For Further Reading

Text References

  • Jurafsky, D., & Martin, J. H. (2021). Speech and Language Processing (3rd ed.). Pearson Education Limited.
  • Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.
  • NLTK Book: Natural Language Processing with Python [>>]
  • Wikipedia: Natural Language Processing [>>]
  • Hodges, A. (1983). Alan Turing: The Enigma. Vintage Books.
  • Anderson, K. (1997). Turing's Cathedral: Origins of the Digital Universe. Bloomsbury.
  • Wikipedia: Alan Turing [>>]
  • Wikipedia: Georgetown-IBM Experiment [>>]
  • McCarthy, J. (1955). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence.
  • Wikipedia: Dartmouth Summer Research Project on Artificial Intelligence [>>]
  • Wikipedia: Machine Translation [>>]
  • Wikipedia: Syntactic Parsing (computational linguistics) [>>]
  • Shortliffe, E. H. (1976). Computer-Based Medical Consultation: MYCIN. Elsevier.
  • Weizenbaum, J. (1966). ELIZA: A computer program for the study of natural language communication between man and machine.
  • Biber, D., Conrad, S., & Leech, G. (2006). Longman Student Grammar of Spoken and Written English.
  • McEnery, T., Wilson, A., & Xiao, R. (2006). Corpus-Based Language Studies.
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning.
  • Wikipedia: Deep Learning [>>]
  • Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Efficient estimation of word representations in vector space.
  • Pennington, J., Socher, R., & Manning, C. D. (2014). Glove: Global vectors for word representation.
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention Is All You Need.
  • Wikipedia: Transformer (deep learning architecture) [>>]
  • Brown, T. B., Mann, B., Ryder, N., Subbiah, J., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners.
  • OpenAI: GPT-3 [>>]
  • Wikipedia: GPT-3 [>>]
  • Google AI: LaMDA [>>]
  • Wikipedia: LaMDA [>>]

Image References

  • First Public Demonstration of Machine Translation Occurs [>>]
  • Original Proposal of Dartmouth Project [>>]
  • Headline of IBM's Article on ALPS [>>]
  • A diagram of syntactic parsing [>>]
  • Formula for calculating mycin's certainty factor [>>]
  • A model on machine learning performance [>>]
  • A pdf from stanford involving Statistical Machine Translation (SMT) [>>]
  • A simplified representation of a single time step of the abstract numerical scheme [>>]
  • An octopus reading books while fish talk about it. [>>]
  • A visualization of word embeddings [>>]
  • A picture showing the architecture of the transformer model. [>>]
  • A figure illustrating the direct use of Large Language Models [>>]