{"schema_version":"onlylabs.public_signal.v1","title":"Scaleway Writing: Doing AI without breaking the bank: yours, or the planet’s","description":"Scaleway writing signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/35eb7e04-3e7c-4aac-a5eb-a6f00b23fe0e","json_url":"https://onlylabs.fyi/signals/35eb7e04-3e7c-4aac-a5eb-a6f00b23fe0e/signal.json","generated_at":"2026-06-08T15:46:51.968+00:00","org":{"slug":"scaleway","name":"Scaleway","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/scaleway","dossier_json_url":"https://onlylabs.fyi/labs/scaleway/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/35eb7e04-3e7c-4aac-a5eb-a6f00b23fe0e","signal_json":"https://onlylabs.fyi/signals/35eb7e04-3e7c-4aac-a5eb-a6f00b23fe0e/signal.json","source":"https://www.scaleway.com/en/blog/ai-planet-datacenters/","lab_dossier":"https://onlylabs.fyi/labs/scaleway","lab_dossier_json":"https://onlylabs.fyi/labs/scaleway/dossier.json","analysis":"https://onlylabs.fyi/analysis/scaleway","analysis_json":"https://onlylabs.fyi/analysis/scaleway/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/scaleway/evidence.json","category":"https://onlylabs.fyi/neoclouds","category_json":"https://onlylabs.fyi/neoclouds.json","category_feed":"https://onlylabs.fyi/neoclouds/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json?category=neocloud","topic":"https://onlylabs.fyi/topics/talking","topic_signals_json":"https://onlylabs.fyi/topics/talking/signals.json?category=neocloud","topic_feed":"https://onlylabs.fyi/topics/talking/feed.xml?category=neocloud","data_business":null},"answer_pack":{"answer":"Scaleway published Doing AI without breaking the bank: yours, or the planet’s. This talking signal gives public context for research themes, product direction, policy, or launch framing. 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Such exponential growth translated into an astounding 300,000x-fold increase over the course of only 6 years - starting from 2012, the year widely recognized as the onset of the deep learning era of AI. This phenomenon is directly linked to the growing complexity of underlying deep learning models: so-called artificial neural networks (ANNs). Loosely inspired by the way our very own brains work, mathematically ANNs amount to matrices of numerical values, termed ANN weights or parameters. Suitable parameters are calculated during the computationally-intensive development stage called model training, and are then used to multiply whatever inputs are fed into the ANN, in order to produce (hopefully sensible) outputs. More parameters, more power A somewhat oversimplified rule of thumb here is: the greater the number of parameters, the more powerful the model. AlexNet , the neural network that kicked off the deep learning revolution in 2012, used 61..."},"evidence_pages":[{"url":"https://papers.nips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf","final_url":"https://papers.nips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf","title":"Doing AI without breaking the bank: yours, or the planet’s","http_status":200,"content_type":"application/pdf","capture_method":"exa","fetched_at":"2026-06-08T15:46:51.968+00:00","bytes":1418820,"raw_path":"a31a3fced303a2857fcd3fd86ae91499b3c688a97c9601d6d91e9dd4d67bc572.pdf","content_hash":"90137160c57217953d5f61857e64ca58e85f06e1b13b4f475c918b1b582b9771","excerpt_chars":1200,"truncated":true,"excerpt":"ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky University of Toronto kriz@cs.utoronto.ca Ilya Sutskever University of Toronto ilya@cs.utoronto.ca Geoffrey E. Hinton University of Toronto hinton@cs.utoronto.ca Abstract We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make train ing faster, we used non-saturating neurons and a very efficient GPU implemen tation of the convolution operation. To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called “dropout” that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of..."},{"url":"https://www.scaleway.com/en/blog/ai-planet-datacenters/","final_url":"https://www.scaleway.com/en/blog/ai-planet-datacenters/","title":"Doing AI without breaking the bank: yours, or the planet’s","http_status":200,"content_type":"text/html; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-07T21:19:17.43626+00:00","bytes":143219,"raw_path":"d3b00d4360573bcbdf3415851143e9685325a18d9976eb5300b626a0878f2046.html","content_hash":"7c80c05af3383ce4e28b1573f6844aa5b914cc18622d83c3300486634b6600f8","excerpt_chars":1200,"truncated":true,"excerpt":"Doing AI without breaking the bank: yours, or the planet’s Build • Olga Petrova • 30/06/21 • 3 min read A 2018 study by OpenAI showed that the amount of compute power needed to train state-of-the-art AI models was doubling every 3.4 months. 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