Artificial intelligence and big data

Explain in your own words the difference between traditional programming (rules written by a person) and machine learning (rules learned from examples), and give an application where each approach is more suitable.

Explain with a simple analogy what an artificial neural network is: layers of 'neurons' that combine inputs with adjustable weights. What is adjusted during training and what does it mean for the network to 'learn'?

Order these three terms from most general to most specific: deep learning, artificial intelligence, machine learning. Explain the inclusion relationship between them with an example of each level.

In supervised learning, what is the label of a data point? Give three examples of labelled datasets (photo → cat/dog, email → spam/not spam, sensor → healthy/faulty) and explain who usually produces the labels and what cost it entails.

Explain the difference between supervised, unsupervised, and reinforcement learning, and classify these applications: a spam filter trained with labelled emails, customer segmentation by buying habits without labels, and a robot learning to walk by trial and error.

Distinguish between a classification problem (predicting a category) and a regression problem (predicting a number). Classify these tasks: estimating the price of a house, deciding whether an X-ray shows a fracture, predicting tomorrow's temperature, and grouping news articles by topic.

A system learns with $80\,\%$ of the data and is evaluated with the remaining $20\,\%$. Explain what the training set and test set are, what overfitting is, and how it is detected by comparing errors in both sets.

A fault classifier evaluated on $200$ machines yields the confusion matrix shown in the figure. a) Calculate the precision ($\frac{VP}{VP + FP}$) and recall or sensitivity ($\frac{VP}{VP + FN}$). b) Explain which error is more serious in maintenance (a false negative: an undetected fault) and how you would change the model threshold to reduce it at the expense of more false alarms.

In bank fraud detection, only one in a thousand transactions is fraudulent. Explain what an imbalanced dataset is, why a model that classifies everything as 'legitimate' achieves $99{,}9\,\%$ accuracy and is completely useless, and what metrics and techniques are used instead (recall, oversampling the minority class, class weighting).

Define 'big data' and explain the three classic 'Vs' (volume, velocity, and variety) with an example of each in the context of an urban transport application.

A local council installs traffic sensors at $200$ junctions that send a reading every $5$ seconds. a) Estimate the number of readings generated per day and per year. b) Reason what storage and processing problems they pose and why conventional single-server databases might fall short.

A tech company decides to train its own 70-billion-parameter language model from scratch in a data centre connected to a grid with an emission factor of 0,26 kg CO₂/kWh. The training process requires 1,5 GWh of electrical energy. Once deployed, the service is estimated to handle 20 million queries per day, consuming an additional 0,5 GWh per month during the inference phase.

1. a) Analyse the three lifecycle phases of this AI system that contribute to its environmental footprint and justify why, over one year of operation, the impact of daily inference can significantly exceed that of the initial training.
2. b) Calculate the mass of CO₂ (in tonnes) emitted solely during the training phase and compare it with the emissions generated by the first full year of inference.
3. c) Design three technical engineering measures (at the level of model architecture, weight reuse, and infrastructure selection) that drastically reduce energy consumption without unacceptably degrading the quality of the responses.