Top 30 AI Projects for Aspiring Innovators: 2024 Edition
Deep Learning For Natural Language Processing
Deep Learning is becoming increasingly important in a technology-dominated world. However, the building of computational models that accurately represent linguistic structures is complex, as it involves an in-depth knowledge of neural networks, and the understanding of advanced mathematical concepts such as calculus and statistics. This book makes these complexities accessible to those from a humanities and social sciences background, by providing a clear introduction to deep learning for natural language processing. It covers both theoretical and practical aspects, and assumes minimal knowledge of machine learning, explaining the theory behind natural language in an easy-to-read way. It includes pseudo code for the simpler algorithms discussed, and actual Python code for the more complicated architectures, using modern deep learning libraries such as PyTorch and Hugging Face. Providing the necessary theoretical foundation and practical tools, this book will enable readers to immediately begin building real-world, practical natural language processing systems.
Natural Language Processing
The Natural Language Processing Research Group, established in 1993, is one of the largest and most successful language processing groups in the UK and has a strong global reputation.
Natural Language Processing (NLP) is an interdisciplinary field that uses computational methods:
To investigate the properties of written human language and to model the cognitive mechanisms underlying the understanding and production of written language (scientific focus)
To develop novel practical applications involving the intelligent processing of written human language by computer (engineering focus)
Research Themes Information AccessBuilding applications to improve access to information in massive text collections, such as the web, newswires and the scientific literature
Language Resources and Architectures for NLPProviding resources - both data and processing resources - for research and development in NLP. Includes platforms for developing and deploying real world language processing applications, most notably GATE, the General Architecture for Text Engineering.
Machine TranslationBuilding applications to translate automatically between human languages, allowing access to the vast amount of information written in foreign languages and easier communication between speakers of different languages.
Human-Computer Dialogue Systems Building systems to allow spoken language interaction with computers or embodied conversational agents, with applications in areas such as keyboard-free access to information, games and entertainment, articifial companions. Detection of Reuse and AnomalyInvestigating techniques for determining when texts or portions of texts have been reused or where portions of text do not fit with surrounding text. These techniques have applications in areas such as plagiarism and authorship detection and in discovery of hidden content.
Foundational TopicsDeveloping applications with human-like capabilities for processing language requires progress in foundational topics in language processing. Areas of interest include: word sense disambiguation, semantics of time and events.
NLP for social mediaSocial Media, Online Disinformation, and Elections: A Quantitative, "Big Data" Perspective.
Biomedical Text ProcessingGATE in Biomedical Text Processing
Core membersAcademic staff
Senior research staff
Research staffHere you can find research publications for the Natural Language Processing Research Group, listed by academic. The head link navigates to the official web page for the relevant academic (with highlighted favourite publications). The remaining links navigate to their DBLP author page, their Google Scholar citations page and optionally a self-maintained publications page.
Academic staffCan ChatGPT Do Data Science?
ChatGPT, a highly advanced language model developed by Meta AI, has left many people wondering about its capabilities. Can ChatGPT do data science? In this article, we'll delve into the capabilities and limitations of ChatGPT and explore its potential in the field of data science.
What is ChatGPT?
ChatGPT is a type of AI model that uses natural language processing (NLP) and machine learning to generate human-like text responses to user queries. It was trained on a massive dataset of text from the internet and can answer questions, summarize content, and even generate creative content such as stories and poems.
Data Science in a NutshellData science is a field that involves extracting insights and knowledge from large datasets. It involves using statistical and machine learning techniques to analyze and visualize data, and to identify patterns and relationships that can inform business decisions. Data science encompasses a range of tasks, including data wrangling, data visualization, machine learning, and storytelling.Can ChatGPT do Data Science?
In short, ChatGPT can assist with some aspects of data science, but it is not a replacement for a human data scientist. Here are some examples of what ChatGPT can do:
1. Data Cleaning and Preprocessing: ChatGPT can help with data cleaning and preprocessing tasks such as data normalization, data transformation, and data Quality control (QC) checks.2. Data Exploration: ChatGPT can help with data exploration tasks such as data summarization, data visualization, and anomaly detection.3. Data Analysis: ChatGPT can assist with data analysis tasks such as data regression, data clustering, and data classification.
However, there are some limitations to ChatGPT's abilities in data science. For example:
1. Lack of Domain Knowledge: ChatGPT lacks domain-specific knowledge in data science, which means it may not be able to understand complex data science concepts or apply domain-specific techniques.2. Limited Contextual Understanding: ChatGPT's understanding of context is limited, which means it may not be able to understand the nuances of a specific problem or situation.3. Limited Creativity: While ChatGPT can generate creative text, it is not capable of generating novel and innovative ideas in the same way that a human data scientist can.
ConclusionIn conclusion, while ChatGPT is a powerful tool that can assist with some aspects of data science, it is not a replacement for a human data scientist. Human data scientists bring a unique set of skills and expertise that are necessary for complex data science tasks. However, ChatGPT can be a useful tool for data science tasks that require a high degree of automation and speed, such as data cleaning, data exploration, and data visualization. As the technology continues to evolve, it will be interesting to see how ChatGPT and other AI models can be used to augment and augment human data science capabilities.
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