Readit NewsI think the most likely outcome of a criminal organization doing this is that they train a public architecture model from scratch on the material that they want to reproduce, and then use without telling anyone. Would your detector prevent this attack?
As for actual lead time associated with our actual strategy, that’s probably not something I can talk about publicly. I can say I’m working on making it happen faster.
1) Produce AI tool 2) Tool gets used for bad 3) Use anti-AI/AI detection to avoid/check for AI tool 4) AI tool introduces anti-anti-AI/detection tools 5) Repeat
Yeah its sounds great until you need mass transport system to support this idea which means only mega cities can benefit the most when tier 2 and tier 3 cities is having a hard time investment
see: japan
The synopsis, as far as my tired brain can remember:
- Here's a brief summary of the last 10 years
- We're reaching the limit of our scaling laws, because we've trained on all the data we have available on the limit
- Some things that may be next are "agents", "synthetic data", and improving compute
- Some "ANNs are like biological NNs" rehash that would feel questionable if there was a thesis (which there wasn't? something about how body mass vs. brain mass are positively correlated?)
- 3 questions, the first was something about "hallucinations" and whether a model be able to understand if it is hallucinating? Then something that involved cryptocurrencies, and then a _slightly_ interesting question about multi-hop reasoning
I notice with Ilya he wants to talk about these out there speculative topics but defends himself with statements like “I’m not saying when or how just that it will happen” which makes his arguments impossible to address. Stuff like this openly invites the crazies to to interact with him, as seen with the cryptocurrency question at the end.
Right before this was a talk reviewing the impact of GANs that stayed on topic for the conference session throughout.
Open projects on github often (at least superficially) require specific versions of Cuda Toolkit (and all the specialty nvidia packages e.g. cudann), Tensorflow, etc, and changing the default versions of these for each little project, or step in a processing chain, is ridiculous.
pyenv et al have really made local, project specific versions of python packages much easier to manage. But I haven't seen a similar type solution for cuda toolkit and associated packages, and the solutions I've encountered seem terribly hacky..but I'm sure though that this is a common issue, so what do people do?