Introduction to Artificial Intelligence - Coursework Assignment Question

Coursework

Question 1 (25 pts)

The idea for this question is to cluster words; for the question you will need a substantial piece of text. I suggest you use Project Gutenberg and download a few novels and concatenate them. Try to pick reasonably recent novels since they will have a more contemporary language use, however, because of the copyright restrictions the books tend to older. In what follows there are a series of “marking waypoints” indicated with numbers in square brackets of the form [m1] to [m6], these will be explained after the overall requirements are discussed.

When you have created your text [m1] pick a set of about 100 words, these should not be function words like “the” or “of”, so, for example, pick 100 nouns or a set split between nouns, verbs, adjective and adverbs. These might be the most common words in these classes or they may all be words in a specitic category, abstract nouns for example, or words related to needlework [m2]. Call this list L.

Now we want to create a set of similarities or distances between pairs of words in L. The similarity I have in mind is how often two words occur in the same sentence, or the average distance apart the words are in the text [m3].

Use this distance or similarity measure to perform unsupervised learning, any algorithm you want, and describe the results [m4].

The distance between two words may be greater than distance you get if you go by way of a third word: for example, and this example is a completely made up, image

d(soup, bed) = 34.4 
d(soup,spoon) = 2.8 
d(spoon, bed) = 4.6

Now it is quicker to get from soup to bed if you go by way of spoon and it might be more correct to regard
d(soup, bed) = 2.8 + 4.6 = 7.4

Use Dijkstra’s algorithm to find the shortest distance between an pair of words in L [m5] and repeat the unsupervised learning. Is it any different?

[m6]
The mark waypoints [m1]-[m6] are there to give an idea of the marking scheme but is intended to be a rough guide, not a rigid scheme. At [m1] four marks will have been allocated, to get a particularly good mark here make sure the data has been cleaned. At [m2] an additional two marks have been allocated, as an aside, this is a place an LLM could sensibly be used, for example, to pick the nouns from a list. At [m3] another four marks have been allocated, be careful to explain how you are calculating the distance, why you made that choice and what you see, for example, by noting which words are closest and which furthest apart. Six more marks are allocated by [m4], for a very good mark discuss the choice of algorithm and any meta-parameters, use graphs to compare different choices. Six more marks are allocated by [m5];
obviously the challenge here is implementing the algorithm. Explain clearly what you have done. Finally there are three more marks to be allocated by [m6].

Question 3 (15 marks)

Generate a data set in two dimensions with a division boundary of the form y = ax2 +x, so points one side of this boundary belong to class A and to the other, class B. Investigate how well logistic regression works for these data as a is varied. What about a small neural network? How are these approaches affected if the number of points is varied, or the balance between class A and class B in the number of points? How does changing the size of the network change the performance.

Question 3 (10 marks)

Do large language models have rights or other attributes of personhood? Do they have rights and will they acquire them? Discuss this in an essay of about one page.

• Your essay should stay focused on that topic throughout.
• If you argue for or against a position, ensure your points directly support your stance. If you’re presenting both sides, weigh the pros and
  cons.
• Start with a clear introduction that states your main argument or the scope of your discussion.
• Organise your points logically in the body of your essay. Each paragraph should advance your argument or analysis.
• Conclude with a summary of your argument or final reflection on the topic.
• Reference key studies or theories that support your argument. Provide brief explanations of why the cited studies are important for your
  argument.
• Don’t just summarise; engage critically with the literature.
• For higher marks, address counterarguments or limitations in the approaches you’re discussing.
• Aim for clear and precise writing. Avoid jargon unless necessary and explain technical terms.
• Ensure that your essay is free of grammatical and spelling errors, and that your ideas flow smoothly from one to the next.