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

The foundation we lay today in data annotation plays a profound role in ensuring AGI aligns with human-centered ethical standards. Data labeling—often seen as the granular, manual work behind machine learning—is critical for the responsible deployment of AGI. Its impact goes beyond technical accuracy to shape ethical AI governance, worker equity, and the fundamental values that future AI systems will hold. In our newest blog, we discuss how data labeling practices today have a profound influence on the ethical standards guiding future AGI systems... and humanity at large. From ensuring fair labor practices to embedding diversity and transparency into datasets, we explore how data annotators play a crucial role in setting the ethical framework for AI. If you're interested in the future of work or ethical AI development, you should definitely have a read. Let us know your thoughts!

Foothill Ventures recently hosted a co-investor and founder mixer event at Los Altos Golf and Country Club. With over 30 CEOs/executives from our portfolio and an incredible group of co-investors, the event was filled with meaningful conversations and a gathering of some of the brightest minds in tech innovation and investing. The highlight of the event was a panel discussion moderated by our Managing Partner Xuhui Shao, featuring visionary CEOs: Phoebe Yao at Pareto.AI, Sam Liang at Otter.ai, Bob S. at Goodcall, and Bryan Lee at Ruli, who shared valuable insights on how AI is transforming productivity for startups. A big thank you to our panelists for sharing these insights and to all attendees for engaging in a lively, forward-looking discussion. We extend a heartfelt thank you to everyone who attended, especially our amazing co-investors from Qualcomm Ventures, SIERRA Ventures, SignalFire, GV (Google Ventures), Tau Ventures, Dell Technologies Capital, Samsung Ventures, Samsung Catalyst Fund, TDK Ventures, Applied Ventures, Matter Venture Partners, Intel Capital, Industry Ventures, and many others. We are deeply grateful for each of you and the contributions you bring to this thriving ecosystem. Here's to many more exciting ventures together! ??

AI's future depends on experts at the vanguard of their fields. Data annotation is evolving into a crucial and intellectually challenging industry that now places a premium on expertise and specialized knowledge. For PhDs and researchers seeking careers beyond academia, data annotation is a legitimate avenue to leverage their skills and experience. At Pareto, we’re witnessing firsthand how researchers are making significant contributions to AGI development while earning huge sums of money—and it’s absolutely well-deserved. It’s about time that academics receive the recognition and compensation they truly deserve for their invaluable expertise. Many scientists and PhD's have already begun to work as AI trainers, and we predict this trend to grow significantly. In our latest blog, we cover how data annotation is becoming an increasingly lucrative role for PhDs and scientists. Have a read!

Thanks for hosting us, Xuhui Shao ??

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.