Lessons Learned: 3 Essentials for Your NLP Data Workforce

The recent failure of IBM’s Project Debater in its contest against global debate champion Harish Natarajan offers the latest in a series of lessons learned about the deployment of natural language processing (NLP) technology in the real world. Project Debater is one of numerous attempts in a decades-old quest to use machines to automate the analysis of language to gather insights and make decisions.

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How CloudFactory Workers Help Train NLP Models

“Hey Siri,” I said. “Call Nancy. Mobile.” Siri replied, “Calling Lindsay. Mobile.” It began to ring. I hadn’t worked with Lindsay in six years. I lunged for my phone and touched the red button just in time to avoid what would no doubt have been an awkward, out-of-the-blue conversation with her. Then, I contemplated the complexities of NLP development.

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Scaling Quality Training Data: Choosing the People in Your AI Tech Stack

Bringing artificial intelligence (AI) to life in the real world is a lot like the 20th-century “space race” for dominance in spaceflight capability. Few can fathom the level of innovation and sheer effort it takes. From model development and data prep to testing and deployment, AI requires a pioneering spirit, sharp minds, and a lot of hard work. AI innovators encounter countless challenges and frustrating defeats.

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