Observatorio IA

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Large language models like ChatGPT efficiently provide users with information about various topics, presenting a potential substitute for searching the web and asking people for help online. But since users interact privately with the model, these models may drastically reduce the amount of publicly available human-generated data and knowledge resources. This substitution can present a significant problem in securing training data for future models. In this work, we investigate how the release of ChatGPT changed human-generated open data on the web by analyzing the activity on Stack Overflow, the leading online Q\&A platform for computer programming. We find that relative to its Russian and Chinese counterparts, where access to ChatGPT is limited, and to similar forums for mathematics, where ChatGPT is less capable, activity on Stack Overflow significantly decreased. A difference-in-differences model estimates a 16\% decrease in weekly posts on Stack Overflow. This effect increases in magnitude over time, and is larger for posts related to the most widely used programming languages. Posts made after ChatGPT get similar voting scores than before, suggesting that ChatGPT is not merely displacing duplicate or low-quality content. These results suggest that more users are adopting large language models to answer questions and they are better substitutes for Stack Overflow for languages for which they have more training data. Using models like ChatGPT may be more efficient for solving certain programming problems, but its widespread adoption and the resulting shift away from public exchange on the web will limit the open data people and models can learn from in the future.
This week, I attended a round table discussion at the House of Commons with politicians and experts from across the education sector to feed into UK policy on AI in Higher Education. Unsurprisingly, one of the key areas of concern and discussion was the impact of AI on academic integrity: in a world where AI can write an essay, what does AI mean for what we assess and how we assess it? And how do we respond in the short term? In this week’s blog post I’ll summarise the discussion and share what we agreed would be the most likely new model of assessment in HE in the post-AI world.
MLA Style Center MLA (17/03/2023)
The MLA’s method for citing sources uses a template of core elements—standardized criteria that writers can use to evaluate sources and create works-cited-list entries based on that evaluation. That new technologies like ChatGPT emerge is a key reason why the MLA has adopted this approach to citation—to give writers flexibility to apply the style when they encounter new types of sources. In what follows, we offer recommendations for citing generative AI, defined as a tool that “can analyze or summarize content from a huge set of information, including web pages, books and other writing available on the internet, and use that data to create original new content” (Weed). 
Timothy McAdoo APA Style (07/04/2023)
In this post, I discuss situations where students and researchers use ChatGPT to create text and to facilitate their research, not to write the full text of their paper or manuscript. We know instructors have differing opinions about how or even whether students should use ChatGPT, and we’ll be continuing to collect feedback about instructor and student questions. As always, defer to instructor guidelines when writing student papers. For more about guidelines and policies about student and author use of ChatGPT, see the last section of this post.
Recently, I’ve been doing a lot of work helping businesses to explore how to integrate AI to improve the efficiency and effectiveness of their organisations, especially their HR and L&D teams. Fear of AI hallucinations along with concerns for data privacy means that most business leaders in start with the assumption that the power and potential of AI lies in building bespoke LLMs - i.e. building an internal search + retrieve engine based on internal data using a chat interface.
Google Youtube (13/07/2023)
As we bring Bard to more people and places around the world, we’d like to share more about how we’re building Bard responsibly, its limitations, how we manage your data, and how you can best use Bard as your creative and helpful collaborator. 

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Sobre el observatorio

Sección en la que se recogen publicaciones interesantes sobre inteligencia artificial no dedicadas específicamente a la enseñanza de ELE. Los efectos de la IA sobre la enseñanza y aprendizaje de lenguas van a ser, están siendo, muy notables y es importante estar informado y al día sobre este tema.

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