When projects get rolling and eventually when the team is in crunch time, there is a strong need to keep the PM/BAs/Developers all on the same page. To that end, here is what we do:
Morning and Evening Check-Ins:
Review the open issues/bugs/next steps
Schedule the day of bug fixing, testing and any other analysis work needed
One of the challenges we face are the need to innovate and transform our institutions to be more dynamic and regenerative. Whether it be the WHO, World Bank, UN, or a government agency, etc. — these institutions were made for a world that was far more stable; they were designed in a world where rapid pace of technological was not a part of the discussion.
This podcast discusses the concepts of the commons of the future, the idea of welfare state in the future, and ideas around new industrialist. Hilary Cottam and Azheem Azhar have an intense and dynamic conversation.
One of the key ideas from Hilary, we must move from Homo econonomicus to Sapiens integra:
– “Homo econonomicus — guided by ration to maximize economic gains”
– “Sapiens integra — a new theoretical human with stronger connections to nature and other humans”
The concept of data portability is all too often simplified to the question “Can we have our data in a machine readable format?”
At best, this ensures the data can be loaded to a new system and at worst the data can be added to a data warehouse.
Palantir, Peter Thiels company, provides a lens into the importance of “thinking from the end” with systems — i.e. what happens what the system potentially reaches obsolescence or you simply want to leave it for whatever reason.
Can you get the data out?
And, can you output the analysis in machine readable format to continue the work in another system?
I assume most companies will not hand over queries as they could claim they are proprietary.
However, this complexity, clear provides the importance of creating in-house capabilities for analytics, and building in the end of contract clauses and what they actually mean into contracts. 
The department has created a new system to replace Palantir, and it wants to transfer the analysis generated by Palantir’s software to the new system. But Palantir, the NYPD claims, has not produced the full analysis in a standardized format — one that would work with the new software — despite multiple requests from the police department in recent months.
It’s tempting to get caught in the trap of applying a promising new technology like Machine Learning to every problem, but try and resist this temptation. Instead, try to follow Joshua Porter’s advice as he states in his Principles of Product Design:
“…[P]roduct innovation isn’t about new products that solve new problems. Product innovation is about new products that solve existing problems better than they’re currently solved.”
The primer is great, and a quick read. Here is my quick summary below:
The basics of deep learning is to think about how the brain breaks up a specific task. For example, let’s say you are hiking the Appalachian Trail, and you see something in the distance running towards you. First, you might notice it is moving. Then, you might notice what shape it is. Then, you might notice how fast it is going. Then, you might notice a big snout. Then, your brain will determine that this is an animal.
The process would continue until your brain evaluated, classified and predicts what object it is seeing. The joy of the mental exercise (for me) is to understand how the human mind works to break down ideas.
Inputs > Algorithm > Prediction > Training:
The following are the key concepts for thinking about deep learning concepts. Yes, this is overly simplified, but it is still a helpful start.
Inputs: Labels/Images
Algorithm:
Levels of Abstraction 1: Is this a shape?
Level of Abstraction 2: Is this shape an ear?
Level of Abstraction 3: Is this a cat?
Prediction = Yes or No. Is this prediction correct?
Current-State of Deep Learning:
Supervised Deep Learning: In effect, this is attempting to clone human behavior via labeled images, video, text or speech.
Reinforcement Learning: This is where the model attempts to “learn” behaviors, codify those behaviors (i.e. what does that mean), and then implement strategies to optimize based on those strategies. As the article suggests, the following are some examples:
E-Commerce: model learns customer behaviors and tailors service to suit customer interests.
Finance: model learns market behavior and generates trading strategies.
Robots: model learns how physical world behaves (through video) and then navigates that world.
Network Architecture to Detect Objects in Images:
Input: Image
Extract Feature: Extract the specific features
Classification: Classify based on the probability of those features
