Microsoft Business Intelligence Platform: A Detailed Analysis
Building Natural Language Generation Systems
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The Rise Of AI-Infused Application Generation Platforms
GenAI, extrapolated to its extreme, would mean the destruction of the software industry.
In theory, a powerful GenAI model could generate and deploy perfectly designed and analyzed software in perfectly optimized machine code from natural language or other instructions – or even autonomously, in its wisdom – without human review. This would remove the need for all business applications (adios, SAP), all flavors of development platform (peace out, Pegasystems), key software components (au revoir, Oracle database), and most of the existing tools, processes, and roles of the software world, including developers (so long and thanks for all the fish).
We don't buy this faith-based, apocalyptic scenario; it is absurd. But the opposite view – that all the individual products, practices, and roles of the software industry will just continue as they are, with AI pixie dust sprinkled gently on top – is naïve.
Between these two extremes, the rapid advancements in TuringBots (AI tools aiding the varied tasks of the software development life cycle) and low-code platforms point to a more realistic future for much of software: Application Generation Platforms.
Application Generation Platforms
ForresterApplication Generation Platforms (or "AppGen" for short) are not and will not be magic. The category represents the evolution of practical platform engineering to take full advantage of AI (especially generative AI), while mitigating its drawbacks. AppGen will integrate the steps of software analysis, development, security, testing, and delivery by providing Turingbots for both low-code and high-code development spanning every step – incorporating principles of Agile and DevOps along the way. To enable the generation of larger chunks of functionality (or even entire applications), the core authoring experience will be a cycle of natural language prompting and subsequent iteration through efficient-and-visual mediums: drawings, graphical user interfaces (GUIs), visual low-code models, and domain specific languages. Lower-level code generation for custom components, extensions, and visibility will also be central. And critically – business-and-industry "domain knowledge" and "best practices" will be baked into the AI models supporting this generation process, eliminating the distinction between "software development" and "off-the shelf applications" in which business excellence is theoretically predefined.
In the short term, there are hurdles to overcome: most natural-language-based AppGen features are only suitable for generating "simple" things, and the typical security and privacy concerns with use of public LLMs apply. But AppGen is not a theory: its component parts already exist, as do modest examples of the pattern – such as the firm that told us of their experience generating a logistics app to help manage sea containers.
Low-code platform vendors have a head start on AppGen and are the current standard bearers of the category — but global hyperscalers, other end to end development platforms, and new startups will also be significant players. In its maturity, AppGen will compress and blur the steps and roles of the SDLC, democratize development further and faster, enable real-time collaboration to design and deliver applications, enable dynamic end-user experiences, and redefine not only the software development industry but also broad swathes of software generally. We estimate AppGen platforms will mature over the next three years.
This post was written by Principal Analyst John Bratincevic and VP, Principal Analyst Diego Lo Giudice and it originally appeared here.
Ashwini Challa: Pioneering Innovation In Natural Language Generation
(Photo : Ashwini Challa)
In the realm of Natural Language Generation (NLG), few names resonate as profoundly as Ashwini Challa's. Her groundbreaking innovations have not only captivated the industry but have also attracted the attention of some of the most esteemed figures in the field. With a series of patents and research articles, Challa's work has propelled the boundaries of NLG, earning recognition from luminaries including Tom Gruber (Co-founder Siri), Eric T. Muller (IBM Watson), Alex Acero (Senior Distinguished Engineer, Apple), Jerome Bellegarda (Distinguished Scientist Etsy, Apple), and Eugene Agichtein (Professor at Emory University and Amazon Scholar).
Pushing the Boundaries of NLGChalla's contributions have been instrumental in pushing the frontiers of NLG, a domain crucial for modern-day virtual assistants, AI chatbots, and language-based applications like Meta Oculus, Portal, and Apple Siri. Notably, Tom Gruber, one of the founding fathers of Apple's Siri, has cited the significance of Challa's work and patents, recognizing its potential to redefine user interaction with technology. Gruber's citation highlights the transformative nature of Challa's research in reshaping the landscape of human-computer interaction.
Eric T. Muller, renowned for his contributions to MIT and IBM Watson, has Cited Challa's research for its depth and innovation. Muller, who has been at the forefront of AI and natural language processing, sees Challa's patents and articles as pivotal in advancing NLG technology, particularly in enhancing the capabilities of AI-driven systems. His patent citation underscores the technical prowess and impact of Challa's work on the broader AI community.
Industry EndorsementsAlex Acero, distinguished scientist at Apple Siri, emphasizes the practical implications of Challa's work by citing her patents, highlighting its relevance in shaping the future of Apple products and services. Acero's citation underscores the industry's recognition of Challa's contributions to NLG research and development, indicating a direct influence on the products that millions of users interact with daily.
Ashwini Challa with Mark Zuckerberg
Challa's patents and research articles, accessible via leading academic databases such as Google Scholar, have become essential references for scholars, engineers, and practitioners in the NLG domain. They provide insights into novel techniques, methodologies, and applications that are shaping the landscape of NLG and driving innovation in AI-driven language technologies.
Driving Innovation Beyond AcademiaAshwini Challa, a pioneering figure in Natural Language Generation (NLG) and Artificial Intelligence (AI), has been making waves across the tech industry with her groundbreaking innovations and patents. With a formidable portfolio of projects undertaken during her tenure at Microsoft and Meta's portal and Oculus AR VR, Challa's work has not only shaped the trajectory of NLG but has also been cited and implemented by top-tier companies including Apple, Amazon, Meta, and Google.
Revolutionizing Conversational InterfacesChalla's contributions span a spectrum of projects, each bearing significant implications for the advancement of NLG and AI systems. Her work on grammatical classification for NLG in assistant systems has garnered widespread attention. The project, published at the prestigious NAACL conference in 2019, introduced a model-based grammatical classifier that filters ungrammatical responses, thereby enhancing the quality assurance of NLG systems in production environments.
Similarly, Challa's endeavors in generating compositional natural language by assistant systems have revolutionized how responses are formulated in conversational interfaces. Through the introduction of a Compositional NLG model, her project has significantly enhanced user satisfaction and reduced ambiguity in user queries while maintaining flexibility across diverse domains.
Addressing Industry ChallengesChalla's commitment to efficiency and effectiveness in NLG is exemplified in her project on data-efficient modeling, published in COLING 2020. By pioneering novel sampling and modeling techniques, this project has reduced latency, improved correctness, and lowered data requirements for NLG model development, making deployment more accessible and cost-effective.
Moreover, Challa's contributions extend beyond NLG to the realm of Machine Learning Operations (MLOps), where her work on advancing the efficiency and effectiveness of Machine Learning with a data-centric MLOps platform has been transformative. The platform, developed at Snorkel AI, streamlines the model development lifecycle by significantly reducing the time and costs associated with data collection.
Industry AdoptionChalla's projects have been recognized and implemented by tech giants even before the rise of OpenAI's ChatGPT. Companies like Apple, Amazon, Meta, and Google have integrated her methodologies and frameworks into their NLG and AI systems, underscoring the industry-wide impact of her research and innovation.
As the tech industry continues to evolve, Ashwini Challa's pioneering work serves as a guiding light, inspiring future generations of researchers and practitioners to push the boundaries of what is possible in NLG, AI, and beyond.
In summary, Ashwini Challa's journey in the realm of Natural Language Generation is one of innovation, impact, and industry recognition. Through her groundbreaking research, patents, and interdisciplinary approach, she has not only advanced the field but has also influenced how technology giants shape their products and services. As NLG continues to play a central role in human-computer interaction, Challa's work stands as a testament to the power of visionary thinking and the relentless pursuit of excellence.
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