Global Partnerships Reflect AI’s Immense Power and Potential Risks

Technology is transforming how we live and work in ways we never thought possible. At the center of those changes is artificial intelligence (AI), which has the potential to fulfill the promises of our favorite buzzwords: data-driven, on-demand, automated, real-time, predictive, integrated. Already, we are seeing breakthroughs in machine learning (ML), robotics, quantum computing, nanotechnology, and the Internet of Things (IoT).

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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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The New AI Factory Model, Part I: How to Scale Quality Training Data [Transcript]

AI training data operations are a lot like the assembly lines of yesterday’s factories. Data is your raw material, and you have to get it through multiple processing and review steps before it’s ready for machine learning. If you want to develop a high-performing ML model, you need smart people, tools, and operations. We hosted a webinar to discuss this topic with experts in workforce and tooling for machine learning. This is a transcript of that November 14, 2018 webinar. It includes minor edits for clarity.

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Scaling Quality Training Data: Best Practices for Your Data Production Line

“Houston, we’ve had a problem.” Astronaut Jack Swigert made the words famous when he communicated to NASA mission control that an explosion had rocked the Apollo 13 capsule that was transporting him and two other people to the moon in April 1970. To get the astronauts home safely, the engineers at Johnson Space Center in Houston, Texas would have to do something they had never attempted before: use the descent engines on the lunar lander to send it home.

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Scaling Quality Training Data: The Hidden Costs of the Crowd

NASA estimated that it took 400,000 engineers, scientists, and technicians to send astronauts to the moon on the Apollo missions. The massive workforce was comprised of people from four major enterprise companies and a host of subcontractors who worked for them.

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Successful AI Development Means Fielding The Best Team

If AI development were a sport, it’d be closer to baseball than boxing. Headlines might make it seem like AI breakthroughs happen with a big knockout punch, but the reality is more akin to a baseball team grinding through a 162-game season. It’s a process that involves having the right people in place over a long stretch, and fielding the best team is essential for success.

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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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AI Bias And The 'People Factor' In AI Development

Oscar Wilde once argued that life imitates art more than art imitates life. Strangely, that’s proving to be the case when it comes to AI development – but not in the way some had hoped.

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The New AI Factory Model: How to Scale Quality Training Data

A Production Problem (Solved)

When Henry Ford attempted to produce the Model T at a rapid pace and with high quality, he ran into a problem. It was difficult to organize teams of specialized workers to assemble automobiles, and with so many workers needed to scale the process, it was highly inefficient. To make matters worse, late delivery of parts caused pile-ups of workers vying for space to work and delays in production.

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