Anytime you speak to Alexa, it records your voice and entire conversation. Amazon then stores those voice recordings forever, unless you opt to delete these in a more timely manner. If you already own an Amazon Alexa, you know how easy they make your day-to-day life. You can ask questions, avoid your phone altogether, or utilize the different skills you can set up and create new routines. How much information are you allowing Alexa and Amazon to collect from you?

The most likely explanation for these results is that ChatGPT was trained on content containing political biases. ChatGPT was trained on a large corpus of textual data gathered from the Internet. Such a corpus would probably be dominated by establishment sources of information such as popular news media outlets, academic institutions, and social media. It has been well-documented before that the majority of professionals working in those institutions are politically left-leaning (see here, here, here, here, here, here, here and here). It is conceivable that the political leanings of such professionals influences the textual content generated by those institutions.

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He is a serial entrepreneur and former management consultant. He holds an MBA and PhD in business. You may not know it yet, but you don't need to look too far to realize that generative AI is already a big part of our lives. Whether you've opted for a cartoon version of yourself as a profile picture, peeked at what you might look like at 90 (gulp), or your personal identity has been transformed into a synthetic version of you somewhere out there in the ether--using a synthetic data generator--that's generative AI doing its thing. And if the last example sounded like something out of a sci-fi movie, know that this is indeed already happening--and you'll be happy to learn why this is a good thing for you.

This thesis introduces quantum natural language processing (QNLP) models based on a simple yet powerful analogy between computational linguistics and quantum mechanics: grammar as entanglement. The grammatical structure of text and sentences connects the meaning of words in the same way that entanglement structure connects the states of quantum systems. Category theory allows to make this language-to-qubit analogy formal: it is a monoidal functor from grammar to vector spaces. We turn this abstract analogy into a concrete algorithm that translates the grammatical structure onto the architecture of parameterised quantum circuits. We then use a hybrid classical-quantum algorithm to train the model so that evaluating the circuits computes the meaning of sentences in data-driven tasks. The implementation of QNLP models motivated the development of DisCoPy (Distributional Compositional Python), the toolkit for applied category theory of which the first chapter gives a comprehensive overview. String diagrams are the core data structure of DisCoPy, they allow to reason about computation at a high level of abstraction. We show how they can encode both grammatical structures and quantum circuits, but also logical formulae, neural networks or arbitrary Python code. Monoidal functors allow to translate these abstract diagrams into concrete computation, interfacing with optimised task-specific libraries. The second chapter uses DisCopy to implement QNLP models as parameterised functors from grammar to quantum circuits. It gives a first proof-of-concept for the more general concept of functorial learning: generalising machine learning from functions to functors by learning from diagram-like data. In order to learn optimal functor parameters via gradient descent, we introduce the notion of diagrammatic differentiation: a graphical calculus for computing the gradients of parameterised diagrams.

Automatic speech recognition research focuses on training and evaluating on static datasets. Yet, as speech models are increasingly deployed on personal devices, such models encounter user-specific distributional shifts. To simulate this real-world scenario, we introduce LibriContinual, a continual learning benchmark for speaker-specific domain adaptation derived from LibriVox audiobooks, with data corresponding to 118 individual speakers and 6 train splits per speaker of different sizes. Additionally, current speech recognition models and continual learning algorithms are not optimized to be compute-efficient. We adapt a general-purpose training algorithm NetAug for ASR and create a novel Conformer variant called the DisConformer (Disentangled Conformer). This algorithm produces ASR models consisting of a frozen 'core' network for general-purpose use and several tunable 'augment' networks for speaker-specific tuning. Using such models, we propose a novel compute-efficient continual learning algorithm called DisentangledCL. Our experiments show that the DisConformer models significantly outperform baselines on general ASR i.e. LibriSpeech (15.58% rel. WER on test-other). On speaker-specific LibriContinual they significantly outperform trainable-parameter-matched baselines (by 20.65% rel. WER on test) and even match fully finetuned baselines in some settings.

The rapid advancement of AI technology has made text generation tools like GPT-3 and ChatGPT increasingly accessible, scalable, and effective. This can pose serious threat to the credibility of various forms of media if these technologies are used for plagiarism, including scientific literature and news sources. Despite the development of automated methods for paraphrase identification, detecting this type of plagiarism remains a challenge due to the disparate nature of the datasets on which these methods are trained. In this study, we review traditional and current approaches to paraphrase identification and propose a refined typology of paraphrases. We also investigate how this typology is represented in popular datasets and how under-representation of certain types of paraphrases impacts detection capabilities. Finally, we outline new directions for future research and datasets in the pursuit of more effective paraphrase detection using AI.