An excellent resource on keeping sites/services up and running — after all, reliability is also feature.
Google – Site Reliability Engineering
— Read on landing.google.com/sre/sre-book/toc/
Deep Learning Basics
While reading Hung Lee’s Recruiting Brainfood, I stumbled upon this deep learning primer:
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
- Output: Image prediction
Enjoy your deep learning explorations!
Ethical OS
Guiding principles for technologists.
A Culture Shift: Appreciation
The old adage says, “What you appreciate, appreciates.”
Problem Finding Machines:
In the day-to-day of work, we become habituated to finding problems, and hopefully solving those problems.
In some ways, we become problem finding machines, and this creates a mindset — we look at processes, we look at technology and we look at people and we try to make things work better. This is not good or bad, rather it is simply an important aspect of accomplishing any goal.
A Mindset:
With the intent of solving problems — we can and do take on a mindset. A mindset that finds issues and risks; and, tries to ensure these countless impediments or underlying cultural challenges do not prevent clients from meeting their goals.
Over the days, months and sometimes years — these challenges — cultural and implementation — can feel like a swarm of mosquitoes, that continue to drain teams of their energy and enthusiasm. The heartbreak for a team or individual is to know they are making progress, but losing their energy as they swim against underlying currents of cultural, communication and leadership challenges.
An Experiment
We decided to experiment with our team meeting to see if we could create the conditions for a new sense of well being by temporarily shifting the focus from problem-finding-solving machines to instead seek what we are grateful for in the day-to-day grind.
We gathered for our weekly team meeting, and instead of status check-ins with issues and risks we conducted an “Appreciation Meeting.”
In this meeting, we each shared words of gratitude to one and other for their actions, their character, or their way of being. We also focused on bringing up what we are grateful for with the work we do, and the clients we have the opportunity to work with.
The Results
As we went around the circle, and shared with one and other words of appreciation. We each felt the initial discomfort, followed by feelings of connection and camaraderie not only for one and other, but also the positive aspects of our work.
In any given workplace, there are countless challenges — from culture and leadership to strategy and operations; there are also countless positive conditions — from humorous encounters to small and bigger wins.
The real challenge is to create the internal capacity to continue identifying and solving problems, while also being keenly aware of the positive aspects of the day-to-day work.
AI & Machine Learning and Knowledge Work
AI and Machine Learning will slowly arrive make its way into all of our apps in both seen and unseen ways.
How do we embrace this? How do we plan for this? How will this change the work all of us do?
Here’s Microsoft’s “Design Ideas” in PowerPoint:
The Importance of Guiding Principles (or, Key Decision Making Criteria)
At the heart of getting something done is getting everyone on the same page to move the project forward. This is especially relevant in an environment of a variety of individuals from department teams, and, ultimately, different walks life.
The diversity of views creates a challenge: Do we argue about the rightness or wrongness of ideas or a decision? Or, do we agree on a set of “guiding principles” and make decisions?
What is a guiding principle?
A guiding principle is a statement that summarizes a criteria or value-based mechanism. Let’s take the following situation in pricing strategy:
- Situation: Company X has developed a patented Water Retention System that helps trees grow faster, while using 80% less water.
- Complication: The founder and owner of Company X wants to target low-income farmers that cannot afford an expensive system. The venture capitalists wants the founder to charge higher rates to ensure maximum distribution, and ultimately a strong return on their investment.
- Question: How should Company X price the Water Retention System?
If you were in this situation, how would you facilitate a decision? Clearly, both the owner and the venture capitalists have a strong case to make regarding the rightness of their decision.
Option A: Conduct a pricing analysis, and present different prices and see if there is a price that meets both needs.
Option B: Develop a core set of guiding principles around making key business decisions, and then conduct a pricing analysis, and evaluate the options based on the guiding principles.
In Option A, there is an implicit debate about what the Owner and the Venture Capitalists value. In Option B, there is an explicit debate about what the Owner and the Venture Capitalists value.
Why is this important?
The point of this example is codifying the unsaid in guiding principles, each individual can evaluate what they value and see whether it resonates.
If there is resonance, then decision making and team dynamics can be more fluid (or, at the leaser — easier).
If there is not resonance, then decision making will be stalled and inauthentic — team members may grudgingly go along, but there will continue to be dissension as increasingly complex decisions are made, and the team will need to decide whether to continue together or not.
Originally posted on Karma Advisory’s medium page here.
Agile Government: A Pipe Dream?
The finest hour of any organization — is in its time of crisis. It may not feel like it at the time, but it is the greatest opportunity to see what emerges, when the best is needed.
Enter Kirk Rieckhoff and J. R. Maxwell, the author’s of McKinsey article, “How the public sector can remain agile beyond times of crisis.”
Having supported government clients in times of crisis, much of Rieckhoff and Maxwell’s experiences resonates. Here are some of my thoughts based on the article.
Please note: all quotes below are from the linked article
What is the reality of the public sector within the context of volatility, uncertainty, complexity, and ambiguity?
Whether it be the information overload, changing consumer/citizen preferences and/or severe partisan politics, agencies are charged with being mission-driven, while not necessarily having the tools, resources or support to get the job done.
Today’s public-sector leaders feel pressure to do more with less, to address a complex and ever-expanding range of issues, and to address them more quickly than ever before.
There are groups of stakeholders — each has its influence on the policies, the rules & regulations, and the funding. By the time a budget passes or a policy comes to fruition, the agency implementing the solution is usually “behind the eight ball.”
From the point of funding, the policy needs to be operationalized, and the corresponding technology needs to be created to support the operations. This is no small task.
Let’s think about this effort a moment:
- Federal, State and/or City Elected Officials determine the Policy, Legal and Budget Process
- The specific-agency determines the specifics of the policy details
- The specific-agency will need to
- operationalize via a complex planning effort — e.g., what existing operations and infrastructure can the programs be tied to?
- development the technology to support the operations
With a multi-stakeholder and competing priorities, this effort is complex and time consuming.
Don’t Waste a Crisis:
A crisis creates the context to throw fear to the side, and get the job done — including, making mistakes. Why? Because lives of fellow human beings are at stake.
In times of crisis, the people within agencies take leaps of faith, try new ideas and simply “make it work.” There is an iterative nature to the job, and no one is faulted for trying their best to serve when others are in need.
The cultural aversion to sharing information across agencies and acting in concert was replaced by an urgently felt need to collaborate.
Policy, Operations and Technology work together to serve those in need. However, when there is no crisis or no pressing need, then according to Rieckhoff and Maxwell, agencies regress to:
- a cultural aversion to risk
- functional silos
- and, organizational complexity
A cultural aversion to risk? Or, simply a complex risky environment?
Government officials, for example, worry about being wrong, angering superiors, and alienating other agencies more than they become excited about proposing new programs, developing faster operating models, or piloting new partnerships.
To a large extent there is a lot of truth to this above statement, but I am uncertain whether this is simply a public sector challenge — or, a leadership and communication issue.
In my view, these belief systems around what can or cannot be done is the true challenge to progress. Whether it be at a fortune 500 company, or within government, I have seen the individuals who have taken the extra step to be unsung leaders to find champions behind ideas and to help get things done.
Is this a profit-motive issue?
Without an imperative to act (such as the profit motive in the private sector), it’s rational to seek ever more information, to conduct additional analyses, to await permission, or to optimize for the interests of the “tribe” rather than the organization as a whole.
This is a reductionist and subjective correlation of what compels individuals and organizations to act: IF X, THEN Y.
If profit-motive, then action….If crisis, then action.
The binary view is helpful, but it is in the gray areas that there are some insights. In the gray areas, we can find countless examples of public and private organizations that went above and beyond to serve others.
There in lies the opportunity: servant leadership to have the tough conversations; to facilitate discussions on shared values; and, to rally organizations to get things done.
The ultimate currency of an organization is communication.
Originally posted on Karma Advisory’s medium page here.
Satya Nadella: A Servant Leader?
Ever since I read Leaving Microsoft to Change the World, I have had a curiosity about the leadership of Microsoft. Bill Gates was seemingly an intellectual leader. Steve Balmer seemed like an autocratic leader. And, then came along Satya Nadella.
As an Indian American with parents from South Africa and Fiji, seeing Nadella as the leader of Microsoft is inspiring. From the little I have seen of him, he seems to be a different type of leader.
So when I read the article from The Economist, “What Satya Nadella did at Microsoft” I was very curious.
Note: All quotes in the article are from http://www.economist.com/news/business/21718916-worlds-biggest-software-firm-has-transformed-its-culture-better-getting-cloud
Enter Satya Nadella:
Create the Conditions to be Agile: Embracing Obstacles vs. Trying to Kill Them
Since as far as I can remember, Microsoft has revolved around its crown jewel: Microsoft Windows. Under Nadella that has clearly shifted to an ecosystem-agnostic approach, whereby users are encouraged on any platform.
Technologies come and go, he says, so “we need a culture that allows you to constantly renew yourself”.
Given that Balmer used to call Linux a cancer, I can only imagine the depth of change this was at Microsoft to hear from its leader.
Leading through Power vs. Leading through Hearts & Minds
Gate’s used to often say, “That’s the stupidest fucking thing I’ve ever heard.”
Balmer used to run across the stage yelling “I love this company.”
With Gates or Balmer, by using language to create strong dichotomies or generalities, a couple things can occur within the culture:
- Employees may be afraid of being wrong or say something in contract to the leader
- Managers may treat subordinates accordingly
- A culture of rightness and wrongness emerges which stifles innovation.
On the other hand with Nadella, he can often be seen sitting in the audience, listening.
In many ways this style of leadership seems to revolve around something deeper — a slow and steady wins the race approach.
Mr Nadella doesn’t seem to be worried by such unknowns, which are to be expected in a fast-changing industry.
Instead, he frets about too much success. “When you have a core that’s growing at more than 20%, that is when the rot really sets in,” he says.
A statement such as this is so counter culture— it seems filled with a sense of stoicism — a dispassionate and unattached objectivity to the realities of rapidly changing times.
The leadership will require a steady hand that is focused on the long game. I am curious to see how Nadella will steer this ship.
Originally posted on Karma Advisory’s medium page here.
Smart Citizen
Smart Citizens — adding to the collective to do interesting data and analytics…this is mainly focused on whether, but these open source platforms are simply the beginning.
Developer Resources
- IBM Learn Tutorials: https://www.ibm.com/developerworks/learn/