Well it's there in the name, they add machine learning to Postgres. They are not the only ones doing this, I am working on a similar idea and I don't think all of us are as misguided as the crypto bros of 2022. This is as much about Postgres as it is about AI. No one has yet successfully communicated what those in the know are seeing, and it is sort of hard to explain but relational databases are a good match for AI. Personally my strategy is to try to build compelling application instead of an infrastructure play, but to each its own and I wish them the best.
This maybe seems like a dumb question if you spend your life thinking about it, but I'm not seeing what the interesting relationship is between those two things.
Honestly just look at their homepage: https://postgresml.org. I'm usually the first to criticize products for using buzzwords and making it very unclear what they do, but of all the AI products I've seen their homepage has to be one the clearest pages I've encounter in the space.
The example there makes it immediately obvious what it does and I won't repeat it here. If it's not clear to you probably you are not the target market.
I already told you I don't want to talk about this here and now. It is not dumb but it is disrespectful to demand explanations from strangers, like I have time for this.
stock prices are proxy for perception of company. that perception matters in different ways for those that don't have a ticker (founder prestige, fundraising, hiring, etc)
Stimulating different parts of the body stimulates different parts of the brain[1]. Through cross talking neurones this might influence parts of your brain to be rewired differently and relieve some over burdened circuits, just like it might happen with traumatic events, but the rigour and precision by which this can be done is still very much an open question.
Universal grammar in the way that Chomsky suggested, a sort of structure all humans must have in there brain, is not a thing. But I believe there is something like the "universality of grammar" where the human vision system or motor system can have a grammatical quality to them. In that sense that there is ambiguity in which tree structure represent the world. I've seen this in many places but I am not sure if someone has put this into (better) writing.
Yale finance could argue that the https://en.wikipedia.org/wiki/Perpetual_bond has priority with the oldest example being 1624 as the holder has the option to collect coupon payment. Currently, Yale has the opportunity to write up and sell the option on the coupons (back to the water board if they wanted to make it a little less unfeasible of being "perpertual") and turn the original "bond" into a zero coupon "bond"? (more of an annuity given the principal can never be collected - unless doubtfully they were written as callable).
Far more money has probably been stolen with the "heads I win, tails you lose" approach -- just put on massive leverage and stick someone else with the bill if it blows up -- than has been stolen by all the Ponzi schemes in history.
The big lie about the semantic web is this ridiculous notion that it is machine readable just because it has an unambiguous grammar to parser and has ontologies to disambiguate homonyms.
Thanks, I was rather involved in the research community. It has little to do with parsing text NLP-style. It has everything to do with annotating documents with formally structured KR data, or embedding the same into them.
In fact, my own involvement in the field started with Jim Hendler suggesting I look into the possibility of developing a spider that would wander across HTML pages and glean knowledge from them with an NLP technique. I worked on that for a while, then abandoned it and proposed to him instead that web pages should be marked up with formally parseable structured semantic data. Why parse a student's page saying he "Goes to U Maryland" when he could just write <claim obj1="me" obj2="UMaryland" rel="attend" ontology="http://ontology.org/university-ontology/">. That was 1995.
Sentence embeddings have been great for improving semantic search, but I am still struggling with finding relevant documents for numerical values. Questions like "what people where born in 1992" or "people with at least 4 children". One thing I can do is pre-process the data by transforming the date of birth into boomers/zoomers/millenials and the like but this does not help on the question side if people don't know what to ask
Lets break down the three clains in reverse order:
1. Are there primitive societies of hunter-gatherer that are not self aware, as in they don't pass neurological test that measure this?
2. From a evolutionary perspective what is the advantage of this sexual dimorphism and given that this advantage exists why did this advantage disappear afterwards and become present in both sexes. Are there any other traits where woman are shown to have larger divergence. I thought men where the "weak" sex in this regard.
3. If consciousness is recent then how do you explain conscious trails and self awareness in lower primates, is this also a case of convergent evolution?
Yes, in my career I've seen a OO extension of C written in XML that required a custom eclipse plugin to write. A language where you defined a function like: function foo() while (...) ... end while; if (...) ... end if; end foo, because those guys loved the C++ style of putting comments at the end of control structures. Finally there was the language you had to write in excel that was exported to CSV that in turn was used to generate XML that was parsed before compile time to generate java code with System.out.println and was finally loaded at runtime through some dynamic loading. I never had any respect for these people as engineers, I was very KrugerDunnings early on in my career but I don't think I was to entirely to blame for always knowing better.
One time, someone did something in a dumb way that other people sometimes do in a good way. That said, I decided that the something someone can do is dumb, because I saw it done in a dumb way. Thank you for coming to my Ted Ted Talk.
There are a lot more middle man then that, a lot of CEO and CTO i've met had an almost active disintrest in anything technical or even practical knowledge of there product, not knowing the priorities is in there own backlog or not knowing what an init system is or does but ask you how to start a process when a computer starts. Unbelievable what people complain about in meetings without having googles that problems in MONTHS. Most people just secretly hate technology and will grasp at anything that gives them an excuse not to care.
