Trusted, curated, subject-specific models are needed for academia Earlier this year a thread from Tom Dietterich, who chairs arXiv’s computer science section, made the rounds. He was clarifying the consequences for unchecked LLM output on the platform. If a submission contains “incontrovertible evidence” that the authors did not

Small teams have always innovated. But AI means a) We can split research into big and small teams b) The small can do absolutely massive things now In October 2024, a Nobel Prize in Chemistry went, in effect, to a piece of software. More precisely it went to the people

As a tech founder who can, at best, describe himself as a hacky coder, I've spent most of my career having to either hire engineers or do without when an idea was too technically demanding. That's changed quite a lot recently. Tools like Claude Code mean

What Problem Are We Trying to Solve? Article Processing Charges at major journals run to thousands of dollars per paper. The editorial infrastructure - submission systems, copyediting, typesetting, hosting - is expensive. What I want to know is how much of the process can be automated, particularly for machine generated academic content.

What problem are we trying to solve? Peer review is structurally failing. The volume of preprint submissions has grown faster than the reviewer pool for decades. Average review turnaround times have lengthened. Reviewer fatigue is well-documented. And review quality is inconsistent in ways that are impossible to fix when

What problem are we trying to solve? OpenScience.ai is trying to look forwards. OpenScience.ai aims to be a gap filler in academic data and literature. Given the current state of open scientific databases, what patterns are present in the data that haven't yet been written up

What problem are we trying to solve? Most major funders now require FAIR data management plans. Most generalist repositories (Zenodo, Figshare, Dryad, Mendeley Data) allow data published on them to be FAIR, but not all data published on them is FAIR. The gap between 'technically FAIR' and '

Looking for Surprisals in your data using AstaLabs Autodiscovery from Allen AI I founded Figshare over a decade ago with a simple thesis, if you make research data openly available, good things will happen. Things you can't predict. This week, I got a rather specific reminder of why

The best way for humankind to benefit from research is to prioritize machines over people when sharing data. Here’s why. We push out the lines that academic research needs to be Findable, Accessible, Interoperable and Re-usable (FAIR) for humans and machines. This suggests humans and machines should get