PALS0039 Introduction to Deep Learning for Speech and Language Processing
UCL Division of Psychology and Language Sciences

Week 10 - Deep Learning and Artificial Intelligence

In which discuss the current deficiencies of deep learning for NLP, and the challenges to the creation of a human-level artificial intelligence.

Learning Objectives

By the end of the session the student will be able to:


  1. Goals of artificial intelligence
  2. We revisit the goals of artificial intelligence, and suggest the most sensible objective is to create artefacts that exhibit intelligent behaviours rather than attempting to think like a human or to duplicate human intelligence. Such systems might allow us to become super-intelligent rather than being intelligent themselves. Even though AI systems are not models of human cognition, they can still influence our understanding of cognition, particularly if the emergent behaviours of AI systems parallel those of humans. This might show, for example, that certain human behaviours are consequences of solving a problem in an efficient way, and so contigent on the problem rather than on the structure of human brains.

  3. Deficiencies of deep learning for speech and language processing
  4. We discuss some clear weaknesses in the deep learning approach to processing speech and language: (i) that DL systems simulate the use of language rather than knowingly use language, (ii) that they appear to understand more than they actually do, (iii) that the systems have no models of interlocutors in the way that people do, (iv) that the systems treat speech as if it were spoken text, (iv) that chatbot systems treat conversations as stimulus response, (v) that systems have no meta-awareness of language, and cannot talk about language itself.

  5. Philosophical limitations of deep learning
  6. We discuss what it would mean for an AI system to "understand" language, through the presentation of the Chinese Room thought experiment. This returns us to the problem of symbol grounding and the need for robots to experience how language relates to the physical world rather than just to language itself. We comment that building models to predict future events in the world, whether that be of physical objects or words in a sentence, could provide an underlying model of how to acquire knowledge and understanding. In turn this could create a unified model of human learning and cognition.

  7. Ethical considerations
  8. We discuss the essential amorality of machine learning. Models trained from data containing the inequalities, biases and prejudices of the human world will reflect those baises, but without being aware of them. This means we should not use deep learning to make moral judgements, and we should acknowledge this when we use deep learning to make life and death decisions.

Research Paper of the Week

Web Resources


Be sure to read one or more of these discussions about the role of deep learning in artificial intelligence and NLP.

Word count: . Last modified: 09:42 23-Dec-2021.