Why must the abstractions be tokenized? I can notice myself feeling things, and surely that counts as awareness of the self. If you notice that you are full of anger or joy, you notice that _you_ exist.
Well you can even fall back to saying that all sensations are abstractions. After all when you feel wet there is no part of your brain that “gets wet”. But the constant mirroring of the external world by your mental abstractions is not something over which you have full control and you couldn’t stop it if you wanted to. Language has nothing to do with it. After all you don’t personally assemble the images you see from your rods and cones, nor did you ever get to choose what cinnamon smells like.
We're two Northeastern University students (jfz and drassaby on HN), and one professor (not on HN), who started this project because there were no frameworks that allowed people to productionize evolutionary algorithms by running them in distributed environments. We wanted to make a type-safe, user-friendly interface to a parallel backend that would allow optimization problems to be solved more easily. We have validated the framework, using it in a paper that evolved fair and accurate machine learning models (https://github.com/julian-zucker/evolving-fair-models/blob/m...). This project is still in early beta, but it is mature enough to be used for proofs of concept and to play with high-performance evolutionary algorithms.
Let us know what you think and how you want to see the project develop! We'd be happy to answer any questions you might have either here or at: julian.zucker@gmail.com and drassaby@gmail.com
I've been working on a Genetic Algorithm/Evolutionary Computing framework in Scala, using network-parallelism to solve optimization problems fast. If you're interested, check it out at https://github.com/evvo-labs/evvo
This is critical - even if no individual study has positive results, the data from each study can be aggregated and that meta-analysis can find results much more easily. I remember reading (but can't find the link to) an article about a medicine that took many trials to have one trial with statistical significance, but a meta-analysis of the first five or so trials would have revealed the efficacy of the drug under test much sooner and at a much lower cost.
Meta-analyses suffer from publication bias, though. Studies with one outcome are more likely to be published than those with some other outcome, and therefore the input for the meta-anslysis is biased.
There is a massive difference between doing something that will clearly hurt someone and choosing to do only work that benefits you. Researchers are currently under no obligation to write up their null findings, and it would be hard to get null findings in prestigious journals. This should be fixed at an institutional level, like the pre-registration of studies in the article, not as a mandate to each individual researcher to write up and publish every study.
>There is a massive difference between doing something that will clearly hurt someone and choosing to do only work that benefits you.
This is the most-downvoted comment I've ever made on HN, and I think your explanation is in summary what people disagree with.
However, the problems of bias and integrity in scientific research can and do have costs in terms of harm to human life. It's just that the connection between just following incentives and bad scientific research is much more abstracted, and therefore is not clearly intentional negligence, as the case with something like food safety.
I have seen companies saying they have a thousand microservices to mean that they have a thousand microservice servers running, but they might have hundreds of application servers in each service pulling from the same queue.
