Challenges at the Base of the Pyramid

Is « Base of the Pyramid » (BoP) the new Eldorado for companies or only « smoke and mirrors »? It is extremely challenging for companies to make profits through offering products and services to the world’s poorest populations, while supposedly tackling social or environmental issues. This article nevertheless aims to show that companies need to push on their initiatives at the BoP. We propose solutions to get over economic, social and political obstacles facing companies’ BoP initiatives and discuss the crucial role of these initiatives in terms of innovation and growth

Sequencing and timing of strategic responses after industry disruption: evidence from post-deregulation competition in the U.S. railroad industry

This paper examines the sequencing and timing of firms’ strategic responses after significant industry disruption. We show that it is not the single strategic choice or response per se, but the sequencing and patterns of consecutive strategic responses that drive a firm’s adaptation and survival in the aftermath of a shift in the industry. We find that firms’ renewal efforts involved differential adaptability in finding balance at the juxtaposition of responding to demand-side pressures and choosing a path of new capability acquisition efficiently. Our study underscores the importance of taking a sequencing approach to studying strategic responses to industry disruption

XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages

Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs)-languages for which NLP research is particularly far behind in meeting user needs-it is feasible to annotate small amounts of data. Motivated by this, we propose XTREME-UP, a benchmark defined by: its focus on the scarce-data scenario rather than zero-shot; its focus on user-centric tasks-tasks with broad adoption by speakers of high-resource languages; and its focus on under-represented languages where this scarce-data scenario is most realistic. XTREME-UP evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks that are of general utility. We create new datasets for OCR, autocomplete, question answering, semantic parsing, and transliteration, and build on and refine existing datasets for other tasks. XTREME-UP provides a methodology for evaluating many modeling scenarios including text-only, multi-modal (vision, audio, and text), supervised parameter tuning, and in-context learning. We evaluate commonly used models on the benchmark

Software for the frontiers of quantum chemistry:An overview of developments in the Q-Chem 5 package

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange–correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear–electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an “open teamware” model and an increasingly modular design

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG- bench). BIG-bench currently consists of 204 tasks, contributed by 450 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood develop- ment, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google- internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Horizontal Alliances as an Alternative to Autonomous Production: Product Expansion Mode Choice in the Worldwide Aircraft Industry 1945-2000

This study investigates why firms choose to undertake product expansion through alliances with competitors rather than on their own. We highlight product heterogeneity as a determinant of this make or ally choice. We propose that firms turn to horizontal alliances in order to implement product expansion projects that require greater resources than those available to them. More precisely, we hypothesize that a firm is more likely to launch a new product through a horizontal alliance rather than autonomously when the resource requirements of the project are greater, the resources available to the firm are more limited, there is a mismatch between resource endowment and requirement, and the firm's collaborative competence allows it to better cope with the interorganizational concerns that collaboration with competitors raises. We find support for our arguments on a sample of 310 new aircraft developments launched between 1945 and 2000, either by a single prime contractor or as a horizontal alliance in which prime contractorship is shared with another industry incumbent. (Copyright © 2009 John Wiley & Sons, Ltd)