19-05-2025
4 New Creative Directions For AI
AI and creativity
getty
We all know that AI is changing enterprise in dramatic ways right now. But there are so many vectors of this progress that it's hard to isolate some of the fundamental ideas about how this is going to work.
I wanted to highlight a survey of ideas on new breakthrough research and applying neural network technologies.
These come from a recent panel at an Imagination in Action event where innovators talked about pushing the envelope on what we can do with this technology as a whole. These insights, I think, are valuable as we look at the capabilities of what we have right now, and how companies can and will respond.
To a large extent, past research in AI has been focused on working with text. Text was the first sort of data format that became the currency of LLMs. I would say that happened for a number of reasons, including that text words are easy to parse and separate into tokens. Also, text was the classical format of computing: it's easier to build systems to work with text or ASCII than to work through audio or video.
In any case we're now exploring boundaries beyond text, and looking at how other data formats respond to AI analysis.
One of these is audio.
'We certainly have text banks, but we haven't even scratched the surface on sound and decoding our voices,' said Joanna Pena-Bickley of Vibes AI, in explaining some of what her company is doing with wearables. 'Bringing these things together is really about breaking open an abundant imagination for creators. And if we can use our voices to be able to do that, I think that we actually stand a chance to actually (create) completely new experiences, sensual experiences.'
It's great for auditory learners, and we may be able to do various kinds of diagnosis and problem-solving that we didn't before. Pena-Bickley explained how this could help with cognitive decline, or figuring out people's personal biological frequencies and how they 'vibe' together.
In the world of gaming, too, scientists can apply different kinds of AI research to the players themselves.
In other words, traditional gaming was focused on creating an entertainment experience, but as people play, they generate data that can be useful in building solutions beyond the gaming world.
'We are trying to create dynamic systems, systems that respond to players,' said Konstantina Yaneva, founder of Harvard Innovation Labs. 'So we map … cognitive science, game design, and then (use) AI-driven analytics to help map and then improve decision-making patterns in players. But this is also a very creative endeavor of how to collaborate with consumers of entertainment, and how to meet them where they are, and then to help them self-realize in some way.'
Another area of pioneering is extended reality: in addition to AR and VR, XR is a technology that seems like it's due to have its day in enterprise.
It's always been time consuming, difficult and hard to keep up in terms of your teaching or if you're doing research,' explained renowned multi-technology artist Rus Gant, who has some ties to MIT. 'So the idea is to use AI as a way to create content in near real time.'
In addition, Gant talked about various aspects of applying AI, and in thinking out of the box, which could be interesting for anyone trying to meld AI with the humanities.
Here's another part of how companies are using the agentic AI approach that's only developed in the last few years.
Noting that agentic is now a big 'buzzword,' Armen Mkrtchyan of Flagship Pioneering talked about the practice of applying this concept to science.
It basically has to do with understanding the patterns and strategies of nature, and applying them to the digital world.
This starts with the idea of analyzing the human brain and comparing it to neural nets. But it can go far beyond that – nature has its own patterns, its own cohesion and its own structures.
Mkrtchyan talked about how scientists can use that information to simulate things like proteins. He also mentioned that the company has a stealth project that will be revealed in time, that's based on this kind of engineering.
Generally, he says it's possible to create a system that's sort of like a generative adversarial system (this is my reference, not his) where some AI agents come up with things, and others apply pressure to a decision-making process.
'About two years ago, we asked ourselves how we could try to mimic what nature does … nature is very good at creating new ideas, new things, and the way it does (this), it creates variability, creates optionality, and then applies selective pressure. That's how nature operates. And we thought at the time that we potentially AI agents, we could try to mimic the process … think of biology asking, can you create a specific peptide that binds with something else? Can we create an RNA molecule? Can it create a tRNA molecule?'
Expanding on this, Mkrtchyan noted some of the possible outcomes, with an emphasis on a certain kind of collaboration.
if we can say that intelligence consists of nature's intelligence, humans, intelligence, machines, intelligence, then can we leverage the power of all three of them to come up with new ideas, new concepts, and to drive them forward?
I also wanted to include this idea from Gant about audiences. Our audiences are changing, and we should be cognizant of that. We have Generation X, the sort of 'bridge' generation to AI, and then we have AI-native generations who have never known a world without these technologies.
'There is a very distinct difference when you come down the line from the Boomers to Gen X to Millennials to Gen Z, Gen Alpha, these are different groups,' Gant said. 'They think differently. They absorb information differently. They're stimulated in different ways, as to what excites them and gets them motivated. And there's no one size fits all. And right now, I think there's a big danger, and in the AI world, particularly when you productize AI, to go for the largest number of customers, and sort of leave the margins alone, because there's no money there. I think… the students that I have (who are) most responsive are basically the Gen X and soon to be Gen Alpha, that they basically look at you and say, 'Why didn't you do this sooner? Why did you do it in a way that doesn't make any sense to me?' I think in a sort of multiplex way. I multitask, I take input in various ways … we don't know what they're going to do with this. Whether it's Millennials or Gen Z or Gen Alpha, they're going to do really interesting things, and that's why I'm interested in how we can work with the AI in its non-traditional role, the solutionization world, where it's thinking outside the box.'
All of this is interesting information coming out of the April event, and reflects a survey of what companies can do now that they were not able to do in the past. Check out the video.
