I wish it would revolutionize spelling.
[1] https://www.merriam-webster.com/dictionary/revolutionise
11–20 of 53 posts
I wish it would revolutionize spelling.
[1] https://www.merriam-webster.com/dictionary/revolutionise
Would researchers have less issue publishing papers that contradict sensitive findings in earlier papers if they can pass responsibility back to the robot? (e.g. robot hypothesizes and disproves widely accepted result that years of other research is based and that anyone who challenged in the past has been dismissed as a quack.)
As a tenure-track scientist who works in ML applications for astrophysics, I disagree with this sentiment. The main issue isn't that enough scientists are using tools to search through literature or form new hypotheses, the main issue is that scientists now have to validate and sift through AI-generated outputs in order to find useful signals, rather than validate and sift through experimentally derived or observed signals.
AI can be useful for hypothesis generation in my field [0], and I think that there are lots of great use cases where it can be used to summarize information. However, it always comes with the possibility that it might output complete nonsense [1], so scientists who adopt these tools will have to spend some of their time verifying their outputs.
[0] https://arxiv.org/abs/2306.11648
[1] https://web.archive.org/web/20230913230733/https://www.msn.c...
I wish it would revolutionize spelling.
According to Merriam Webster [1], "revolutionise" is the British spelling of "revolutionize" (I did not know). [1] https://www.merriam-webster.com/dictionary/revolutionise
https://www.studyenglishtoday.net/british-american-spelling....
Any specific examples? What new understanding do we have of our world because of AI?
Here's the heart of how they say it can be done > Two areas in particular look promising. The first is “literature-based discovery” (LBD), which involves analysing existing scientific literature, using ChatGPT-style language analysis, to look for new hypotheses, connections or ideas that humans may have missed. LBD is showing promise in identifying new experiments to try—and even suggesting potential research collabo…
I think actually the second idea extends quite a bit further than that. The idea is that you enable the agent to interact with an entire collection of laboratory equipment through tool-use. The agent not only generates hypotheses and designs experiments to test them, but then also actually executes the experiments through tool-use and iterates.
But it remains totally unclear whether those things are achievable, and to what extent they can actually do real, useful science, rather than just exist as a novelty. Of course AI "can" revolutionize science. But the proof is in the pudding. Write an article when something has happened, rather than predicting that it will (and being wrong, like every such article written for the past 70 years).
Things are re-discovered across adjacent fields of study all the time. There are also a bunch of times I've come across a paper when I was a year into a project, and wished that I'd had it at the beginning of the project.