Servant of Christ, Summit Professor of Learning Technologies; Researcher on AI, CS, faith and technology, XR/VR/AR, gaming, simulations, health, online learning
As #AI gets better at the work involved in science, more scientists see it as essential rather than a nice to have.
But researchers tell @ScienceCareers access to the best tools today depends less on need than on who can pay—and that the costs of AI are falling especially hard on early-career scientists, many of whom are reluctant to raise the issue because it feels taboo.
Learn more: https://t.co/YEuf0fDR3K
Google published a paper that might end the PhD.
It’s called “ScientistTwo”
For centuries, the bottleneck to human knowledge has been the human brain.
It takes five years of grueling labor to read the literature, find a gap, formulate a hypothesis, run the experiments, and write the paper.
But, Google automated the entire pipeline.
They built an AI that reads a problem, forms hypotheses, runs experiments, writes the paper, AND simulates its own peer review. no human touches it.
It reads millions of papers to find blind spots in the current science.
It generates novel hypotheses.
It designs the experiments. It writes the code to test them.
When the code fails, it debugs itself.
When the results come in, it analyzes the data, generates the charts, and writes the final academic paper.
It even simulates a brutal peer-review process to critique and improve its own work before it considers the project finished.
This isn't an LLM summarizing a PDF. This is a fully autonomous digital scientist.
What takes a human candidate half a decade of massive intellectual effort... the AI accomplishes in a fraction of the time, at a fraction of the cost.
The implications are staggering.
If you can spin up 10,000 autonomous researchers overnight, the rate of scientific discovery is about to go vertical. We are moving from artisanal, human-speed research to industrialized knowledge creation.
But it raises an uncomfortable reality for academia.
When an AI can read the literature, design the experiment, execute the code, and write the paper...
What exactly is the human supposed to do?
I think there is way too much concern expressed about verification of AI output in everyday work due to a risk of factual errors.
It used to be true, but data on more recent models suggest they beat human experts, including PhD specialists, in finding facts and checking claims.
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