Artificial Intelligence used in business – interview with Daniel Hulme

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Daniel Hulme (PhD) is a leading expert in Artificial Intelligence (AI) and emerging technologies, and is the CEO of Satalia. Satalia is an award-winning company that provides AI products and solutions for global companies such as Tesco and PwC. Daniel is also UCL’s Computer Science Entrepreneur in Residence and a lecturer at LSE’s Marshall Institute, focused on using AI to solve business and social problems. Daniel is a contributor to numerous books, podcasts and articles on AI and the future of work. He is also a faculty member of Singularity University. Daniel Hulme will be one of the keynote speakers at the Masters&Robots conference held online on September 21-24, 2020.

How artificial intelligence might be used in business?

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 you can’t benchmark machine intelligence against human intelligence. 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.

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.

Behavior is how quickly or frictionlessly I can move resources to achieve the objective. For example, if my goal is to sell lots of ice cream, how can I allocate my resources to make sure that I’m achieving the objective?

But the key word for me in the definition of goal-directed adaptive behavior is adaptive. If your computer system is not making a decision and then learning whether that decision was good or bad and adapting its own internal model of the world, I would argue that it’s not true AI. And it’s OK for companies at the moment to be calling machine learning AI. So for me, the true definition of AI involves systems that can learn and adapt themselves without the aid of a human. Adaptability is synonymous with intelligence.

Which sectors note the greatest demand for such technological solutions?

Every sector. But companies don’t have machine learning problems, they have optimization problems.

Optimization is the process of allocating resources to achieve an objective, subject to some constraints. Optimization problems are exceptionally hard to solve. For example, how should I route my vehicles to minimize travel time, or how do I allocate staff to maximize utilization, or how do I spend marketing money to maximize impact, or how do I allocate sales staff to opportunities to maximize yield? There are only a handful of people across the world who are good at solving problems like this with AI.There is a misunderstanding that these machine learning and deep learning models will solve many companies’ problems. Machine learning, data science, and statistics are great at finding patterns in data. But the most important thing is making decisions that leverage the patterns found in data. This requires a completely different set of skills: discrete mathematics, operations research, and optimization. These skills are massively underrepresented in industry.

Many CEOs feel they need to bring AI into their organization. There’s this fear factor that if you’re not on the AI bandwagon, then you’re going to lose out to competitors that are going to be eating your market, because they’re using technologies to make decisions faster and better than you.

CIO’s are trying to hire data scientists, whose work represents a kind of proxy for AI. But data scientists only have a certain type of skill. They understand how to use statistics and machine learning to find patterns in data. They’re not necessarily good at building production-grade systems that can make decisions or that can adapt themselves.

Where can we meet machine learning solutions on a daily basis?

There is a bubble in AI. 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 a small part of the AI journey.  And the next part of the journey for most big companies is optimization and decision making.

As 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.

The vast amount of content people are spreading is not AI. More and more people claim to be an AI or ethics expert, but only a small handful truly understand what these technologies are and what they are capable of achieving. It’s like claiming you’re a surgeon because you know how to knit. Companies are hiring data-scientists, thinking they will solve their AI problems, but this is hugely naive. There has probably been more hype around AI than any other technology I can think of, and whilst I suspect there might be a bubble in the short term, AI will impact our businesses and lives in more than perhaps any other technology.

I used to think that technology was a threat, in the sense that my competitors had access to advanced technologies and data. But now I think it’s getting the talent to use that technology.  How do you attract and retain that talent?  And that comes back to culture and purpose.

What are the benefits of introducing artificial intelligence into business?

AI will enable Digital Twins. 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.

Does artificial intelligence have any disadvantages that affect running a business?

In the short term, over the coming decade, I believe that AI will create jobs. In the long term, it will remove more jobs than it creates. I spend a lot of time thinking about the concept of economic singularity. This is the point at which AI will free people from their jobs and those people won’t be able to retrain fast enough to get another job, because AI will have taken it, too. Some experts believe that this could happen in the next 10 to 20 years, and that governments and our economy aren’t prepared for it. Satalia’s purpose is to try to address these future problems. We need to somehow create a global infrastructure that supports those people who are going to be out of work.

There’s another concept called the technological singularity, in which we build AI smarter than us in every possible way. It will be the last invention humanity needs to create, because it will be able to think infinitely faster and better than humans. Many scholars predict we will birth a superintelligence around the middle of our century. It will either be the most glorious thing to happen to humanity or perhaps our biggest existential threat. My concern is that if we are not cooperating as a global species by the time we create it, then it will see us as a threat and remove us from the equation. My purpose is to steer the world toward cooperation, and that means reinventing our political and economic models, and agreeing on a new objective function for humanity. The impulse for countries to increase GDP and companies to make profits means that more and more investment will be made to drive efficiencies and profits, which is leading us to a global economic and environmental crisis. We need a sustainable objective function, and we need to get everyone on the planet contributing to it; otherwise, we may destroy ourselves. I don’t believe that governments are prepared or can act quickly enough, so I hope the change will come from business leaders who have a huge influence and responsibility to steer us toward a positive future.