captaincaveman

Unattributed thoughts I'm not convinced that is giving back, further I do think this is susceptible to attack, how many flat earth articles do I need to pump out for chatGPT to consume and come to very wrong conclusions?

Perhaps there are some mitigations for this I'm unaware of?

They should be able to use it to check for contradictions against what the Bard has generated and facts in the KG.

For example I asked chatGPT about birds that can't fly and it started bullshitting about all sorts of birds that clearly can, those facts would likely be in the KG.

I remember making mobile games/apps on early devices, I don't think any of those were particularly compelling either ...

I think VR for games is fine, still clunky as a physical experience at the moment with relatively basic graphics. Improve those things and I'll be upgrading my Quest 1.

Money isn't lost its been spent on research, of course finding a 100 ways something doesn't work doesn't mean that much if you don't find the 1 way that does.

Meta are trying to force AR into the world, like trying to produce a baby in 3 months by having having 3 women. Maybe they are close, maybe they are producing a load of useful research for someone else to exploit in the future. Apple supposedly bringing out their thing 'this year', but there has been little exposed of it, maybe they are just egging on Facebook to waste there time/money .. time will tell.

That is painfully close to being true, and truth is, no one wants to maintain code.

I don't get how you can't see your in the same sinking boat of tech?

It's called abstraction, which is used when modelling categories of things, you do it lots when you write code right, so things are reusable.

Like you probably have something called an iterrable in your programming language of choice, so why not just call it a linked list, or a vector, is basically the argument you're making in my opinion.

To be fair, I do find some modelling languages do disappear into too much abstraction, they basically end up like being a Upper Ontology [1] and lack instances/examples that make them approachable in practice.

[1] https://en.wikipedia.org/wiki/Upper_ontology

So what?

This argument gets thrown up by someone nearly every time documentation gets mentioned.

1. Does it matter if not entirely accurate with the as-is? It showed the previous state or intention, that is typically very useful and a lit better than nothing.

2. Your unit test are out of date when your change code yet that typically gets updated, or new tests added, and is typically more work than updating the docs.

3. If what you document is so wildly different to the solution, you're probably documenting too much detail (this is where most junior devs go wrong), if it's that your arch has actually radically changed it sounds like your documenting too early, do you do spikes etc to figure out your arch before you commit to it?

robots.txt lacks nuance about copyright, it doesn't really serve the same purpose.

I also have this problem and has been something I've been thinking about for some time, and have started building.

All the comments here have been great validation, that there is others with a need, which is encouraging.

I've mostly been working on the data aspects, so not much to see as yet, but if a new approach to this is of interest to you, please signup.

https://ont.fyi

I think the other solutions out there focus too much on note taking, and manually organising stuff, hopefully I can create something more compelling for the rest of us!

normally you go : data -> information -> knowledge.

Although the definitions are some what murky, this is the generally accepted model.

I think what you are talking about is personal knowledge management.

Assuming you're talking about inside an enterprise, rather than something minor or personal, then yes (although not necessarily my decisions).

A trap that sometimes gets laid out is it a binary build or buy decision, there is typically options in between in my opinion. Build isn't necessarily as onerous as it once was either, the use of cloud, frameworks, libraries, low code products and SaaS means you can often construct something from these legos.

In my experience, there are some in enterprises with procurement and IT management skills who tend not to have a clue about building modern software, and often push for buying stuff (keeps them busy), and sell it as a win, bought a thing, set it up, declared victory and fucked off to the next project leaving the users and technologists to figure out how to unfuck the mess that has accumulated around this clunky COTS product that is now a critical part fo the business.

yeah it could use something like pictorial writing, lets call it hieroglyphics or better still a logogram language where each symbol means a thing like Chinese, hmm or maybe we could create an alphabet of characters where on there own they don't mean anything but with a simple small set we could combine create as many words as we want and we could program with ...

half joking I don't really know much about languages, I just don't see how a graphical programming model would be better than a text based one in expressiveness or compactness?

It's a structure for representing Knowledge and optionally reasoning. A relational database is fine for lots of instances of data that are basically the same (i.e. rows in a table), if you want the table to be really flexible to deal with the complexity of modelling a great number of things you end up bastardising the relational model and using strings and other quirks, and the semantics of what this all means isn't in the data but in the applications/humans on how to interpret it ... knowledge graphs deals with this. Additionally ones like RDF support the Open World view of data, where mostly not having information means you just don't have it (the world has a lot of information), rather than assuming in a Closed World that missing data means it's false (not true), and has other affordances for modelling the world, especially where you may have many data sources and need to integrate them together.

It's more than just a graph database, which is typically misunderstood by most devs (including me), when they start looking at this.

However designing an Ontology is hard, just as getting a good database schema is hard, failure of most systems is the data model builds up tension over time, which leads to code complexity/hairballs, effort in adding new concepts to the model, operational work arounds, which then start to poison the data quality (start adding freeform text to store 'data').

There is no god given ontology or one way of modelling the world, crowdsourcing is a good approach, but looking at this one you can see some questionable practices (tradeoffs to be fair), and there is a big gap in people who have skills and experience.

it works well for laying out information for reading, and has some additional markup beyond just layout (unlike markdown), I'd argue it is fairly rudimentary and not extensible.

yet has no metadata or any semantics, its basically just how you layout text ...

Which is basically my point!

Spent many a Sunday afternoon wondering around that castle, did you find the knights mess hall, has an awesome echo, used to visit just to shout in there with the kids :)

Markdown is fine for formatting text, not convinced it's great for knowledge management.

do_something if not already_done?

Problem with so much syntactic sugar is you need to retain/recall the semantics of the vocabulary. As a polyglot programmer who doesn't write much code anymore, it slows me down.

I'm not entirely sure we have even identified all the problems of AGI yet, if so would be interested to see that list?

Generally not a fan of Atlassian, but I can't say Confluence search has been a problem, I have more problems searching within Google Workspace (if its still called that this week).

"Top Programmers", hmm depends what you mean by this, do you mean solving the sort of very narrow problems as done in Olympiads and the like, or something more useful.

I suspect the 'Top Mathematicians' are not those that are best at calculation, like you might get in Olympiads, but those which are able to apply maths to solve real world problems or produce creative breakthroughs. Being good enough at calculation very likely helps, but isn't sufficient.

I work for a reasonable sized enterprise, I can literally not think of a single use for these VR headsets, apart from some novelty, such as trying to make compulsory training more interesting. And they aren't going to buy everyone headsets for that, nor are they going to deal with the faff of sharing them out to teams (hygiene hassle).

Text is okay, but describing a graph is difficult and not as intuitive as a diagram. This doesn't mean UML is needed for the diagram, however the alternative is a semantically weak ad hoc diagram, and I'm not a fan of UML even though have plenty of experience, I agree that UML basically had good intentions for a real problem, poorly executed.

Can you show an example of the text you was trying to extract entities from, interested to see the problem.

Well shouldn't be too difficult to test these hypotheses, if the AI approach is better then actually run a study and show that!

Sounds like he invented something gimmicky.

Yeah, this guy doesn't (as he admits) understand a skaters mindset or culture. If you ride Indy's you're buying into classic solid trucks, and if you been skating a long time you are likely to be nostalgic.

On a more practical aspect, you don't want to relearn the feel of your trucks, and for what benefit, if the word was they grind better, or are lighter without a reduction in strength, maybe, but neither are likely to be more than marginal. Also skaters can be a bit superstitious, have a crap session, blame the stoopid new fangled trucks.

Anyway may take on it, but not skated for years.