[roykishony]

Thanks everyone for engagement and discussion. Following the range of comments, just a few thoughts:

1. Traceability, transparency and verifiability. I think the key question for me is not only whether AI can accelerate science, but rather how we can use AI to accelerate science while at the same time enhancing key scientific values, like transparency, traceability and verifiability.

More and more these days when I read scientific papers, published either at high impact journals or at more specialized journals, I find it so hard, and sometimes even frustratingly impossible, to understand and check what exactly was done to analyze the raw data and get to the key results, what was the specific chain of analysis steps, what parameters where used, etc, etc. The data is often not there or is poorly annotated, the analysis is explained poorly, the code is missing or is impossible to track, etc etc. At all, it became practically impossible to repeat and check the analysis and the results of many peer reviewed publications.

Why are papers so hard to follow and trace? Because writing clear and fully traceable and transparent papers is very hard, and we don’t have powerful tools for doing this, and it requires the scientific process itself (or at least the data analysis part) to be done in an organized and fully traceable way.

Our data-to-paper approach is designed to provide ways to use AI powerfully, not only to speed up science (by a lot!), but also at the same time to use AI to enhance transparency, traceability and verifiability. Data-to-paper sets a standard for traceability and verifiability which imo exceeds the current level of human created manuscripts. In particular:

1. “Data-Chaining": by tracing information flow through the research steps, data-to-paper creates what we call “data-chained” manuscripts, where results, methodology and data are programmatically linked. See this video (https://youtu.be/mHd7VOj7Q-g). You can also try click-tracing results in this example ms: https://raw.githubusercontent.com/rkishony/data-to-paper-sup...

See more about this and more examples in our preprint: https://arxiv.org/abs/2404.17605

2. Human in the loop. We are looking at different ways to create a co-piloted environment where human scientists can direct and oversee the process. We currently have a co-pilot app that allows users to follow the process, to set and change prompts and to provide review comments at the end of each steps (https://youtu.be/Nt_460MmM8k). Will be great to get feedback (and help!) on ways in which this could be enhanced.

3. P-value hacking. Data-to-paper is designed to raise an hypothesis (autonomously, or by user input) and then go through the research steps to test the hypothesis. If the hypothesis test is negative, it is perfectly fine and suitable to write a negative-result manuscript. In fact, in one of the tests that we have done we gave it data of a peer reviewed publication that reports a positive and a negative result and data-to-paper created manuscripts that correctly report both of these results.

So data-to-paper on its own is not doing multiple hypothesis searches. In fact it can help you realize just how many hypotheses you have actually tested (something very hard for human research even when done honestly). Can people ask data-to-paper to create 1000 papers and then read them all and choose only the single one in which a positive result is found? Yes - people can always cheat and science is built on trust, but it is not going to be particularly easier than any other of the many ways available for people to cheat if they want.

4. Final note: LLMs are here and are here to stay and are already used extensively in science doing (sadly sometimes undisclosed: https://retractionwatch.com/papers-and-peer-reviews-with-evi...). The new models of ChatGPT5, ChatGPT6, ... will likely write a whole manuscript for you in just a single prompt. So the question is not whether AI will go into science (it already does), but rather how to do so and use AI in ways that fosters, not jeopardizes, accountability, transparency, verifiability and other important scientific values. This is what we are trying to do with data-to-paper. We hope our project stimulates further discussions on how to harness AI in science while preserving and enhancing key scientific values.

[uniqueuid]

Hi,

thanks for the honest and thoughtful discussion you are conducting here. Comments tend to be simplistic and it's great to see that you raise the bar by addressing criticism and questions in earnest!

That said, I think the fundamental problem of such tools is unsolvable: Out of all possible analytical designs, they create boring existing results at best, and wrong results (i.e. missing confounders, misunderstanding context ...) as the worst outcome. They also pollute science with harmful findings that lack meaning in the context of a field.

These issues have been well-known for about ten years and are explained excellently e.g in papers such as [1].

There is really one way to guard against bad science today, and that is true pre-registration. And that is something which LLMs fundamentally cannot do.

So while tools such as data-to-paper may be helpful, they can only be so in the context of pre-registered hypotheses where they follow a path pre-defined by humans before collecting data.

[1] http://www.stat.columbia.edu/~gelman/research/unpublished/p_...

[roykishony]

Thanks much for these thoughtful comments and ideas.

I can’t but fully agree: pre-registered hypothesis is the only way to fully guard against bad science. This in essence is what the FDA is doing for clinical trials too. And btw lowering the traditional and outdated 0.05 cutoff is also critical imo.

Now, say we are in a utopian world where all science is pre-registered. Why can’t we imagine AI being part of the process that creates the hypotheses to be registered? And why can’t we imagine it also being part of the process that analyzes the data once it’s collected? And in fact, maybe it can even be part of the process that help collects the data itself?

To me, neither if we are in such a utopian world, nor in the far-from-utopian current scientific world, there is ultimately no fundamental tradeoff between using AI in science and adhering to fundamental scientific values. Our purpose with data-to-paper is to demonstrate and to provide tools to harness AI to speed up scientific discovery while enhancing the values of traceability and transparency and make our scientific output much more traceable and understandable and verifiable.

As of the question of novelty: indeed, research on existing public datasets which we have currently done cannot be too novel. Though scientists can also use data-to-paper with their own fascinating original data. It might help in some aspects of the analysis, certainly help them keep track of what they are doing and how to report it transparently. Ultimately I hope that such co-piloting deployment will allow us delegating more straight forward tasks to the AI and letting us human scientists to engage in higher level thinking and higher level conceptualization.

[alchemist1e9]

> That said, I think the fundamental problem of such tools is unsolvable: Out of all possible analytical designs, they create boring existing results at best, and wrong results (i.e. missing confounders, misunderstanding context ...) as the worst outcome. They also pollute science with harmful findings that lack meaning in the context of a field.

This doesn't seem correct to me at all. If new data is provided and the LLM is simply an advanced tool that applies known analysis techniques to the data, then why would they create “boring existing results”?

I don’t see why systems using an advanced methodology should not produce novel and new results when provided new data.

There is a lot of reactionary or even luddite responses to the direction we are headed with LLMs.