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Insights and best practices for unraveling the complexities of digital data

Applied AI For Finance

Explore how AI can be leveraged to yield value from unstructured data within Financial Documents. This discussion will uncover both the risk factors and the great potential associated with applying AI to financial industry use cases.
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Bogged Down by Annotation

Artificial Intelligence is only as smart as the data its fed. While it’s not always easy to turn raw data into smart data, high-quality data annotation performed by experts provides structure to data that is otherwise just noise to a supervised learning algorithm. Listen in as Chief Product & Marketing Officer Rahul Singhal explains.
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The Art & Science of Data Annotation

Innodata and Société Generale’s Lourenco Miranda discuss how to prepare data for machine learning.
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Webinar

Smart Data for AI: Everyday Cases for the Advanced Business

With the expansion of essential AI systems, new uses for smart and clean data to accelerate business have been surging. This episode will dive into use cases that some of the most advanced businesses are uncovering to stay ahead of the competition.
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Webinar

Your company’s Artificial Intelligence project will fail if you don’t do this...

An interview with Rahul Singhal, the Chief Product and Marketing Leader for Innodata at the Open Data Science Conference. C-suite leaders are becoming increasingly frustrated that many of their Artificial Intelligence projects are failing. There is strong evidence that one of the core factors contributing to this high failure rate is poor data annotation. Rahul explains the issue.
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(NASDAQ: INOD) Innodata is a leading data engineering company. Prestigious companies across the globe turn to Innodata for help with their biggest data challenges. By combining advanced machine learning and artificial intelligence (ML/AI) technologies, a global workforce of over 3,000 subject matter experts, and a high-security infrastructure, we’re helping usher in the promise of digital data and ubiquitous AI.

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Your company’s Artificial Intelligence project will fail if you don’t do this

An interview with Rahul Singhal, the Chief Product and Marketing Leader for Innodata at the Open Data Science Conference. C-suite leaders are becoming increasingly frustrated that many of their Artificial Intelligence projects are failing. There is strong evidence that one of the core factors contributing to this high failure rate is poor data annotation. Rahul explains the issue.

Innodata's Chief Product Officer, Rahul Singhal

Rahul Singhal- Chief Product & Marketing Officer

As Chief Product Officer at Innodata, Rahul drives the innovation behind the leading Ai-based platforms that handle end-to-end data solutioning for some of the largest companies in the world. Prior to joining Innodata, he was Chief Product Officer at Equals 3, an AI marketing platform which won several accolades including Gartner Cool Vendor, CES Top 5, and IBM Watson ISV award. Before Equals 3, Rahul spent 12 years at IBM, the last three of which he spent leading the product portfolio for the Watson Platform which included a collection of APIs for vision, speech, data and language. During his tenure at Watson, he grew usage of the services by over 100X and launched over 15 new services. Rahul is also an Adjunct professor at New York University (NYU) where he teaches Competitive Strategy and Advanced Experimental Design and Machine Learning.