Understanding that the term "Artificial Intelligence" is broad, we focus on distinguishing between AI, Machine Learning, and Deep Learning, as well as exploring different algorithms, such as supervised and unsupervised models, and highlighting the unique capabilities of Generative AI compared to traditional AI. By providing use case examples for each, we foster a solid grasp of their practical applications.

Ask for the evidence, the source data, the weights and measures, the assumption of hidden layers, the feature or predictor, the social biases of the engineers involved, the choices that were made, the team that made the choices, the sponsor, the intent, the target, the cost function, the proxy for Bayesian error, the test/retest reliability, the total cost of ownership, the full life cycle cost of cradle to grave implementation, the retroactive cost of “space junk,” the variance and standard deviation of the data set, what xx% inaccurate looks like. . the understanding of the impact the model could have on the world and why you’re accountable.

You constantly update them on the top models of AI that exist out their and their applications. Hold discussions as to where everyone thinks AI may reach in the near future and where it is currently.

Ensuring your team understands AI's realistic limitations requires a clear and structured approach: 1. Clarify AI's Capabilities: Begin by providing a straightforward explanation of what AI can achieve and where it has limitations. Focus on its strengths, such as automating repetitive tasks, analyzing large datasets, and providing predictive insights. 2. Showcase Real-World Examples: Use tangible examples to illustrate both the power and the boundaries of AI. Case studies can be particularly effective in demonstrating these points. 3. Encourage Open Dialogue and Questions: Foster an environment where team members feel comfortable asking questions and voicing concerns.

It’s essential to channel the excitement around AI into a balanced understanding of its capabilities and limitations. I encourage open discussions that emphasize the importance of critical thinking about AI technologies. By facilitating workshops and training sessions, we can explore real-world case studies that highlight both successes and failures. Additionally, I promote a culture where questioning assumptions is valued. We need to remind our teams that while AI can drive significant advancements, it also has constraints—such as data biases, ethical dilemmas, and the necessity of human oversight.