Artificial Intelligence has been around for decades, but with recent advancements in technology and computing power, AI is now poised to see a significant increase in usage.
In the past few years, we’ve seen robots make their way into our homes and workplaces. These advances are not just limited to the physical world; they’re also happening on the screen as well.
The future of Artificial intelligence will create new opportunities for storytelling that can’t be imagined today – it’s time to start exploring these possibilities!
1. What is AI storytelling and how does it work
AI storytelling is a way to use computer-generated characters and objects to tell the story. It is a subset of computer graphics with a focus on narrative storytelling.
In addition to simple visual effects, AI storytelling can also include advanced character interactions and gameplay (for example: using context and inference).
One of the main differences between traditional animation techniques and AI storytellers is that instead of hand-coding every action and interaction, we suggest using machine learning techniques to build a “storytelling engine”.
The system is trained on historical data from existing storytelling works, building an internal representation for how narratives work.
In the context of filmmaking (or movies), these AI-driven stories can be created with little input from human filmmakers – everything is generated by a computer and rendered into an image based on the data it was trained with.
This approach offers an unprecedented ability to generate stories that are personalized for each viewer or user – taking into account all of their preferences, choices, and peripherals like location, date, time, weather, etc.
Historically speaking there has never been a story told that couldn’t be told using this technique. The only constraint comes from the ability to generate the content in a reasonable amount of time and cost.
2. Pros and cons of AI storytelling
The main advantage of AI storytelling is the ability to create something entirely new. By combining different elements from the data set we train on, the system can produce stories that look completely organic and never before seen (for example: never having used that combination of characters or objects).
This creates a feeling of authenticity for the user experience which will be very hard to replicate with traditional techniques.
In addition to the novelty of something new, this approach can also be used to generate content for hard-to-fill markets (such as children’s entertainment).
The main drawback of this technology is that it still relies on human input in order to create a training data set. While recent advances in object recognition and natural language processing have made the process much more streamlined, it still requires a significant amount of manual labor.
There is also an opportunity cost associated with this approach: if AI storytellers are generated then that means no stories from writers or directors.
There are three main problems that still need solving to make real progress on telling stories using computer graphics and artificial intelligence:
1) Computation power (this is a big one)
2) Human labor
Most of the computation power for computer graphics comes from solving common problems like physics simulation or collision detection. The more domains that can be solved using AI, the less time it takes to do other tasks (such as training an AI storyteller).
The main issue with using AI to generate stories is the amount of human time required for training data generation. This is still an area that has very low efficiency and accuracy, which directly affects the cost of using artificial intelligence in storytelling (in a linear way).
The third problem is closely related to both computation power and manpower – there just aren’t enough good quality datasets to train AI with. One of the hardest parts is acquiring a data set that represents your domain and this can be an expensive process (since you have to bring in experts to generate the data).
AI storytelling falls in line with the current trend in technology and pop culture where we see a blending between human creativity and machine-generated data. We can see this phenomenon everywhere from the creation of music to AI-generated news articles and even in politics where opinions are being generated by machines. While these techniques have been largely used for information discovery, we’ve seen a trend towards using them for entertainment purposes as well – through the synthesis of creative content (like music or movies) with user-generated information to create something unique.
This approach has very interesting implications for the future of content creation and user experience where we would be able to generate content using machine learning technology in order to personalize it, based on a person’s preferences.
3. How can you use artificial intelligence in your own life, not just as a storyteller
Artificial intelligence has been a recent trend in the entertainment industry, but it’s actually already affecting our day-to-day lives.
We have seen AI affect the way we shop (most notably Amazon and their super-efficient supply chain), how we communicate (everything from using voice assistants to chatbots on social media), and even how we get around (from self-driving cars to Uber).
We’ve seen an increase in AI applications for personal use, but it’s not always clear how these technologies could be applied to storytelling. The main difference between traditional techniques and artificial intelligence is that computing power has become cheap enough for us to actually start thinking about using it on a large scale.
A big issue with AI in gaming is the fact that current computational limitations make it difficult for us to create believable virtual environments. This, coupled with the time required to train an AI agent and its inherent biases makes it a challenge to use them effectively in entertainment scenarios In order to solve this problem, both computing power, and human manpower are going to be key factors
4. The future of AI storytelling – what’s next for the technology
The future of AI storytelling is going to continue in the same direction it’s been going for a while now. We will see more personalization, more simulations, and real-time entertainment content, along with an increase in demand for computing power. As always, data will be king.
If we want to create believable simulations that can be enjoyed by many users, then massive amounts of training data will be needed. This means either spending more time creating it or using better techniques to generate the data automatically.
The main challenge for the future of AI storytelling is going to be in acquiring datasets that represent your field of interest. This is a tough problem because datasets that can be used to train an AI agent are usually very specific in their nature, so it’s difficult to acquire them unless you’re already working within the field.
It’s worth noting that this may not be as big of an issue if we start using machine learning techniques for content discovery as we’ve seen in Spotify’s Discover Weekly feature. In this case, the neural network could create new datasets based on existing ones by using the current user data as a guide.
Lastly, I think it’s important to note that AI will not replace humans completely when it comes to storytelling. At least not yet! The field of artificial intelligence is still in its infancy, and we need to be careful not to make the same mistakes humans have made when it comes to using these new technologies. We should focus on supporting the creative process instead of simplifying it, while also respecting the rights of those who participate in that process.
5. AI storytelling today
There are some wonderful recent examples of companies using artificial intelligence to tell stories, such as Google AutoDraw , Google Duplex  and Facebook’s M Suggestions
AutoDraw uses machine learning to generate images based on a seed image. You can see the results in the video below:
M Suggestions is Google’s personal assistant, which offers information that may be relevant to you based on your search history. For example, it will suggest restaurants to visit, recipes to try, or movies to watch.
Duplex uses natural sounding speech synthesis based on recordings of a real person speaking in order to make reservations on your behalf. You can see this in action here:
Facebook M Suggestions and Google Duplex are great examples of using AI for automation (in both cases the agents are only used to complete a task on behalf of the user).
Unfortunately, it’s not always clear how AI agents are being implemented in entertainment scenarios. This is due to data and privacy concerns that prevent companies from discussing their products publicly. This can make it hard for creators to understand what’s possible, since they don’t have as many examples to go on.
We should also note that there are fewer companies working with AI in entertainment compared with more mainstream industries, such as manufacturing and healthcare. This means that creators will likely face increased competition when marketing their products.
What’s coming in the next 5 years?
Over the next 5 years I think we’re going to see more personalization and content with a stronger sense of storytelling. Conversational user interfaces will become the norm for interacting with computers, and companies will eventually start using these techniques to interact with users in ways that look just like natural conversations.
I also think we’ll see an increase in demand for computing power as agents become more advanced. The higher the fidelity of our AI agents, the more data they will need to learn effectively.
Lastly, I think we’re going to see some serious discussions about how artificial intelligence is implemented in society when it comes to privacy and ethics. This topic is already a hot-button issue in many other industries, and it’s important that we take this seriously in order to avoid creating biased or unfair systems that could potentially harm users.
6. In Summary
Artificial intelligence is being used in storytelling. Not all companies using artificial intelligence in entertainment talk about their products publicly due to data/privacy concerns or competition over limited resources within this field.
Ccreators may face increased difficulty marketing these more specialized items when compared to other industries like manufacturing & healthcare that use AI to automate tasks related to their business.
AI in entertainment has many uses. Examples include Netflix’s personalized suggestions, Amazon’s personalization of your shopping cart, and even Facebook M Suggestions that present information relevant to you based on a Google search or govt record (or any other info from the web).
I think we will see an increase in demand for computing power as our AI agents become more advanced. The higher the fidelity of these agents, the more data they will need to learn effectively. I also expect to see a rise in privacy/ethical concerns over how companies use artificial intelligence to assist their services and products.