In an era where artificial intelligence (AI) is not just a buzzword but a fundamental driver of innovation, the importance of the data that fuels these AI systems cannot be overstated. As we navigate the complexities and potentials of 2024, Easy Engineering is thrilled to welcome Michael Abramov, CEO of Keylabs, for an in-depth discussion on the evolving landscape of image data annotation and its critical role in shaping the future of AI. 

Michael’s insights promise to illuminate the intricacies of this field and the pioneering work of Keymakr in pushing the boundaries of image annotation.

EasyEngineering: Welcome, Michael. It’s a pleasure to have you with us today. As we step into 2024, the image data annotation industry is evolving rapidly. Could you share your insights on this transformation?

Michael Abramov: Thank you for having me. Indeed, the industry is at a critical juncture. Historically seen as a niche, it’s now pivotal in AI and technology. This shift is largely driven by advances in AI, which demand not only expanded applications of image data annotation but also heightened quality and accuracy. It’s an exciting yet challenging time for us in the field.

E.E: Speaking of quality, there’s a lot of emphasis on the ‘garbage in, garbage out’ principle in AI. How does Keymakr approach this challenge?

M.A: Absolutely, the quality of input data is paramount, and our approach at Keymakr is deeply rooted in this principle. Recognizing the unique demands of specific projects, we often find it necessary to collect or even create data, tailoring them meticulously to meet each project’s unique needs. This tailored approach is crucial because open source data or synthetic data don’t always suffice, especially when we need to train models on real-world data and diverse scenarios. The real world is complex and varied, and synthetic data often falls short in capturing this complexity. 

E.E: AI-assisted annotation is a hot topic right now. How is Keymakr leveraging this trend?

M.A: AI-assisted annotation is indeed transforming our work. At Keymakr, we use AI tools to reduce manual labor significantly, sometimes by as much as 70%. However, we firmly believe in the blend of AI efficiency and human discernment. This combination is essential to maintain data integrity, especially for complex tasks.

E.E: With AI taking a more prominent role in annotation, how important are data curation and validation?

M.A: Data curation and validation have become the new frontier. With AI leading the way in annotation, our focus has shifted to ensuring that the data is accurate, relevant, and unbiased. Biases in data, algorithms, or management can lead to erroneous AI project outcomes. So, our role in data curation and validation is more critical than ever.

E.E: Keymakr has been a significant player since 2015. What makes your approach unique?

M.A: Our strength lies in our team of over 400 in-house annotators, who bring niche expertise to diverse projects. This specialization is vital as the one-size-fits-all approach is obsolete. Our annotators ensure that the data is not just accurate but also relevant and nuanced.

E.E: How do you balance the use of AI tools and the need for human oversight in your annotation process?

M.A: At Keymakr, we view AI-assisted annotation as a collaborative process. Our annotators are trained to work seamlessly with AI tools, understanding their strengths and limitations. This harmony allows us to offer services that are fast, efficient, and, most importantly, deeply accurate and reliable.

E.E: Looking ahead, what do you see as the future of data annotation and its role in AI?

M.A: The future of data annotation is intertwined with AI’s evolution. Companies like our, with comprehensive approaches to data curation, annotation, and validation, are setting benchmarks in the AI revolution. Our commitment to quality and customization in data annotation will play a crucial role in the development of AI technologies.

E.E: Any final thoughts you’d like to share with our readers?

M.A: As we move forward, it’s essential to remember that the foundation of effective AI lies in the quality of the data it’s trained on. At Keymakr, we are committed to ensuring this foundation is as robust as possible, setting the stage for the next wave of AI innovations.

E.E: Thank you, Michael, for sharing your valuable insights with us today.

M.A: It’s been my pleasure. Thank you for having me.