I just don't agree that it's a false equivalency. I see them both as "tools I use to get the job done". For me, the job is not "writing code" - it is "deliver feature", "fix bug", and the accountability, responsibility, and communication that comes with it.
Would you also say that "someone who wants to use an IDE / LSP features to code and not give credit to the IDE / LSP is the worst kind of person"? If not, what is the difference between the two for you?
> No military or aid interference of any kind, no taxes or laws but also no import/export commerce, communication or organizing of any government or militia/military will be allowed
Using Authentik as a part of my selfhosted setup, mostly positive things to say. I tried with Keycloak first but had too much trouble getting the Docker image to work, so switched to Authentik.
I also checked out some other options along the way, and ultimately realized that pretty much all of the options come with enterprise-oriented features that are just added complexity for the self-hosting use case.
Ultimately, I've gotten at least somewhat familiar with all the complexities of Authentik, so I'd have a hard time switching off. Would definitely love to see a solution geared towards selfhosting that's more barebones, though.
Getting a Reddit API key seems to be a more complex process than the usual, with the first step being submitting a service ticket [0]. I imagine that this would also help Reddit prevent people from doing what you're suggesting, if it were to become popular.
No, but instead means that GPT can't be smarter than the people who are writing the published content on the web (generally, the very smartest humans if you're considering high quality published works alone).
If you could do a perfect job of training a GPT on the entirety of published academic literature, the total of what that GPT could spit out would be limited by the knowledge contained in academic literature. At the same time, you'd have created a tool that is cheap and does a good job of synthesizing knowledge/answering questions across all disciplines. The model will never replace the scientists who are working at the very bounds of their fields, but it doesn't have to in order to be extremely useful, even useful enough to replace a majority of knowledge workers.
Just because GPTs can't be smarter than the smartest humans doesn't mean they can't be smarter than most humans.
Google has a whole bunch of products/tools in the healthcare space, and it seems like their contribution there is only growing. I've been working with FHIR/EHR adjacent tooling lately for a personal project, and a good number of both open source resources and SAAS products I've seen have been from Google.
More broadly, all big 3 cloud providers (Azure, AWS, and Google) have offerings for FHIR data storage and API access, as well as common NLP based healthcare data analysis workflows. Many of these seem relatively new, or as if they have had a lot of recent attention focused on them. I'm definitely interested in how/why these companies (as well as some other VC funded ones, like Medplum), are entering this space with products that are not directly sellable, but are rather things that other tools would have to build upon. It seems like AWS works directly with end-customers to use their APIs to build products, but I'm not sure what Azure and Google are doing.
This one's probably too complex for my use case, but I thought the concept looked very neat and wanted to share.
I agree that it's probably not anything more than statistics, but why can't statistics alone generate emergent phenomena? What convinces you that the human brain isn't also just statistics at a massive scale?
I've been working in systems neuroscience for a few years (something of a combination lab tech/student, so full disclosure, not an actual expert).
Based on my experience with model organisms (flies & rats, primarily), it is actually pretty amazing how analogous the techniques and goals used in this sort of research are to those we use in systems neuroscience. At a very basic level, the primary task of correlating neuron activation to a given behavior is exactly the same. However, ML researchers benefit from data being trivial to generate and entire brains being analyzable in one shot as a result, whereas in animal research elucidating the role of neurons in a single circuit costs millions of dollars and many researcher-years.
The similarities between the two are so clear that I noticed that in its Microscope tool [1], OpenAI even refers to the models they are studying as "model organisms", an anthropomorphization which I find very apt. Another article I saw a while back on HN which I thought was very cool was [2], which describes the task of identifying the role of a neuron responsible for a particular token of output. This one is especially analogous because it operates on such a small scale, much closer to what systems neuroscientists studying model organisms do.
But that's entirely dependent on the data - if you read the article, you'll see that the author is pointing out how different datasets are reporting different values for the same healthcare service. A correct, properly labeled example is just that - a document where values are accurate rather than misreported.
If you want to get a sense of what a correct, properly labeled example might look like, you should visit the CMS's guidance [1]. Health insurers should be providing documents that follow the format described there, and most importantly, reporting values that are accurate.