A conversation with Dr Dan Nicolau and Dr Michael Bennett
In this interview, they discuss why the programme was created, why professionals need to move beyond simply using AI tools, and how a deeper understanding of the technology can help people apply AI confidently and responsibly within their own fields.
What inspired the creation of the new online Applied Artificial Intelligence MSc?
Dr Dan Nicolau: There’s a gap between the research frontier, the work both academics and companies are doing, and the professionals who need to use these technologies every day.
Take law as an example. AI is already being used to support everything from judicial review to evidence analysis and legal research. But if you’re a lawyer, or work in an allied profession, there hasn’t really been an obvious way to upskill for the age of AI.
You might read headlines saying AI is transforming a profession, but what professionals actually need is a deeper understanding of how these tools work and how they can apply them responsibly within their own careers.
People often compare AI to the invention of the horse or the railway, but I don’t think those comparisons are particularly useful. It’s probably much closer to electricity. When electricity came along, plumbers still needed to be plumbers, but they also needed to understand electricity. That’s exactly where we are with AI today.
Professionals still need their expertise, but they also need to understand this new technology that’s changing how every profession operates.
Why is applied AI such a significant area of study today?
Dr Michael Bennett: We wanted to “open the bonnet” on AI.
It would have been easy to create another programme that simply teaches people how to prompt ChatGPT more effectively. Instead, we wanted students to understand where AI comes from, how it works, and the technical foundations that underpin it.
By doing that, we’re helping professionals across a wide range of sectors use AI responsibly, efficiently and productively, not simply consume whatever tools happen to be available.
The first third of the programme is deliberately rooted in the technical foundations of AI. We know many of our students may not have studied mathematics since they were 16, but we’re ready for that challenge. We take students on that journey, so they develop the confidence to understand AI, rather than simply use it.
What makes this course different from a traditional Computer Science or AI degree?
Dr Michael Bennett: This isn’t a programme designed to produce AI researchers.
It’s designed for professionals who want to understand AI well enough to apply it confidently within their own sector.
Rather than focusing purely on coding or theory, students build an understanding of programming, machine learning and responsible AI before applying those skills within areas such as healthcare, law, public policy, education, security and the creative industries.
The goal is to produce graduates who understand both the technology itself and the responsibilities that come with applying it.
Who is this course designed for, and what kinds of students will benefit most?
Dr Dan Nicolau: The students who will benefit most are those who really engage with the technical side of the programme.
Prompt engineering is a little like knowing how to operate a light switch. That’s very different from understanding how electricity actually works or getting the most out of it.
The students who get the most from this programme will combine the technical foundations – programming, machine learning and understanding data – with expertise in their own profession.
You don’t need to become an AI researcher. But there’s a big difference between someone who can build and evaluate a machine learning model using the right data, understand where bias comes from, stress-test that model appropriately and recognise its limitations, and someone who’s simply experimenting with AI tools at the surface level.
The deeper students engage with the foundations of AI, the more they’ll get out of the programme.
How has the field of AI evolved over the past decade?
Dr Dan Nicolau: The biggest technical development has been transformer models.
What transformer models allow AI systems to do is maintain long-range relationships across huge amounts of information. That’s what gives modern AI systems context and allows them to generate answers that make sense.
Language is the obvious example, but exactly the same principles apply in fields like medicine, where AI can help design proteins or analyse extremely complex biological data.
What’s interesting is that many of the techniques behind AI are actually much older than people realise. Some of the mathematical foundations go back to the nineteenth century, while machine learning itself has existed for decades.
What’s really changed over the last ten years is the availability of data. The internet has digitised an enormous amount of human knowledge and information, creating the datasets that modern AI systems need in order to learn. So while the algorithms themselves aren’t necessarily new, our ability to train them at scale has transformed what AI is capable of.
Which emerging issues should students pay particular attention to over the next 5–10 years?
Dr Dan Nicolau: One of the biggest challenges will be trust. AI systems have always been capable of producing incorrect information, but as reasoning models become more sophisticated, those mistakes become much harder to recognise because they appear logical and convincing.
Whether you’re working in law, healthcare, education or science, understanding when to trust AI, and when to question it, will become an essential professional skill. At the same time, these capabilities may also become one of AI’s greatest strengths.
Hallucinations may ultimately be part of what allows AI to become genuinely creative. The challenge isn’t necessarily eliminating them altogether, but understanding how to work with AI responsibly while recognising its limitations.
How is AI changing different professions?
Dr Dan Nicolau: One of the most fascinating things about AI is that it’s beginning to blur the boundaries between professions because it’s turning many professional challenges into data problems.
Take two very different examples. One is identifying a face in a crowd. The other is a pathologist examining tissue under a microscope to diagnose cancer.
They seem like completely different professions, but as machine learning problems, they’re remarkably similar. Both involve recognising patterns, comparing them against known data and making informed decisions.
The same applies when comparing predicting the outcome of clinical trials with analysing investment strategies. On the surface they’re completely different industries, but many of the underlying AI techniques are surprisingly similar.
To thrive in the age of AI, professionals need to understand which boundaries between professions are beginning to dissolve, and which remain just as important as ever.
Why is King’s uniquely placed to deliver this programme?
Dr Dan Nicolau: King’s excels in disciplines where AI has the potential to make the biggest difference, and where it also needs to be applied most responsibly.
We’re recognised for areas such as medicine, law, defence and public policy. These are highly regulated professions where AI has enormous potential to improve outcomes, but where ethical, legal and societal considerations are absolutely critical.
That places King’s in a unique position. Where we already have world-leading expertise also happens to be where AI presents both its greatest opportunities and its greatest challenges. That gives us a real opportunity to shape how AI is developed and applied for the benefit of society.
Discover King’s online Applied Artificial Intelligence MSc programme
The online Applied Artificial Intelligence MSc is designed for professionals who want to move beyond surface-level familiarity with AI and develop the technical knowledge to understand, evaluate and apply it responsibly.
Study 100% online and part-time, building core knowledge in programming, machine learning and responsible AI before specialising through a pathway in Creativity and Culture, Education and Society, Public Policy or Security.