Appen

In a world driven by data, Retrieval Augmented Generation (RAG) is transforming the way businesses interact with AI. From automating customer support to enhancing knowledge management and search, RAG's ability to blend large language models with fact-driven retrieval is a game-changer. But here's the catch—no AI system is perfect without human expertise. Ensuring context, relevance, and accuracy requires more than just algorithms; it demands a human touch. 💡 At Appen, we integrate human expertise at every stage—data preparation, prompt crafting, and continuous quality control. This holistic approach ensures your AI systems are not just functional but optimized for accuracy, precision, and relevance. Let’s elevate your AI initiatives— Download our latest eBook to discover how Retrieval Augmented Generation (RAG) and human expertise can elevate your AI's performance and unlock its full potential: https://lnkd.in/g67hRPyr 

Exploring the Latest in AI Innovation: Meta's Llama 3.2 Appen is excited to see Llama 3.2 bring exciting new possibilities with customizable vision and edge AI models. Llama 3.2 includes vision models (11B and 90B) for visual understanding tasks and text lightweight models (1B and 3B) built for mobile and edge devices. 🔍 What’s new? • Vision models (11B and 90B) supporting image reasoning use cases, including document-level understanding, image captioning, and visual grounding tasks  • Lightweight models (1B and 3B) supporting context length of 128K and on-device use cases like summarization and instruction following  • Pre-trained and aligned models available to be fine-tuned for custom applications  • Llama Stack for developers to simplify the deployment of retrieval-augmented generation (RAG) with integrated safety tools to ensure responsible AI development Check out Meta's announcement to learn more about Llama 3.2! 🔗 https://lnkd.in/gvRWRB94 

🚀 Exciting Times for AI Reasoning! 🚀 OpenAI just launched the o1-preview, a new series of reasoning models designed to tackle complex problems with a step-by-step approach —much like how humans think through challenging tasks. These models are setting new standards in science, coding, and math by refining their reasoning processes and learning from mistakes. In a recent test, the o1-preview model solved 83% of International Mathematics Olympiad problems, compared to just 13% by GPT-4o. At Appen, we’ve been ahead of the curve. Last month at Ai4, we released our Chain-of-Thought (CoT) eBook, showcasing our extensive work in building high-quality training data for AI models. Our expertise in creating datasets has been instrumental in improving the capabilities of Large Language Models (LLMs) like OpenAI’s o1 series. We’re helping AI think more like humans by improving reasoning step-by-step. Curious to know more? In our eBook, we share insights on how we built a mathematical reasoning dataset that empowers AI to solve complex problems more accurately and efficiently. 👉 Download the eBook and discover how Appen’s high-quality training data is driving the future of LLMs and Chain-of-Thought reasoning: https://lnkd.in/e-yiu8Zg 👉 Learn more about the o1-preview models and ChatGPT's latest announcement: https://lnkd.in/eQZG9qXd #AI #MachineLearning #LLM #ChainOfThought #Appen #DataDrivenAI 

Si Chen, Head of Strategy at Appen, recently discussed the future of LLM reasoning and Chain-of-Thought (CoT) reasoning with The AI Journal's Hannah Algar. Si highlights how CoT brings AI reasoning closer to human cognition, enabling LLMs to solve complex, multi-step tasks—from business decision-making to mathematical reasoning—with greater transparency. 𝗞𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: 𝗕𝗿𝗶𝗱𝗴𝗶𝗻𝗴 𝗔𝗜 𝗮𝗻𝗱 𝗵𝘂𝗺𝗮𝗻 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴: CoT reasoning enhances an LLM’s ability to think in a step-by-step manner and communicate that thought process clearly, increasing model alignment with human users.  𝗧𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻: Chain-of-thought prompting is shown to increase the transparency of model output and boost accuracy in real-world applications, like solving elementary math problems.  𝗟𝗟𝗠𝘀 𝗮𝘀 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝘁𝗼𝗼𝗹𝘀: CoT reasoning has great potential in industries like healthcare, finance, and education, where increased transparency around how the model is processing data and reaching conclusions is highly valued. Check out the full conversation for more insights on how CoT prompting is shaping the next generation of AI capabilities. https://lnkd.in/evRnJnqG #AI #MachineLearning #LLM #CognitiveAI #Appen #AIReasoning #FutureOfAI 

As AI continues to evolve, the importance of high-quality, ethically sourced data has never been more critical. In the latest edition of 𝘛𝘩𝘦 𝘉𝘪𝘨 𝘋𝘢𝘵𝘢 𝘋𝘦𝘣𝘳𝘪𝘦𝘧 with Alex Woodie, Appen's CEO, Ryan Kolln, sheds light on the transformative role of data annotation and labeling in the AI ecosystem. 𝗪𝗵𝘆 𝗶𝘀 𝗗𝗮𝘁𝗮 𝗔𝗻𝗻𝗼𝘁𝗮𝘁𝗶𝗼𝗻 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁? Data annotation involves labeling data—like text, images, and videos—to make it understandable to machines. This process is the backbone of training AI models, ensuring they can accurately recognize patterns, make decisions, and generate insights. However, not all data is created equal. 𝗧𝗵𝗲 𝗥𝗶𝘀𝗲 𝗼𝗳 𝗚𝗲𝗻𝗔𝗜 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗡𝗲𝗲𝗱 𝗳𝗼𝗿 𝗧𝗿𝘂𝘀𝘁𝗲𝗱 𝗗𝗮𝘁𝗮 With the emergence of Generative AI, the stakes are even higher. GenAI models, which can create new content, rely on vast amounts of annotated data. But to produce reliable results, this data must be curated with precision and ethics at the forefront. 𝗛𝗼𝘄 𝗔𝗽𝗽𝗲𝗻 𝗶𝘀 𝗟𝗲𝗮𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗖𝗵𝗮𝗿𝗴𝗲 Appen has been at the forefront of data annotation for nearly 30 years, providing solutions that prioritize quality and ethical standards. Ryan Kolln discusses how companies can harness Appen's expertise to ensure their AI initiatives are built on a foundation of trusted, human-curated data. Discover how Appen is shaping the future of AI, and learn why ethical data practices are key to responsible AI development: https://lnkd.in/eQXaQFdG #AI #DataAnnotation #EthicsInAI #GenAI #DataQuality #Leadership #BigDataDebrief

We had an incredible time at Ai4 - Artificial Intelligence Conferences 2024! Our team was at the forefront of discussions around the adoption and practical application of LLMs in business environments. Christopher Stephens joined AI leaders in a panel where they tackled the real-world challenges of integrating AI into enterprise operations. Key Highlights: • Practical Applications: The discussion highlighted the importance of prioritizing use cases that deliver tangible value. Organizations are keen on understanding where to invest their resources to maximize the benefits of LLMs. • Composite Models: The future of AI is not about relying on a single LLM but orchestration of multiple models to address diverse needs. This approach was echoed by many on the panel as a more practical and effective strategy. • Human Intelligence: The data strategy behind the problem is critical and enriching data with domain expertise will be a key unlock. This will also help address the randomness that innately exists with generative AI models. • Challenges on the Horizon: Risk management, regulation, and alignment with corporate policies are top concerns. As LLMs become more integrated into business operations, the need for robust data governance and ethical AI practices will only intensify. These were particularly emphasized by leaders in the banking, healthcare, and industrial sectors. We also had the chance to reconnect with industry peers, gaining valuable insights into the landscape. It was a reminder of the importance of agility and innovation in AI.   A big thank you to everyone who visited our booth and joined the conversation. We’re excited about the future of AI and the role Appen will play in driving it forward! Si Chen, Mat Wilcox, Erik Hindman, Alyssa Belardi Pumphrey, Jordan Hagan, Lu Lu

Happening now at Ai4 - Artificial Intelligence Conferences 2024—Booth 221! 🚀 Our team is ready to dive into how high-quality data and human expertise can supercharge your AI models. Whether you're looking to boost performance or explore new AI frontiers, we’ve got the insights you need. Stop by and meet Mat Wilcox, Alyssa Belardi Pumphrey, Erik Hindman, Si Chen, Jordan Hagan, and Lu Lu. Plus, catch Christopher Stephens' panel on LLMs tomorrow at 10:35 AM at 165-168 (L1)! Let’s drive AI innovation together—see you there! https://lnkd.in/gBXabbsy

Ai4 - Artificial Intelligence Conferences 2024 is just around the corner, and we’re eager to see you at Booth 221! Discover how our bespoke data solutions and human expertise can elevate your AI strategies. Meet our incredible team— Mat Wilcox, Alyssa Belardi Pumphrey, Erik Hindman, Si Chen, Jordan Hagan, and Lu Lu—and explore how high-quality data can enhance your AI models. Don’t miss our panel on “Making Use of LLMs” with Christopher Stephens on Wednesday, August 14, from 10:35 AM to 11:20 AM (US/Pacific), where we’ll delve into the practical applications of LLMs. 🚀 Let’s unlock AI’s full potential together! 🚀 https://lnkd.in/gBXabbsy 

We’re thrilled to introduce new enhancements to Appen's AI Chat Feedback tool on our AI Data Platform (ADAP)! Designed to refine and perfect your conversational AI models, this tool empowers your AI teams to test and evaluate models in realistic scenarios, ensuring accuracy and reliability.    What Makes Appen's AI Chat Feedback Tool Unique? 📈 Graphical Editor Activation: Streamline your job setup by activating the AI Chat Feedback tool directly from the Graphical Editor. 🔄 Min/Max Turns: Define the expected conversation length with minimum and maximum turns to simulate real user interactions. 🤖 Live Multi-Chatbot Support: Gather real-time completions and human feedback across different models simultaneously. 📝 Rich Text Editor: Enjoy rich text rendering and editing, with support for code formatting and LaTeX for complex equations. ✏️ Custom Response Edit: Allow contributors to improve model responses or provide their own when the model’s reply isn’t ideal. 📜 Live-preamble: Contributors can create preambles or contexts before engaging in conversations, enhancing interaction quality. Appen's enhanced AI Chat Feedback tool helps create better conversational AI models. It combines new features with human input to enhance accuracy and ethical performance. Leverage this innovation to improve your AI development and applications.   Check out our demo video to see it in action and learn how Appen is helping advance AI to be both effective and trustworthy.    Interested in learning more: https://lnkd.in/eXbwVPgr Alice Desthuilliers Emily G.

Elevate Your AI Journey with Appen at Ai4 2024! 🚀 Excited about the future of AI? So are we! Swing by Booth 221 at Ai4 - Artificial Intelligence Conferences 2024 in Las Vegas to chat with our team about how high-quality, human-in-the-loop data can elevate your AI models. We’ll be sharing insights on everything from boosting model performance to expanding AI capabilities. Meet our fantastic team members— Mat Wilcox, Alyssa Belardi Pumphrey, Erik Hindman, Si Chen, Jordan Hagan, and Lu Lu—and see how our bespoke solutions and expertise can tackle your toughest AI challenges. Plus, catch our panel discussion “Making Use of LLMs” with Christopher Stephens on Wednesday, August 14, from 10:35 AM to 11:20 AM (US/Pacific). We can’t wait to connect and explore how we can drive AI innovation together! 🌟 See you in Las Vegas! 🌟 https://lnkd.in/gBXabbsy #Ai4 #AI #Appen #MachineLearning #Innovation #DataDriven #LLMs