Engineers In The Loop

Introduction

Engineers-in-the-loop (EITL) is a variation of the humans-in-the-loop (HITL) imperative for AI.

Engineers are responsible for approximately 80% of economic growth due to their contributions to technological advancements. Their involvement in EITL is crucial because they ensure that AI applications are not only innovative but also practical and sustainable. This integration leads to enhanced productivity, which is a key driver of long-term economic growth. In other words, EITL is literally how money is made in Boomspace.

As AI becomes increasingly integrated into basic infrastructure such as energy, transportation, buildings, and agriculture, the uniform specifications for EITL will become crucial.

Enhancing Accuracy and Precision

Human involvement in AI processes helps maintain high levels of precision, especially in critical domains where errors cannot be tolerated. For instance, in manufacturing critical equipment for sectors like aerospace, human oversight ensures safety and reliability.

Eliminating Bias

AI systems can develop biases during their training phases or even after deployment as they continue to learn. Human intervention is necessary to identify and correct these biases early on, ensuring fairness and objectivity in AI-driven decisions.

Ensuring Transparency

AI algorithms often operate as “black boxes,” making decisions based on complex data patterns that are hard to interpret. Engineers in the loop can provide transparency by explaining how decisions are made, which is essential for building trust in AI systems, particularly in sensitive fields like healthcare and finance.

Augmented Decision-Making

The HITL approach allows for a balance between the speed and efficiency of AI algorithms and the ethical considerations that humans bring to decision-making processes. This combination ensures that AI decisions align with human values and interests, making them more responsible and trustworthy.

Supporting Continuous Improvement

Human feedback is vital for refining AI models over time. By actively participating in the training and validation processes, engineers can help AI systems adapt to new data and scenarios more effectively than they would on their own.

Conclusion

Boomspace.ai is focused on building a graph neural network (GNN) among engineers, scientists, and technologists. By providing EITL in the same structure as AI organizes itself, engineers will seamlessly integrate into AI processes. The goal is to deliver the right knowledge to the right place at the right time with speed, scale, and power.