The ideology of data by Sasha Gryjicic, @SashaG, Dentsu Aegis Network, #BigDataTO #BigData #AI #Intelligence
Notes from the #BigDataTO conference in Toronto
- Data marketing and artificial intelligence are headed in the wrong direction
- Marketing is the pursuit of convincing someone they need something, marketing is a commercial outcome to propel the broader economy forward, marketing uses media and communications to convince, largely based on human language
- Data is a digital expression of something in the world, organized and stored in many ways. We are finally getting the external world to use a single language but we can’t read this language. Humans don’t read binary code or extrapolations of code.
- Data violates the notion of scarcity and data is almost always out of both time and space context for necessity. Data is necessarily incomplete and it is not that thing itself. Data has inherent biases, is super messy, and contradictory
- We use data to optimize things that have already happened, or we generalize what we learn from data to engineer more of those outcomes, e.g., when managing an online store, we optimize data to get optimal business outcomes but this doesn’t help us learn why or what drives the decisions
- Intelligence is the ability to gather, category, organize inputs, store, reflect, and respond to them. For humans, intelligence is innate, structured, organized, and process oriented. We have a fixed capacity of intelligence and are creative with it. It is not the result of external stimulus.
- Language is the best way for humans to get access to our intelligence. It’s the language we use when we think. We talk to ourselves more than we talk to others.
- The AI we’re building is like automated statistics. We brute force relationships and create a black box of intelligence. We don’t understand why a computer makes certain decisions because we cannot hold enough variables in our mind to understand. Are algorithms intelligence or optimization? We are drifting further from understanding what intelligence really is. It’s not AI at all.
- We’re accelerating the fatigue of positive reinforcement. We’re following bad after bad. We’re heading away from language which is the only way to understand ourselves.
- Intelligence seems to include morality, the ability to store and reflect and take decisions based on reflections.
- We need to back away from disorganized data. We need to pause and relfect on what we see in the data to understand ourselves better. We need to dive into our own intelligence better. Reflecting on something is more important that acting on something.