4 Ways AI Companies Can Leverage Remote Work
In the past ten years alone, 83% of US businesses have introduced a flexible workspace policy, or are planning to adopt one. Whether your company has already incorporated remote work into its data management or has yet to do so, the reality is that remote work and telecommuting are quickly becoming the new normal.
Many companies are understandably hesitant about embracing the shift to flexible remote work. However, as reported by Mark Dixon, CEO of IWG:
“...flexible working is considered by many to be the new norm for any business that is serious about productivity, agility, and winning the war for top talent.”
When companies leverage remote work appropriately, their businesses and bottom lines benefit greatly. AI companies, in particular, stand to gain immensely from remote work (link Committing to a Digital Workforce here).
Here are 4 ways AI companies can leverage remote work to optimize their data management.
1. Wrangle data and mine text.
Wrangling and text mining can help companies derive important information from their data. However, while critical to a company's growth, in-house data management like data wrangling and text mining can also demand time and divert attention away from core products or services. By sourcing their work to remote teams, companies can benefit from important data management while saving important resources like time and effort for their bottom lines.
2. Transcribe, caption, and understand speech.
The field of natural language processing relies heavily on well-structured data sets. Without appropriately maintained and organized data sets, important AI functions like speech recognition and natural-language generation would be impossible. Luckily, important data management tasks for NLP — such as transcription and captioning — can be performed remotely at scale to save a company time and money without compromising on quality.
3. Annotate, label, and tag images.
Many burgeoning fields like autonomous vehicles use images as important training data sets for their AI. With self-driving cars in particular, these images and bounding boxes help train important AI systems for identifying and avoiding obstacles. By sourcing this image annotation, labelling, and bounding to remote workers, companies can rapidly scale the production of their training data sets and efficacy of their data management and optimize their iteration.
4. Moderate and track video.
In the same way that images can be used for training data sets, videos are also critical in preparing and building many autonomous AI systems. Data management tasks like video object tracking make autonomous systems possible.
On a more public sphere, more than 500 million hours of video are watched on YouTube alone every day (https://buffer.com/resources/social-media-video-marketing-statistics/). Video constantly needs to be tracked and monitored in order to maintain brand image and restrict offensive content.
Both types of tasks can be outsourced to remote teams at scale. With so much content to moderate and track at any given time, leveraging a digital workforce to fulfill these tasks remotely enables companies to maximize their talent and focus on their primary services.
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