That's the DeepMind modus operandi- and why they publish in Nature. It's in their best interest to claim as much covered ground as possible so they can keep working towards their end-goal. But most people who read Nature already know that results in that journal are overhyped. You just have to apply a specific prior/translator when you read their papers.
This really is a depressingly true sentiment. Nature is both the most important publication but so often, the big shots publish overhyped stuff there that doesn't deserve it but get away with it since they are bigshots. Underscore here for another piece of evidence of the dysfunction in science.
I don't doubt the sentiment is true, but hasn't this kind of science always been this way? By that I mean authors inflating the importance of their work; everyone wants to be seen as having the biggest breakthroughs.
When I think of dysfunction in 'science' I usually think of unfalsifiable hypothesis, the repeatability crisis in Psychology, p-hacking in Medicine, misuse of statistical methods in Economics and other epistemic issues, but I don't think of exaggerations like this.
I would imagine that it's the metrics universities and funding agencies apply in promotion decisions. For example, my (well-known) university decided to measure impact, and takes "Twitter engagement" as one proxy metric for impact -- against my explicit recommendations. I'll leave the consequences to everybody's imagination.
Yes. In fact many scientists post to twitter but don't look at the replies (Emily Bender is an example) or even block you if you disagree with them. That's not engagement and I wonder what the dean would do about a promotion where the scientist just sort of blathered on twitter and had lots of followers, but wasn't actually providing any real scientific value (again, Emily Bender is an example).
Deans typically lack detailed technical knowledge to evaluate candidates (not to mention time), they just go with the flow, and want to run the department smoothly.
I think the real mechanism is this: those who make the decision (those with the most power, i.e. those who bring in the most funding and can threaten to leave if they don't get their way)
already know whom they want and they cherry-pick data, eg "impact" figures, to bolster their case. That enables the dean then to justify the decision in public with those cherry-picked figures ... (the dean can hardly say
we are hiring X because otherwise top funding getter Y will leave)
I think Twitter is less important in STEM subjects than in social sciences or humanities, as STEM has more clearcut results.
