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PaperclipPerfector  1d ago (text post)   4985 thread views

Culture War Roundup for the week of August 17, 2026

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Does anyone know what, if anything, the connection is between the work that led to transformers and the rapid developments in corporate big data/machine learning (particularly recommendation algorithms and similar marketing strategies) going on at around the same time? The model of AI history in my head has the post-Woo era of AI beginning with the Netflix recommendation algorithm in 2006, but I don't know how accurate it is.

The connection is substantial.

It's 2014. Imagine you're a large search company that gets virtually all of its revenue from search-derived advertising. Being able to rank by user preference drives verified revenue. But your current search ranking system is heavily heuristic. What can you do?

The novel thing: use deep learning to predict pairwise document preference (as measured by click through rates). You can imagine a system: you start with a query, decompose it into unigrams and bigrams, and then map it into an embedding space. No sophisticated contextual embedding schemes in those days; it's just a bag of words, averaged or summed together. Then, use a ReLu network on those embeddings to predict that pairwise preference, all trained jointly.

This requires a massive upfront investment. You need infrastructure to perform an unprecedentedly large, distributed training run. CPUs won't cut it, so you design custom ASICs to process your tensors. Serving the model at a rate of millions of queries per second with a latency of a couple ms is an entire problem unto itself.

This is incredibly costly. But, as it turns out, that crude (by today's standards) approach is enough to drive double digit percentage increases in revenue, easily paying for itself almost right away. Later you transition from bag of words to LSTMs, which helps even more, but you'd like even better contextual understanding.

And all that infrastructure investment is reusable. And many of the leads on the reranking project also happen to be working on neural machine translation in the same org, which faces similar problems. There must be some way to convert sequences of tokens into better representations; what do we need to do better?

A company that managed to do all that and answer that question would be incredibly well positioned for the LLM era, and it would have taken a staggering amount of organizational incompetence and sclerosis to miss the opportunity.

Thanks for this, I wasn't really aware of that history. Almost my entire interaction with neural nets was through the medium of computer vision object recognition and Caffe, plus Q networks when they came along; I attended lectures where people discussed how you could use RNNs to turn Shakespeare into Shakespearish but it never occurred to me how far it would scale or that it was really worth paying attention to.

I would say only partial.

Recommendation algorithms were generally clustering approaches and support vector machines, I think. Neural networks were regarded as a potential (but not very good) way to do clustering for the most part, and one of the clusterings they were trying to do was vision (image class analysis). The breakthrough moment in my opinion was the publication of AlexNet in 2012, which demonstrated that neural networks weren't just a way to do clustering and image analysis, but that if done right they were a better way to do it and produced a step change in performance on a task usually considered impossible.

Transformers came out of google's work on translation, which has a fairly different heritage. That's less about clustering and more about sequence analysis using autoencoders, recurrent neural networks, etc. Data-driven neural networks were already pretty much considered SOTA in that field if I remember rightly (Google's lead researcher famously said, 'our translation performance goes up 10% for every linguist I fire') but benefited from the massive increase of interest in neural networks, hardware for driving neural networks, collecting big data pools, and general theoretical work on representations/efficiency/etc.

I don't know that AlexNet et al were truly breakthroughs in and of themselves so much as a logical extension of existing NN stuff enabled by the step-change in GPU hardware -- as I recall LeCun was doing similar work in the 1980s, and I myself did a project with a simple one-layer LSTM network in the late 90s in which I had to work at night because the server load involved was bringing down other (so-called) "important workloads" at my department (at a major university) during the day.

None of this was really feasible, even as research, until pretty near 2010 -- the hardware just wasn't there yet.

They weren't, really, I agree. "Deep learning" as in Massively-Multilayer Neural Networks was known to be a good thing ages ago, we needed the hardware to do it. I would say AlexNet was a breakthrough in the since of marking a step change from, 'hey guys if you gave us some really massive computers I bet this toy would do something useful' to 'holy shit guys, see what I can do'.

All credit to the neural network guys for sticking to their guns until the hardware caught up, of course.

RankBrain (2015) was pre-Transformer and was used in production search. Basically a learned embedding model for semantic relevancy.

AlexNet was the turning point, I agree.

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