WNS (Holdings) Limited a leading provider of global Business Process Management (BPM) services, today announced it has acquired Vuram, a global leader in enterprise automation services. Vuram helps companies accelerate digital transformation by aligning, automating, and optimizing processes using a combination of low-code software applications and intelligent automation platforms. By integrating these technologies into core business operations, Vuram is able to drive end-to-end enterprise automation and the creation of custom, scalable BPM solutions. These solutions include the ability to extract, collect, and categorize data using OCR and AI-based document processing, develop rule-based processing engines and ML-based augmentation, and leverage advanced analytics to improve decision-making. Vuram has also created customizable, low-code, "plug and play" solutions across front, middle, and back-office functions, including industry-specific solutions for the Banking/Financial Services, Insurance, and Healthcare verticals.

Global Artificial Intelligence Ai In Construction Market Growth (Status and Outlook) 2021-2028 presents a profound comprehension regarding the …

Dynamo Software, Inc., a market-leading provider of end-to-end cloud software solutions for the alternative investment management industry, announced a partnership with Northfield Information Services, Inc. Through this information technology collaboration, Dynamo will further augment its analytics and reporting by providing Limited Partners (LPs) with enhanced modeling and forecasting of cash flows and fair values, powered by Northfield. "We are thrilled to partner with Dynamo and power their new cash flow reporting engine" The Dynamo-Northfield partnership responds to the growing demand for insight and transparency into investment performance – which investors want readily available in today's dynamic environment. The integration empowers Dynamo's LP clients, including endowments, foundations, pensions, family offices, and fund of funds (FOF), to design allocation plans, assess potential cash flow shortfalls, and conduct stress/scenario testing with increased confidence. The Northfield data integration, combined with Dynamo Data Automation (DDA), also equips LPs to accelerate smart business decisions and optimize investment operations.

Many of us are familiar with Intuit's (NASDAQ:INTU) industry-leading products in personal taxes (Turbo Tax) and small business accounting (QuickBooks). However, the company has expanded well beyond these two areas and assembled a portfolio of products that have improved and will continue to improve the financial lives of its customers. On Intuit's website, CEO Sasan Goodarzi described their mission statement as follows: We are a purpose-driven, values-driven company. Our mission to power prosperity around the world is why we show up to work every single day to do incredible things for our customers. Our values guide us and define what we stand for as a company.

Lightning AI, the startup behind the open source PyTorch Lightning framework, today announced that it raised $40 million in a Series B round led by Coatue with participation from Index Ventures, Bain, the Chainsmokers' Mantis VC and First Minute Capital. CEO William Falcon told TechCrunch that the new money will be used to expand Lightning AI's 60-person team while supporting the community around PyTorch Lightning development. Lightning AI, formerly Grid.ai, is the culmination of work that began in 2018 at the New York University Computational Intelligence, Learning, Vision, and Robotics (NYU CILVR) Lab and Facebook AI Research (now Meta AI Research). After Falcon started developing PyTorch Lightning as an undergrad at Columbia in 2015, he founded Lightning AI in 2019 with Luis Capelo, the former head of data products at Forbes. While working on his PhD at NYU and Facebook AI Research, Falcon open sourced PyTorch Lightning and -- according to him -- the project quickly gained traction.

Metropolis, a startup building payment infrastructure for parking facilities, today announced that it raised $167 million in a Series B round co-led by 3L Capital and Assembly Ventures with participation from Dragoneer, Eldridge Industries, Silver Lake Waterman, UP Partners and former deputy mayor of New York Dan Doctoroff. CEO Alex Israel told TechCrunch via email that the proceeds will be put toward product development, expanding the company's team and expanding into "new mobility adjacent verticals." Israel contends that parking payment infrastructure is outdated on the whole. Parking garages are stuck in the pre-internet age, he asserts -- disconnected from the digital payments ecosystem (e.g., schemes like Apple Pay and Google Pay). Meanwhile, FlashParking, Passport, AirGarage and REEF Technology (formerly ParkJockey) have raised hundreds of millions from SoftBank and others for tech-forward parking management.

Unstructured data, especially text, continues to grow rapidly in various domains. In particular, in the financial sphere, there is a wealth of accumulated unstructured financial data, such as the textual disclosure documents that companies submit on a regular basis to regulatory agencies, such as the Securities and Exchange Commission (SEC). These documents are typically very long and tend to contain valuable soft information about a company's performance. It is therefore of great interest to learn predictive models from these long textual documents, especially for forecasting numerical key performance indicators (KPIs). Whereas there has been a great progress in pre-trained language models (LMs) that learn from tremendously large corpora of textual data, they still struggle in terms of effective representations for long documents. Our work fills this critical need, namely how to develop better models to extract useful information from long textual documents and learn effective features that can leverage the soft financial and risk information for text regression (prediction) tasks. In this paper, we propose and implement a deep learning framework that splits long documents into chunks and utilizes pre-trained LMs to process and aggregate the chunks into vector representations, followed by self-attention to extract valuable document-level features. We evaluate our model on a collection of 10-K public disclosure reports from US banks, and another dataset of reports submitted by US companies. Overall, our framework outperforms strong baseline methods for textual modeling as well as a baseline regression model using only numerical data. Our work provides better insights into how utilizing pre-trained domain-specific and fine-tuned long-input LMs in representing long documents can improve the quality of representation of textual data, and therefore, help in improving predictive analyses.

AI can power phenomenal revenue growth – until it doesn't. That lesson is being learned the hard way at a growing number of companies where issues with AI systems are not caught and remedied before materially impacting revenue. The latest example is Unity Software, a platform for creating and operating interactive and real-time 3D (RT3D) content. On its most recent earnings call, Unity revealed that it missed top line expectations and lowered its revenue guidance for the rest of the year due in part to a "self-inflicted wound" in AI. Specifically, the company's CEO and Executive Chairman John Riccitiello cited several issues related to machine learning (ML) models that caused an estimated impact to the business of approximately $110 million in 2022: When AI fails on the public stage like this, the temptation to pile onto whatever company is on the chopping block is sometimes irresistible (see: Zillow).

In a nutshell, artificial intelligence (AI) is a computer system that exhibits self-learning behavior or cognition. Cognitive computing systems are computers that use techniques such as machine learning to automatically determine how best to execute tasks without being explicitly programmed using rules. For example, let's say you have data showing that people who bought your product were going to buy something new with about a 30% chance of higher profit margin. You also have an algorithm that determines what percent of your sales staff should be responsible for finding new customers in this specific area. With all these variables, why not just test them and see which one generates revenue growth the most?

In Meta Platform's (FB -3.82%) most recent earnings report, there were some signs of a pullback with net income down year over year and more competition has played a part. In this video clip from "The Virtual Opportunities Show" on Motley Fool Live, recorded on May 24, Fool.com contributor Jose Najarro discusses how the company's investment in artificial intelligence is encouraging for the business going forward. Jose Najarro: First if we take a quick look, Meta Platforms for their financial results total revenue was $27.9 billion dollars. That was up 7% year over year. Again, this is a company that was probably growing at strong double digits and now we're seeing a bit of a pullback.