In 2016, Oskarina Fuentes got a tip from a friend that seemed too good to be true. Her life in Venezuela had become a struggle: Inflation had hit 800 percent under President Nicolás Maduro, and the 26-year-old Fuentes had no stable job and was balancing multiple side hustles to survive. Her friend told her about Appen, an Australian data services company that was looking for crowdsourced workers to tag training data for artificial intelligence algorithms. Most internet users will have done some form of data labeling: identifying images of traffic lights and buses for online captchas. But the algorithms powering new bots that can pass legal exams, create fantastical imagery in seconds, or remove harmful content on social media are trained on datasets--images, video, and text--labeled by gig economy workers in some of the world's cheapest labor markets. Appen's clients have included Amazon, Facebook, Google, and Microsoft, and the company's 1 million contributors are just a part of a vast, hidden industry.

On weekdays, between homeschooling her two children, Michelle Curtis logs on to her computer to squeeze in a few hours of work. Her screen flashes with Google Search results, the writings of a Google chatbot, and the outputs of other algorithms, and she has a few minutes to respond to each--judging the usefulness of the blue links she's been provided, checking the accuracy of an AI's description of a praying mantis, or deciding which of two chatbot-written birthday poems is better. She never knows what she will have to assess in advance, and for the AI-related tasks, which have formed the bulk of her work since February, she says she has little guidance and not enough time to do a thorough job. Curtis is an AI rater. She works for the data company Appen, which is subcontracted by Google to evaluate the outputs of the tech giant's AI products and search algorithm.

Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. Quality data is at the heart of the success of enterprise artificial intelligence (AI). And accordingly, it remains the main source of challenges for companies that want to apply machine learning (ML) in their applications and operations. The industry has made impressive advances in helping enterprises overcome the barriers to sourcing and preparing their data, according to Appen's latest State of AI Report. But there is still a lot more to be done at different levels, including organization structure and company policies. The enterprise AI life cycle can be divided into four stages: Data sourcing, data preparation, model testing and deployment, and model evaluation.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! A recent survey from McKinsey showed that 56% of respondents reported AI adoption in at least one function, up from 50% the year prior, with the three most common use cases focused on service-operations optimization, AI-based enhancement of products, and contact-center automation. Businesses are committing huge amounts of money to AI initiatives. According to Appen's 2021 State of AI report, AI budgets increased 55% year-over-year, reflecting a shift from an experimental project mindset to an expectation of business benefits and ROI.

Data is the lifeblood of machine. But organizations continue to struggle to obtain good, clean data to sustain their AI and machine learning initiatives, according to Appen's State of AI and Machine Learning report published this week. Of the four stages of AI–data sourcing, data preparation, model training and deployment, and human-guided model evaluation–data sourcing consumes the most resources, takes the most time, and is the most challenging, according to Appen's survey of 504 business leader and technologists. On average, data sourcing consumes 34% of an organization's AI budget, versus 24% each for data preparation and model testing and deployment and 15% for model evaluation, according to Appen's survey, which was conducted by the Harris Poll and included IT decision makers, business leaders and managers, and technical practitioners from the US, UK, Ireland, and Germany. Finally, 42% of technologists find data sourcing to be the most challenging stage of AI lifecycle, compared to model evaluation (41%), model testing and deployment (38%) and data preparation (34%).

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. A recent survey from McKinsey showed that 56% of respondents reported AI adoption in at least one function, up from 50% the year prior, with the three most common use cases focused on service-operations optimization, AI-based enhancement of products, and contact-center automation. Businesses are committing huge amounts of money to AI initiatives. According to Appen's 2021 State of AI report, AI budgets increased 55% year-over-year, reflecting a shift from an experimental project mindset to an expectation of business benefits and ROI. One reason this shift is happening now is that many businesses have built expert data science teams and matured their understanding of the discipline.

It was meant to be a temporary side job--a way to earn some extra money. Oskarina Fuentes Anaya signed up for Appen, an AI data-labeling platform, when she was still in college studying to land a well-paid position in the oil industry. But then the economy tanked in Venezuela. Inflation skyrocketed, and a stable job, once guaranteed, was no longer an option. Her side gig was now full time; the temporary now the foreseeable future.

Sourcing Specialist at Appen ACTIVELY HIRING Let's connect! Apply and join our team today! We are currently looking for Candidates in different Languages and Dialects in #Greece. Languages and Dialects: #Turkish (Turkey) #Greek (Greece) #German (Germany) #English (United States) Are you ready to help drive advances in computer vision applications for a major technology company? If you have a smartphone and enjoy taking photos then this project is perfect for you.

Sourcing Specialist at Appen ACTIVELY HIRING Let's connect! We are currently looking for Candidates in different Languages and Dialects in #Greece Languages and Dialects: #German (Germany) #Turkish (Turkey) #Greek (Greece) Are you ready to help drive advances in computer vision applications for a major technology company? If you have a smartphone and enjoy videos then this project is perfect for you. We are looking for several video submissions of different rooms in your home environment with the main lighting switched ON and NOT from battery-powered or natural light. These will be taken on your smartphone devices and uploaded through our platform.

Crowd Sourcing Specialist at Appen ACTIVELY HIRING Let's connect! Flexible Work Opportunities #Portugal About Appen Appen collects and labels images, text, speech, audio, video, and other data used to build and continuously improve the world's most innovative artificial intelligence systems. Our expertise includes having a global crowd of over 1 million skilled contractors who speak over 235 languages, in over 70,000 locations and 170 countries, and the industry's most advanced AI-assisted data annotation platform. Our reliable training data gives leaders in technology, automotive, financial services, retail, healthcare, and governments the confidence to deploy world-class AI products. Founded in 1996, Appen has customers and offices globally.