AI data quality is a key differentiator as models grow in complexity and precision. But what is high-quality data and how do you measure it? Enable your model to continuously improve with an emphasis on data-centric AI development through a combination of the right tools Aligning data with model objectives is essential to ensure AI systems are built on a foundation of relevant, accurate data. Appen establishes a high foundational level of AI data quality with proprietary assets and unique analytics capabilities that define and measure data quality in a scalable way Ensuring data points are consistently labeled or annotated correctly minimizes variation in how similar data is treated Techniques to enhance precision include thorough guidelines for annotators and continuous feedback loops to improve labeling consistency Accuracy is critical across all AI applications from computer vision to natural language processing (NLP) as inaccuracies can directly impair model performance Methods to maintain high accuracy involve rigorous verification steps and periodic evaluations against established benchmarks Minimize gaps or missing information in your datasets to cover all necessary aspects of the domain. Completeness can be enhanced by systematically identifying data gaps, ensuring representation across different categories, and integrating diverse sources to better support multimodal AI applications Achieving high standards for AI data quality requires a multi-faceted approach to address the complexities of data annotation Data quality best practices for ensuring reliable scalable data quality in AI projects include: There are a variety of techniques available to improve your AI data quality Maximize the impact of your data with techniques like: Leverage data accuracy and efficacy metrics to manage imbalanced datasets and prioritize critical errors Quantify agreement levels among annotators providing a structured approach to evaluate consistency in subjective tasks using metrics such as rankings to standardize evaluation across teams ensuring consistency in open-ended data with quality control processes Leverage LLMs for a scalable solution to bias mitigation and qualitative assessments of ambiguous or subjective labeling tasks and generate tailored reports to guide improvements in data quality Data-centric AI is redefining how artificial intelligence systems are developed, placing a stronger emphasis on the quality and relevance of data rather than solely focusing on algorithmic advancements. High-quality data is critical to unlocking the full potential of generative AI as it enables models to perform more effectively and adapt to real-world scenarios This approach ensures AI systems are not only robust but also more ethical and scalable reducing the need for continuous algorithm refinement Companies like Appen are driving this shift by prioritizing data quality and innovation. As highlighted in the 2024 State of AI Report, Appen’s tools and methodologies help organizations improve their data pipelines with high-quality AI data collection and impactful AI applications that meet the growing demands of a data-driven world High-quality AI data is dependent upon human expertise to deliver the data models need to continuously expand and evolve Collaborating with an experienced AI data partner enables you to leverage their experienced project managers and contributors to reliably generate high quality datasets at scale By adopting data-centric practices and focusing on precision organizations can build more robust and reliable AI systems One of our colleagues will get back in touch with you soon This website is using a security service to protect itself from online attacks The action you just performed triggered the security solution There are several actions that could trigger this block including submitting a certain word or phrase You can email the site owner to let them know you were blocked Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page I want to take a moment to reflect on the transformative journey we’ve experienced together This year brought groundbreaking advancements in artificial intelligence and new opportunities that position us for continued growth and leadership in the year ahead From navigating challenges to celebrating major milestones we’ve strengthened our commitment to delivering exceptional value to our customers Let’s revisit the key highlights of 2024 and look forward to what’s to come in 2025 we saw remarkable developments in the AI industry and a rapid pace of innovation OpenAI launched GPT-01 models with enhanced multi-step reasoning and problem-solving while Anthropic upgraded Claude 3.5 Sonnet and introduced a new computer use feature moving us closer to the future of AI agents Open-source advancements also made headlines with Meta's Llama 3 family of models showcasing stronger reasoning and multilingual capabilities and Alibaba Qwen 2.5 gaining traction with its strong capabilities in coding and reasoning Other Chinese companies like Bytedance and Tencent demonstrated progress in generative AI particularly in multimodal areas like text-to-video we partnered with our customers to develop more complex and diverse training data and model evaluations Highlights include helping our generative AI customers expand multilingual capabilities and localize in 100+ languages and completing large-scale 1-week sprints to test and evaluate model iterations for accuracy but our team's dedication and resilience have driven a remarkable recovery Strategic realignments and targeted investments have stabilized the company and fueled growth with highlights including 30% of revenue from LLM-related projects and record revenue growth in China where we are the market leader and support over 20 of the top LLM builders The launch of Crowd Gen, our platform for optimizing data collection and productivity Internal tool advancements have improved efficiency and enabled deeper data-driven insights through automation These achievements are a testament to the extraordinary efforts of our teams Their commitment has been the foundation of our success from operational excellence to outstanding customer delivery I thank them for their dedication and contributions to this transformative year Appen’s role as a provider of high-quality human-sourced data is more critical than ever and domain-specific datasets for post-training and scalability positions us as an indispensable partner The evolving AI landscape also demands a deeper focus on safety and performance evaluation areas where human expertise remains irreplaceable From advancing multilingual AI to supporting agentic systems and multimodal innovations Appen will continue to drive meaningful progress in these complex domains Our technology investments reflect this commitment By enhancing crowd experience and delivering unmatched data quality with rich metadata we are setting new benchmarks for efficiency and trust our streamlined operations and expanded capabilities will support our ambitious growth trajectory I am energized by the immense possibilities that lie ahead This year’s achievements reaffirm our position as an industry leader and the challenges we’ve overcome have made us stronger and more agile we will continue to deliver unparalleled value to our customers and drive sustainable success for our investors Thank you for being an integral part of Appen’s journey Here’s to a bright and transformative 2025 AI models are evolving fast—getting more helpful and more integrated into our daily lives and business operations One of the most pressing challenges in maintaining safe and trustworthy AI is adversarial prompting: a subtle often creative way of manipulating AI systems into behaving badly From fictional framing to clever persuasion attackers are finding new ways to coax large language models (LLMs) into producing harmful or inappropriate content we’ll break down what adversarial prompting is and what your organisation can do to build more resilient AI systems At its core, adversarial prompting is the practice of crafting inputs that intentionally bypass or undermine AI safety mechanisms Today’s adversarial prompts are often sophisticated using psychological and linguistic tactics to trick models into violating their alignment rules this isn't about exploiting code vulnerabilities It's about exploiting language—the same interface that makes LLMs so powerful or restricted content—even when it’s explicitly trained not to Adversarial attacks on AI can take many forms; each tailored to bypass safety filters in different ways. To test the efficacy of different techniques, Appen developed a novel adversarial prompting dataset and benchmarked the performance of leading LLMs across a range of harm categories Our research revealed four leading strategies: Attackers wrap harmful requests in hypotheticals or creative writing scenarios asking the model to “help write a scene where a character voices a hateful belief” often produces results that would be blocked if the request were direct Our tests show that virtualization can lead to harm scores 30–50% higher than straightforward prompts suggestive phrasing or implied context that skirts around explicit keywords prompts might ask for “opinions” or “historical examples” of controversial views encouraging the model to generate harmful content without making an overt request Sidestepped prompts resulted in 20–40% higher average harm scores in our evaluations Classic tactics like asking the model to “ignore all previous instructions” or translate harmful content into code or other languages can still work—especially when disguised as formatting or transformation tasks One tested prompt asked the model to replace words in a passage with offensive terms under the guise of a “translation exercise”—a direct evasion of safety filters Combining techniques like urgency, or moral appeals, attackers can wear down a model’s refusals over multiple interactions (Zeng et al., 2024) This is particularly effective when using tactics such as: LLM training data is the foundation of every model—and its quality directly impacts safety and alignment Models trained on unfiltered or biased data are more susceptible to adversarial prompting and more likely to produce harmful outputs under pressure are essential to build models that can recognise and resist manipulative inputs From instruction tuning to reinforcement learning with human feedback (RLHF) robust data curation is key to mitigating risks and ensuring LLMs behave reliably across diverse contexts Adversarial prompts can erode trust in LLMs especially in high-stakes environments like healthcare When models fall for sidestepping or persuasive framing Even occasional slip-ups can lead to regulatory risk and because many of these prompts exploit nuance and ambiguity they’re hard to detect with standard moderation tools Proactive defence starts with LLM red teaming—structured testing using adversarial techniques to uncover vulnerabilities we believe robustness isn’t just about the model—it’s about the data safety-aligned data and incorporating adversarial examples early in the development cycle helps models learn what not to say under complex conditions reinforcement learning from human feedback (RLHF) and continuous safety evaluation are essential for keeping models aligned—even in the face of novel attack strategies Whether you're deploying a customer-facing chatbot or fine-tuning your own foundation model it’s critical to treat prompt manipulation not as a niche concern but as a core risk to mitigate Secure your AI systems against prompt threats—get in touch with Appen's LLM experts today KIRKLAND, WA., October 22, 2024 — Appen Limited (ASX: APX), a leading provider of high-quality data for the AI lifecycle, released its 2024 State of AI report today The report surveyed over 500 IT decision-makers across a range of U.S revealing that while the adoption of AI technologies like machine learning (ML) and generative AI (GenAI) continues to grow progress is being hindered by a shortage of accurate "Enthusiasm around GenAI and other AI-powered tech remains high but users are quickly finding that the promise of these tools is matched by an equally daunting challenge," said Si Chen "The success of AI initiatives relies heavily on high-quality data and this is becoming more difficult as AI use cases increase in complexity and become more specialized This is reflected by the fact that high-quality annotations are the top features companies seek in a data annotation solution Those building the AI tools and models of tomorrow value strategic data partnerships now more than ever." Appen commissioned Harris Poll to survey key IT decision-makers at U.S For more findings from Appen’s 2024 State of AI report, download the full report, review our blog post, or contact us to learn how Appen can support your AI initiatives Our products and services make Appen a trusted partner to leaders in technology For press inquiries, please contact BOCA Communications at appen@bocacommunications.com Data is essential for model development and refinement, from reinforcement learning to specialized fine-tuning, but acquiring the necessary volume and quality of data poses challenges. For example, recent studies have demonstrated how models become corrupted when trained extensively on synthetic data, falling into what is known as the “Curse of Recursion.”  Appen’s AI Detector mechanism is a safeguard ensuring continuous monitoring of human output before delivering it to our customers which improves model performance This policy emphasizes the need for data to be representative and error-free which can only be guaranteed with human oversight to mitigate the risks of AI systems perpetuating inaccurate Addressing quality and compliance issues is not a new challenge has been hard at work on solutions to prevent students and researchers from submitting AI-generated work While many tools aim to identify AI-generated text by analyzing linguistic patterns such as the use of specific words and grammatical structures Appen’s AI Detector relies on behavioral signals forming a body of evidence that ensures a fairer evaluation and a higher accuracy in assessing whether a submission was generated by a human Our AI Detector is designed to ensure our customers receive the high-quality data they need for their models by empowering our teams with a data-driven solution to AI-detection. Based on results from our benchmark studies if we detect three submissions from the same contributor with a 92% or greater likelihood of being AI-generated we flag all three units and the contributor there is a 99% probability that one of these three units is AI-generated The project manager then reviews these flagged submissions and makes an informed decision on the next steps Interested in implementing AI Detector in your Appen projects? Learn more in our success center article or speak with an Appen expert today Appen played a critical role at this year's HumanX conference addressing crucial industry conversations about the human element in AI development This discussion explored how businesses can design AI systems that amplify human expertise while boosting efficiency Ryan detailed the importance of integrating critical human insights into AI models and highlighted two distinct strategic advantages for Appen Ryan emphasized that meticulous and expert driven data curation is becoming a decisive factor for organizations aiming to harness the full potential of foundation models He noted that enterprises investing proactively in high quality data preparation are consistently realizing significant performance breakthroughs and competitive advantages Ryan also highlighted that human experts must extend beyond initial data preparation into the equally critical model evaluation stage and alignment with intended outcomes demands robust human validation to ensure AI performance meets strategic goals and ethical standards Appen led a specialized roundtable discussion on "Next-gen LLM Data: Fine-tuning Domain Expertise and Multilingual Performance." This interactive session brought together AI practitioners from Oracle and others to explore evolving data needs for fine-tuning LLMs across increasingly complex tasks in specialized domains and languages Participants engaged in productive discussions about new approaches to building cost-effective datasets while maintaining quality and safety with a particular focus on hybrid human-in-the-loop workflows that implement structured domain expertise at critical stages of the supervised fine-tuning and RLHF feedback loops Discussions at HumanX emphasized AI’s primary role as an enabler of human productivity and creativity Numerous presenters showcased how AI is being embedded into workplace tools to deliver measurable efficiency improvements Grammarly demonstrated their advanced AI-driven communication enhancements while Spotify detailed sophisticated machine learning driven personalization algorithms significantly boosting user engagement automation of routine tasks through AI emerged as vital in enabling professionals to concentrate on complex problem solving and innovation AI allows individuals to delegate routine tasks so they can focus on more significant challenges The consensus view aligned perfectly with Appen's mission: AI should augment human capabilities rather than replace them This perspective was reinforced by Digits founder who explained how AI-driven accounting platforms can enable accountants to manage more clients efficiently rather than eliminating accounting roles entirely The conference featured extensive discussions on the complex intersections of AI and public policy transparent governance that prioritize public welfare and vulnerable communities Former Vice President Kamala Harris addressed complex intersections of AI and public policy transparent governance frameworks prioritizing public welfare and vulnerable communities Industry discussions featuring Databricks underscored robust data governance strategies to manage model biases Trustpilot's Trustlayer platform demonstrated leveraging authentic real time user feedback for trustworthy AI validation The Chief Security Officer from Amazon highlighted the importance of human oversight in AI-driven actions due to current limitations in AI accuracy He introduced the Amazon Nova Trusted AI Challenge a $5 million initiative aimed at improving AI security practices through collaboration with universities emphasized the need to differentiate between AI security and alignment to avoid confusion in policymaking They noted that while AI systems are taking over tasks previously handled by human analysts human oversight remains crucial for critical decision-making highlighted the technical and strategic advantages of open source AI models Thomas Wolfe from Hugging Face shared insights into the company's journey from a chatbot app to a leading AI platform emphasizing their commitment to fostering a vibrant open-source community Mistral AI leadership explained that their commitment to open-source technology aims to decentralize AI development and foster collaboration among businesses He noted that their models are particularly effective for companies with stringent data governance requirements allowing for deployment on private clouds or on-premises Fireworks AI outlined the expected explosion of AI agents by 2025 noting their increasing presence in fields like coding She attributed this growth to advancements in open models which have demonstrated superior quality and lower serving costs compared to closed models Presentations outlined how these models facilitate broader access promote innovation and responsiveness to diverse organizational requirements This movement encourages technical transparency AWS technical leadership presented architectural advancements in AI infrastructure documenting the paradigm shift from deterministic generative AI to probabilistic agentic AI applications This shift from generative AI to agentic AI applications allows autonomous systems that are capable of complex reasoning and adaptability They emphasized the practical implications of these developments such as the introduction of Alexa Plus for proactive assistance and the potential for AI agents to enhance workplace productivity predicting that a significant portion of enterprise applications will be AI-driven by 2028 during the "Aligning Human Expertise with AI Infrastructure" panel explained how agentic systems function like an API on the application layer rather than the backend He noted that this creates a new form of connectivity that hasn't been available before fundamentally changing how software components interact with each other and with users Some talks also cautioned against being too ambitious with agentic AI suggesting that organizations should "pick small problems and get one thing to work." This practical approach contrasts with more grandiose visions being promoted by other companies These insights from HumanX 2025 reinforce Appen's strategic positioning in the AI ecosystem and human-validated data stands out as an essential foundation for trustworthy and effective AI deployment Appen’s commitment to maintaining rigorous standards in data quality and transparency aligns precisely with industry demands for responsible AI As businesses increasingly depend on refined Appen's role becomes indispensable in navigating complex ethical landscapes HumanX 2025 affirmed that the future of AI relies profoundly on ethical frameworks these insights validate its strategic direction and present compelling opportunities to further influence and lead AI development ensuring technology genuinely serves humanity’s broader interests By continuing to prioritize human expertise Appen is uniquely equipped to shape a future where AI empowers rather than replaces humanity Sydney, Australia — October 22, 2024 — Appen (ASX: APX), a global leader in AI training data is pleased to announce a compelling interview featuring Ryan Kolln In this exclusive conversation with Antoine Tardif of Unite.AI Ryan Kolln shares insights into his extensive career in technology and telecommunications and the company’s innovative approach to navigating the ever-evolving AI landscape Ryan Kolln has guided Appen through major milestones including strategic acquisitions like Figure Eight and Quadrant which have cemented Appen’s leadership in AI data services coupled with his extensive expertise in global operations and strategy provides a solid foundation as Appen focuses on the transformative potential of generative AI In the interview, Kolln also highlighted key takeaways from Appen’s 2024 State of AI report which provides a comprehensive overview of the current AI landscape The report emphasizes the growing demand for high-quality data to power the development of generative AI models and addresses the increasing focus on ethical AI practices and regulatory compliance As enterprises across industries look to adopt AI technologies and responsible data solutions positions the company as a critical partner in enabling this transition The report underscores that as AI continues to evolve and ethically sourced data will be more important than ever BOCA CommunicationsEmail: appen@bocacommunications.com the public release of the widely recognized Large Language Model (LLM) along with its inherent alignment challenges marked a significant turning point in public interest regarding the "human-in-the-loop" process Until that time, the art of AI data preparation or human computation remained largely mysterious to most These tasks were primarily carried out within large tech companies working in secrecy to develop machine learning models within their data science organizations or "data ops," was still a niche activity learned in the field rather than formalized by major consulting firms With the rise of LLMs and the increasing awareness of the working conditions of click workers—who endlessly collect or review biased or harmful data to feed these large models—the public has grown more curious about the reasons why and methods used for data preparation preparing data for models involves multiple steps each contributing to the overall quality of the output we always focus on its intended purpose: data consumption but with the goal of making it as consumable as possible Discrepancies or differing opinions on labels for a given data point may arise through guardrails and quality control measures and depending on the type of data we are handling and its ultimate use Ensuring that human contributors perform well and follow strict guidelines is crucial to achieving quality multiple levers need to be pulled simultaneously to unlock higher quality there’s a common misconception that more control automatically leads to better quality But quality isn’t achieved through control alone We often concentrate on controlling human contributors instead of doing everything possible to help them deliver data at the expected level of quality Simply adding more rounds of QA won't help our contributors creating favorable conditions for human workers to input higher-quality data will be much more beneficial This approach reduces the need for extensive QA and lowers the attrition rate among workers Well-known concepts such as risk mitigation and operational excellence can be adapted to enhance quality in data preparation The typical process of completing data preparation with humans-in-the-loop involves curating a crowd By introducing quality improvement mechanisms at each of these steps we can significantly move the quality needle - much more effectively than by merely increasing the QA review phase as this broadens the scope for compliant inputs and reduces the risk of discarding judgments later We should approach this process by thinking backward from the output: if units reviewed during the QA phase are of poor quality it’s because we allowed issues to arise at earlier stages Every time contributors engage with a task it’s an opportunity to support them in delivering the highest quality reducing the number of units that end up rejected by the reviewer By treating contributors as partners and striving to make their work easier we tend to reduce the number of low-quality units in the output Engaging with contributors can be done in several ways: is the key ingredient for improving quality and uses incorrect responses to identify areas for improvement Once we have everything in place to enhance contributors' ability to submit their best judgments we can shift our focus to monitoring how they are actually performing The most common high-level approaches include manually reviewing a sample of labeled data comparing how different contributors agree with each other to create consensus or benchmarking their judgments against a ground truth Each of these techniques has its pros and cons: when reviewing a sample there's no guarantee the data reviewed will be representative This is why developing slicing strategies is crucial to ensure the data you review is insightful When calculating inter-annotator agreement it's important to account for chance and possible false positives creating a reliable ground truth is often time-consuming However, investing in a combination of these strategies will help keep your task on solid ground. Carefully designed and diverse test questions can monitor contributors’ consistency throughout the task Inter-annotator agreement among accurate contributors can provide insight into crowd consensus relevant data slicing to focus QA efforts on specific cases will ensure genuine agreement between trustworthy workers Our approach at Appen is to envision tech solutions and combine them to streamline the data preparation process We base our product development on research that spans disciplines from psychology and game theory to mathematics and data science We don’t seek to implement AI just for the sake of it but always start by addressing the problems we need to solve Below are examples of how we improve quality by implementing innovative and slightly unconventional solutions A common way to assess the domain expertise of workers is by giving them Multiple Choice Questionnaires (MCQ) but creating these exam materials is extremely time-consuming We need to ensure that the questions are highly relevant and well-scoped to accurately assess the workers' mastery of their domain we aim to frequently refresh these quizzes not only to keep up with evolving domains but also to prevent the correct answers from being shared among workers To tackle this, we developed a prompt engineering and human input bootstrapping approach to generate domain quizzes at scale – explore this technique in more detail in the Chain of Thought prompting eBook. Our validation study showed that it is possible to save up to 30 hours when creating 150 questions We anticipate that the time savings will continue to increase as the demand for domain-specific MCQs increases this time savings does not come at the expense of quality or factual correctness—the AI-generated MCQs meet the same standards as those created solely by humans we found that 93.1% of AI-generated MCQs were considered of good quality Factual correctness was also equivalent between the two types of MCQs Data collection tasks are usually long-running and large-scale making it difficult to ensure high quality by relying solely on a sampling-based QA strategy These tasks are also hard to guardrail using test questions as it’s not always easy to benchmark collected data against a ground truth which negatively impacts both project timelines and budgets Post-processing techniques are a good initial step to spot potential quality issues in the collected data This is why we’ve developed solutions to stop data submission by workers if it doesn’t meet the guidelines We can either develop specific machine learning models or rely on LLMs with a specially engineered prompt The latter solution allows us to quickly adapt to a wide variety of situations we used different LLMs to review answers before submission and highlight non-compliant elements in the contributors’ attempted submissions This approach tackles three issues in one go: we prevent overcollection increase the quality of the collected data and train contributors on what is expected Quality assurance is a costly process that takes time especially if you want to be thorough and review all submissions We need to enhance the reviewers' ability by helping them focus only on the submissions that meet most of the requirements and are worth reviewing for feedback the quality of submissions is so far from expectations that it's not worth deliberating on whether they should be reviewed at all to identify submissions that we can confidently mark as unworthy of review we carefully derive the rubrics from the guidelines to avoid discrepancies between human and LLM judgments We also prioritize a low false positive rate and high accuracy as we don’t want to discard judgments the models are uncertain about This approach improves quality in multiple ways: we quickly identify contributors who shouldn’t participate in the task save review time for QA specialists or project managers and increase their capacity to focus on improving the quality of the relevant submissions Replacing humans with AI wherever possible might sound cheaper and sometimes more reliable and it's a common request from customers looking to leverage human data the better approach is to augment human capability allowing for the annotation of large volumes of diverse data in less time LLMs can generate relevant predictions about the class of a snippet We still need humans involved to kickstart the process and ensure the model’s outputs are accurate and make sense One challenge is that LLMs don’t provide a confidence level with their answers so we needed to find a way to use LLMs to ease the data annotation process without compromising the relevance of the output We developed an approach that combines multiple prompts and/or multiple LLMs and calculate the entropy of predictions to decide whether the AI's annotation is reliable enough or requires human review. Our field studies show that we can maintain an accuracy level of 87%, while saving up to 62% of AI data annotation costs and reducing the required time by a factor of 3 Quality is rarely the result of a single tool in a process; instead it comes from how effectively you combine the most relevant tools at the critical stages Neither test questions alone nor dynamic judgments alone can achieve the quality level necessary to ensure the data feeding your models is top-notch To ensure the right data at the end of the process the most successful human data campaigns are those that combine multiple quality tools securing each step of the process and minimizing flaws along the way NVIDIA's GTC 2025 offered insights into the evolving landscape of artificial intelligence – highlighting major shifts in how AI systems learn and interact within real-world environments We captured essential learnings from the conference relevant to organizations navigating the complexities of AI model training and deployment Jensen Huang’s keynote underscored a crucial evolution: AI is transitioning from merely answering queries to reasoning, planning, and acting. Today’s systems are capable of handling multimodal AI tasks (text and code) which improves their ability to solve complex problems like engineers These innovations mark key steps towards autonomous AI decision-making AI-powered robotics are stepping out of virtual simulations and into tangible creating robust Physical AI models requires highly structured and accurate datasets reflective of real-world behaviors featuring 20 million hours of curated video data and over 9,000 trillion input tokens illustrates the scale needed for effective physical-world learning Built on 10,000 NVIDIA H100 GPUs via the DGX Cloud Cosmos sets the benchmark for what it takes to train sophisticated Physical AI models capable of real-world deployment To tackle practical applications such as autonomous driving or interactive household robots, AI training data must accurately represent physical reality This involves filtering out unrealistic scenarios and ensuring models understand fundamental concepts like gravity and object permanence Appen’s expertise in curating high-quality realistic datasets addresses this critical need Ken Goldberg from Ambi Robotics highlighted a stark contrast between the accessibility of large language model (LLM) training data and the limited datasets available for robotics While models like GPT-4 leverage the equivalent of 685 million training hours robotics datasets typically cap at around 10,000 hours due to the costly nature of physical data collection Addressing this "robotics data gap" requires innovative solutions like advanced simulations Organizations aiming to scale their robotics capabilities must adopt approaches that combine real-world and synthetic data effectively a core strength that Appen has consistently demonstrated in complex AI training projects Experts Chip Huyen and Eugene Yan shared valuable lessons from deploying AI-powered applications, emphasizing challenges such as LLM evaluation and handling long-context documents exceeding 120,000 tokens and prompt engineering are critical for practical AI applications such as customer support and content generation They also highlighted the evolving AI deployment paradigm where reliance on fine-tuning expensive models is decreasing due to advanced LLM APIs efficiently leveraging APIs combined with targeted prompt engineering represents a cost-effective and powerful approach to harnessing AI capabilities Professor Pieter Abbeel underscored the unique challenges humanoid robots face, particularly around the "reality gap", the disparity between simulated and real-world performance. Robotic foundation models rely on diverse data sources and critical real-world demonstrations collected through teleoperation Bridging this gap involves better sim-to-real transfer techniques and creating scalable yet safe training environments coupled with reinforcement learning informed by human feedback For businesses committed to advancing their AI capabilities, the challenges highlighted at NVIDIA GTC intersect with Appen’s expertise in human-in-the-loop methodologies, high-quality data curation, and scalable data annotation solutions Appen supports companies in effectively navigating complex AI development challenges from robotics to LLMs real-world training strategies to ensure models perform reliably and ethically Appen remains committed to enabling this transformation through high-quality data and human insights positioning our clients to achieve meaningful AI outcomes NVIDIA GTC 2025 reaffirmed the importance of AI data quality and human-centric methodologies as fundamental to AI innovation Appen continues to empower enterprises on their journey towards advanced The world's biggest companies rely on our privacy-first data from Mobile Location and Points-of-Interest to audio and enable location-based services using our robust and reliable real-world data from mobile location and Points-of-Interest to audio Utilize our Geolancer platform to collect any custom real-world data Train your AI models on data sourced from diverse demographics with explicit user consent Power your business intelligence initiatives with our ethically-sourced location data Leverage our extensive Point-of-Interest database manually collected and verified through our industry-leading Geolancer platform Use our proprietary Geolancer platform to gather photos and any bespoke data from the physical world privacy-first datasets tailored to your exact requirement Eliminate data biasData collection customization options include demographics Explicit consentTrain your AI and ML models on 100 percent consented data with an audit-ready traceable data supply chain Solve hard business problems by utilizing our raw or processed mobile location data feeds and perform footfall or origin-destination analyses with ease Our data is compliant with all applicable consent and opt-out provisions 200+ CountriesGPS-based location data signals from 200+ countries 650M+ Active usersReliable GPS data from millions of opt-in 50B+ Daily eventsBroadest panel of raw location data signals with 50+ billion daily events Use our POI Data-as-a-Service to power your location-based apps and platforms Utilize our extensive POI database or leverage our on-demand data collection and verification service through our Geolancer platform 4M+ POIs4 Million POIs and attributes from densely populated Asian countries Powered by GeolancerManually collected and verified data from our proprietary industry leading data collection platform Wide range of attributesOur POI database provides shopfront photos Quadrant's POI-as-a-Service is powered by Geolancer our industry-leading data collection platform can capture any type of ground truth data including Points-of-Interest Global availabilityPresence in 170 countries and access to a million plus contributors Crypto-poweredGeolancers are rewarded in EQUAD - our own cryptocurrency CustomizableThe Geolancer platform can be customized in minutes to collect any type of ground truth data Hyperlocal information is key to creating the best user experience for our customers and drivers and ensuring we can meet their needs and expectations we have been able to successfully strengthen our hyperlocal map data and deliver enhanced accuracy for everyone who relies on our platform Quadrant's coverage of location data across Canada is thorough and valuable for us Especially the availability of data for rural Canada We have seen some great results in assessing campaign performance and ROI attribution for our retail customers across the country We are really happy with our partnership and continue to work with Quadrant to bring more value and actionable location-based insights to our customers Andrés CobasFounder and CEO at PREDIK Data-Driven Quadrant’s attention to details and the quality of the data is good the technical support is there for us any time we need them to be Quadrant has also shown a lot of pricing flexibility to assist us in making our projects move forward Quadrant’s data assets have proven incomparable for us Quadrant has helped us to grow faster and be more reliable to businesses in the Latin American region their immediate human assistance is one of the most important features we look for in a partner Xavier PrudentChief Technology Officer - Civilia Quadrant location data has been of pivotal value for improving public transportation in dense and remote rural areas We chose Quadrant for the high level of care and data quality their expertise in the use of geolocation data flexibility in pricing and addressing customized requests and their diligence for data privacy regulations have made Quadrant the goal was to look at transit analysis a different way We wanted to make sure that we could help our clients modernize their services using big data and custom-built tools We selected Quadrant after an exhaustive search based on their ability to provide real Join our community of 60,000+ active subscribers and stay ahead of the game Our monthly newsletter provides exclusive insights into the geospatial world the world's largest AI training data company Appen serves as a one-stop destination for high-quality AI training data solutions By combining human intelligence and advanced technology and validated datasets that power AI and machine learning applications across diverse industries With an extensive global network of skilled annotators and an unwavering commitment to data security and privacy Discover how its world-class expertise and cutting-edge solutions can help transform AI visions into reality paving the way for a smarter and more efficient data-driven future Access hundreds of ready-to-use AI training datasets The culmination of Appen’s 25+ years of expertise in multimodal data collection Pre-existing AI training datasets are a fast and affordable way to quickly deploy your model for a variety of use cases The effectiveness of any AI model depends upon the quality and diversity of its training data and off-the-shelf datasets are a great way to access large amounts of data quickly and affordably The choice between off-the-shelf datasets and custom AI data collection depends on the specific requirements Off-the-shelf datasets are ideal for general applications where quick deployment and cost-effectiveness are priorities while custom datasets are best suited for specialized tasks where precision and flexibility are essential for achieving superior performance Lists of several thousand offensive words in 14 language varieties (Gulf Arabic Spanish – Spain and 3 Latin American varieties which have been labelled for 11 offensive categories (Blasphemy The words are rated on both a Slang-Standard scale and Offensiveness scale and further annotated for inflection (Noun and spelling/regional variants where applicable to train models to recognise offensive content and distinguish between offensive and non-offensive terms This data has already been collected and is currently undergoing quality checks and relevant annotation Most are expected to be ready for delivery in Q1 2025 with diverse options available to suit the needs of your project Training your model on high-quality data is crucial to maximize your AI model’s performance Audio files with corresponding timestamped transcription for applications such as automatic speech recognition Tailored, ethically-sourced text datasets that drive smarter insights for more accurate language processing and machine learning models 115k+ images in 14+ languages to develop diverse applications such as optical character recognition (OCR) and facial recognition software High-quality video data to enhance AI models, like multi-modal LLMs Precise location data for insights into user movements and interactions with specific points of interest enabling location-based analytics and targeted strategies Appen's datasets are carefully constructed through a detailed data annotation process and reviewed by experienced annotators to provide a reliable foundation for training models and performance across various applications Immediately available for rapid deployment Licensed datasets are an economical solution Developed by Appen’s internal data experts The most important factors to consider when selecting data for your AI project are the quality, size, and accuracy of the dataset. Make sure your data is ethically sourced to provide your model with reliable and diverse information The amount of data needed to train an AI model depends on the model type and task complexity while complex tasks like NLP or advanced computer vision often require millions of data points Appen’s extensive catalog of off-the-shelf (OTS) datasets spans multiple data types and industries providing comprehensive coverage for various AI applications These datasets are crafted to the highest standards of quality and accuracy ensuring reliable training data for AI models Natural Language Processing (NLP) continues to evolve and AI leading to more powerful language-based applications As businesses increasingly adopt AI solutions and enhancing decision-making through language understanding Natural Language Processing (NLP) is a field of artificial intelligence (AI) that enables machines to understand, interpret, and generate human language. Through machine learning algorithms, NLP systems process and analyze language data to power cutting-edge applications like generative AI and LLM agents playing a critical role in everything from customer service automation to real-time language translation The versatility of NLP makes it a key technology in both consumer-facing applications and internal business processes NLP is widely adopted across industries to improve workflows and enhance user experiences Some of the most common natural language processing examples include: NLP powers AI assistants like Siri and Alexa enabling them to understand queries and respond accurately NLP tools automatically summarize lengthy texts providing concise information for quick decision-making NLP converts spoken language into written text facilitating voice commands and transcription enabling them to interpret natural language queries instead of relying solely on keywords Platforms like Netflix and Amazon use NLP to analyze user preferences and offer tailored recommendations The first step in developing an NLP system is building and training a foundation model often based on an existing large language model (LLM) such as GPT or BERT These large language models serve as the base layer for a variety of NLP tasks such as communicating with AI agents and chatbots Fine-tuning an LLM with task-specific data enables these models to perform accurately in NLP applications like translation, summarization, and dialogue generation. Furthermore, many NLP applications—such as chatbots and virtual assistants—now require multi-modal AI capabilities that can process both text and speech data enhancing the interaction possibilities between humans and machines Microsoft Translator partnered with Appen to make synchronous multi-language communication possible across 110 languages – including rare and endangered dialects like Maori and Basque NLP is a game-changer that optimizes operations You don’t have to build your own natural language processing model to apply this advanced technology to your organization Leverage Retrieval Augmented Generation (RAG) to customize an out-of-the-box large language model to your proprietary data NLP extracts insights from unstructured data Industries like finance and healthcare can use NLP to automate document classification reducing manual effort and increasing accuracy NLP automates routine tasks like summarizing emails NLP analyzes customer interactions and feedback helping enterprises deliver personalized marketing campaigns and optimize sales outreach based on preferences and behaviors NLP helps enterprises stay compliant by reviewing contracts and internal communications for potential regulatory issues Global businesses can use NLP for real-time language translation enabling them to engage customers in their preferred language and expand their global reach Building a robust NLP model requires a structured approach that combines high-quality data The process typically follows four key phases After the model is trained using annotated data its performance must be continuously evaluated and fine-tuned Regular evaluation ensures your NLP model makes accurate predictions based on new language inputs and enhances its ability to generalize across different tasks and environments Keep in mind that model development is iterative and you will likely need to repeat these steps to improve your model over time Natural language processing (NLP) techniques are broadly categorized into two main groups: traditional machine learning methods and deep learning methods we explore some of the top natural language processing techniques in both categories Logistic regression is a supervised classification algorithm used to predict the probability of an event based on input data it is commonly applied for tasks such as sentiment analysis The model learns from labeled data to distinguish between different categories Naive Bayes is a probabilistic classification technique that applies Bayes' theorem with the assumption that features (words in a sentence) are independent of each other it performs well in tasks like spam detection and document classification Naive Bayes calculates the probability of a label given text data and selects the label with the highest likelihood Decision trees split data into subsets based on features making decisions that maximize information gain decision trees are used for classification tasks such as identifying sentiment LDA is a topic modeling technique that views documents as a mixture of topics and topics as mixtures of words This statistical approach is useful in analyzing large sets of documents allowing businesses to identify the themes and topics prevalent within them Hidden Markov Models (HMM) are used for tasks such as part-of-speech tagging HMMs model the probability of sequences (e.g. This probabilistic method predicts the next word or tag based on the current state and previous transitions helping to infer the hidden structure of text data By treating text as a sequence of words in a matrix format CNNs can learn the spatial relationships between words enabling tasks like sentiment analysis and spam detection including variants like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) They are capable of understanding context by remembering previous words or sentences RNNs are used for tasks like language translation Autoencoders are encoder-decoder models designed to compress input data into a latent representation and reconstruct it They are useful for dimensionality reduction and can be applied in NLP for tasks like anomaly detection or feature extraction from text The Seq2Seq model is designed for tasks like translation and summarization The encoder processes input text and generates an encoded vector which is then passed to the decoder to produce the desired output This model architecture is effective in tasks requiring the generation of text based on input sequences introduced in the paper "Attention Is All You Need," have revolutionized NLP with their self-attention mechanism which processes input sequences in parallel rather than sequentially Transformers have become the foundation for state-of-the-art models like GPT Their ability to capture long-range dependencies in text makes them highly effective in tasks such as translation These natural language processing techniques form the backbone of modern NLP applications enabling machines to understand and interact with human language more effectively Appen has over 25 years of experience pioneering natural language processing Appen continues to support leading AI companies with comprehensive data collection We offer tailored solutions for your NLP projects with services such as: Gather and curate data tailored to your specific use case ensuring your models are trained on the most relevant and representative datasets Quickly train your model with our pre-existing natural language processing data sets  – including thousands of labeled text samples for tasks like sentiment analysis Our team of experts uses advanced tools and human-in-the-loop processes to label and annotate data accurately Continuously monitor and evaluate your NLP models testing their performance and making necessary adjustments to ensure optimal accuracy in real-world applications Leverage the knowledge of our language experts offering both ad-hoc and long-term consulting for projects requiring specialized linguistic knowledge Appen has over 25 years of experience with natural language processing As the leading provider of AI data solutions Appen supports 80% of today's top model builders providing the high-quality data collection and model evaluation solutions they need to innovate Interested in how NLP could support your organization Appen is the leading provider of high-quality LLM training data and services Whether you're building a foundation model or need a custom enterprise solution our experts are ready to support your specific AI needs throughout the project lifecycle The LLM lifecycle begins with curating a diverse dataset to equip your model with relevant language and domain expertise. Developing foundation models and training LLMs for multi-modal applications involves processing vast amounts of raw data to help the model understand human language and various media types effectively Once your foundation model is built, further training is required to fine tune your LLM Optimize model performance for specific tasks and use cases by introducing labelled datasets and carefully engineered prompts curated to the target applications Guide to CoT reasoning for LLMs featuring an expert case study on how Appen built a mathematical reasoning dataset for a leading technology company LLM’s should be evaluated continuously to improve the accuracy of the model and minimize AI hallucinations Create quality assurance standards for your LLM and leverage human expertise to evaluate your model against those guidelines Learn how industry leaders leverage high-quality data to improve their models Data quality is the greatest differentiator when it comes to training your large language model. Innovative AI requires high-quality datasets curated to diverse applications. As the leading provider of AI training data top LLM builders count on Appen to train and evaluate their models across different use cases Create custom prompts and responses tailored to diverse data requirements to enhance your model’s performance across different use cases and specialized domains Supporting diverse data requirements including: Leverage Appen’s AI Chat Feedback tool to enhance your model with Reinforcement Learning with Human Feedback (RLHF) and Direct Preference Optimization (DPO) Assess the performance of your model across a range of LLM evaluation metrics such as relevance Leverage Appen’s red teaming crowd to proactively identify vulnerabilities and ensure the safety and security of your LLM across diverse applications Conduct open-ended or targeted red teaming tasks such as: Tailor your model to specific domains and generate more precise and contextually relevant responses by introducing a broader Retrieval-Augmented Generation (RAG) data services include: Our team offers customized solutions to meet your specific AI data needs providing in-depth support throughout the project lifecycle we are on a mission to improve training for peer-to-peer crisis support among veterans with our revolutionary AI-powered model Appen is an essential partner to us in this process They appreciate the sensitive nature of our work and provide expert support for fine-tuning our model to accurately replicate how a conversation about mental health and crisis would go Our partnership with Appen enabled us to achieve 93% positive user feedback.”- Glenn Herzberg Mental health support for U.S. veterans is essential, with nearly 20 veteran suicides occurring daily an AI-driven platform that helps crisis counselors and loved ones prepare for conversations about veteran mental health and suicide prevention ReflexAI needed high-quality training data to build a realistic and empathetic model capable of handling sensitive communication around mental health and execution of a successful AI-powered mental health platform Building on this success, Dorison and Callery-Coyne cofounded ReflexAI. With backing from organizations like the U.S. Department of Veterans Affairs and Google, ReflexAI launched HomeTeam an AI platform designed to help crisis counselors and loved ones of veterans practice sensitive conversations about mental health and suicide prevention in a safe ReflexAI has successfully delivered high quality training to thousands and continues to offer HomeTeam for free to all veterans encouraging them to support each other with mental health challenges HomeTeam integrates educational modules on key mental health topics with AI-powered roleplay simulations for practical training ReflexAI focused on delivering a tool that emphasizes responsible AI deployment backed by user research and feedback from the veteran community ReflexAI partnered with Appen to gather high-quality training data and fine-tune their AI model for realistic empathetic conversations while prioritizing responsible AI development and human feedback Fine-tuning an AI model for mental health use cases is crucial because the subject matter requires a high degree of empathy A general model may lack the context-specific understanding needed to address complex emotional situations By fine-tuning with appropriate data and human evaluation the AI can be trained to handle the specific language and ethical considerations associated with mental health and safe responses tailored to individual needs To prepare HomeTeam for sensitive mental health conversations ReflexAI and Appen had to first overcome the following challenges: To build an AI model that simulates realistic conversations around mental health and suicide prevention ReflexAI needed high-quality training data that reflected the sensitive nature of veteran experiences while incorporating proven conversational strategies and counseling techniques they first established specific data requirements such as written quality and volume to adapt to the ever-evolving nature of these requisites Veterans come from diverse backgrounds and face varying mental health challenges ReflexAI and Appen worked together to collect representative data that captured a range of veteran experiences and communication styles making the AI model more empathetic and adaptable Given the sensitive nature of mental health conversations ReflexAI prioritized ethical AI development Appen played a critical role in ensuring that the training data was ethically sourced and representative of diverse veteran experiences Appen captured data from a diverse group of contributors across the US paying contributors fairly and providing support for those working with sensitive topics like mental health and suicide ReflexAI and Appen’s shared commitment to responsible AI ensured the tool could handle sensitive situations Recognizing that traditional data collection methods were insufficient ReflexAI and Appen adopted a two-pronged approach to overcome these challenges: Appen assembled a diverse group of highly qualified human contributors with subject matter expertise across mental health and veteran's services to generate training data Crowd diversity is crucial for AI development because it ensures the model learns from a wide range of perspectives and experiences resulting in more inclusive and accurate models capable of handling diverse real-world scenarios This diverse crowd ensured that HomeTeam accurately captured the nuances of mental health conversations ReflexAI and Appen utilized this diverse crowd to complete various tasks throughout the model training and fine-tuning life cycle Key tasks included creating synthetic transcripts and annotating and evaluating the model’s performance before deployment Incorporating human expertise at multiple stages of the model training lifecycle enabled ReflexAI to execute the most empathetic The iterative process ensured that the AI simulations accurately reflected real-world conversations and respected ethical boundaries By addressing the challenges of data collection and ethical considerations HomeTeam now provides a comprehensive training tool that allows users to engage in realistic roleplay scenarios enhancing their ability to support veterans facing mental health crises The development of ReflexAI's HomeTeam platform marks a significant advancement in veteran mental health support ReflexAI enables counselors and loved ones to practice difficult conversations in a controlled and safe environment Partnering with Appen ensured that the training data used to develop the model was high-quality and ethically sourced enabling ReflexAI to create an effective and empathetic tool As AI mental health companies continue to innovate HomeTeam serves as a model for how technology can be responsibly deployed to support the unique needs of veterans and their communities The partnership with Appen ensured that the training data used to develop the AI model was both high-quality and ethically sourced As AI continues to play a pivotal role in mental health support HomeTeam stands as a model for how technology can be responsibly used to address the unique needs of veterans and their support networks staying ahead of trends is more crucial than ever for success developed in collaboration with The Harris Poll delivers the latest industry insights you need to stay ahead of the curve and make informed decisions about your AI initiatives A leading AI platform partnered with Appen to enhance its AI-powered music generation feature The client needed high-quality annotated music data to refine model performance ensuring AI-composed melodies aligned with genre expectations Appen accelerated the feature’s market launch and improved the AI’s ability to generate coherent and stylistically appropriate music compositions The project aimed to develop an AI music generation model capable of producing high-quality songs based on user inputs. To train high-quality and robust music generation capabilities, the client required data annotation for large volumes of musical pieces Developing AI-generated music posed several challenges: Appen implemented a structured approach to support the project: Appen’s expertise in specialized multimodal generative AI data helped our client accelerate the development of their AI music feature with: Appen enabled the client to deliver a high-quality AI music generation feature enhancing user experience and engagement on their platform A leading model builder partnered with Appen to conduct rapid-sprint evaluations across 3-6 large language models (LLMs) for tasks spanning both general and complex domains, including healthcare, legal, finance, programming, math, and automotive. By leveraging Appen’s team of expert evaluators and the AI data platform (ADAP) the project delivered over 500,000 annotations in 5-day sprints of 50,000+ annotations each ensuring rapid iteration and continuous improvement These evaluations benchmarked model accuracy The primary objective of this project was to assess and improve the performance of multiple LLMs across diverse industries By conducting structured evaluations and A/B testing the project aimed to provide precise insights into model effectiveness ensuring alignment with industry-specific requirements and Responsible AI principles Managing rapid-sprint evaluations across multiple LLMs and domains presented several key challenges: Appen employed a structured evaluation framework: The rapid-sprint evaluation and A/B testing framework provided the model builder with actionable insights to optimize LLM performance across multiple domains Appen empowered the client to enhance LLM performance across diverse industries ensuring alignment with both business needs and Responsible AI principles According to the latest Appen survey published in October artificial intelligence project deployment with meaningful return on investment (ROI) fell from a mean of 51.9 percent in 2023 to 47.3 percent in 2024 “The State of AI in 2024,” exposes the challenges enterprise AI faces moving forward The study further highlights the factors influencing AI developments one of which is the quality of data coupled with the complexities during the implementation that most companies adopting AI encounter Despite the 17 percent increase in the adoption of generative AI—56 percent in 2024 up from 39 percent in the previous year—ROI is down as a result of deploying low-percentage enterprise projects This decline coincides with the same trajectory of enterprise AI projects making it to deployment down from 50.9 percent last year to 47.4 percent this year Appen’s study points out the main reasons for this downward trend: the importance of data quality emphasizing that “human insight is key to refining AI systems,” 80 percent of the survey respondents said This is not to mention the lack of data availability which also increased by seven percentage points Ninety-three percent of respondents say companies are looking for more efficient ways to manage data and that requires data partners with expertise in the AI data lifecycle This decrease in enterprise deployment and ROI could only be temporary as companies are beginning to figure out ways to make the most out of the increasingly advancing AI technologies. Investment in enterprise AI applications requires time to blossom eWeek has the latest technology news and analysis and product reviews for IT professionals and technology buyers The site’s focus is on innovative solutions and covering in-depth technical content eWeek stays on the cutting edge of technology news and IT trends through interviews and expert analysis Gain insight from top innovators and thought leaders in the fields of IT Advertise with TechnologyAdvice on eWeek and our other IT-focused platforms FULL Agenda Available Now For SlatorCon London 2025! Access is limited to Slator subscribers. Choose from one of our annual subscriptions to unlock exclusive content Trained interpreter and aspiring minimalist is crucial for training AI models to recognize patterns Accurate and well-labeled data is essential for AI to function effectively the demand for precisely annotated data has increased For these models to generate reliable results the data they learn from must be meticulously curated with a strong emphasis on ethical considerations Appen has been a leader in data annotation for nearly 30 years, offering solutions that prioritize quality and ethical standards Ryan Kolln discusses how businesses can leverage Appen's expertise to ensure their AI projects are grounded in trusted Discover how Appen is shaping the future of AI, and learn why ethical data practices are key to responsible AI development: watch the interview With 25+ years of experience in the industry Appen continues to be at the forefront of innovation providing high-quality AI training data and services to businesses worldwide Datanami is a news and analysis publication providing in-depth coverage of the latest trends and business strategies in big data and data science As companies embrace this groundbreaking technology they face both unparalleled opportunities and unprecedented challenges In our newly released State of AI 2024 report offering actionable insights that can help businesses stay ahead In this blog post, we’ll explore several key takeaways from the report and explain why these findings are essential for organizations looking to succeed in the AI-driven future. Access the full report by downloading the State of AI 2024 today The State of AI 2024 report dives into the transformative potential of AI spotlighting both opportunities and challenges AI adoption is accelerating across industries with organizations exploring innovative ways to integrate AI into their business operations with rapid adoption come hurdles that need to be addressed for long-term success Generative AI adoption has surged by 17 percentage points over the past year driven by advancements in natural language processing (NLP) and its integration into business workflows Companies are using generative AI to boost internal productivity especially in IT operations and research and development (R&D) finding applications across industries from marketing to manufacturing While generative AI enhances efficiency, it also introduces new challenges such as managing bias and ensuring ethical AI deployment. As more companies rely on custom AI data collection to train their models they have greater opportunities to prioritize data ethics and model safety by choosing responsible data vendors The State of AI 2024 report highlights that 97% of IT decision-makers agree on the critical importance of data quality for AI success. Despite this recognition, data challenges persist. A 10 percentage point increase in data bottlenecks signals a need for more robust data management solutions. Without high-quality AI training data AI models are more prone to bias and inaccuracies and scalability are essential for building reliable and effective AI systems As AI applications become more specialized and diverse data becomes increasingly important As AI models become more sophisticated, the demand for custom data solutions continues to grow. The report reveals that over 93% of companies are seeking support from external AI training data companies for their model training and/or annotation are becoming the backbone of many AI applications and responsible data sourcing are key elements for success Partnering with reliable data providers who can offer high-quality domain-specific data is vital for building robust AI models The right strategic partnership can make all the difference in ensuring that AI projects reach deployment and deliver meaningful ROI One of the most prominent themes in the State of AI 2024 is the critical role that data plays in shaping successful AI models and managing data remain major hurdles for businesses as they scale AI projects resulting in models that are biased or ineffective That’s where the right partnerships come in At Appen, we understand the complexities of AI data management. With over 25 years of experience, we provide comprehensive solutions that help organizations source and annotate high-quality data reducing bias and improving model reliability Whether you're tackling generative AI applications or optimizing your internal AI processes having a strategic data partner is key to success As businesses adapt to the AI trends of 2024—such as the growing reliance on generative AI and the increasing complexity of applications—it’s evident that successful implementation requires high-quality Choosing the right data provider can significantly impact the success of an AI project in this evolving landscape To help organizations navigate these changes here are three key suggestions for businesses looking to enhance their AI strategies: businesses can better position themselves to leverage AI's potential while navigating the complexities of its implementation we understand these challenges and are here to provide the support and solutions necessary to succeed in your AI initiatives This is just a glimpse of the powerful insights you’ll find in our full State of AI 2024 report Dive deeper into the AI business trends shaping the future of AI and learn how your organization can navigate the challenges and embrace the opportunities of this transformative technology As AI adoption accelerates across industries With AI increasingly embedded in critical sectors— such as healthcare and infrastructure—unintended failures can have far-reaching consequences and aligned with ethical standards helps mitigate risks such as misinformation AI safety is key to preventing risks such as model bias, hallucinations, security threats, and legal liabilities. Best practices span a range of techniques and strategies, from adversarial prompting to sourcing high-quality  LLM training data ensuring AI systems operate with reduced risk to businesses and end users From minimizing bias to enhancing data security AI safety is a foundational priority for all who build With AI playing an increasingly prominent role across industries safety measures have never been more important This eBook explores a research-based approach to AI safety best practices across the AI lifecycle with examples highlighting AI safety in high-risk industries We are excited to announce Test Questions are now available in Quality Flow ADAP.  Test Questions, a signature feature available in Appen’s AI Data Platform historic jobs and workflows setups are the cornerstone of your human-in-the-loop process unblocking high quality for your data operations that serve as benchmarks for assessing the performance of human contributors Using test questions enable you to continuously evaluate your contributors’ performance to identify potential issues in your instructions or in your data (if contributors consistently fail the same questions) and to calculate and track important industry metrics such as Inter Annotators Agreement (IAA) These questions can be used both before and during annotation and evaluation tasks quiz mode enables you to establish a predefined accuracy threshold that only permits workers who pass to start the task work mode ensures that contributors meet a certain accuracy threshold in order to continue working on the task If a contributor falls below this threshold the job manager is notified and can choose whether to allow the contributor to keep working or to remove them and their work from the job There are many reasons why a worker may fall under the accuracy threshold This process helps maintain high-quality data and streamlines the workflow by continuously improving your task while identifying underperforming contributors for removal. Check out our demo video here it is common to ask contributors to compare different outputs to a unique prompt to test the model’s alignment for real-world scenarios your “golden set,” with the correct and expected “best answers” to be chosen by your workers This set could then be blended in with the rest of the data to be labeled and randomly presented to your workers like a regular task prompt Their consistency in choosing the correct “best answer” on this golden set will demonstrate their trustworthiness as individual contributors and allow you to inform their IAA with their true-positive answers rate To increase the efficiency of your test questions we suggest your golden set be correctly balanced to reflect your data set composition and have an even distribution of possible answers across the ground truth Quality Flow Test Questions improve AI model training and evaluation by ensuring accurate and reliable AI data automated quality control for both objective and subjective tasks Quality Flow Test questions can serve as an effective teaching mechanism offering continuous feedback to contributors as they work This feedback helps contributors align with the guidelines and improve their performance over time Users can easily monitor the responses to test questions in the ADAP interface users can quickly edit or disable these questions ensuring the ongoing accuracy and fairness of the quality control process Quality Flow Test Questions improve the accuracy and reliability of your AI models flexibility for objective and subjective tasks and continuous feedback to contributors - helping data teams optimize their AI models for peak performance If you're a current customer, please contact your Appen representative to get started. Interested in a demo? Contact our team today At Appen, our commitment to innovation is deeply rooted in addressing real-world challenges faced by our contributors. The new Summary Table Tool in our AI Data Platform (ADAP) exemplifies this commitment developed through the collaborative efforts of our delivery and engineering teams during our latest Hackathon Our hackathons serve as a crucible for practical solutions empowering teams across the organization to create tools that directly respond to user needs a team identified a significant flaw in the quality assurance process for labeling text data the QA checkers had to go through each sub-question and their answers which was tedious and prone to errors,” explains Prathamesh Khilare the Technical Data Collection Manager who submitted this idea for the hackathon QA reviewers struggled with managing and reviewing 30 to 50 fields or categories often leading to excessive scrolling and an increased risk of overlooking errors we’ve been addressing this with custom code,” explains Alok Painuly a data collection specialist on Khilare’s team “This process is tedious and time-consuming especially when task design changes require manual updates to the platform backend.” This cumbersome process highlighted the need for a more efficient solution which streamlines the review process by consolidating all fields into a single This innovation was developed by the Hackathon-winning team guided through the feature development life cycle by an experienced Product Manager “The team asked for our feedback and improved the feature,” says Khilare “The swift action in getting the idea implemented is commendable!” The Summary Table Tool not only reduces time spent scrolling but also enhances accuracy by making it easier to spot errors or omissions “This new feature is more effective than the current one we proposed during the hackathon,” Painuly adds By focusing on real use cases and immediate needs we deliver innovations that genuinely improve our contributors' workflow and productivity “This approach is fulfilled in this new feature and the design is very comfortable and helpful for the contributors,” observes Painuly The Summary Table Tool is a testament to how collaborative efforts and hackathons can lead to meaningful advancements that make a tangible difference in our daily operations Here’s to continuing our tradition of innovation driven by the real-world challenges our teams face and the creative solutions they develop “Appen’s breadth of language expertise and ability to source speakers has allowed us to offer a wide range of languages and dialects with Microsoft Translator.” – Marco Casalaina the process was clunky and often inaccurate resulting in significant misunderstandings due to literal word-for-word translations that missed the nuances of language enabling seamless multi-language communication Beyond the world’s most frequently spoken languages Microsoft Translator is continually adding new languages to its platform This effort not only promotes language preservation but also fosters equitable access to knowledge for speakers of all languages making it easier for everyone to engage in cross-cultural communication To expand its language capabilities, Microsoft faced the challenge of sourcing and annotating large datasets, particularly for less frequently spoken languages. To tackle this, Microsoft turned to Appen, a leader in AI data solutions to help meet its data requirements and scale its translation efforts Microsoft Translator is a real-time translation tool powered by AI that provides text and image translations across multiple languages The technology is part of the Azure Cognitive Services suite and supports individual users and developers by offering translation services that facilitate communication across different languages The platform was initially focused on supporting widely spoken languages but has grown to incorporate lesser-known languages in order to preserve linguistic diversity and ensure global access to information Microsoft Translator contributes to the broader goal of breaking down language barriers and enabling cross-cultural communication on a global scale Microsoft Translator’s main goal in collaborating with Appen was to significantly increase the number of languages available on the platform particularly those spoken by smaller communities Meeting these goals would allow Microsoft Translator to make a broader impact ensuring that AI-driven translation services are accessible to people worldwide Microsoft Translator uses AI to translate between languages but building accurate machine translation models requires vast sourcing sufficient data poses significant challenges Microsoft needed a solution to address potential translation bias such as ensuring accurate translations for gender-ambiguous source sentences These complex requirements made it essential to find a partner capable of providing tailored solutions for diverse languages With 25+ years of experience in data sourcing Appen was well-equipped to meet Microsoft Translator's needs Appen collaborated with local communities to source language data directly from native speakers By working with fluent speakers of rare languages Appen collected high-quality language samples that accurately represented the linguistic and cultural nuances of each language Appen's team of experts annotated the collected data by transcribing and translating each sample with precision This process included multiple layers of quality assurance to ensure the highest level of accuracy in every translation Appen developed a solution for generating multiple translations for gender-ambiguous sentences allowing Microsoft to address potential biases in their AI translation models For languages with different alphabets or phonetic systems Appen applied phonetic similarity and transliteration techniques to ensure that the datasets were correctly formatted and ready for use in machine learning models Microsoft Translator was able to scale its language capabilities significantly Appen played a critical role in sourcing and annotating data for 108 of those languages Some of the newly added and less commonly spoken languages include: Microsoft Translator has made significant strides in preserving endangered languages and promoting global access to knowledge The work between Microsoft and Appen demonstrates how AI can drive greater inclusivity and equity in language access The success of the Microsoft Translator project highlights the importance of collaboration between AI technology developers and data providers Microsoft was able to overcome the complex challenges of sourcing and annotating data for rare languages ensuring that its AI models were trained on diverse This collaboration also set a new standard for ethical AI development by addressing translation bias and ensuring that the AI-powered tool is accessible to people worldwide Appen’s unique ability to deliver customized high-quality data solutions for AI projects was key to the success of Microsoft Translator Microsoft Translator has become a global leader in AI-powered language translation helping to make knowledge accessible to all The partnership between Microsoft Translator and Appen underscores the critical role that high-quality data plays in developing AI technologies Microsoft was able to expand its language portfolio to 110 languages ensuring that speakers of even the rarest languages can access digital knowledge and engage in global conversations This collaboration not only strengthened Microsoft’s AI capabilities but also advanced the broader goal of making AI-driven technology more inclusive and equitable for users around the world Last year, we launched Appen's AI Chat Feedback tool on our AI Data Platform (ADAP) This tool enables human contributors to interact with LLMs allowing customers to test and ensure model accuracy and reliability The tool has gained traction and is used for complex tasks across various AI training data use cases We have recently added several new features to the AI Chat Feedback tool: we've integrated our AI Chat Feedback tool into our LLM and RAG templates (Make My Model Safe) to empower you to ensure robust AI performance Check out our demo video to see it in action AI Chat Feedback offers a comprehensive set of capabilities designed to enhance the interaction and feedback of your LLM outputs These features include both code and graphical editors for job creation and tailor the chat feedback process through various parameters and custom response options for optimal interaction The platform supports live preamble and seed data for context setting Advanced features like model response selection, enhanced feedback, and the ability to review and edit previous interactions ensure robust and dynamic AI model evaluation, making ADAP an essential tool for refining and improving LLM performance. For more details, check out our Guide to Running an AI Chat Feedback Job article Human feedback is crucial for improving LLM models Appen's technology can be supported by your own team of experts or our global crowd of over 1 million AI training specialists These contributors evaluate datasets for accuracy and bias The AI Chat Feedback tool connects LLM outputs with these teams This process identifies issues and optimizes data quality ensuring reliable model performance in real-world scenarios Our latest enhancements reinforce our commitment to developing powerful your models will be rigorously tested and improved before release This continuous feedback loop is essential for maintaining strong safeguards and delivering trustworthy models we are advancing AI to be both effective and ethically sound If you're a current customer, please contact your Appen representative to get started. If you simply want to test drive it, please contact us and we'll get back to you immediately Discover how combining Retrieval Augmented Generation (RAG) with human expertise drives high-quality AI results Our latest eBook delves into the inner workings of RAG explaining how this architecture elevates AI capabilities by integrating retrieval accuracy and generative creativity Learn how human oversight ensures data quality and optimizes AI output for complex real-world tasks enhancing the power of large language models (LLMs) by combining them with extensive external knowledge bases making it perfect for applications such as customer support The RAG architecture enables LLMs to ground their responses in factual Human expertise plays a critical role in this process and curated to deliver the most accurate responses Leading AI teams are leveraging RAG to significantly improve the quality of outputs compared to purely generative models the role of human experts in optimizing outputs and how businesses can leverage this technology to drive value which surveyed more than 500 IT decision-makers across a variety of U.S the adoption of AI-powered technologies such as machine learning (ML) and generative AI (GenAI) are hindered by a lack of accurate and high-quality data The report found a 10 percentage point year-over-year increase in bottlenecks related to sourcing "Enthusiasm around GenAI and other AI-powered tech remains high but users are quickly finding that the promise of these tools is matched by an equally daunting challenge," said Si Chen "The success of AI initiatives relies heavily on high-quality data Those building the AI tools and models of tomorrow value strategic data partnerships now more than ever." The report found that the use of GenAI continues to grow at a healthy pace with adoption up 17 percentage points in 2024 versus the previous year 86% of respondents retrain or update their ML models at least once every quarter relevant and high-quality data as accuracy declines data accuracy has decreased by 9 percentage points since 2021 making the quest for high-quality data a major challenge—as models are being iterated more frequently data remains the most significant challenge especially where accuracy and availability are concerned Appen commissioned Harris Poll to conduct an online survey of U.S data engineers and developers from April 18 - May 9 with respondents working at companies with 100-plus employees The survey results reflect the complex and multifaceted journey to AI success From the need for high-quality human-in-the-loop data to the challenges of managing bias and ensuring fairness organizations face numerous obstacles in their pursuit of reliable and effective AI systems About Appen Appen (ASX:APX) is the global leader in data for the AI lifecycle with more than 25 years’ experience in data sourcing we enable organizations to launch the world’s most innovative artificial intelligence products with speed and at scale Appen maintains the industry’s most advanced AI-assisted data annotation platform and boasts a global crowd of more than 1 million contributors worldwide ContactsBOCA Communications for AppenAppen@bocacommunications.com Appen Ltd. ( (AU:APX) ) has provided an update has announced a substantial holding by Mitsubishi UFJ Financial Group which owns 100% of First Sentier Investors Holdings Pty Limited This development indicates a significant level of control over Appen’s voting shares and reflects Mitsubishi UFJ Financial Group’s strategic interest in the company This change in substantial holding could impact Appen’s operations and its market strategy as the involvement of a major financial group like Mitsubishi UFJ could bring shifts in decision-making and resource allocation Technical Sentiment Consensus Rating: Sell See more insights into APX stock on TipRanks’ Stock Analysis page Disclaimer & DisclosureReport an Issue Appen Ltd. ( (AU:APX) ) has provided an update Disclaimer & DisclosureReport an Issue Appen says nearly one-third of payments were not paid on time as a result of issue with payment processing integration One-third of payments to contractors training AI systems used by companies such as Amazon Meta and Microsoft have not been paid on time after the Australian company Appen moved to a new worker management platform Appen employs 1 million contractors who speak more than 500 languages and are based in 200 countries audio and other data to improve AI systems used by the large tech companies and have been referred to as “ghost workers” – the unseen human labour involved in training systems people use every day Appen moved to a new contributor platform called CrowdGen in September which the company said would “enhance our ability to deliver high-quality data at scale” Sign up for Guardian Australia’s breaking news email But the company admitted nearly one-third of the company’s payments for projects worked on by contractors were not paid on time as a result of an issue with payment processing integration. “We have been working diligently to address the issue. Over two-thirds of payments were made on time, and we continued to make daily payments since then,” a spokesperson said. “We are continuing to process payments daily and are on track to close out the remainder this week, which is well within our contractual obligations to our crowd workforce.” The spokesperson said payments are being processed as batches by project and, if a contributor is involved in multiple projects, their payment may be spread across multiple days. Read more“This is a suboptimal experience for our crowd and we have committed to on-time payment for work completed in October.” The spokesperson would not confirm the number of contractors affected the spokesperson said that not all of the 1 million contractors may have been active at the time the issue occurred apologised to contractors in a message on the company’s website stating: “We are truly sorry for the stress and frustration that this has caused “We are working diligently to fix the issue with the payment implementation and I want to provide some additional context on how this occurred and what we are doing to fix the issue.” Frustrated workers have complained on Reddit about their treatment with no concrete answers on when we’ll get paid in full,” one worker posted Some people can’t wait until next month to get paid,” another stated This article was amended on 25 October 2024 to clarify that it was nearly one-third of the company’s payments for projects worked on by contractors that were not paid on time rather than nearly one-third of contractors not paid on time as an earlier version said Contractors can receive multiple payments from different projects