#appen

[ebizmarket]

Appen Tasks: Is live or is it Memorex?

Could everyone who is currently doing micro-tasks on Appen please raise their hand? :) 🤚

[sloppypears-ash-sg]

Letters To Match Fanchildren Pt 2

Finally, I made more of them!

Got various inspirations for many fanchildren. (Ship inspo: character.ai)

yay rarepairs 🌟🎶

Assets!

[hustlealley]

Discover AI Chat Bot Training Jobs on Leading Remote Work Platforms

Discover AI Chat Bot Training Jobs on Leading Remote Work Platforms! 🌐 Ready to dive into the world of AI? Check out my latest blog post for insights on finding and landing exciting remote jobs in AI chat bot training. Don’t miss out!

Have you ever dreamed of earning money from the comfort of your home? Training AI chat bots and diving into various remote jobs can make that dream a reality. In my previous posts, I introduced you to platforms like OneForma and Remotasks, which offer some fantastic remote job opportunities. Today, I’m thrilled to share even more companies where you can find similar remote jobs and earn a steady…

[howandreviews]

My Experience Working at Appen

Appen

Appen, an internationally acclaimed technology services company, has been at the forefront of providing high-quality training data, annotation, and linguistic services since its inception in 1996. The company has emerged as a significant player in the field of data annotation and machine learning services. From personal experience, Appen offers an excellent platform for individuals seeking to work remotely, providing an array of work-from-home jobs. 

Independent Contractor

As an independent contractor, I’ve been involved in numerous projects, ranging from search engine evaluation to micro tasks and voice projects. If you don’t succeed in one project, Appen provides a plethora of options, ensuring that there’s always an opportunity to explore.

The working hours at Appen vary significantly depending on factors like the project you’re working on, your availability, and workload requirements. There might be requirements to work a specific number of hours per week, while at other times, your workload would depend solely on your availability. My projects ranged from 1 to 4 hours, and occasionally, I managed to work up to 8 hours a day when extra work was available. Therefore, the workload can vary extensively depending on the project.

Flexible Work

One of the significant advantages of working with Appen is the flexibility it offers. As a contractor, you have the freedom to set your own schedules and work at your convenience, provided the project requirements and deadlines are met. In my role as a social media evaluator, I had the luxury of starting my work early and finishing by late morning, offering me ample flexibility. However, it’s essential to note that workload and availability requirements can differ based on the project and may change over time.

Appen pays its contractors competitively, with rates varying based on factors like the project, contractor’s location, experience, and skills. According to the company’s website, the hourly rate for some projects ranges from $5 to $30 per hour, while other jobs may pay by task or project. 

The company’s remote jobs are an excellent opportunity for students, stay-at-home parents, retirees, or anyone needing a flexible work schedule that allows them to work from anywhere. However, one must note that consistency of work might not be guaranteed and contracts could be terminated without warning. Therefore, it’s crucial to have a backup plan or side hustles. Despite these caveats, my overall experience working at Appen has been positive, offering a significant learning experience and a considerable degree of flexibility.

Conclusion

Appen offers a valuable platform for individuals seeking flexible, remote work opportunities with a range of projects to choose from. The flexibility extends to both working hours and the freedom to set personal schedules. Although the pay is competitive, the consistency of work can vary and contracts might be terminated without prior notice. Therefore, while Appen presents a significant opportunity, it’s crucial for potential contractors to consider these factors and have backup plans or supplementary income streams in place. 

For a comprehensive understanding of the roles I undertook, as well as an evaluation of their advantages and disadvantages, please visit Lifeafterfiftyish for an in-depth review.

[bealove19]

Mensagem positivas 2023

[jcmarchi]

Ryan Kolln, CEO at Appen – Interview Series

New Post has been published on https://thedigitalinsider.com/ryan-kolln-ceo-at-appen-interview-series/

Ryan Kolln, CEO at Appen – Interview Series

Ryan Kolln is the Chief Executive Officer and Managing Director of Appen. Ryan brings over 20 years of global experience in technology and telecommunications, along with a deep understanding of Appen’s business and the AI industry.

His professional career began as an engineer, with a focus on mobile network data engineering in Australia, Asia and North America. On completion of an MBA from New York University, Ryan joined The Boston Consulting Group (BCG) in 2011 as a strategy consultant. During his time at BCG he specialised in technology and telecommunications and gained deep strategy expertise across a variety of growth and operational topics.

Joining Appen AI in 2018 as VP of Corporate Development, he led strategic acquisitions like Figure Eight and Quadrant, and supported the establishment of the China and Federal divisions. Prior to his appointment as CEO, he served as Chief Operating Officer, overseeing global operations and strategy.

With over 20 years of experience in technology and telecommunications, how has your career path shaped your approach to leading Appen through the rapidly evolving AI landscape?

My career began as a telecommunications engineer, where my role was to build and optimize networks and involved a huge amount of data, analytics, and finding innovative solutions to optimize network performance and customer experience.

After completing my MBA at NYU, this evolved into leadership roles in tech strategy and mergers & acquisitions, where I focused on bigger strategic questions, such as emerging trends, investment opportunities, and business models. This background has given me a deep understanding of both the technical and business aspects of emerging technologies.

At Appen, we work at the intersection of AI and data, and my experience has allowed me to lead the company and navigate complexities in the rapidly evolving AI space, moving through major developments like voice recognition, NLP, recommendation systems, and now generative AI. This strategic vision is crucial as AI continues to transform industries globally.

You’ve been with Appen since 2018, driving major acquisitions like Figure Eight and Quadrant. How have these strategic moves positioned Appen as a leader in AI data services, and what do you see as the next big opportunity for the company?

The acquisitions of Figure Eight and Quadrant were key to expanding our AI data capabilities, particularly in areas like data annotation and geolocation intelligence.  Figure Eight’s data annotation platform was particularly impactful.  The platform is highly customizable, and we have used it for work in many different domains.  More recently, we have been utilizing the platform to run most of our generative AI dataflows.

In addition to the acquisitions, about 5 years ago we set up an operation in China called Appen China.  We are now the largest AI data company in China, with revenue almost double that of our nearest competitors.

Looking forward, the focus for Appen is on supporting the development and adoption of generative AI.  There are major growth opportunities in both the model builders and companies looking to adopt generative AI into their products and operations.  We feel we are just at the beginning of the largest AI wave.

Data quality plays a crucial role in AI model development. Could you share how Appen ensures the accuracy, diversity, and relevance of its datasets, especially with the increasing demand for high-quality LLM training data?

Appen’s strength is our ability to create high-quality data consistently and at scale. We work closely with our customers to understand their AI model objectives and develop high-quality data for their needs through a multi-layered approach that combines automated tools and human feedback. We have a global workforce of over 1 million across 200+ countries, which allows us to curate a group of qualified and diverse contributors. Through rigorous quality control and feedback loops, we ensure that the data is accurate, consistent, and relevant, and can be used to effectively improve the performance of AI models. This allows AI systems to operate effectively in real-world environments and can also be used to improve robustness and reduce bias, especially for LLMs.

Synthetic data generation is gaining popularity, and Appen’s investment in Mindtech highlights your interest in this area. Could you discuss the advantages and disadvantages of using synthetic or web-scraped data versus crowdsourced data for training AI models, and how you see synthetic data complementing the crowdsourced data Appen is known for?

­­High-quality data is crucial but can be costly and time-consuming to produce, which is why synthetic data is gaining attention. It works well for structured data in traditional AI/ML tasks, especially in industries with strict privacy regulations like healthcare and finance, as it avoids using personal information.

However, synthetic data often lacks the depth and nuance of real-world data, especially for complex Generative AI tasks that require diversity and deep expertise. It can also perpetuate errors or biases from the original data. Web-scraped data, commonly used for LLMs, presents its own challenges with low-quality content, bias, and misinformation, requiring careful curation.

Crowdsourced data, which Appen specializes in, remains the “ground truth.” Human expertise is vital for generating the diverse, complex data needed to improve AI model accuracy and ensure alignment with human values.

We view synthetic data as complementary to our human-annotated data. While synthetic data can accelerate parts of the process, human-labelled data ensures models reflect real-world diversity. Together, they provide a balanced approach to creating high-quality training data for AI.

The EU AI Act and other global regulations are shaping the ethical standards around AI development. How do you see these regulations influencing Appen’s operations and the broader AI industry moving forward?

The EU AI Act and similar global regulations are likely to influence Appen’s operations by setting new ethical standards for AI model development and performance. We may see changes in how we handle data, ensure model fairness, and address ethical considerations. This could lead to more rigorous processes and potential adjustments in our approach to model training and validation.

Broadly, these regulations will likely drive the industry towards higher ethical standards, increase compliance costs, and potentially slow down some aspects of innovation. However, they will also push for greater accountability and transparency, which could ultimately lead to more responsible and sustainable AI development.

With growing concerns around bias in AI, how does Appen work to ensure that the datasets used to train AI models are ethically sourced and free from bias, particularly in sensitive areas like natural language processing and computer vision?

We actively work to reduce bias by fostering diversity and inclusion across our projects. It is encouraging to see that many of our customers are focused on capturing broad demographics in data collection and model evaluation tasks. Having a global crowd that resides in most countries enables us to source data from a wide range of perspectives and experiences, which is especially important in sensitive areas like natural language processing and computer vision.

Since 2019, we formalized our best practices into the Crowd Code of Ethics, showing our dedication towards diversity, fairness, and crowd wellbeing. This includes our commitment to fair pay, ensuring our crowd’s voice is heard, and maintaining strict privacy protections. By upholding these principles, we aim to deliver high-quality, ethically sourced data that supports responsible AI development.

As AI becomes more integrated into industries like automotive, advertising, and AR/VR, how is Appen positioning itself to meet the increasing demand for specialized training data in these sectors?

Over the last 27 years, we have provided specialized training data for a diverse range of industries and use cases, and we continue to evolve as our customer needs evolve.

As an example, in automotive, we worked with leading automotive companies and in-cabin solution providers to build in-vehicle speech systems. Now, we are helping our customers in new areas like video data collection of drivers to help safety by monitoring driver distraction.

In advertising, we helped a leading global advertising platform improve the quality and accuracy of ads for user relevance over a large multi-year global program with 7M+ evaluations. Now, as many of the platforms are adopting generative AI solutions, our crowd are not only assessing the relevance of ads but also helping evaluate the quality of generated ads.

We have been able to do all of this through our robust annotation platform which can be customized to support complex workflows and various data modalities including text, audio, image, video, and multimodal annotation. But ultimately, our ability to move with the changing industry comes down to our deep expertise in data for AI development and strong partnership with our customers.

Appen has been a leader in providing high-quality data for a variety of AI applications. Looking forward, how do you see Appen’s role evolving as generative AI and LLMs continue to develop and influence global markets?

Generative AI and LLMs are transforming industries, and we will continue to play a critical role in providing high-quality data to support these advancements. When it comes to global markets, our ability to source across 200 countries and 500+ languages will become even more valuable, and we have a strong history of this as we helped companies like Microsoft launch Machine Translation models for over 110 languages.

As the deployment of LLM applications grows, we see a growing demand for aligning with human end users, including localization capabilities to ensure language and cultural nuances are addressed in various global markets. We’re committed to helping companies develop AI systems that are both performant and responsible by ensuring that the data used to train these models is diverse, relevant, and ethically sourced.

Appen is known for powering some of the world’s most advanced LLMs. What are some of the innovations in data annotation and collection that Appen is focusing on to enhance the performance of these models?

We’re continuously innovating our data annotation and collection processes to enhance the performance of LLMs. One area of focus is improving the efficiency and accuracy of data annotation through advanced AI-assisted tools, which help to streamline and automate parts of the process while maintaining high-quality standards.

We can identify data points that need further human input, ensuring that annotation efforts are targeted where they will make the most impact. We have integrated features in our platform like Model Mate which can be used to help accelerate data production and improve data quality. We are also focused on best practices in contributor management, which is important as the complexity of tasks increases.

The ability to understand contributor-level performance and provide feedback to continuously improve the quality of our human-generated data. These innovations allow us to provide the high-quality, large-scale data required to power and fine-tune the world’s leading LLMs.

As you step into your new role as CEO, what are your top priorities for Appen over the next few years, and how do you plan to drive the company’s growth in the highly competitive AI space?

As I transition into the role of CEO, my strategic priorities are designed to ensure Appen’s leadership in the competitive AI landscape:

Supporting the development of generative AI models: Over the last 18 months, generative AI has become a key component of our service offering, with 28% of group revenue coming from generative AI-related projects in June 2024 compared to 8% in January. We see significant potential in the generative AI market, which is projected to reach $1.3 trillion by 2032 according to industry forecasts.

Supporting the adoption of generative AI models: We see growth in new segments as enterprises leverage generative AI solutions for their use cases. Although the percentage of generative AI projects reaching deployment is low, we anticipate that FY24/25 will be a transition period where experiments move to production, and drive demand for custom high-quality and specialized data.

Optimizing and automating the way we prepare data: By utilizing AI for quality assurance and automating certain steps of the data preparation process. This will allow us to enhance data quality while also improving operational efficiency, improving our gross margins.

Evolving the experience for our crowd workers: Our new CrowdGen platform enables us to scale projects quickly and flexibly in line with our customer needs, utilizing AI for automated screening and project matching. This will also improve our contributor experience personalized support. Appen has been an early adopter in promoting transparency, diversity, and fairness in our data sourcing, and we remain committed to our Crowd Code of Ethics.

These priorities will position Appen for sustained growth and innovation in the evolving AI landscape.

Thank you for the great interview, we urge readers who wish to learn more to visit Appen.

[ammg-old2]

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. Countless people do similar work around the world for Google; the ChatGPT-maker, OpenAI; and other tech firms. Their human feedback plays a crucial role in developing chatbots, search engines, social-media feeds, and targeted-advertising systems—the most important parts of the digital economy.

Curtis told me that the job is grueling, underpaid, and poorly defined. Whereas Google has a 176-page guide for search evaluations, the instructions for AI tasks are relatively sparse, she said. For every task she performs that involves rating AI outputs, she is given a few sentences or paragraphs of vague, even convoluted instructions and as little as just a few minutes to fully absorb them before the time allotted to complete the task is up. Unlike a page of Google results, chatbots promise authoritative answers—offering the final, rather than first, step of inquiry, which Curtis said makes her feel a heightened moral responsibility to assess AI responses as accurately as possible. She dreads these timed tasks for the very same reason: “It’s just not humanly possible to do in the amount of time that we’re given.” On Sundays, she works a full eight hours. “Those long days can really wear on you,” she said.

Armughan Ahmad, Appen’s CEO, told me through a spokesperson that the company “complies with minimum wages” and is investing in improved training and benefits for its workers; a Google spokesperson said Appen is solely responsible for raters’ working conditions and job training. For Google to mention these people at all is notable. Despite their importance to the generative-AI boom and tech economy more generally, these workers are almost never referenced in tech companies’ prophecies about the ascendance of intelligent machines. AI moguls describe their products as forces akin to electricity or nuclear fission, like facts of nature waiting to be discovered, and speak of “maximally curious” machines that learn and grow on their own, like children. The human side of sculpting algorithms tends to be relegated to opaque descriptions of “human annotations” and “quality tests,” evacuated of the time and energy powering those annotations.

The tech industry has a history of veiling the difficult, exploitative, and sometimes dangerous work needed to clean up its platforms and programs. But as AI rapidly infiltrates our daily lives, tensions between tech companies framing their software as self-propelling and the AI raters and other people actually pushing those products along have started to surface. In 2021, Appen raters began organizing with the Alphabet Workers Union-Communications Workers of America to push for greater recognition and compensation; Curtis joined its ranks last year. At the center of the fight is a big question: In the coming era of AI, can the people doing the tech industry’s grunt work ever be seen and treated not as tireless machines but simply as what they are—human?

[djdejong]

Vrijdag begonnen met de boel buitenzetten, dag 1084

Vrijdag begonnen met de boel buitenzetten, dag 1084

Nu is het maandag wat een chaos is dit, snappen zeker niet hoe dit werkt in Nederland.

[eluminium]

svensk sommar

[hiwelcometothemonstersancturary]

What would poppy do when she was given all rights to playtime co. if so what will she turn the building into?

Poppy only receives all the rights to PlayCo. after Angel inevitably passes away. She keeps the building intact, maintaining it as a memorial site, as it was agreed by all the toys. She keeps licensing products and taking care of the brand, wanting to bring smiles to children all around the world, just like her parents.

[the-dye-stained-socialite]

i am being niceys. please give me a round of applause

[sloppypears-ash-sg]

More Letters To Match Pairs!

Pencehole (Black Hole/BH x Pencil), Rublock (Ruby x Blocky), Appen (II Apple x Pen), and...

surprisingly, Stapoldy is here (prefer that ship name over Stoldy. Although Stoldy is an okay name).

Inspo: character.ai Salt and Pepper

[nyaskitten]

Rlly wanted to draw my wife with his one tit out.

[cooali]

ik ben gewoon oprecht sprakeloos?? de kans dat hier een regering uit komt die goed samen gaat werken is klein dus you know ik hou hoop MAAR dat oprecht zoveel mensen op de pvv hebben gestemd??? kan het echt totaal niet bevatten eigenlijk

[ellypop99]

one day i want to unlock a vamoura and suddenly there are NO MORE TORNADOS. i even thonk about shararook? NO MORE BLOOD MOONS.

[intrigd-voyagr]

me when the huh!!! Me when the whuh!!!