Daniel J. Hulme

thumb|Daniel Hulme in 2019 Daniel James Hulme (born 21 February 1980) is a British businessman, investor, academic and commentator, working in the field of Artificial Intelligence (AI), applied technology and ethics. He is the CEO and founder of Satalia that exited to WPP plc in 2021 for a rumoured $100,000,000 where he is also Chief AI Officer. Hulme is also an angel investor in emerging technology companies. In 2024 Daniel co-founded Conscium, an AI Safety company which tests AI Agents and verifies that they do what they are supposed to do. It is also investigating whether AIs will soon become conscious, and how to test for that, and developing more efficient approaches to AI development using neuromorphic computing. Quotes AI will be disruptive, but will ultimately provide opportunities. We all have an innate desire to do something for humanity Hierarchies are not structured for good decision making We can build AI to represent every corner of society… we can use it to ensure we build a less biased experience I’ve always been interested in what it means to be human, and in the nature of the universe. I did my undergraduate [degree] in AI and my master’s in AI, and my Ph.D. in AI, so I guess I’ve been doing 19 years’ worth of activity in AI. What drew you to artificial intelligence? There are two definitions of AI, and the more popular one is the weakest. This first definition [concerns] machines that can do tasks that were traditionally in the realm of human beings. Over the past decade, due to advances in technologies like deep learning, we have started to build machines that can do things like recognize objects in images, and understand and respond to natural language. Humans are the most intelligent things we know in the universe, so when we start to see machines do tasks once constrained to the human domain, then we assume that is intelligence.But I would argue that humans are not that intelligent. Humans are good at finding patterns in, at most, four dimensions, and we’re terrible at solving problems that involve more than seven things. Machines can find patterns in thousands of dimensions and can solve problems that involve millions of things. Even these technologies aren’t AI — they’re just algorithms. They do the same thing over and over again. In fact, my definition of stupidity is doing the same thing over again and expecting a different result. What is your definition of AI? The best definition of intelligence — artificial or human — that I’ve found is goal-directed adaptive behavior. I use goal-directed in the sense of trying to achieve an objective, which in business might be to roster your staff more effectively, or to allocate marketing spend to sell as [much] ice cream as possible. It might be whatever goal you’re seeking. And the second definition of AI? There are four categories of AI skills. The first category is the data. Companies should ask: Are we getting our data into the shape where people can consume it? There are lots of companies out there that are throwing money at building data lakes — that’s all the raw data that a company holds from code generation to sales information — because they think at some point in the future data lakes will be useful. That’s not a bad investment, but I would also suggest that you need to be building applications straight away on top of that data lake that drive value into your business. Companies…should be thinking about building digital twins of their organizations, i.e., a perfect digital representation of their physical assets, like their infrastructure and employees. On the question of talent and skills: You say data science is only part of it. What other categories should companies be looking at? Next is recruiting data scientists who have the machine learning and statistics skills to find insights from the data. Then, the third is [finding] what I call the decision scientists: people who can understand how to make decisions or solve optimization problems that leverage those insights. What’s the second AI category? And fourth, crucially, for true AI, you need to have an AI architect who understands how to glue these three components together: the data, the machine learning, and the optimization to build adaptive systems. And at the moment, it’s the CIO who is trying to step into that role of overseeing this. But I don’t know of many companies out there that have true AI architects. For now, companies are managing maybe part of this, but not all four categories. What’s the second AI category? Digital twins are the next evolution of digital transformation. To be able to adapt more quickly to a changing world, companies need to create a digital replica of all of their physical assets, their infrastructure and people. Once you have a twin, you can start to run experiments and simulate scenarios to operate your business more effectively. Further down the line we may even have AI setting those experiments, and running experiments without the aid of the human. The role of the strategist and of leadership is to develop a strong vision and purpose, i.e., [determining] what key objective the organization needs to aspire to. I hope that organizations will realize that this objective needs to be much more sophisticated than a financial return to be able to attract, empower, and motivate talent. Exceptional talent wants to align with a strong purpose and inspirational leaders. Can you say more about the digital twin concept? What can digital twins be used for? There is a bit of a bubble in AI. I don’t think that it’s going to go to waste. I think that all this investment will be additive, but there’s an over-expectation of what machine learning can bring right now, because of a lack of appreciation of the fact that machine learning is only part of the journey. And the next part of the journey for most big companies is optimization and decision making. Are we going to have a wave of wasted AI spending? As [futurist] Roy Amara noted, the impact of technology tends to be overestimated in the short run and underestimated in the long run. For now, you can probably ignore the idea of having adaptive systems in your business. That will come later. In the short run, you can use AI to remove the friction of mundane and repetitive tasks across the organization. If used correctly, this can absolutely change your business. But there’s a lot of hype out there, and a lot of people investing in these technologies don’t know what they’re doing. You’re saying that AI is simultaneously overhyped and underexploited. The world is changing so quickly, it’s very difficult to actually have all the necessary data points to be able to help you forecast accurately. At the moment, that’s still in the realm of human beings. How well equipped is AI to help business leaders forecast the future? External links Category: living people Category:Businesspeople from England Category:University College London alumni Category:Academics from England Category:1980 births