The Best Explanation I’ve Seen for How Machine Learning Works
How machines learn, as demonstrated by a pile of matchboxes playing tic-tac-toe.
How machines learn, as demonstrated by a pile of matchboxes playing tic-tac-toe.
The challenges offered by artificial intelligence require a different approach than that seen for plagiarism detection.
Was a recent Scholarly Kitchen piece analyzing the capabilities of ChatGPT a fair test? What happens if you run a similar test with an improved prompt on LLMs that are internet connected and up to date?
What uses for artificial intelligence (AI) might we expect outside of the publication workflow? Some answers to this question can be found through the lenses of sustainability, justice, and resilience.
To identify both benefits and risks of generative AI for our industry, we tested ChatGPT and Google Bard for authoring, for submission and reviews, for publishing, and for discovery and dissemination.
New data literacy and artificial literacy standards are necessary and emerging. The workflows and iterative mindsets the Digital Humanities can help inform our approaches.
Are scholarly publishers primed to become the critical content suppliers for the big Generative AI companies?
In this article, Minhaj Rain explores how human intelligence tasks (HITs) and not simply more AI tools could be the way forward as a reliable and scalable solution for maintaining research integrity within the scholarly record.
Revisiting a post from 2019 in light of the acquisition of protocols.io by Springer Nature. As community-owned and -led efforts to build scholarly communications infrastructure gain momentum, what can be done to help them achieve long term sustainability?
Last January we wrote a group post about “Twexit” and with the launch of Threads we wondered how the Chefs were feeling about the emerging and existing social media options.
An update on how generative AI has progressed and how it has been applied to research publishing processes since ChatGPT was released, looking at business, application, technology, and ethical aspects of generative AI.
This year, Ithaka S+R is examining the shared infrastructure for scholarly communication and will ultimately make recommendations for its future. This week, we issued a draft of our project report. Please share your comments, suggestions, and other feedback by the end of August.
The AI takeover isn’t all doom and gloom. Finally, a long running musical question can be answered.
The current uproar over artificial intelligence does not show us what the future of AI will look like, but rather how a human population falls into predictable patterns as it contemplates any new development: we are observing not AI but ourselves observing AI.
A new collaboration between JSTOR and the social annotation tool Hypothesis has seen more instructional uses of content and greater engagement among students with the material.