I like your breakdown, and I've observed similar things in my experience as an engineering focused data person! I've had many discussions with my colleagues about how to manage effectively these different blends of roles and skills.
I'm looking for someone for an engineering type of data role right now. Is there a way to get in touch with you about it?
Our product helps companies listen to their customers by unifying natural language feedback across various channels, applying signals using various natural language modeling techniques, then aggregating them to help teams deliver better outcomes using more relevant information.
Hope to hear from you (brandon at frame.ai) :)
edit: forgot to share agreement for your breakdown
We are agreeing that NYC is not at a moment of urban collapse. The processes that drives away the tax base includes policies and social and market forces that erode the city's effectiveness as a sustaining economic and social hub.
The 1960s and 1970s crisis had a lot to do with the end of NYC's industrial epoch. Suburban development and globalization eliminated manufacturing and pulled workers and residents out of the city. The recovery of NYC was bringing high-value services, retail, and tourism back along with arts and culture.
In the time since, NYC has become increasingly a luxury experience, which is indeed part of its strength but also its weakness, since it accelerates decline when people can up and leave without having roots.
>Instead, what kills cities is a long period in which their leaders fail to reckon honestly with ongoing, everyday problems—how workers are treated, whether infrastructure is repaired. Unsustainable, unresponsive governance in the face of long-term challenges may not look like a world-historical problem, but it’s the real threat that cities face.
The feels correct to me.
I lived in New York City for 15 years. Until last year. I've thought about this theme all year. Decades of policy supporting foreign investment and developer speculation gutted the chance for even affluent upper middle class New Yorkers to afford housing and setup a home base, and so many left. The situation has been incomparably more challenging for low income residents.
I agree the urban collapse meme is much easier to spread than a thoughtful discussion about policy and priorities and how to balance the economic strength of a city's major players with the daily priorities of everyday citizens. I hope the New York remainders shift priorities and initiate a different kind of prosperous era than the one I got to enjoy.
I had the same initial thought based on the title. Unfortunately, the answer is no.
The article discusses a low-dimensional KNN problem. The curse of dimensionality guides intuition that the methods here likely will not apply to extremely high-dimensional problems.
faiss actually comes with a lot of excellent documentation that describes the problems unique to KNN on embedding vectors. In particular, for extremely large datasets, most of the tractable methods are approximations that make use of clustering, quantization, and centriod-difference tricks to make computation efficient.
I like your breakdown, and I've observed similar things in my experience as an engineering focused data person! I've had many discussions with my colleagues about how to manage effectively these different blends of roles and skills.
I'm looking for someone for an engineering type of data role right now. Is there a way to get in touch with you about it?
Our product helps companies listen to their customers by unifying natural language feedback across various channels, applying signals using various natural language modeling techniques, then aggregating them to help teams deliver better outcomes using more relevant information.
Hope to hear from you (brandon at frame.ai) :)
edit: forgot to share agreement for your breakdown