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cjyabraham · cjyabraham · commit 922cc2f61110 · 2024-12-17T12:59:18.000-08:00
Signed-off-by: Chris Abraham &lt;cjyabraham@gmail.com&gt;
diff --git a/_community_stories/15.md b/_community_stories/15.md
diff --git a/_community_stories/43.md b/_community_stories/43.md
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+---
+title: 'Using deep learning and PyTorch to power next gen aircraft at Caltech'
+ext_url: https://www.youtube.com/watch?v=se206WBk2dM
+date: Nov 14, 2019
+tags: ["Research", "Aeorospace"]
+---
+Learn how Caltech’s Center for Autonomous Systems and Technologies (CAST) uses PyTorch to build deep learning systems that can understand the aerodynamics of how aircrafts interact with the ground to enable much smoother and safer landings. 
diff --git a/_community_stories/44.md b/_community_stories/44.md
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+---
+title: 'Deepset achieves a 3.9x speedup and 12.8x cost reduction for training NLP models by working with AWS and NVIDIA'
+ext_url: https://aws.amazon.com/blogs/machine-learning/deepset-achieves-a-3-9x-speedup-and-12-8x-cost-reduction-for-training-nlp-models-by-working-with-aws-and-nvidia/
+date: Jan 27, 2021
+tags: ["Research", "NLP"]
+---
+At deepset, we’re building the next-level search engine for business documents. Our core product, Haystack, is an open-source framework that enables developers to utilize the latest NLP models for semantic search and question answering at scale. Our software as a service (SaaS) platform, Haystack Hub, is used by developers from various industries, including finance, legal, and automotive, to find answers in all kinds of text documents. You can use these answers to improve the search experience, cover the long-tail of chat bot queries, extract structured data from documents, or automate invoicing processes.
diff --git a/_community_stories/45.md b/_community_stories/45.md
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+---
+title: 'PyTorch at Dolby Labs'
+ext_url: https://www.youtube.com/watch?v=K5hD0et_wUc&list=PL_lsbAsL_o2BY-RrqVDKDcywKnuUTp-f3&index=20
+date: Nov 6, 2019
+tags: ["Research", "NLP"]
+---
+Hear how Dolby Labs is using PyTorch to develop deep learning for audio, and learn about the challenges that audio AI presents and the breakthroughs and applications they’ve built at Dolby to push the field forward.
diff --git a/_community_stories/46.md b/_community_stories/46.md
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+---
+title: 'Using a Grapheme to Phoneme Model in Cisco’s Webex Assistant'
+ext_url: https://blogs.cisco.com/developer/graphemephoneme01
+date: September 7, 2021
+tags: ["Research", "NLP"]
+---
+Grapheme to Phoneme (G2P) is a function that generates pronunciations (phonemes) for words based on their written form (graphemes). It has an important role in automatic speech recognition systems, natural language processing, and text-to-speech engines. In Cisco’s Webex Assistant, we use G2P modelling to assist in resolving person names from voice. See here for further details of various techniques we use to build robust voice assistants.
diff --git a/_community_stories/47.md b/_community_stories/47.md
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+---
+title: 'AI21 Labs Trains 178-Billion-Parameter Language Model Using Amazon EC2 P4d Instances, PyTorch'
+ext_url: https://aws.amazon.com/solutions/case-studies/AI21-case-study-p4d/
+date: June 7, 2021
+tags: ["Research", "NLP"]
+---
+AI21 Labs uses machine learning to develop language models focused on understanding meaning, and in 2021 it set a goal to train the recently released Jurassic-1 Jumbo, an autoregressive language model with 178 billion parameters. Developers who register for beta testing will get access to Jurassic-1 Jumbo and can immediately start to customize the model for their use case. The software startup wanted to train the model efficiently, so it looked to Amazon Web Services (AWS) and built a solution using Amazon Elastic Compute Cloud (Amazon EC2), a web service that provides secure, resizable compute capacity in the cloud. Choosing Amazon EC2 gave the company control over the training process, including node allocation.
diff --git a/_community_stories/48.md b/_community_stories/48.md
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+---
+title: 'The Why and How of Scaling Large Language Models'
+ext_url: https://www.youtube.com/watch?v=qscouq3lo0s
+date: Jan 4, 2022
+tags: ["Research", "NLP"]
+---
+Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Over the past decade, the amount of compute used for the largest training runs has increased at an exponential pace. We've also seen in many domains that larger models are able to attain better performance following precise scaling laws. The compute needed to train these models can only be attained using many coordinated machines that are communicating data between them. In this talk, Nicholas Joseph (Technical Staff, Anthropic) goes through why and how they can scale up training runs to use these machines efficiently.
diff --git a/_community_stories/49.md b/_community_stories/49.md
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+---
+title: 'University of Pécs enables text and speech processing in Hungarian, builds the BERT-large model with just 1,000 euro with Azure'
+ext_url: https://www.microsoft.com/en/customers/story/1402696956382669362-university-of-pecs-higher-education-azure-en-hungary
+date: August 10, 2021
+tags: ["Research", "NLP"]
+---
+Everyone prefers to use their mother tongue when communicating with chat agents and other automated services. However, for languages like Hungarian—spoken by only 15 million people—the market size will often be viewed as too small for large companies to create software, tools or applications that can process Hungarian text as input. Recognizing this need, the Applied Data Science and Artificial Intelligence team from University of Pécs decided to step up. Using Microsoft AI Solutions and ONNX Runtime solutions, it built and trained its own BERT-large model in native Hungarian in under 200 hours and total build cost of 1,000 euro.
diff --git a/_community_stories/50.md b/_community_stories/50.md
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+---
+title: 'Mapillary Research: Seamless Scene Segmentation and In-Place Activated BatchNorm'
+ext_url: /blog/mapillary-research/
+date: July 23, 2019
+tags: ["Research"]
+---
+With roads in developed countries like the US changing up to 15% annually, Mapillary addresses a growing demand for keeping maps updated by combining images from any camera into a 3D visualization of the world. Mapillary’s independent and collaborative approach enables anyone to collect, share, and use street-level images for improving maps, developing cities, and advancing the automotive industry.
diff --git a/_community_stories/51.md b/_community_stories/51.md
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+---
+title: 'How 3DFY.ai Built a Multi-Cloud, Distributed Training Platform Over Spot Instances with TorchElastic and Kubernetes'
+ext_url: https://medium.com/pytorch/how-3dfy-ai-built-a-multi-cloud-distributed-training-platform-over-spot-instances-with-44be40936361
+date: Jun 17, 2021
+tags: ["Research"]
+---
+Deep Learning development is becoming more and more about minimizing the time from idea to trained model. To shorten this lead time, researchers need access to a training environment that supports running multiple experiments concurrently, each utilizing several GPUs.
diff --git a/_community_stories/52.md b/_community_stories/52.md
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+---
+title: 'SearchSage: Learning Search Query Representations at Pinterest'
+ext_url: https://medium.com/pinterest-engineering/searchsage-learning-search-query-representations-at-pinterest-654f2bb887fc
+date: Nov 9, 2021
+tags: ["Research"]
+---
+Pinterest surfaces billions of ideas to people every day, and the neural modeling of embeddings for content, users, and search queries are key in the constant improvement of these machine learning-powered recommendations. Good embeddings — representations of discrete entities as vectors of numbers — enable fast candidate generation and are strong signals to models that classify, retrieve and rank relevant content.
diff --git a/_community_stories/53.md b/_community_stories/53.md
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+---
+title: 'IBM Research: Bringing massive AI models to any cloud'
+ext_url: https://research.ibm.com/blog/ibm-pytorch-cloud-ai-ethernet
+date: Nov 17, 2022
+tags: ["Research"]
+---
+The field of AI is in the middle of a revolution. In recent years, AI models have made images, songs, or even websites out of simple text prompts. These types of models with billions of parameters, called foundation models, can with little fine-tuning be repurposed from one task to another, removing countless hours of training and labelling, and refitting a model to take on a new task.
diff --git a/_community_stories/54.md b/_community_stories/54.md
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+---
+title: 'ChemicalX: A Deep Learning Library for Drug Pair Scoring'
+ext_url: https://arxiv.org/abs/2202.05240
+date: Feb 10, 2022
+tags: ["Research", "Healthcare"]
+---
+In this paper, we introduce ChemicalX, a PyTorch-based deep learning library designed for providing a range of state of the art models to solve the drug pair scoring task. The primary objective of the library is to make deep drug pair scoring models accessible to machine learning researchers and practitioners in a streamlined this http URL design of ChemicalX reuses existing high level model training utilities, geometric deep learning, and deep chemistry layers from the PyTorch ecosystem. Our system provides neural network layers, custom pair scoring architectures, data loaders, and batch iterators for end users. We showcase these features with example code snippets and case studies to highlight the characteristics of ChemicalX. A range of experiments on real world drug-drug interaction, polypharmacy side effect, and combination synergy prediction tasks demonstrate that the models available in ChemicalX are effective at solving the pair scoring task. Finally, we show that ChemicalX could be used to train and score machine learning models on large drug pair datasets with hundreds of thousands of compounds on commodity hardware.
diff --git a/_community_stories/55.md b/_community_stories/55.md
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+---
+title: 'Graph Convolutional Operators in the PyTorch JIT'
+ext_url: https://www.youtube.com/watch?v=4swsvOLzL_A&list=PL_lsbAsL_o2BSe3eS4spnodObBa3RL08E&index=3
+date: Dec 2, 2020
+tags: ["Research", "Science"]
+---
+In this talk, scientist Lindsey Gray and Ph.D. student Matthias Fey co-examine how the challenges of High Energy Particle Physics are driving the need for more efficient research and development pipelines in neural network development. In particular, they look at the additions made to PyTorch Geometric, which allow Graph Neural Network models to be compiled by the PyTorch JIT, significantly easing the process of deploying such networks at scale.
