2,041 karma · joined March 28, 2013
Firstly, even for non vector data, read/write transactional database vs read-optimized store purely for fast serving are already markedly different. Then, the shape of data that is used to generate embeddings is markedly different than the shape of data that is ready to transact or serve.
So, no matter where it is stored, it has to leave that store, get transformed and enriched and then run through an embeddings generator (ML inference).
Then, it has to be stored in a manner that is optimal for retrieval ranking. If you are doing ANN that's one thing, but if you are doing attribute based filtering while retrieving and you wish to accelerate it through GPU to do more exhaustive search, that's another thing altogether.
All these lead to fairly sophisticated optimized implementation. Sure, a singular database product solution that has all these different optimized engines can emerge over time but surely it is too early today to converge like this.
The advent of faster and cheaper network speeds and feeds made it economically feasible to recreate decades-old circuit switching network topologies like Clos/Fat-tree in packet switched networks. This solved the problem of packet blocking and dropping for any-to-any traffic patterns, even at line speed. This simplification of the network topology made it easier to place workloads within the datacenter and to build larger workloads.
However, there was still a problem of head-of-line blocking due to flow routing imbalances. This could cause packet drops and latency spikes. This problem was solved with the development of end-to-end traffic aware routing techniques using software defined networks (SDNs).
As companies built more datacenters, they needed to interconnect them with high-speed campus/metro networks. These networks could be fully meshed, but there was a limit to how much bandwidth they could support. This led to the need for software defined controls for managing traffic priority and quality of service (QoS) across these high-demand links. This was especially important when campus/metro links failed and degraded capacity.
However, implementing all of these network functions in routers would have made them very complex, expensive, and difficult to manage. This led to a deeper dive into SDN.
As companies gained more experience with SDN, they realized that the traffic patterns between large pods of hardware (cells) within a single datacenter were fairly stable. This meant that the full-clos topology between these cells was underutilized. Additionally, the next generation of network speeds and feeds was so fast that it would have required a complete rip and replace of the existing infrastructure.
To avoid this rip and replace, companies looked for ways to make the existing layers feed/speed agnostic and to improve their utilization. This led to an interest in optical switching, but with much faster switching speeds and software defined control (unlike traditional telecom optical circuit switching).
Today, the hardware and software stack needed to implement all of this is still very bespoke and not as mature as electrical packet switching networks. This means that it is not yet a viable solution for smaller players. But the amount of investment AI/ML workloads are attracting and how much of a big difference these OCS interconnects can make between these large pods will likely lead to renewed interest in commercializing the OCS solutions for smaller players. So, in that way, this publication is very timely.
We act as though we are finding out the ads powered free software services business model today. All of user facing google products – Chrome, Android, Gmail, Youtube, Photos, GSuite etc all exists because they are paid for by ads revenue from ads side of the business. One can make a very strong case that the net benefit far far out weights any harm on the other side.
Secondly, I would trust Google more than Apple or Meta to keep my data safe from technical hacks or leaking or selling it to third parties. And I understand they are going to use my data to power their ads business where they personalize and target ads to me on their own and 3rdparty properties.
Finally, we need to keep the regulatory oversight and pressures to move and keep them in line with increasing privacy and security expectations.
If you are James Bond and you want to have secure communications with M, you are better off using a custom appliance type mobile device meant only to do that one function and nothing else. You will have to rely on anonymous 3rd-party burner devices to leave messages encoded in pre-agreed sequence of rotating geo-locked codebooks on random message boards. Any less opsec and you are toast!
If you are a normal human-being using connected digital devices normally, then you should assume anything you put on those devices is already stolen. If you want to keep things truly private, keep it off digital (paper, old-school analog tape etc). Then, at least they will have to physically steal it from you – which, depending on the situation, might be much harder for them, but not necessarily much safer for you. So, you lose either way.
Only real way out is to have well-functioning democratic governments with strong transparency and checks and balances with strongly enforced privacy laws to protect its constituents from both domestic and foreign adversaries and strong support for initiatives like Citizen Labs.
senior-father - ದೊಡ್ಡಪ್ಪ (doddaappa) for elder brother of father,
senior-mother - ದೊಡ್ಡಮ್ಮ (doddammA) for elder sister of mother,
junior-father – ಚಿಕ್ಕಪ್ಪ (chikkappa) for younger brother of father,
junior-mother - ಚಿಕ್ಕಮ್ಮ (chikkammA) for younger sister of mother.
Their spouses get the same seniority title with appropriate gender.
Then, opposite gender siblings of your parents get pronouns that are more conceptually closer to uncle/aunt. But here, there's no seniority distinction.
Edit: Forgot to mention backdoors built into basic technologies they may already be using – like the Cavium HSM thing that came to light earlier this week.
If I'm running a popular app/web service, I would have my own AS number and I will have purchased a few blocks of IP addresses under this AS and then I would advertize these addresses from multiple owned/rented datacenters around the world.
These BGP advertisements would be to my different upstream Internet service providers (ISPs) in different locations.
For a given advertisement from a particular location, if you see a regional ISP as upstream, you can make an educated guess that this particular datacenter is in that region. If these are Tier 1 ISPs who provide direct connectivity around the world, then even that guess is not possible.
You can see the BGP relationships in a looking glass tool like bgp.tools – https://bgp.tools/prefix/193.142.125.0/24#connectivity
If you have ability to do traceroute from multiple probes sprinkled across the globe with known locations, then you could triangulate by looking at the fixed IPs of the intermediate router interfaces.
Even this is is defeated if I were to use a CDN like Cloudflare to advertise my IP blocks to their 200+ PoPs and ride their private networks across the globe to my datacenters.