Part One

Data privacy in an observational (human) economy.

Part Two

The importance of PII data as we move to a computational economy.

Part Three

Central intermediaries, not data privacy is not the problem.

Part Four

Data regulations are not good for AI and economy.

Part Five

NLP is the future of data privacy.

Part One

Is there a better model than incubator, accelerator and/or studios?

Part Two

The importance of the Pareto Principle for founders.

Part Three

Founders can leverage fractional team members in four critical functions.

Part Four

How does a vendor fit within his/her industry?

Part Five

Decide what you want to do and stick to the plan.

Part One

The imapct of curve fitting on structured data.

Part Two

AI does not like anomalies. This is going to create more fat tail events.

Part Three

The left brain is AI. The right brain cannot be replicated by AI.

Part Four

Society is being caught off guard with more and more fat tail events.

Part Five

The fox, hedgehog and the importance of consensus models.

Part Six

We are missing anomalies because our brains are overwhelmed.

Part Seven

Skewness and kurtosis are important.

Part Eight

Companies manage decisions around the mean.

Part Nine

P-hacking and curve fitting data are hiding fat tails.

Part Ten

Machine learning = regression to the mean.

Part One

Problem statement, elevator pitch and user interface.

Part Two

Features, discrimination types, alerts and marketing.

Part Three

Approach to app and ways to report discrimination.