101–110 of 202 posts
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This is true for public universities and private universities; you see the same thing happening in academic papers (and especially the university PR around the paper)
I would say anecdotal. This hasn't been my case across four universities and ten years.
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I do hallucinate a better future as well.
It bothers me that the word 'hallucinate' is used to describe when the output of a machine learning model is wrong. In other fields, when models are wrong, the discussion is around 'errors'. How large the errors are, their structural nature, possible bounds, and so forth. But when it's AI it's a 'hallucination'. Almost as if the thing is feeling a bit poorly and just needs to rest and take some fever-reducer before b…
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I've built microscopes intended to be installed inside workcells similar to what companies like Transcriptic built ( https://www.transcriptic.com/ ). So my scope could be automated by the workcell automation components (robot arms, motors, conveyors, etc). When I demo'd my scope (which is similar to a 3d printer, using low-cost steppers and other hobbyist-grade components) the CEO gave me feedback which was very educ…
That’s similar to how Google won in distributed systems. They used cheap PCs in shipping containers when everyone else was buying huge expensive SUN etc servers.
The market seems excited to charge in whatever direction the weathervane has last been pointing, regardless of the real outcomes of running in that direction. Hopefully I’m wrong, but it reminds me very much of this study (I’ll quote a paraphrase) “A groundbreaking new study of over 1,000 scientists at a major U.S. materials science firm reveals a disturbing paradox: When paired with AI systems, top researchers becom…
I only recently started using aider[1].
My experience with it can be described in 3 words.
Wow!
Oh wow!
It was amazing. I was writing a throwaway script for one time use (not for work). It wrote it for me in under 15 minutes (this includes my time getting familiar with the tool!) No bugs.
So I decided to see how far I could take it. I added command line arguments, logging, and a whole bunch of other things. After a full hour, I had a production ready script - complete with logs, etc. I had to debug code only once.
I may write high quality code for work, but for personal throwaway scripts, I'm sloppy. I would not put a command line parser, nor any logging. This did it all for me for very cheap!
There's no going back. For simple scripts like this, I will definitely use aider.
And yeah, there was definitely no satisfaction one would derive from coding. It was truly addictive. I want to use it more and more. And no matter how much I use it and like the results, it doesn't scratch my programmer's itch. It's nowhere near the fun/satisfaction of SW development.
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That applies to absolutely everyone. Convenient results are highlighted, inconvenient are either not mentioned or de-emphasized. You do have to be well read in the field to see what the authors _aren't_ saying, that's one of the purposes of being well-read in the first place. That is also why 100% of science reporting is basically disinformation - journalists are not equipped with this level of nuanced understanding.
yes, but google has a long history of being egregious, with the additional detail that their work is often irreproducible for technical reasons (rather than being irreproducible for missing methods). For example, we published an excellent paper but nobody could reproduce it because at the time, nobody else had a million spare cores to run MD simulations of proteins.
I recently ran across this toaster-in-dishwasher article [1] again and was disappointed that the LLMs I have access to could replicate the "hairdryer-in-aquarium" breakthrough (or the toaster-in-dishwasher scenario, although I haven't explored it as much), which has made me a bit skeptical of the ability of LLMs to do novel research. Maybe the new OpenAI research AI is smart enough to figure it out? [1] https://jdsti…
Tbh I don’t see why I would use this. I don’t need an ai to connect across ideas or come up with new hypothesis. I need it to write lots of data pipeline code to take data that is organized by project, each in a unique way, each with its own set of multimodal data plus metadata all stored in long form documents with no regular formatting, and normalize it all into a giant database. I need it to write and test a data…
Agreed - AI that could take care of this sort of cross-system complexity and automation in a reliable way would be actually useful. Unfortunately I've yet to use an AI that can reliably handle even moderately complex text parsing in a single file more easily than if I'd just done it myself from the start.
Tbh I don’t see why I would use this. I don’t need an ai to connect across ideas or come up with new hypothesis. I need it to write lots of data pipeline code to take data that is organized by project, each in a unique way, each with its own set of multimodal data plus metadata all stored in long form documents with no regular formatting, and normalize it all into a giant database. I need it to write and test a data…
Exactly, they want to automate the most rewarding part that we don’t need help with… plus I don’t believe they’ve solved the problem of LLMs generating trite ideas.
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I would say anecdotal. This hasn't been my case across four universities and ten years.
The actual papers don't overhype. But the university PR's regarding those papers? They can really overhype the results. And of course, the media then takes it up an extra order of magnitude.
But yes, it's normally: "science paper says an experiment in mice shows promising results in cancer treatment" then "University PR says a new treatment for cancer is around the corner" and "Media says cure for all cancer"