AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework

This technical report presents AutoGen, a new framework that enables development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools. AutoGen's design offers multiple advantages: a) it gracefully navigates the strong but imperfect generation and reasoning abilities of these LLMs; b) it leverages human understanding and intelligence, while providing valuable automation through conversations between agents; c) it simplifies and unifies the implementation of complex LLM workflows as automated agent chats. We provide many diverse examples of how developers can easily use AutoGen to effectively solve tasks or build applications, ranging from coding, mathematics, operations research, entertainment, online decision-making, question answering, etc.Comment: 28 page

TARDis Project Final Report

The TARDis Project Final Report outlines the background, methodology and implementation of e-Prints Soton. It identifies outcomes of the project and its evolution to a centrally funded University research repository, embedded within the research landscape of the organization

Enhancing Geospatial Data: Collecting and Visualising User-Generated Content Through Custom Toolkits and Cloud Computing Workflows

Through this thesis we set the hypothesis that, via the creation of a set of custom toolkits, using cloud computing, online user-generated content, can be extracted from emerging large-scale data sets, allowing the collection, analysis and visualisation of geospatial data by social scientists. By the use of a custom-built suite of software, known as the ‘BigDataToolkit’, we examine the need and use of cloud computing and custom workflows to open up access to existing online data as well as setting up processes to enable the collection of new data. We examine the use of the toolkit to collect large amounts of data from various online sources, such as Social Media Application Programming Interfaces (APIs) and data stores, to visualise the data collected in real-time. Through the execution of these workflows, this thesis presents an implementation of a smart collector framework to automate the collection process to significantly increase the amount of data that can be obtained from the standard API endpoints. By the use of these interconnected methods and distributed collection workflows, the final system is able to collect and visualise a larger amount of data in real time than single system data collection processes used within traditional social media analysis. Aimed at allowing researchers without a core understanding of the intricacies of computer science, this thesis provides a methodology to open up new data sources to not only academics but also wider participants, allowing the collection of user-generated geographic and textual content, en masse. A series of case studies are provided, covering applications from the single researcher collecting data through to collection via the use of televised media. These are examined in terms of the tools created and the opportunities opened, allowing real-time analysis of data, collected via the use of the developed toolkit

Oral Communication in Genre Theory and Software Development Workplaces

My dissertation defines how software developers have abandoned traditional documentation practices for other kinds of media that work better in their workplace practices. Ultimately, even though other media like white boards, sticky notes, and “oral communication” are vastly different than traditional, written software documentation, they match the fast paced, decision-making situations of contemporary developer communities. I focus particularly on oral communication because it is the most unacceptable means to “document,” according to traditional standards. I use North American Genre Theory to describe those decision-making situations contemporary developers and note how the theory does not account for all the documentation I expect to find. Via several projects and interviews I confirm that oral communication is a new means of “documentation” and reconciles North American Genre Theory

Widening Access to Applied Machine Learning with TinyML

Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this paper, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML both leverages low-cost and globally accessible hardware, and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia (Harvard University) and industry (Google) produced a four-part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for learners from a global variety of backgrounds. It introduces pupils to real-world applications, ML algorithms, data-set engineering, and the ethical considerations of these technologies via hands-on programming and deployment of TinyML applications in both the cloud and their own microcontrollers. To facilitate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project competition. We also released the course materials publicly, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies.Comment: Understanding the underpinnings of the TinyML edX course series: https://www.edx.org/professional-certificate/harvardx-tiny-machine-learnin