In May 1926, a thirteen-year-old boy set off from Southampton on his bicycle, heading towards the rolling hills of Dorset and his new school. The General Strike had disrupted transport, so he cycled the more than sixty miles to Sherborne School. His name was Alan Turing, and his mathematical ideas would help shape the world we live in today.

But what can we learn from him that might inspire us to follow his example?

Much has been written about Alan Turing: his life, his mathematics, his work at Bletchley Park, the birth of the computer and his ideas about artificial intelligence. Yet another story deserves our attention: how he tackled problems when he did not know the answer. Again and again, he stripped away unnecessary complications, looked at problems from a fresh angle and found simple ideas that revealed something others had missed.

By looking at how Turing approached the problems he worked on, we can discover five steps that anyone can use.

1. Question the problem

Before trying to solve a problem, ask whether you are looking at it in the right way. Could you frame it differently? Are you making assumptions that you do not need to make?

Turing’s work on computation began with a straightforward question: What does it mean to calculate?

Rather than starting with existing calculating machines, he imagined a person following a set of instructions, step by step. He asked how this process could be described in a simple, precise way. From this idea, he developed what became known as the Turing machine, a mathematical model that helped him investigate fundamental problems in logic and computation.

He took a similar approach to artificial intelligence. The question Can a machine think? is difficult to answer without first deciding what we mean by think. Turing suggested a different question: could a machine hold a conversation so convincingly that we could not reliably tell it apart from a human? This gave researchers a more practical way to explore the question.

Sometimes, the first step towards solving a problem is to ask a better question.

2. Simplify

Once you have questioned the problem, look for its essentials. Ask yourself: What is the simplest explanation that could work?

Turing applied this approach to biology. Living organisms are enormously complicated. Their cells contain countless interacting processes involving genes, proteins and chemical reactions. Rather than trying to explain everything at once, Turing asked a simpler question: Could a small number of basic processes explain how patterns develop in living things?

He developed mathematical equations to describe how chemicals react and spread through a substance. His work showed how these simple processes could interact to produce patterns across a much larger area.

The lesson is important. You do not always need to understand every detail of a complicated problem before you can begin to understand how it works. Sometimes, finding the right simple model is the key to making progress.

3. Explore possibilities

Do not dismiss an idea simply because it seems unlikely. Explore it. Ask what would happen if it were true.

Turing’s work on biological patterns provides a striking example. We might expect an orderly pattern to need something to organise it, like a gardener arranging plants in a flower bed. Turing showed mathematically how patterns could arise through the interaction of chemicals, without an external organiser laying them out.

This was a surprising idea. The chemicals did not need to follow a plan for a pattern to emerge. Their interactions could produce one.

When faced with a difficult problem, do not assume that something is impossible just because you cannot yet see how it could happen. Explore the possibility, work out its consequences and see where the reasoning leads.

4. Persist

Some problems cannot be solved at the first attempt. When one approach fails, look for another. Be prepared to change your thinking and try again.

Turing’s work on Enigma, the German cipher machine used during the Second World War, shows why persistence matters. Enigma could produce an enormous number of possible settings, making it impractical to test every possibility one by one.

Turing and his colleagues at Bletchley Park looked for clues and mathematical patterns that could help eliminate possibilities and narrow the search. Their work helped them find ways through a problem that had seemed impossibly difficult.

Persistence does not mean repeating the same action and hoping for a different result. It means continuing to work on a problem, learning from what fails and looking for a better approach.

5. Make it work

Finally, put your ideas into practice. A solution is not necessarily complete just because it looks convincing on paper. You need to find out whether it works.

Turing combined abstract mathematical thinking with practical problem-solving. His work at Bletchley Park helped turn mathematical ideas into methods that could be used to break German codes. His ideas about computation also helped lay the foundations for the computers we use today.

This lesson matters particularly in the age of artificial intelligence. An AI system can produce an answer that sounds convincing. But sounding convincing does not prove that the answer is correct. We still need to test ideas, examine the evidence and find out whether they work in practice.

Understanding an idea is important. Putting it to the test is equally important.

In 1926, Alan Turing set off on his bicycle for a school in Dorset. He could not have known what he would go on to achieve. His importance lies not only in his mathematical discoveries, but also in the way he approached problems.

He questioned what others took for granted. He stripped complicated problems back to their essentials. He explored ideas that seemed unlikely, persisted when answers were hard to find and put his ideas to the test.

These are not skills reserved for great mathematicians. They are skills we can all develop.

The next time you face a difficult problem, whether at school, at home or with something you want to achieve, try asking a different question. Look for a simpler explanation. Explore possibilities, keep going when your first attempt fails and test whether your ideas work.

You don’t have to be Alan Turing to solve problems like Alan Turing.

Many years ago I wrote a post about a scene in Star Trek II: The Wrath of Khan in which Captain Kirk is confronted with a training simulator designed to test his ability to survive a battle against the Klingons.

He was the only person in the history of Starfleet Academy to beat it. As the film progresses, we discover how. Kirk had not simply outmanoeuvred the simulator. He had reprogrammed it.

Imagine you are Captain Kirk, flying the USS Enterprise in the simulator. The Klingon fleet divides into two groups. One moves around the Enterprise from the left, while the other circles around from the right. Their intention is to attack simultaneously from both sides, leaving you nowhere to turn. Yikes!

Kirk realised that if he could change the rules of the simulator, he could change the battle.

But what happens when the simulator is powered by AI?

A traditional simulator is controlled by a program: a set of rules written in advance. An AI system works differently. It can be trained on thousands of battles and learn patterns from them. When a new battle begins, it uses what it has learned to decide what to do next. There is therefore no simple set of battle rules for Kirk to rewrite.

So how could Captain Kirk beat an AI simulator?

Perhaps he would do what Kirk has always done: look at the problem differently. Instead of changing the rules, he could change the data from which the AI learns. A small conventional program could manipulate the training data, subtly altering the patterns that the AI discovers. The simulator would still be following its instructions. It would simply have learned the wrong lesson.

This points to an interesting shift. With traditional programming, influence comes from changing the rules. With AI, influence can come from changing the data from which the system learns.

A program designed to manipulate the information presented to an AI could cause its decisions to become systematically biased towards a particular conclusion. This is a real area of research in AI security and robustness.

Computer hackers like giving things names. So perhaps we should give this one a name of our own: Silent Whisper. Silent, because the influence may go unnoticed. Whisper, because it does not need to shout or take control. It simply nudges the system towards learning something different.

In Star Trek II, Kirk receives a commendation for “original thinking” after finding a way to beat the simulator. He goes on to become a Starfleet Admiral.

But suppose Kirk faced the simulator today. He could no longer simply reprogram its battle rules, because an AI simulator learns its behaviour from the battles it has been shown. To beat it, he would have to find a new way of interfering with what it learns.

And perhaps that is the real challenge for the age of AI.

The old Kirk changed the rules. The new Kirk would change what the AI learned from.

So, Captain Kirk, the question is no longer whether you can outwit the simulator.

Can you outwit the way it learns?

It struck me recently, while our plumber fixed a leaking toilet, that neither AI nor a robot could simply replace him. I started thinking about the skilled people we rely on every day: electricians, metalworkers, car mechanics, builders and many others.

As the world changes, these jobs will change too, and AI will become another tool of the trade. But we will still need people who can work with their hands, make decisions, solve problems and, above all, build relationships and interact with other people.

So, which other jobs might remain fundamentally human in an AI-driven world?

Skilled trades matter because the real world rarely follows a set pattern. A plumber may arrive expecting to fix a leaking pipe and find old fittings, awkward spaces or a problem no manual could predict. AI can help plan the job, suggest ways to fix it and identify possible faults. But the plumber must assess the situation, draw on past experience and decide what to do next.

Some jobs also require people to understand what others need. Imagine a patient sitting with their GP after receiving difficult test results. They want more than a diagnosis. They want someone to explain the results, answer their questions and help them decide what to do next. AI may read a scan more accurately than a human, but many patients will still want a doctor beside them when they face a life-changing decision.

A teacher faces a similar challenge. They notice when a student starts to give up. They explain the idea differently, encourage the student and share their delight when the idea finally clicks. AI can explain a subject, but it cannot replace the relationship between teacher and student.

Creative work raises another question. An artist starts with an idea. AI can suggest colours, compose music, write poetry and draft stories, but it has no life of its own to draw upon. AI may become a powerful creative partner, but people bring the imagination, judgement and experience that give creative work meaning.

We can break any job into its separate tasks and ask what AI can do. Some tasks suit AI because they involve information, repetition or clear rules. Others require people to judge situations, work with others, draw on experience, create something new or take responsibility for the result.

So the question should not be, “Will AI replace this job?” but, “Which parts can AI do, and which still need a person?”

Rather than making humans compete with machines, we should use AI where it helps, while keeping what makes us human.

Next time you read that AI will bring about the demise of humanity, stop and ask: what makes us human, and can a machine ever learn it?

Like most people, there have been various points in my life where I have come into contact with the legal system. Whether buying a house or a car, dealing with matters related to home life, or handling business issues ranging from employment to intellectual property law, I have often had to rely on a solicitor or lawyer to interpret the law and advise me on the situation at hand. With AI beginning to cast a shadow over the legal field, I wonder whether there will come a time when I can simply describe a situation and receive clear, effective legal guidance in return. To explore this question, I decided to ask AI. The following is its response.

Me:   Why is the UK legal system so complicated that it requires expertise to provide advice?

AI:   UK legal system is complex because it has evolved over centuries, combining thousands of statutes with court decisions that interpret them. Legal systems also need to handle many different situations and exceptions, so simple rules often become detailed and technical to avoid unfair or inconsistent outcomes. As a result, understanding what the legal system actually means in a specific case often requires specialist knowledge. Some complexity is unavoidable in a modern society, but much of it also comes from layers of historical amendments and legal precedent accumulating over time.

Me:   As your powers increase will there be a point where you take over from the experts?

AI:   As AI becomes more capable, it may perform much of the technical work currently done by experts, especially in fields that involve analysing information and applying rules. However, experts don’t just provide knowledge—they also take responsibility, exercise judgment, and are accountable for decisions. So AI may replace many expert tasks, but whether it replaces experts themselves depends as much on trust, legal system, and accountability as on technology.

Me:   I have heard a lot about the next stage in AI development - Agentica - what is it and how could it be applied to the legal system?

AI:   “Agentica” refers to more advanced AI systems that can plan, use tools, and take actions over time rather than just answering questions. In legal system, such systems could handle tasks like legal research, drafting documents, managing cases, and even suggesting strategies or outcomes. This could make self-representation and access to legal help much easier, especially for simple cases. However, humans would likely still be needed for accountability, courtroom advocacy, and high-stakes judgment.

Me:   Put forward a case for applying you to the legal system, in particular for the general public.

AI:   The case for applying AI to legal system is mainly about improving access, cost, and understanding for the general public. AI could translate complex UK legal rules into plain English, guide people through procedures like claims or appeals, and help draft documents without needing expensive legal support. It could make legal help more widely available, especially for simple disputes where hiring a solicitor is often too costly. Overall, the goal is to reduce barriers to justice and make it easier for ordinary people to understand and use the legal system themselves.

Me:   Looking into a crystal ball how long before the ideas that we have discussed will become reality?

AI:   Here is the summary with possible stages of development realistic timescales added. The dates are approximate and not predictions because adoption will depend on technology, regulation, public trust and how quickly legal institutions adapt. The transition to an AI-enabled legal system should be gradual rather than sudden.

Over the next 1–3 years, AI will increasingly be used as an assistant for tasks such as legal research, summarising documents, drafting routine correspondence and explaining legal information in plain English. Lawyers and judges will remain responsible for all important decisions.

Over the next 3–7 years, AI is likely to become a more active adviser, helping analyse cases, identify relevant precedents, predict likely outcomes and suggest possible solutions. Human professionals will still provide oversight and make the final decisions.

Over the next 5–10 years, some routine legal processes may become largely automated, particularly where the rules are clear, such as simple claims, standard applications and administrative decisions. People should still have the ability to appeal to a human decision-maker.

Beyond 10 years, AI could transform access to justice by providing everyone with a personal legal assistant capable of explaining rights, preparing documents and helping people navigate legal processes. Lawyers would continue to play a vital role in complex cases, but routine legal support could become available to a much wider group of people.

Throughout this transition, the key principles should remain the same: AI should support rather than replace human judgement, decisions must remain accountable, and the legal system should become simpler and more accessible. The goal is not to remove lawyers, but to create a system where technology helps people understand and use the legal system more effectively.

Me:   Do you have any final thoughts?

AI:   The future of the legal system is not AI or people — it is AI helping people deliver better justice.

Many species reintroductions in the UK, including the Red Kite, Eurasian Beaver, and White-tailed Eagle, have shown promising signs of restoring biodiversity and rebuilding damaged ecosystems. Yet nature is a complex network of interactions, and even successful reintroductions can produce unexpected effects on other species. Conservation decisions are often based on limited data and observations made after changes have already occurred. What is needed is a new approach.

To understand how ecosystems behave, scientists often begin with simple models of interaction. One of the most common is the relationship between foxes and rabbits.

When rabbit populations are high, foxes thrive. Food is abundant, survival improves, and their numbers begin to rise. But this success does not last indefinitely. As foxes become more numerous, the pressure on rabbits increases, and their population begins to fall. With fewer rabbits available, foxes in turn begin to decline, and the cycle gradually resets.

At first glance, this pattern suggests a predictable rhythm in nature—almost like a natural balance that repeats over time.

But real ecosystems are not this simple.

Foxes do not depend on a single food source. They hunt rodents, birds, and amphibians, and they also scavenge carrion. They may even consume fruit and berries when available. At the same time, their survival is shaped by disease, habitat change, climate, and human activity.

As more species and interactions are added, the system stops behaving like a simple cycle and becomes a complex web of dependencies.

And in systems this interconnected, predicting the long-term effects of a single conservation decision becomes extremely difficult.

This is where AI could play a transformative role. By analysing vast amounts of ecological data, AI can begin to model the intricate relationships within ecosystems and predict the likely outcomes of conservation decisions before they are made.

But its power increases significantly when combined with the idea of a digital twin.

A digital twin of an ecosystem would be a living virtual representation of the natural world. It would be continuously updated using data from satellites, drones, camera traps, weather stations, acoustic sensors, and field surveys. Together, this stream of information would allow the AI to build a detailed picture of the environment—tracking populations of plants and animals, mapping their interactions, and monitoring changes in habitat and climate over time.

Rather than relying only on historical data, the system could then explore the future. It could simulate thousands of possible outcomes, allowing conservation managers to test different actions within the virtual ecosystem before any intervention takes place in the real world. The reintroduction of a predator, the restoration of a wetland, the planting of a woodland, or the removal of an invasive species—all could be explored safely in advance.

Crucially, the digital twin would not be static. As new data flows in from the real ecosystem, the model would be continuously refined. Each real-world outcome would feed back into the system, improving its accuracy and strengthening its predictions. Over time, this would create a dynamic feedback loop in which the AI becomes increasingly capable of understanding ecological complexity.

If we can build a digital twin of our ecosystems and use AI to explore the consequences of our actions before we take them we may finally begin to shift from reacting to environmental change to anticipating it.

The question is no longer whether such systems are possible. It is whether we can develop and deploy them quickly enough to make a difference before biodiversity loss reaches a point of no return.