There's the Khadas Mind series of mini pcs. They have a proprietary docking interface though. Agree that it would be great if this form-factor was more common.
The weight and reinforcement of trees, poles, deer, etc. are difficult to change. Single-vehicle crashes cause more than half the crash deaths in the US[1].
I don't think the safety record of a motorcycle is a standard to aim for -- but even then a kei truck is at least twice the weight and likely has significantly less sophisticated brakes.
It's not mentioned in the original paper, but DiskANN also supports PQ at build-time via `--build_PQ_bytes`, though it's a tradeoff with the graph quality as you mention.
One interesting property in benchmarking is that the distance comparison implementations for full-dim vectors can often be more efficient than those for PQ-compressed vectors (straight-line SIMD execution vs table lookups), so on some systems cluster-and-merge is relatively competitive in terms of build performance.
I think you might be right that encouraging curious discussion of these topics is important to the mission of HN.
However, if the moderation approach is going to change, I think it would be better to do so explicitly through changes to the site guidelines rather than in an ad-hoc way. I don't think that this article is covering an 'interesting _new_ phenomenon' (emph. mine) as discussed in the guidelines, and indeed most of the comments are talking about moderation policy or the conflict in general as opposed to the details presented in the article. Perhaps it would be better to have a thread explicitly focused on members of the community engaging with each other as individuals, such as a hypothetical 'Ask HN: How has conflict personally affected you?' or 'Ask HN: How/why have your views on this conflict changed over time?'
The stories that the article has to tell are important, but they aren't the thing that people are discussing here. And moderating submissions instead of explicitly discussion-focused posts invites some of the concerns about sourcing and bias that have been raised in other comments.
Something I don't fully understand, from [1], Altman was an employee of the for-profit entity. So to fire him, wouldn't the non-profit board be acting in it's capacity as a director of the for-profit entity (and thus have a fiduciary duty to all shareholders of the for-profit entity)? Non-profit governance is traditionally lax, but would the other shareholders have a case against the members of the non-profit board for acting recklessly w/ respect to shareholder interests in their capacity as directors of the for-profit?
This corporate structure is so convoluted that it's difficult to figure out what the actual powers/obligations of the individual agents involved are.
Altman has claimed before that he doesn't hold equity in OpenAI. He could have some kind of more opaque arrangement that gives him a material stake in the financial success of OpenAI, and downplayed it or didn't disclose it to the board.
Who knows, though -- I'm sure we'll find out more in the next few weeks, but it's fun to guess.
There are a lot of directions people try to go, making different tradeoffs in the complexity of the clustering, the loss from the quantization, the impact on performance (esp. trying to get some subset of the tables to fit in cache). Readers might be interested in [1], which gives a survey of some of the directions.
In general though PQ is a pretty good baseline. I'm glad all these vector DB companies seem to have decided that the best form of marketing is high-quality summaries/tutorials about fundamental concepts, it's a good contribution to the community.
Apart from the comments you've already gotten, another goal of geospatial systems is to support range queries (e.g. for the bounding box of the user's screen, what are all the businesses in that box). In higher dimensions range queries are mostly useless and the focus is on NN queries.
But as the other comments have mostly said, it's mainly dimensionality and scale differences that drive the design differences (e.g. graphs end up working better than trees in high dimensions)
> Let's put deep learning aside for a second. Can anyone here name a single academic in any other subfield of computer science who had a string of groundbreaking research works where every one of those works is from 1980 or after?
Shafi Goldwasser (Probabilistic encryption, zero-knowledge proofs, etc.) got her PhD in 1984.
Leslie Lamport is just a bit before your deadline, his 'Time, Clocks' paper came out in 1978, but the bulk of his work was after 1980, including paxos.
While there's definitely some truth to your Kuhnian view of 'times of revolution' in a field, I think it's hard to apply that to recent progress because it may just be that it's not clear which research works were groundbreaking without the benefit of hindsight. To me, the revolutionary period of CS research is still ongoing.