There is a national need to increase the number of minority students entering STEM fields with essential computing skills. To increase minority students’ interest and engagement in computing, a researcher-practitioner partnership between the University of Texas at El Paso and the El Paso Independent School District, developed and implemented a culturally and linguistically responsive curriculum and pedagogy to introduce computational thinking (CT) in two middle schools across different subject areas in a borderland region. The curriculum leveraged the Sol y Agua game – a bilingual, culturally-responsive game designed to engage students of this region in CT. This paper describes the process and initial findings of this project. The quantitative data from in-game analyses show that students utilized the language change feature to switch from English to Spanish more frequently than the other way – highlighting the need for educational platforms relatable to students through language, environment, and cultural context. Analyses of the qualitative data indicate that while teachers/team members understood CT and translanguaging concepts and taught lesson units that provided opportunities to practice both, CT and translanguaging were largely implicit in the curriculum. In collaborative analyses of these patterns, teachers described additional supports that would help them to make CT instruction and translanguaging strategies more explicit in the content and pedagogy, highlighting the need for systematic, targeted integration of these concepts.

The Sol y Agua researcher-practitioner partnership (RPP) project introduces computational thinking (CT) in the middle school of the Paso del Norte region using a linguistically and culturally responsive approach. At the core of this RPP is the Sol y Agua game, a bilingual, culturally- and environmentally-relevant educational game developed at the University of Texas at El Paso to introduce computing and STEM topics in middle school. The Sol y Agua RPP includes some critical areas for a successful RPP, including partnership building and the focus on a linguistically and culturally-responsive pedagogy and content development. We describe our approach to build a sustainable RPP, incorporating bilingual pedagogy, and integrating CT through a culturally- and environmentally-relevant game as part of our RPP experience.

Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach for this task based on a transformer that autoregressively models the text and image tokens as a single stream of data. With sufficient data and scale, our approach is competitive with previous domain-specific models when evaluated in a zero-shot fashion.

We demonstrate that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even becoming competitive with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks. We also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora.

The FoundationDB Record Layer is an open source library that provides a record-oriented data store with semantics similar to a relational database implemented on top of FoundationDB, an ordered, transactional key-value store. The Record Layer provides a lightweight, highly extensible way to store structured data. It offers schema management and a rich set of query and indexing facilities, some of which are not usually found in traditional relational databases, such as nested record types, indexes on commit versions, and indexes that span multiple record types. The Record Layer is stateless and built for massive multi-tenancy, encapsulating and isolating all of a tenant's state, including indexes, into a separate logical database. We demonstrate how the Record Layer is used by CloudKit, Apple's cloud backend service, to provide powerful abstractions to applications serving hundreds of millions of users. CloudKit uses the Record Layer to host billions of independent databases, many with a common schema. Features provided by the Record Layer enable CloudKit to provide richer APIs and stronger semantics with reduced maintenance overhead and improved scalability.

CloudKit is Apple's cloud backend service and application development framework that provides strongly-consistent storage for structured data and makes it easy to synchronize data across user devices or share it among multiple users. Launched more than 3 years ago, CloudKit forms the foundation for more than 50 Apple apps, including many of our most important and popular applications such as Photos, iCloud Drive, Notes, Keynote, and News, as well as many third-party apps. To deliver this at large scale, CloudKit explicitly leverages multi-tenancy at the application level as well as at the user level to guide efficient data placement and distribution. By using CloudKit application developers are free to focus on delivering the application front-end and logic while relying on CloudKit for scale, consistency, durability and security. CloudKit manages petabytes of data and handles hundreds of millions of users around the world on a daily basis.

Deep convolutional neural networks take GPU-days of computation to train on large data sets. Pedestrian detection for self driving cars requires very low latency. Image recognition for mobile phones is constrained by limited processing resources. The success of convolutional neural networks in these situations is limited by how fast we can compute them. Conventional FFT based convolution is fast for large filters, but state of the art convolutional neural networks use small, 3 3 filters. We introduce a new class of fast algorithms for convolutional neural networks using Winograd's minimal filtering algorithms. The algorithms compute minimal complexity convolution over small tiles, which makes them fast with small filters and small batch sizes. We benchmark a GPU implementation of our algorithm with the VGG network and show state of the art throughput at batch sizes from 1 to 64.