Generative tools like ChatGPT and Stable Diffusion have got everyone talking about artificial intelligence (AI) – but where is it headed next?
It’s already clear that this exciting technology will have a big impact on the way we live and work. UK energy provider Octopus Energy has said that 44% of its customer service emails are now being answered by AI. And the CEO of software firm Freshworks has said that tasks that previously took eight to 10 weeks are now being completed in days as a consequence of adopting AI tools into its workflows.
But we’re still only at the beginning. In the coming weeks, months, and years we will see an acceleration in the pace of development of new forms of generative AI. These will be capable of carrying out an ever-growing number of tasks and augmenting our skills in all manner of ways. Some of them may seem as unbelievable to us today as the rise of ChatGPT and similar tools would have done just a few months back.
So, let’s take a look at some of the ways we can expect generative AI to evolve in the near future and some of the tasks it will be lending a hand with before too long:
Text-based generative AI is already pretty impressive, particularly for research, creating first drafts, and planning. You might have had fun getting it to write stories or poems, too, but probably realized it isn’t quite Stephen King or Shakespeare yet, particularly when it comes to coming up with original ideas. Next-generation language models – beyond GPT-4 – will understand factors like psychology and the human creative process in more depth, enabling them to create written copy that’s deeper and more engaging. We will also see models iterating on the progress made by tools such as AutoGPT, which enable text-based generative AI applications to create their own prompts, allowing them to carry out more complex tasks.
Generative Visual AI
As well as text, current generative AI technology is quite good at creating images based on natural language prompts, and there are even some tools that use it to generate video. However, they have some limitations due to the intensive nature of the required data processing. As this domain of generative AI becomes more advanced, it’s likely that it will become easy to create images and videos of just about anything, to the extent that it becomes difficult to distinguish generative AI content from reality. This could lead to issues such as deepfakes becoming problematic, resulting in the spread of fake news and disinformation.
Generative AI in the Metaverse
There are many predictions about how the way we interact with information and each other in the digital domain will involve. Many of these focus on immersive, 3D environments and experiences that can be explored through virtual and augmented reality (VR/AR). Generative AI will speed up the design and development of these environments, which is a time and resource-intensive process, and Meta (formerly Facebook) has indicated that this could play a part in the future of its 3D worlds platforms. Additionally, generative AI can be used to create more lifelike avatars that help to bring these environments to life, capable of more dynamic actions and interactions with other users.
Generative Audio, Music, and Voice AI
AI models are already impressively capable when it comes to generating music and mimicking human voices. In music, generative AI is likely to increasingly become an invaluable tool for songwriters and composers, creating novel compositions that can serve as inspiration or encourage musicians to approach their creative process in new ways. We are also likely to see it being used to create real-time, adaptive soundtracks – for example, in video games or even to accompany live footage of real-world events such as sports. AI voice synthesis will also improve, bringing computer-generated voices closer to the levels of expression, inflection, and emotion conveyed by a human voice. This will open new possibilities for real-time translation, audio dubbing, and automated, real-time voiceovers and narrations.
AI can be used by designers to assist in prototyping and creating new products of many shapes and sizes. Generative design is the term given for processes that use AI tools to do this. Tools are emerging that will allow designers to simply enter the details of the materials that will be used and the properties that the finished product must have, and the algorithms will create step-by-step instructions for engineering the finished item. Airbus engineers used tools like this to design interior partitions for the A320 passenger jet, resulting in a weight reduction of 45% over human-designed versions. In the future, we can expect many more designers to adopt these processes and AI to play a part in the creation of increasingly complex objects and systems.
Generative AI in Video Games
Generative AI has the potential to significantly impact the way video games are designed, built, and played. Designers can use it to help conceptualize and build the immersive environments that games use to challenge players. AI algorithms can be trained to generate landscapes, terrain, and architecture, freeing up time for designers to work on engaging stories, puzzles, and gameplay mechanics. It can also create dynamic content – such as non-player characters (NPCs) that behave in realistic ways and can communicate with players as if they are humans (or orcs or aliens) themselves, rather than being restricted to following scripts. Once game designers get to grips with implementing generative AI into their workflows, we can expect to see games and simulations that react to players’ interactions on the fly, with less need for scripted scenarios and challenges. This could potentially lead to games that are far more immersive and realistic than even the most advanced games available today.