Pareto.AI

🚀 Exciting news: We have just launched expert AI/LLM training services at Pareto.AI! We are proud to introduce advanced data labeling, annotation, and evaluation services powered by elite data teams for AI businesses around the globe. Our mission has always been to empower professionals to deliver exceptional results for customers. As we transition into LLM training, human feedback and expertise take center stage. Our dedication to collaboration and high-quality data sets us apart in AI and LLM development. By placing elite data workers at the forefront, we ensure a nuanced approach to data evaluation, resulting in robust and effective LLM models. We have already completed successful projects with notable AI companies and aim to expand our footprint even further. Our offerings include RLHF for fine-tuning AI models, engine annotation, computer vision training, LLM hallucination testing, and more. Learn how we ensure model excellence through every phase of development by reading our latest blog below. Today marks an important new chapter in our journey, and we look forward to demonstrating our superior methodology to all our new customers! #ParetoAI #LLMTraining #AI #RLHF #LaunchAnnouncement

We are excited to share a new edition of our "Behind the Data" series! 🤖 Once a month, we feature one of our top AI trainers. This series highlights their professional journey, how they found Pareto.AI, their experiences working with us, their interests, and their thoughts about both the AI industry and the future of work. This month, we had a chat with Gilbert Mungai—a data scientist and valued Pareto.AI community member whose journey from actuarial science to cutting-edge AI work is packed with insights. Gilbert opens up about the challenges and rewards of balancing AI projects with his career, shares his thoughts on the future of AI in the job market, and reflects on his learning curve in data labeling. He also offers an honest take on the highs and lows of task availability, the importance of community, and the importance of staying curious and persistent to succeed as a professional. Read the full interview below!

As AI evolves, the purpose of human labor is likely to shift from generalized functions to specialized tasks that make the best use of each individuals strengths, enhance productivity, and promote job satisfaction. In other words, the future of work is shifting towards an environment where tasks will be increasingly "atomized." This change allows for a more individualized approach, matching roles to the unique strengths of each worker. Certain skills will become more valuable than ever before: Creativity, emotional intelligence, critical thinking, and ethical reasoning, among others. How can society prepare for this shift? In our blog, we provide an introduction to the concept of "atomized tasks" along with some practical steps on how to prepare for this change. Read more below! #FutureOfWork #Atomization #PostAGI #HumanCapital #ParetoAI #RLHF

You might have heard that being polite when prompting an LLM leads to better results—but is that really the case? Perhaps you've considered whether adding pressure, like threats, might influence the output. How can you tailor prompts effectively across different contexts to get the best results? We discuss the findings of a research paper by VILA Lab at Mohamed bin Zayed University of AI, introducing 26 principles for optimizing LLM prompts by up to 50%. These principles simplify how to structure queries for different models, analyzing LLaMA-1/2 (7B, 13B, 70B), GPT-3.5, and GPT-4 through extensive experimentation to enhance user understanding and improve outcomes. Have a read below and let us know what you think!

AI advancement faces four key constraints: 1. Data scarcity 2. Power consumption 3. Chip manufacturing limitations 4. The infamous “latency wall”—a fundamental speed limit arising from unavoidable delays in AI training computations. Out of these four bottlenecks, data availability stands out as the most unpredictable, characterized by significant variability in quality. The effectiveness of multimodal data in enhancing reasoning is uncertain, and its availability, quality, and the efficiency of tokenization methods are less reliable than for text data. While synthetic data could allow for unlimited scaling, it incurs significant computational costs and risks such as model recursion. It’s not just about collecting large datasets; it’s essential to ensure their accuracy, relevance, and diversity. Small-batch human data at the frontiers of human expertise is vital, providing the reliable ground truth necessary for effective AI systems. This foundation is also essential for generating synthetic data , which *may* enhance scalability when based on trustworthy inputs. In our blog, we discuss the aforementioned AI scaling constraints in more detail and do a deep dive into the implications of data scarcity for AI advancement. Let us know what you think!

In the midst of the major AI advancements from companies like OpenAI and Meta, one might wonder—what is Apple’s AI play? This summer, Apple introduced DCLM-7B, an open-source model that outperformed Mistral-7B. What's even more surprising is that Apple made its weights, training code, and dataset publicly available. The key to it’s performance? Thoughtful data curation. Apple’s success with DCLM-7B underscores a key principle we share at Pareto AI: it’s not just about data volume, but the *right* data. While Apple's use of automated data pipelines is a step in the right direction, truly unlocking the next level of model performance—and potentially reducing dependence on OpenAI to power Apple Intelligence (wink)—requires the integration of small-batch, high-precision expert data, something that frontier AI labs have been the first to realize. In our blog, we touch on some interesting topics: a summary of Apple’s DataComp research paper, its limitations, the shifting focus towards data quality, and Apple’s plans on integrating AI in consumer tech.

Last month, OpenAI launched o1 and o1-mini, the first in a series of "reasoning" models designed to tackle complex tasks in science, coding, and math. We see the o1 models as foundational technologies, much like GPT-2 was for AI evolution. While still in the early stages, these models point to a future where AI can handle intricate, agentic tasks. The biggest advance with o1 is its ability to manage complex reasoning natively, thanks to Chain-of-Thought (CoT) reasoning at inference... or as OpenAI calls it: "thinking." This reduces the need for step-by-step prompts, allowing the model to solve multi-step problems more efficiently as it works through different possibilities before arriving at an answer (although this does lead to increased latency.) As a result, prompting methods need adjustment. Currently popular prompting strategies may no longer work since o1 handles reasoning internally. We've put together a short guide with tips, such as using delimiters, limiting Retrieval-Augmented Generation (RAG), and streamlining steps to optimize your use of o1's #CoT features. We also explain some known limitations of the o1 siblings in case you are wondering if these models would be best suited to your needs. Give it a read!

This week we sent out our sixth edition of People Watching, a newsletter to help you discover great, under-the-radar people. A few highlights from this edition: → Phoebe Yao is the founder of Pareto.AI, which helps with data labeling for AI & LLM training. They work with expert-vetted labelers who can help complete more complicated tasks than typical labelers would do. → David Li is a creative programmer who builds immersive, sometimes silly experiences that capture the magic of frontier technologies. → Alannah Connealy is the founder of Raena Health, which tests and treats hormone imbalances with a whole-person approach, from the comfort of your own home. → Jackson Oswalt is working as a hardware engineer this summer at Midjourney, where he’s building new physical computing interfaces. When he was 12, he built a nuclear fusion reactor. → Amir Bolous has done a wide variety of exploring, including backpacking for 2 months, training to run a marathon in 7 weeks for a bet, raising $8k off a viral tweet to run a reading retreat, playing with some numbers at the Stanford Cancer Institute, working early at Spellbound and Glide, and hosting a podcast. → Marley Xiong is a self-identifying curious person. She's done research at Harvard and interned in data science & machine learning at Google and Google X. She’s very interested in neurotech. → Stephen Fay works on the real-time signal processing for the ALBATROS radio telescope. He also occasionally writes poetry and fiction. → Somin (Mindy) Lee is an undergraduate student at the University of Toronto working towards a BASc of Computer Engineering, and planning to double minor in Robotics & Mechatronics and Bioengineering. Her research interests span from topics such as implantable devices and biomimetic soft robots to BCIs and ML. To see more of this, subscribe to the newsletter: pplwatching.substack.com. We've also included a form where you can recommend people you think we should feature. Thanks for reading and supporting! And stay tuned for some exciting updates coming soon. 🙂

Good news! 🎉 A few months ago, we shared our involvement in groundbreaking AI safety research with UCL, ML Alignment & Theory Scholars, and Anthropic. Today, we're thrilled to announce that this collaborative effort has been recognized with the Best Paper Award at the International Conference on Machine Learning (ICML)! Check out the brilliant presentation given by akbir khan on the ICML website: https://lnkd.in/gCHjQfa4 We're immensely grateful to have been part of this project. This recognition not only validates our hypothesis about the effectiveness of talent-first systems in complex data collection projects but also highlights the critical role of human expertise in advancing AI safety. Want to dive deeper into our methodology? Explore our case study on Pareto's role in gathering high-quality data for this award-winning research: https://lnkd.in/g_WH3z-t We're more motivated than ever to continue our mission of bringing human expertise to AI development in new and incentive-aligned ways. Here's to many more collaborations ahead! 🚀 #AIResearch #ICML2024 #BestPaperAward #AIOversight #ParetoAI

Happy Independence Day to our clients, colleagues, experts, and partners in the USA. Let's continue pushing boundaries and creating opportunities in the world of AI. #ParetoAI #IndependenceDay #AIInnovation #DataLabeling