Natural language processing (NLP) is among the fastest growing applications of artificial intelligence (AI). It’s also one of the most difficult to build. NLP powers a growing number of tools, such as chatbots, virtual assistants like Amazon’s Alexa, and even the spell check for the communication apps we use to send text messages from our devices.
Cloud workforce solutions and integrations with leading data labeling platforms support higher quality training data for machine learning
Durham, NC – April 30, 2019 – CloudFactory, a global leader in managed workforce solutions for artificial intelligence (AI), today announced it has expanded its product portfolio and established partnerships to meet the unique data processing needs of companies applying AI and machine learning (ML). New partnerships with technology providers Hivemind, Labelbox and Dataloop combine CloudFactory’s managed cloud workforce and digital expertise with the best annotation and labeling tools on the market to deliver high quality data processing for a wide variety of use cases.
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).
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.
“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.
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.
“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.
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.
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.
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.