Artificial intelligence personal statement example

Why do you want to study this course or subject?

At four each morning, before the vans leave our bakery depot, someone decides which of forty-odd shops each driver visits and in what order. For six of my nine years there, that job has been mine. I began with a whiteboard and local knowledge. When the depot bought routing software, I spent weeks comparing its plans with what drivers actually did. Sometimes it found an order none of us had considered. Sometimes it sent a seven-tonne van down a lane no driver would attempt, because nobody had told it the lane was too narrow. That gap between a system that optimises well and one that understands its problem is what drew me to artificial intelligence. I want to learn how these systems represent the world, search for solutions and learn from data, and why they can be so capable in one respect and so blind in another. Melanie Mitchell's Artificial Intelligence: A Guide for Thinking Humans sharpened that question. Her account of image classifiers fooled by small changes, and of how hard analogy is for machines, matched what I had seen on a far smaller scale. I am applying now because evening study has shown me I can handle the mathematics, and I want the depth that full-time study allows.

How have your qualifications and studies helped you to prepare?

I left school with GCSEs and an unfinished BTEC, so I have rebuilt my qualifications around full-time work. I completed A-level Mathematics through evening classes last summer and am now taking an Access to HE Diploma in Computing. Calculus was the part I enjoyed most. It later made sense of material that would otherwise have been a black box. In my Access programming unit, I wrote a Python program that orders delivery stops. It first builds a route using a nearest-neighbour heuristic, then improves it with 2-opt swaps. On invented sets of thirty stops, 2-opt consistently shortened the routes. However, I realised straight-line distance ignores roads, rivers and one-way systems. For a smaller set, I built a distance table from an online map, and the rankings of some routes changed. Writing that up taught me that a model is only as sensible as its assumptions. Alongside the diploma, I worked through Andrew Ng's Machine Learning Specialization online. I implemented gradient descent for linear regression myself rather than relying only on the library call. I also saw how feature scaling speeds convergence, something the partial derivatives from A-level helped me follow. A shorter computed route is useful only if the distances represent the journey being planned.

What else have you done to prepare outside of education, and why are these experiences useful?

My job has given me habits I expect to use as a student. Dispatch runs to a fixed deadline, so I plan, check and act calmly when a driver calls in sick or a road closes. I keep a weekly spreadsheet of late deliveries. Sorting it by shop and weekday showed my manager that most problems came from two routes, which we then redesigned. I have also trained four new planners. Explaining why an obvious-looking route is wrong made me better at stating my own reasoning. Outside work, I am a volunteer timekeeper at my local parkrun most Saturdays, which rewards precision more than speed. For years I have solved cryptic crosswords with my dad. Recently I have started setting them for the depot tea room. Building a fair clue means anticipating how a solver will parse it, a small lesson in thinking about how another mind handles language. To prepare for full-time study, I have saved for my first year and arranged to work reduced weekend shifts. I am used to studying from five until seven before work, so I come to this degree with tested routines and a clear reason for being there.