Darren Abramson

In this paper, I provide a number of state-of-the-art results for Winograd- related problems by applying a public technique for zero-shot scoring with language models. I explain how I discovered this advance and my recent experience in sharing these results with the scientific community. We are on the threshold of major advances and opportunities, but also dangerous trends. A strengthened discourse on the public benefit derived from the use of computers is badly needed and starting to emerge; here, I focus on that discourse in the context of apparent sudden, giant leaps in our ability to process text with computer: natural language processing (NLP). There is no shortage of lessons for science and ethics. Fortunately, despite the vulnerable status of public University research, the prognosis for informed public debate about the sciences of machine learning and artificial intelligence remains excellent.

The use of language models in Web applications and other areas of computing and business have grown significantly over the last five years. One reason for this growth is the improvement in performance of language models on a number of benchmarks — but a side effect of these advances has been the adoption of a “bigger is always better” paradigm when it comes to the size of training, testing, and challenge datasets. Drawing on previous criticisms of this paradigm as applied to large training datasets crawled from pre-existing text on the Web, we extend the critique to challenge datasets custom-created by crowdworkers. We present several sets of criticisms, where ethical and scientific issues in language model research reinforce each other: labour injustices in crowdwork, dataset quality and inscrutability, inequities in the research community, and centralized corporate control of the technology. We also present a new type of tool for researchers to use in examining large datasets when evaluating them for quality.

In this paper I argue that Turing’s responses to the mathematical objection are straightforward, despite recent claims to the contrary. I then go on to show that by understanding the importance of learning machines for Turing as related not to the mathematical objection, but to Lady Lovelace’s objection, we can better understand Turing’s response to Lady Lovelace’s objection. Finally, I argue that by understanding Turing’s responses to these objections more clearly, we discover a hitherto unrecognized, substantive thesis in his philosophical thinking about the nature of mind.

In this paper I argue that whether or not a computer can be built that passes the Turing test is a central question in the philosophy of mind. Then I show that the possibility of building such a computer depends on open questions in the philosophy of computer science: the physical Church-Turing thesis and the extended Church-Turing thesis. I use the link between the issues identified in philosophy of mind and philosophy of computer science to respond to a prominent argument against the possibility of building a machine that passes the Turing test. Finally, I respond to objections against the proposed link between questions in the philosophy of mind and philosophy of computer science.