Microsoft has officially introduced its first GPT-3-powered feature in a customer product, eight months after it exclusively licensed the sophisticated OpenAI language model. The tech giant has announced at the virtual Build developers conference that it's integrating GPT-3 in Power Apps, which even people with no coding experience can use to build business productivity apps. With the new features in place, Power Apps will be even easier to use -- in fact, it'll give users the power to code by using plain conversational language. GPT-3 is the largest language model ever trained and is capable of generating text so human-like, it could write believable fake news. Microsoft invested $1 billion in OpenAI back in 2019 and got access to the language tech for its own use and for its Azure cloud customers.

Microsoft has announced its first commercial use case for AI language model GPT-3, for which the company purchased an exclusive license last year. Developed by OpenAI, GPT-3 is capable of generating accurate passages of text based on only a few basic prompts. Soon after the model was released, one tester also found it could be taught to compose code with just a few tweaks, leading to speculation over how Microsoft might utilize the technology. At its Build 2021 event, Microsoft has revealed that GPT-3 will be put to work in combination with Power Fx, the company's low-code open source programming language. The pairing will allow developers to code applications using natural language inputs, expediting application development and helping devs pick up advanced concepts more quickly.

Build Any souls wondering what Microsoft would do with its GPT-3 investment have been given an answer with a Power Fx update lightly seasoned with the AI tech. Microsoft gained exclusive rights to use OpenAI's GPT-3 in September last year, allowing it to embed the text-and-code-generating machine-learning model into its own products. Available in preview from next month, the technology was shown off at Microsoft's Build 2021 shindig today, and represents the latest attempt by the Windows giant to get folks from low code to no code and bring its Power platform closer to the masses. Looking initially like a jumped-up version of IntelliSense, the technology attempts to parse natural language entered by the user and generate the corresponding Excel-like language of Power Fx to perform the requested task. The idea is that you type in something like, "show me the readers who commented at the weekend," and it should generate the formulas to retrieve that information.

Microsoft is using the power of GPT-3's natural language artificial intelligence (AI) to help people who don't know how to code write their own software using Microsoft's PowerApps development platform, unveiled at Microsoft's Build developer conference. Redmond has hoped that PowerApps would become a powerful corollary to its Office suite, but the platform has languished a bit. Microsoft originally set up PowerApps in 2015 around a set of programming templates, pulling data from user-defined sources and then outputting results. Think of it like the next level of a traditional macro in Microsoft Office--it's a way for an average user to write a program to instruct Windows to perform a task, but with minimal or no knowledge of program coding. The problem is that even what Microsoft calls a "low code" or "no code" approach can be time-consuming and complex.

In September 2020, Microsoft purchased an exclusive license to the underlying technology behind GPT-3, an AI language tool built by OpenAI. Now, the Redmond, Washington-based tech giant has announced its first commercial use case for the program: an assistive feature in the company's PowerApps software that turns natural language into readymade code. The feature is limited in its scope and can only produce formulas in Microsoft Power Fx, a simple programming language derived from Microsoft Excel formulas that's used mainly for database queries. But it shows the huge potential for machine learning to help novice programmers by functioning as an autocomplete tool for code. There's a million-developer shortfall in the US alone," Charles Lamanna, CVP of Microsoft's Low Code Application Platform, tells The Verge. "So instead of making the world learn how to code, why don't we make development environments speak the language of a normal human?" Microsoft has been pursuing this vision for a while through Power Platform, its suite of "low code, no code" software aimed at enterprise customers. These programs run as web apps and help companies that can't hire experienced programmers tackle basic digital tasks like analytics, data visualization, and workflow automation. GPT-3's talents have found a home in PowerApps, a program in the suite used to create simple web and mobile apps. Lamanna demonstrates the software by opening up an example app built by Coca-Cola to keep track of its supplies of cola concentrate. Elements in the app like buttons can be dragged and dropped around the app as if the users were arranging a PowerPoint presentation. But creating the menus that let users run specific database queries (like, say, searching for all supplies that were delivered to a specific location at a specific time) requires basic coding in the form of Microsoft Power Fx formulas. "This is when it goes from no code to low code," says Lamanna. "You go from drag and drop, click click click, to writing formulas.

Microsoft's integration of the OpenAI GPT-3 technology into Power Apps is the first time GPT-3 will be available commercially in one of its own products.

Medical entity retrieval is an integral component for understanding and communicating information across various health systems. Current approaches tend to work well on specific medical domains but generalize poorly to unseen sub-specialties. This is of increasing concern under a public health crisis as new medical conditions and drug treatments come to light frequently. Zero-shot retrieval is challenging due to the high degree of ambiguity and variability in medical corpora, making it difficult to build an accurate similarity measure between mentions and concepts. Medical knowledge graphs (KG), however, contain rich semantics including large numbers of synonyms as well as its curated graphical structures. To take advantage of this valuable information, we propose a suite of learning tasks designed for training efficient zero-shot entity retrieval models. Without requiring any human annotation, our knowledge graph enriched architecture significantly outperforms common zero-shot benchmarks including BM25 and Clinical BERT with 7% to 30% higher recall across multiple major medical ontologies, such as UMLS, SNOMED, and ICD-10.

Distributional semantics based on neural approaches is a cornerstone of Natural Language Processing, with surprising connections to human meaning representation as well. Recent Transformer-based Language Models have proven capable of producing contextual word representations that reliably convey sense-specific information, simply as a product of self-supervision. Prior work has shown that these contextual representations can be used to accurately represent large sense inventories as sense embeddings, to the extent that a distance-based solution to Word Sense Disambiguation (WSD) tasks outperforms models trained specifically for the task. Still, there remains much to understand on how to use these Neural Language Models (NLMs) to produce sense embeddings that can better harness each NLM's meaning representation abilities. In this work we introduce a more principled approach to leverage information from all layers of NLMs, informed by a probing analysis on 14 NLM variants. We also emphasize the versatility of these sense embeddings in contrast to task-specific models, applying them on several sense-related tasks, besides WSD, while demonstrating improved performance using our proposed approach over prior work focused on sense embeddings. Finally, we discuss unexpected findings regarding layer and model performance variations, and potential applications for downstream tasks.

This is a basic Python shell (really, it's a fancy wrapper over the system shell) that takes a task and asks OpenAI for what Linux bash command to run based on your description. For safety reasons, you can look at the command and cancel before actually running it. To be clear, I'm not trying to convince you that having an AI model figure out what Linux command to run based on your written description is a good idea, but the commands that it generates are, well - watch the video if you want to see. There are several pre-canned ways of interacting with the models that OpenAI provides (the "GPT" models): completing a provided fragment, answering a question, generating "ideas" from a topic, summarizing a passage, etc. This shell uses the question-and-answer format and provides the model with an "example context" and examples of input and output.

Microsoft is using the power of GPT-3's natural language AI to help people who don't know how to code write their own software using Microsoft's PowerApps development platform. The announcement was made at Microsoft's Build developer conference today. Microsoft has hoped that PowerApps would become a powerful corollary to its Office suite, but the platform has languished a bit. Microsoft originally set up PowerApps in 2015 around a set of programming templates, pulling data from user-defined sources and then outputting results. Think of it like the next level of a traditional macro in Microsoft Office--it's a way for an average user to write a program to instruct Windows to perform a task, but with minimal or no knowledge of program coding.