Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data. As the word suggests it generates, i.e. Selecting content for email marketing based on predictive ai personalisation, and creating a personalised product selection email from it, is the perfect example. The recent buzz around generative AI has been driven by the simplicity of new user interfaces for creating high-quality text, graphics and videos in a matter of nano-seconds.
The technology, it should be noted, is not brand-new. Generative AI was introduced in the 1960s in chatbots. But it was not until 2014, with the introduction of generative adversarial networks, or GANs — a type of machine learning algorithm — that generative AI could create convincingly authentic images, videos and audio of real people.
On the one hand, this newfound capability has opened up opportunities that include better movie dubbing and rich educational content. It also unlocked concerns about deepfakes — digitally forged images or videos — and harmful cybersecurity attacks on businesses, including nefarious requests that realistically mimic an employee’s boss.sTwo additional recent advances that will be discussed in more detail below have played a critical part in generative AI going mainstream: transformers and the breakthrough language models they enabled.
Transformers are a type of machine learning that made it possible for researchers to train ever-larger models without having to label all of the data in advance. New models could thus be trained on billions of pages of text, resulting in answers with more depth. In addition, transformers unlocked a new notion called attention that enabled models to track the connections between words across pages, chapters and books rather than just in individual sentences. And not just words: Transformers could also use their ability to track connections to analyse code, proteins, chemicals and DNA.
The rapid advances in so-called large language models (LLMs) — i.e., models with billions or even trillions of parameters — have opened a new era in which generative AI models can write engaging text, paint photorealistic images and even create somewhat entertaining sitcoms on the fly. Moreover, innovations in multimodal AI enable teams to generate content across multiple types of media, including text, graphics and video. This is the basis for tools like Dall-E that automatically create images from a text description or generate text captions from images.
These breakthroughs notwithstanding, we are still in the early days of using generative AI to create readable text and photorealistic stylized graphics. Early implementations have had issues with accuracy and bias, as well as being prone to hallucinations and spitting back weird answers. Still, progress thus far indicates that the inherent capabilities of this type of AI could fundamentally change business. Going forward, this technology could help write code, design new drugs, develop products, redesign business processes and transform supply chains.
How does generative AI work?
Generative AI starts with a prompt that could be in the form of a text, an image, a video, a design, musical notes, or any input that the AI system can process. Various AI algorithms then return new content in response to the prompt. Content can include essays, solutions to problems, or realistic fakes created from pictures or audio of a person.
Early versions of generative AI required submitting data via an API or an otherwise complicated process. Developers had to familiarize themselves with special tools and write applications using languages such as Python.
Now, pioneers in generative AI are developing better user experiences that let you describe a request in plain language. After an initial response, you can also customise the results with feedback about the style, tone and other elements you want the generated content to reflect.
Generative AI models
Generative AI models combine various AI algorithms to represent and process content. For example, to generate text, various natural language processing techniques transform raw characters (e.g., letters, punctuation and words) into sentences, parts of speech, entities and actions, which are represented as vectors using multiple encoding techniques. Similarly, images are transformed into various visual elements, also expressed as vectors. One caution is that these techniques can also encode the biases, racism, deception and puffery contained in the training data.
Once developers settle on a way to represent the world, they apply a particular neural network to generate new content in response to a query or prompt. Techniques such as GANs and variational autoencoders (VAEs) — neural networks with a decoder and encoder — are suitable for generating realistic human faces, synthetic data for AI training or even facsimiles of particular humans.
Recent progress in transformers such as Google’s Bidirectional Encoder Representations from Transformers (BERT), OpenAI’s GPT and Google AlphaFold have also resulted in neural networks that can not only encode language, images and proteins but also generate new content.
What are the benefits of generative AI?
Generative AI can be applied extensively across many areas of the business. It can make it easier to interpret and understand existing content and automatically create new content. Developers are exploring ways that generative AI can improve existing workflows, with an eye to adapting workflows entirely to take advantage of the technology. Some of the potential benefits of implementing generative AI include the following:
- Automating the manual process of writing content.
- Reducing the effort of responding to emails.
- Improving the response to specific technical queries.
- Creating realistic representations of people.
- Summarizing complex information into a coherent narrative.
- Simplifying the process of creating content in a particular style.
What are the limitations of generative AI?
Early implementations of generative AI vividly illustrate its many limitations. Some of the challenges generative AI presents result from the specific approaches used to implement particular use cases. For example, a summary of a complex topic is easier to read than an explanation that includes various sources supporting key points. The readability of the summary, however, comes at the expense of a user being able to vet where the information comes from.
Here are some of the limitations to consider when implementing or using a generative AI app:
- It does not always identify the source of content.
- It can be challenging to assess the bias of original sources.
- Realistic-sounding content makes it harder to identify inaccurate information.
- It can be difficult to understand how to tune for new circumstances.
- Results can gloss over bias, prejudice and hatred.
What are the concerns surrounding generative AI?
The rise of generative AI is also fueling various concerns. These relate to the quality of results, potential for misuse and abuse, and the potential to disrupt existing business models. Here are some of the specific types of problematic issues posed by the current state of generative AI:
- It can provide inaccurate and misleading information.
- It is more difficult to trust without knowing the source and provenance of information.
- It can promote new kinds of plagiarism that ignore the rights of content creators and artists of original content.
- It might disrupt existing business models built around search engine optimisation and advertising.
- It makes it easier to generate fake news.
- It makes it easier to claim that real photographic evidence of a wrongdoing was just an AI-generated fake.
- It could impersonate people for more effective social engineering cyber attacks.
What are some examples of generative AI tools?
Generative AI tools exist for various modalities, such as text, imagery, music, code and voices. Some popular AI content generators to explore include the following:
- Text generation tools include GPT, Jasper, AI-Writer and Lex.
- Generative AI predictive personalised product selection for ecommerce SwiftERM
- Image generation tools include Dall-E 2, Midjourney and Stable Diffusion.
- Music generation tools include Amper, Dadabots and MuseNet.
- Code generation tools include CodeStarter, Codex, GitHub Copilot and Tabnine.
- Voice synthesis tools include Descript, Listnr and Podcast.ai.
- AI chip design tool companies include Synopsys, Cadence, Google and Nvidia.
Use cases for generative AI, by industry
New generative AI technologies have sometimes been described as general-purpose technologies akin to steam power, electricity and computing because they can profoundly affect many industries and use cases. It’s essential to keep in mind that, like previous general-purpose technologies, it often took decades for people to find the best way to organize workflows to take advantage of the new approach rather than speeding up small portions of existing workflows. Here are some ways generative AI applications could impact different industries:
- Finance can watch transactions in the context of an individual’s history to build better fraud detection systems.
- Legal firms can use generative AI to design and interpret contracts, analyse evidence and suggest arguments.
- Manufacturers can use generative AI to combine data from cameras, X-ray and other metrics to identify defective parts and the root causes more accurately and economically.
- Film and media companies can use generative AI to produce content more economically and translate it into other languages with the actors’ own voices.
- The medical industry can use generative AI to identify promising drug candidates more efficiently.
- Architectural firms can use generative AI to design and adapt prototypes more quickly.
- Gaming companies can use generative AI to design game content and levels.
Generative AI vs. AI
Generative AI produces new content, chat responses, designs, synthetic data or deepfakes. Traditional AI, on the other hand, has focused on detecting patterns, making decisions, honing analytics, classifying data and detecting fraud.
Generative AI, as noted above, often uses neural network techniques such as transformers, GANs and VAEs. Other kinds of AI, in distinction, use techniques including convolutional neural networks, recurrent neural networks and reinforcement learning.
Generative AI often starts with a prompt that lets a user or data source submit a starting query or data set to guide content generation. This can be an iterative process to explore content variations. Traditional AI algorithms process new data to return a simple result.
Best practices for using generative AI
The best practices for using generative AI will vary depending on the modalities, workflow and desired goals. That said, it is important to consider essential factors such as accuracy, transparency and ease of use in working with generative AI. The following practices help achieve these factors:
- Clearly label all generative AI content for users and consumers.
- Vet the accuracy of generated content using primary sources where applicable.
- Consider how bias might get woven into generated AI results.
- Double-check the quality of AI-generated code and content using other tools.
- Learn the strengths and limitations of each generative AI tool.
- Familiarize yourself with common failure modes in results and work around these.
The future of generative AI
The incredible depth and ease of ChatGPT have shown tremendous promise for the widespread adoption of generative AI. To be sure, it has also demonstrated some of the difficulties in rolling out this technology safely and responsibly. But these early implementation issues have inspired research into better tools for detecting AI-generated text, images and video. Industry and society will also build better tools for tracking the provenance of information to create more trustworthy AI.
Furthermore, improvements in AI development platforms will help accelerate research and development of better generative AI capabilities in the future for text, images, video, 3D content, drugs, supply chains, logistics and business processes. As good as these new one-off tools are, the most significant impact of generative AI will come from embedding these capabilities directly into versions of the tools we already use.
Grammar checkers are going to get better. Design tools will seamlessly embed more useful recommendations directly into workflows. Training tools will be able to automatically identify best practices in one part of the organization to help train others more efficiently. And these are just a fraction of the ways generative AI will change how we work.