In our four-year journey as Heartex, we've successfully built Label Studio, a top-notch data labeling platform used by tens of thousands of organizations. Today, we're taking a bold step forward as HumanSignal, harmonizing human insights and feedback with AI progression.
Why HumanSignal? In an era of rapid AI advancement and even fears of AGI taking over, we are betting big on people (a.k.a humans!). Let us tell you why.
As AI/ML integration continues at an accelerating pace, it's clear that every company will become an AI company. This phenomenon is evident from the rapid growth of AI models and applications such as ChatGPT, which gained a record 100 million active users in just two months.
Even with an unsurpassed rate of adoption, the potential of foundation models is yet to be fully realized. For the first time, AI-powered applications are no longer solely developed by ML specialists, but also by classically trained software developers, hackers, and even students. This shift towards democratization and the potential of emerging AI applications are truly exhilarating.
However, generic foundational models trained on public data may not fully capture the unique nuances and context of organizations and individuals on their own. The lack of specialized knowledge hinders optimal performance, accuracy, relevance, and even ethical alignment. What’s missing is the integration of human expertise and feedback, which can trigger continuous innovation, scalability, and differentiation.
The differentiator in the era of readily available AI models is the proprietary data and specialized knowledge, which comes from people. Each organization possesses a rich pool of proprietary data and expert knowledge that can be harnessed today through labeling, offering the AI system a valuable human signal.
Distribution of knowledge across public and private data
Such human signal plays a crucial role in various stages of AI applications. It's needed not just for training datasets but also for validating the model's accuracy and guiding the model to generate desired outputs, a process known as “prompt engineering.”
Our open source and enterprise versions of Label Studio are designed to capture these invaluable human signals. We believe in the enduring importance of human signal and are committed to evolving alongside it, understanding that the extent of feedback required and labeling methodology varies with different use cases and training strategies.
At HumanSignal, we aim to help companies transform into AI-powered enterprises by capturing and utilizing human signals to build, refine, and validate models. We strive to automate as many steps as possible, allowing human expertise to focus solely on tasks that add unique value to the models. We're creating a system that facilitates data discovery, knowledge encoding, model supervision, and business automation.
While the interfaces for gathering human feedback and signals will continually evolve, the importance of the human signal will persist.
Our belief in the power of community, openness, and open-source is unwavering. The success of Label Studio and the growth of our community are testaments to this belief. As we move forward, we are more resolute about the importance of open-source, especially considering the potential risks of a few entities dominating the AI ecosystem.
We truly believe that humans will drive the AI revolution and ensure its safety and freedom from bias. Therefore, fostering an open community is more crucial than ever.
Thank you for your continued support as we navigate this exciting intersection of humans and AI. We're thrilled about the journey ahead and look forward to your feedback.
Michael, Nikolai, Max from HumanSignal
We're excited to showcase some new features we've added to Label Studio Enterprise specifically designed to help create datasets for fine-tuning Large Language Models (LLMs) like ChatGPT or LLaMA.
We’ve hit a big milestone for the company—securing our next funding round of $25 million in funding led by Redpoint, with participation from all our existing investors, Unusual Ventures, Bow Capital, and Swift Ventures.
From active learning to autonomous agents, learn the use cases, strategies, and tradeoffs for automated data labeling.
