Truth Behind Thames: Archaeological and Historical Investigations of the “Missionary Whaleship”

Transporting the second detachment of American missionaries to the Hawaiian Islands earned Thames its novel moniker, but this was only one of several unique distinctions it earned during its sailing career. It was the largest vessel ever constructed at Potapaug, Connecticut (today's Essex) upon its launch in 1818, and the first four-years of Thames' employment involved competing for freight and cargo against the famed "packets" of the Black Ball Line. While Thames did not adhere to a sailing schedule, it completed more crossings per-year than the early packets and its crossings were almost invariably shorter. Thames' merchant service ended in 1822 and it was subsequently acquired by a newly-formed investment firm out of New Haven, Connecticut. There Thames was converted for an arguably more demanding role: whaling. Its maiden voyage as a whaleship facilitated the aforementioned mission group to their destination without incident and Thames finally returned with some 1,900 barrels of sperm whale oil after a three-year cruise. Despite achieving a full cargo, however, Thames' owners were dissatisfied with its outcome. They opted to sell the ship rather than outfit it for a second voyage, and Thames entered into the Sag Harbor, New York (Long Island) whaling fleet. Thames was altogether a "greasy" (lucky) Sag Harbor whaler until 1838, when it was deemed unfit for further use, condemned, and purposefully scuttled as a breakwater and barrier against erosion in the harbor's near shore area. The ship's decaying hull gradually receded from view and memory until the late-1960s, when a significant quantity of its remains were removed during a marina construction project. Today, the reconstructed keel and disarticulated structural members of the merchant vessel-turned-whaleship Thames are permanently exhibited at Mystic Seaport in Mystic, Connecticut. As the construction characteristics of repurposed vessels and the processes of merchant vessel-to-whaleship conversion have seemingly escaped archaeological and historic research, alike, the social and historical contexts and the broader significance of converted vessels has been understudied--to say the least. In truth, conversions were far more popular than purpose-built whaleships throughout the majority of the "Golden Age" of American whaling (roughly 1820-1850) and "recycled" vessels played an important role in the industry's unprecedented resurgence following the War of 1812. Furthermore, these vessels provided a chance to experiment with emerging designs and construction methods that later appear in the forms of purpose-built whaleships. This thesis informs upon these topics by examining the history and archaeology of Thames, an unparalleled example of a converted vessel whose documentary and physical evidence have yet been carefully considered. A combined theoretical approach involving aspects of object biography and a recent development known as object itinerary orient this study. As such, the extensive, dynamic, and socially-meaningful "entanglements" that Thames may be said to have experienced during its 203 year existence are considered critical aspects for generating a better understanding of its various roles, as are the present and future entanglements it experiences as a museum object at Mystic Seaport

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc