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Tag: NLP (page 1 of 1)

IRCTC’s AI-chatbot improve customer interactions by 70%

AI chatbot, AskDISHA powered by Microsoft Azure and developed by CoRover for IRCTC had helped improve customer interactions by 70%. The chatbot used AI, ML, and NLP to answer passenger queries. Customers can now receive instant responses on frequent queries with almost no wait time. Supporting both voice and text capabilities in English and Hindi, it had benefitted more than 178 mn passengers since its inception in Oct 2018. 

TCS launches AI-based Workforce Analytics solution for talent management

TCS launched an AI-based Workforce Analytics solution designed to help organisations to deal with talent management challenges of the digital era. The product uses ML, NLP and a patented cognitive engine to professionally evaluate skills, personality traits, strengths, cultural compatibility, workplace values, and the learning style of an employee. The solution can be used for leadership development, career management, performance improvement, and safety and wellness interventions.

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IBM signs its first-ever esports deal

IBM has signed its first-ever multi-year deal with Activision Blizzard. The Overwatch League will use IBM’s cloud and AI tech to “power analytical and interactive content” in its broadcasts. Starting next year, the league will tap into IBM’s ML and NLP smarts to improve OWL ranking systems. It will also use Watson AI for predictive analysis. IBM’s cutting-edge technologies will help Activision bring interactive engagement to fans and teams.

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Top 4 AI trends for 2021

1. Using AI’s branches – ML, NLP etc – to analyse large data sets and deriving useful information in healthcare and other domains. 2. Using facial recognition and AI-equipped drones in remote surveillance, especially at a time like this, when social distancing and contactless jobs are essential. 3. AI, ML help businesses better understand customers needs, hence better customer servicing and better revenues.4. AI will also be used to accurately predict outbreaks.

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IIT Madras faculty develops AI model to process 11 Indian regional dialects

Contributing to the progress of Indian NLP, IIT Madras faculty jointly with AI4Bharat has developed AI models to process text in 11 Indian regional languages. These models and datasets will be freely available for Indian start-ups, students etc to solve India-centric problems with technology. The models take advantages of the similarities between Indian languages to make efficient use of data.

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4 major areas where companies are using Natural Language Processing

1. NLP-based speech recognition helps in analysing and extracting info from large data sets just by querying. 2. NLP is also being used in analysing customer satisfaction through sentiment analysis. 3. NLP tools such as MeaningCloud, ML Analyser automatically summarize long docs into short, fluent and accurate summaries. 4. NLP-powered chatbots are being used everywhere – from customer service to sales to even HR.

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How SirinLabs’ AI-powered CLM solution helps enterprises manage tedious contracts

1.SirionLabs’ contract lifecycle management platform uses AI, ML, NLP to auto-extract key info from contracts and computes performance requirements.  2. Integrates with enterprise systems (ERP, P2P etc), and validates supplier performance against the contract in real-time. 3. Performs auto-validation of invoices as per contracts, resulting in easy identification of invoice discrepancies. 4. Uses permission ledger architecture to enable shared access to data.

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How Maveric Systems uses tech to spot right clients, candidates

Maveric Systems uses advanced analytics and data engineering to know clients’ needs. It collates banks customers’ feedbacks using automation. NLP, ML identify key themes in the feedback. Detailed engineering analysis of channel tech validates their dissatisfiers and delighters. They’re experimenting ML, AI-based candidate spotting to find talented, appropriate candidates. However, this is in its infancy now. 

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How AI can help researchers be more productive in less time

1. Discovery: NLP, ML-based tools can show relevant content, narrowing down the filtering, unnecessary reading time. 2. Research, data gathering phase: consisting of data gathering, experimenting, analysing & representing research outputs, this is the actual research phase. Open-source AI tools along with statistical ML can ease this process. 3. Publishing: AI tools can correct grammar, do text formatting, write manuscripts etc.

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