patterns and relationships and create new text. This means:
- New generative AI apps – such as Chat GPT – empower almost anyone to harness the vast potential of AI to create something new from a virtually unlimited number of sources. In seconds.
- Recent advances mean that LLMs can learn autonomously and acquire “common sense”. With potentially billions of parameters, LLMs can make accurate predictions with little training. This means they can be adapted quickly and easily to a wide range of tasks.
- Although generative AI is perceived as cutting-edge technology, it will quickly become the baseline of business success. Every successful organisation in every sector needs a generative AI strategy.
What’s the use of generative AI?
- Advances in LLMs will also transform business and how work gets done. Many of your time-consuming repetitive tasks can be automated, freeing staff to focus on tasks that add more value. As well as boosting productivity, AI will help gain a deeper understanding of their customers by analyzing their online activity and becoming data driven.
- Although the number of use cases for generative AI is potentially unlimited, customer assistance is an area of focus. Chatbots powered by LLMs are already transforming customer service. By enabling 24/7 multilingual customer support, a growing range of powerful applications can increase customer engagement, answer queries and build loyalty.
- LLMs can work across mutiple data types, including voice, text and images, so companies can align customer support with a multichannel-marketing strategy and to build a culture that truly revolves around the customer.
Key concepts in generative AI
Recent examples of generative AI that can generate text include ChatGPT and Perplexity AI. But evolving generative AI apps can also generate new images, video, sounds or code. Popular models include:
- Transformer models: These are designed to learn the contextual relationships between words. They learn through a mechanism known as self-attention, which weighs the importance of words in a sequence based on their context.
- Generative Adversarial Networks (GANs): These models employ two neural networks – a generator and a discriminator. The generator generates new examples, and the discriminator model classifies these as either real or fake. Over time the generator learns to create more realistic content that can fool the discriminator, while the discriminator gets better at distinguishing content. The two neural networks learn in parallel.
- Variational Autoencoders (VAEs): In essence these are probabilistic generative models that analyse a pattern in a dataset to generate new content. They can be used to generate text, image and video. VAEs have a wide range of applications, ranging from generating fake human faces, to producing purely synthetic music.
The above models already have many applications in business, ranging from generating personalised content, deploying chatbots and virtual assistants, to automated code generation and debugging.
Generative AI empowers business transformation
Although generative AI is in constant flux, there are some fundamentals that remain constant. All generative AI applications rely on data, so the AI strategy must be aligned to the data strategy. An effective enterprise data strategy built on modern flexible architecture is essential. And with growing data volumes and the high demands of generative AI this increasingly needs to be cloud based.
GFT is committed to AI as the next stage of digitalisation. We have developed the GFT AI.DA Marketplace that will help bring your generative AI strategy to life using our proven tools and accelerators.
We can help you define the business purpose and objectives of generative AI; develop a prototype within a target technical framework and architecture; deploy, run and quantify the success of a generative AI deployment.
Generative AI – the baseline of business success
- Artificial intelligence is not the same as human intelligence. All content must be carefully monitored and inspected.
- There are widespread reports of AI models ‘hallucinating’ or generating harmful results. Any deployment must have clear guardrails that prevent damage to the company, its brand or reputation.
- There are genuine legal concerns around data privacy, the legal ownership of machine-generated content and the data used for training models.
- As with all new technologies, data security is a concern as AI offers a bigger attack surface for criminals.
Achieving success with generative AI is never easy, but it’s a lot easier with an expert partner. Discover how GFT can help you bring your AI strategy to life.
