If there’s one technology that’s promised to turn every corner of world industry on its head, it’s artificial intelligence. Not since the dawn of the internet and the advent of the mobile phone has there been such a transformative technology that stands to completely change the way the world gets work done.
Leading this paradigm shift are innovators like Nicholas Desmond, tech gurus who specialise in artificial intelligence technology and figure out new ways to apply it. He previously worked to implement AI-driven security to combat financial fraud at NuData Security Mastercard, and he’s currently the director of data science at Huge. But recently, he’s been laying a foundation for how generative AI can be used safely and ethically in creative realms such as writing, film production and art.
Learn more about how Nicholas Desmond is unlocking unprecedented efficiency and innovation in the creative industries.
From cybersecurity expert to generative AI guru
Nicholas Desmond graduated with a master’s in mathematics from University College London, focusing on applied mathematics in areas like probability and statistics. Fresh out of college, he worked as a data engineer for organisations like Experian and Kindred Group, building a strong foundation in data science and infrastructure.
He would go on to work at NuData Security, a Mastercard company. There, he created AI and machine learning models to combat the rising tide of cyber threats. This included the development of three patented innovations for detecting and mitigating cyberattacks, such as a fraud detection system that recognises suspicious activity and secures compromised accounts, as well as an anomaly detector that monitors web traffic and identifies harmful activity like DDoS attacks or hacking attempts. These technologies not only enhanced security for millions of customers but resulted in significant savings for several major institutions.
Later, Nicholas joined design and technology company Huge as its director of data science, leading the development of machine learning and generative AI systems for several high-profile clients. His most notable contribution was the designing and implementing of the machine learning pipeline for Culture Decoder, an AI-powered tool that helps brands better understand the cultural trends that are popular with their customers.
Culture Decoder analyses billions of data points across news outlets, social media, and other sources to assess the relevancy of various trends for target customer groups. Then, it calculates the sentiment and momentum of each trend, gives guidance on whether to act on or ignore specific trends, and provides actionable, data-driven recommendations to help brands drive engagement.
It was through the development of Culture Decoder that Nicholas started to develop a passion for using his AI expertise to explore human creativity. He realised that emerging tech like AI could provide insight into how people think and feel and create targeted content that’s purpose-built to connect with human sentiment.
Addressing the shortcomings of generative AI
Generative AI is the current hot topic in tech and entrepreneurship, with companies around the world looking to leverage it for cost savings, leaner workforces and personalised customer service.
However, while many AI developers are recycling the same models and workflows, making minor adjustments for convenience and personalisation, few innovators are making strides to improve generative AI over the long haul and turn it into a sustainable methodology. The fact of the matter is that generative AI is still in its infancy. Its true potential has yet to be reached — a potential that will always remain out of reach if AI experts never address its fundamental flaws.
Nicholas is one such expert working to tackle the many shortcomings of current-gen AI. For one, he recognises that generative AI models are highly probabilistic, which means their outputs can vary wildly and be quite inconsistent, leading to a wide range of unexpected scenarios.
While at Huge, while developing a generative AI assistant for a client, he worked to specifically address this flaw, implementing safeguards to enhance the reliability and relevance of AI-generated content. This required a lot of collaboration with his team as they worked to identify potential issues that could arise from the unpredictable nature of AI, and they had to brainstorm various methods to “guardrail” the solution — testing, identifying and resolving edge cases that could impact the user experience or produce undesirable outcomes.
One such method they explored was semantic caching, a technique that stores previously computed outputs in an internal database and allows for faster retrieval when a similar input is encountered again. This not only made the system more efficient, but it also helped to manage the scalability and relevancy concerns that are commonplace with heavy generative AI models.
The potential for generative AI in creative media
Image by Kohji Asakawa from Pixabay
Nicholas believes that when generative AI is closely monitored and kept on guardrails like these, it can be used more efficiently and effectively in a wide range of use cases.
For example, one of Nicholas’ recent endeavours was the creation of a model that’s capable of creating AI-generated children’s books. By leveraging generative AI to develop narratives and imagery with minimal human intervention, Nicholas was able to build an automated pipeline that would open new possibilities for content creation in publishing. This also allows for a higher volume of unique content that’s capable of catering to various audiences and preferences.
Looking forward, Nicholas envisions how AI can be used to augment human creativity in fields like animation and interactive media. He’s currently exploring how AI might be used throughout every stage of production, from generating initial concepts to refining the final output. This includes the fascinating possibility of real-time storytelling and interactive film, where AI can adapt the narrative on the spot based on audience engagement and feedback.
But Nicholas stresses that his aim is never to replace humans. His goal is simply to augment their work, free them from the time-consuming minutiae of their work and allow them to focus on what they do best: big-picture creativity.
Central to this philosophy is Nicholas’ commitment to human-in-the-loop systems — workflows and methodologies that encourage a balance between AI automation and human creativity. While AI handles repetitive or time-consuming tasks, human creatives monitor the AI and step in where needed to ensure the content retains originality and emotional depth. This frees creators to focus on the high-level aspects of storytelling and artistic expression, enhancing the overall quality of their work.
By marrying the efficiency of AI with the raw genius of human creativity, Nicholas believes that generative AI can be a game-changer for creative industries, allowing for faster content creation, reduced costs and more creative output. This can enable production studios and individual creatives to scale their work without sacrificing quality, expanding artistic expression and pushing the boundaries of what AI can achieve in creative fields.
Nicholas Desmond: Leading Innovation in AI Creativity
While Nicholas Desmond got his start in combating fraud and cyber threats, he’s now hoping to use his AI expertise to improve the way media is created and consumed. By bridging the gap between emerging technology and human expression, he’s working to shape a future where AI serves to amplify creativity — not replace it.
To learn more about how Nicholas is using AI to augment human creativity, check out his LinkedIn.
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