AI Leadership

Is your company struggling with AI adoption? A likely cause is that the trust and AI competencies of your non-engineering workforce is low. A great way to solve this problem is to embed non-technical employees into the engineering process, using a company sponsored initiative that capitalizes on the holistic knowledge of the entire workforce. This process requires the leadership of both the CIO and CHRO, with each respective organization working together to create a Domain-Ad

The Age of AI requires engineers to lead machines, lead people that build machines, and lead organizations that adopt AI. A key part of this framework is developing the right cognitive skills. Our research indicates that six elements play a significant role in AI/ML engineering performance. The number sequence is purposeful. Big C Creativity (Frontier Thinking) should be cultivated first before placing constraints on the problem set.

Large Language Models (LLM) that utilize self attention mechanisms are challenged by the context window. When the sequence becomes too long, models can make mistakes with tokens that are located far apart and in the middle of the text. The trick is finding the right balance between cost and decision requirements.

How do Robots map an environment? Welcome to the SLAM Algorithm. The wheel encoder and IMU tracks rotation and movement. The LIDAR and RGB-D sensors capture data from the environment. Noisy data is filtered using a formula that predicts the Robot's position versus landmarks (known as Kalman Filter). This information is fused using the onboard computing device, which helps optimize drift from the IMU. During the mapping process, the Robot constantly checks is it has revisited

Linear Algebra is a critical skill for AI leadership. It should be mandatory learning as part of high school and undergraduate curriculums. CHROs should emphasize this skill in their emerging leadership programs. Having basic skills in Linear Algebra allows professionals to proactively manage machines. You do not need to perform the calculations yourself. However, understanding the process will make your conversations with the engineering team more effective.

Understanding how to construct effective times series forecasts is an essential skill for AI Leaders. Engineers should understand which charts are important and what questions to ask. This will make the LLM prompt questions and conversations with the engineering team more impactful.

In the age of AI, leaders are required to do three things: lead machines, lead people that build machines, and lead organizations that adopt AI. Occlusion is a common problem in Computer Vision. AI Leaders must find a way to balance technical requirements with cost considerations, employee skills, and the customer experience.

In the age of AI, leaders are required to do three things: lead machines, lead people that build machines, and lead organizations that adopt AI. To effectively lead in this environment, executives must understand AI. This means going beyond prompt engineering and AI agent courses to learn how things actually work.

The Age of AI requires new leadership skills. One of the cognitive processes that has become increasingly important for managing AI is short-term memory. George Miller's magic number of 7 plus or minus 2 remains a useful standard for measuring working memory. Our research indicates that AI Engineers that can remember more build better products. The reason is because the job places a premium on short-term recall to ensure daily updates, MLOPs process, model design changes, and