Month: May 2018

We only deserve a styrofoam cup

As a response to a post on power recently, Ashay shared the following story from Simon Sinek. It is one I’ve thought about it a few times since and I thought I’d share.

I heard a story about a former Under Secretary of Defense who gave a speech at a large conference. He took his place on the stage and began talking, sharing his prepared remarks with the audience. He paused to take a sip of coffee from the Styrofoam cup he’d brought on stage with him. He took another sip, looked down at the cup and smiled.

“You know,” he said, interrupting his own speech, “I spoke here last year. I presented at this same conference on this same stage. But last year, I was still an Under Secretary,” he said.

“I flew here in business class and when I landed, there was someone waiting for me at the airport to take me to my hotel. Upon arriving at my hotel,” he continued, “there was someone else waiting for me. They had already checked me into the hotel, so they handed me my key and escorted me up to my room. The next morning, when I came down, again there was someone waiting for me in the lobby to drive me to this same venue that we are in today. I was taken through a back entrance, shown to the greenroom and handed a cup of coffee in a beautiful ceramic cup.”

“But this year, as I stand here to speak to you, I am no longer the Under Secretary,” he continued. “I flew here coach class and when I arrived at the airport yesterday there was no one there to meet me. I took a taxi to the hotel, and when I got there, I checked myself in and went by myself to my room. This morning, I came down to the lobby and caught another taxi to come here. I came in the front door and found my way backstage. Once there, I asked one of the techs if there was any coffee. He pointed to a coffee machine on a table against the wall. So I walked over and poured myself a cup of coffee into this here Styrofoam cup,” he said as he raised the cup to show the audience.

“It occurs to me,” he continued, “the ceramic cup they gave me last year . . . it was never meant for me at all. It was meant for the position I held. I deserve a Styrofoam cup.”

“This is the most important lesson I can impart to all of you,” he offered.

“All the perks, all the benefits and advantages you may get for the rank or position you hold, they aren’t meant for you. They are meant for the role you fill. And when you leave your role, which eventually you will, they will give the ceramic cup to the person who replaces you. Because you only ever deserved a Styrofoam cup.”

Asking why and understanding why

We know that it is good practice to understand why things are being done a certain way. The better we understand why, the more effective we can become.

There are many ways to understand why. We can understand why by listening, observing, asking, or doing some combination of the three. Each has its strengths and weaknesses. For example, asking why optimizes for speed and can be effective in some situations. But, it can also backfire. That’s because being on the receiving end of why questions is challenging and, occasionally, annoying.

When I was recently reminded to understand why things are being a done a certain way, I realized that, in my mind, asking why is often synonymous to understanding why.

But, on further thought, I realized it isn’t the same thing. Asking is just one approach to understanding why. If we seek to understand why, it helps to realize this and learn to tailor our approach based on the situation.

Rivers and clarity

What makes a river so restful to people is that it doesn’t have any doubt – it is sure to get where it is going, and it doesn’t want to go anywhere else. | Hal Boyle

Clarity of direction is a powerful calming force. Here’s to seeking and finding it this week.

Phone detoxing

I’ve been experimenting with phone detoxing over weekends in the past few months. While my normal approach has been to play hide-and-seek with the phone by putting it in some obscure place and forgetting about it, I decided to do a complete switch off this weekend. My 3 lessons from switching the phone off for a 60 hour period –

1. I missed 3 use cases – i) Waze/maps when we were driving, ii) Ability to call contact other when we split ways at a crowded area, and iii) Whatsapp to send the occasional message to framily.

2. I did not miss the following – i) Checking if there’s any new email or message because the phone is close by and ii) Reading articles on my phone – I prefer a larger screen but the phone is really convenient.
Overall, I can’t say I missed the phone all that much. I did cheat a bit by sending some messages from my wife’s phone too coordinate with friends – but, it was minimal. I enjoyed doing all my writing and reading from a larger screen – it was more targeted and intentional than reflexively picking up my phone.

3. I’ve been disconnecting from work email for a full 48 hours between Friday evening – Sunday evening for a few months now. And, while that has enabled me to be better engaged through the weekend, there was something wonderfully liberating about switching off completely. We normally associate detoxing with the body. But, there’s something to be said for detoxing for the mind.

I look forward to doing this more.

From AI doomsday to IA, Orwell and Social Support

Was the invention of the axe a good thing or a bad thing? The axe was among the first simple machines — a breakthrough in technology that propelled humanity forward. It helped our ancestors chop wood and hunt. But, it was also used as a weapon in war.

Every incredible advance has had a dark side. We have prevented infant mortality thanks to advances in ultrasound technology. And, yet, the same technology was responsible for female infanticide. Industrial farming has helped us feed billions of humans with fewer humans involved in agriculture than ever before. However, it has also resulted in routine horrible treatment of farm animals.

Given this context, it is often amusing to see the discussion around artificial intelligence. We see talk of doomsday one day (“all the jobs are going away”) and techno-optimism on another (“AI is going to help us by freeing us from repetitive tasks”). Of late, I’ve been seeing more media devoted to the latter. It is worth examining both sides of the conversation.

Not doomsday. The central hypothesis behind the idea that there is no doomsday on the cards is the idea that we’re moving into a world with IA or “Intelligence Augmentation.” The idea here is that AI is great at finding answers. But, it is on us to find questions. We’ll find new and interesting questions to keep us occupied while AI helps us eliminate repetitive tasks and make us more efficient. And, we’ll use ingenuity to create new jobs that don’t exist — just as we created “Yoga instructor” or “Zumba instructor” jobs after the industrial revolution.

One example of this is a painting robot that was featured on Wired (see video — 4 mins) that increased the productivity of human laborers by 4x while taking over all the repetitive tasks. You’ve probably come across similar stories.

The surge in recent positivity is also thanks to an OECD research report that classified ~10% of American jobs as high risk. This is much lower than previous forecasts that labelled ~50% of jobs as high risk.

Maybe doomsday. From The Atlantic on WalMart’s future workforce —

Walmart executives have sketched a picture of the company’s future that features more self-checkouts and a grocery-delivery business — soon escalating to 100 cities from a pilot program in six cities. Personal shoppers will fill plastic totes with avocados and paper towels from Walmart store shelves, and hand off packages to crowdsourced drivers idling in the parking lot. Assembly will be outsourced, too: Workers on Handy, an online marketplace for home services, will mount televisions and assemble furniture.

Such examples are also dime-a-dozen these days. More automation promises more returns to shareholders => happier executives and boards.

Of course, it is also easy to counter all examples of optimism. For example, the same painting robot (featured above) that increased productivity of human laborers by 4x is a great place to start. At some point — assuming other painting firms invested in robots — we will have 4x the amount of painting capacity at hand. Are there as many jobs to go around?

And, the above OECD report that said risks of “massive technological unemployment” are overblown cautioned that we face risks of “further polarisation of the labour market” between highly paid workers and other jobs that may be “relatively low paid and not particularly interesting.”

This Economic graph summarizing some of the findings was particularly interesting.

Notice how the percentage of jobs at risk of automation decreases as a country gets richer?

The polarization that the report warns may not be limited to high skill and low skill jobs then. There is reason to believe that we might see a growing schism between richer and poorer countries.

The truth likely lies somewhere in the middle. All this brings us back to the story of the axe. Every technology breakthrough has a dark side. The challenge, then, is to not get caught in all the techno-optimism that accompanies the emergence of breakthrough technology and to take the effort to think through the second and third order consequences.

As we’ve seen in the revelations about the effects of social media in the past 2 years, the absence of such thought can have serious long term consequences.

So, how do we proceed?

My recommendation would be to stop any debate about whether we’re heading to an AI induced doomsday and, ask the three following questions —

1. Are we clear on what we’re talking about when it comes to AI? There are three major domains of AI that we discuss –

  • AGI or Artificial General Intelligence. This is when robots become capable of being human (a.k.a. West World). Scientists like Alan Turing and John McCarthy envisioned this 70–80 years ago and we’re no closer to it now than we were then.
  • IA or Intelligence Augmentation. A classic current example of this a search engine as it augments our memory and factual knowledge. Many of the machine learning applications today are in this domain.
  • II or Intelligence Infrastructure. An example of this would be machine learning powered security systems that make use of a web of devices (infrastructure) to make human environments safer or more supportive. While we’re still in the early days, there’s plenty of investment in start-ups and fledgling companies directed here.

It is important to be clear about these domains because a lot of mainstream discussion bandwidth is wasted in talking about the dangers of Artificial General Intelligence. That is a waste of time.

Instead, our discussions should center around IA and II. We’ve made plenty of progress using techniques like Deep Learning. And, while both extend human capabilities, they also automate tasks that currently employ large groups of humans in the near term.

2. Are we conscious about the possible dark side of AI — specifically the use of artificial intelligence for surveillance? 
The Economist outlined this in a piece about the Workplace of the future —

And surveillance may feel Orwellian — a sensitive matter now that people have begun to question how much Facebook and other tech giants know about their private lives. Companies are starting to monitor how much time employees spend on breaks. Veriato, a software firm, goes so far as to track and log every keystroke employees make on their computers in order to gauge how committed they are to their company. Firms can use AI to sift through not just employees’ professional communications but their social-media profiles, too. The clue is in Slack’s name, which stands for “searchable log of all conversation and knowledge”.

The good news is that most of the preceding portions of the article talked about the benefits of algorithms in the workplace — fairer pay rises and promotions, improve productivity and so on.

It will be on us to strike a good balance.

3. Are we designing the right social support systems to be able to prepare us?
In a great piece titled “The Robots are coming, and Sweden is fine.” by The New York Times, I found 3 notes fascinating –

  • “In Sweden, if you ask a union leader, ‘Are you afraid of new technology?’ they will answer, ‘No, I’m afraid of old technology,’” says the Swedish minister for employment and integration, Ylva Johansson. “The jobs disappear, and then we train people for new jobs. We won’t protect jobs. But we will protect workers.”
  • 80% of Swedes express positive views about robots and artificial intelligence versus 72% of Americans who declared themselves “worried” per a Pew Research survey.
  • The challenge, of course, is taxation. Taxes are ~60% in Sweden and are a key part of the social contract.

While the thought of ~60% taxes in the US would be morally repulsive, it is unclear how long we’ll be able to sustain the current reality.

German Economist Heiner Flassbeck had a powerful graph showing the declining share of national wealth in rich countries (except Norway).

National wealth in the US and UK is now negative. Low public wealth limits the government’s ability to regulate the economy, redistribute income and mitigate rising inequality.

Regardless of Artificial intelligence, income inequality has been rising everywhere.

If AI is expected to further increase the level of inequality, we’ll need to double down on the discussion on social support systems.

For the record, I’m not optimistic that this will happen. Our ability to prepare for changes before they hurt us is poor (see: climate change).

But, I’m hopeful that we can begin by changing how we approach conversations around AI. Maybe next time we hear a conversation about sentient machines, we’ll put a stop to the conversation and focus it on the actual issues like Orwellian uses of data and investing in social support systems to counter inequality. Maybe that, in turn, will mean thoughtful uses of AI in the the organizations we’re part of.

And, maybe, just maybe, we’ll succeed in making the transition to a world with Intelligence augmentation and Intelligence Infrastructure in the coming decades a lot less painful…

Links for additional reading

  • Shor’s algorithm to solve factorization with quantum computers — on Wikipedia
  • How to Become a Centaur — on MITpress
  • The Painting Robot that didn’t take away anyone’s job — on Wired
    A respite from the robots (but a retraining emergency) — on Axios
  • Machines will take fewer jobs but low-skilled workers will still be badly hit — on The Financial Times
  • OECD research visual — on The Economist
  • The Artificial Intelligence revolution hasn’t happened yet — on Medium
  • The origins of Artificial Intelligence — on Rodney Brooks’ blog
  • The workplace of the future — on The Economist
  • AI State of the Union on YouTube
  • The Exponential View — a curated newsletter that is the source of many of these links — Thanks Azeem
  • The robots are coming, and Sweden is fine — on The New York Times (a must read)
  • How inequality is evolving and why — on Flassbeck Economics (another must read)

Mmm Yummy

Our daughter is going through a phase when she says “Mmm Yummy” every time she eats something she loves. It is an expression of pure, untainted, happiness at getting to eat what she enjoys. It also helps that the bar for what she loves isn’t all that high. :-)

When I saw her do it this morning, I asked myself why I don’t do it more often. Just like her, I’m clearly fortunate enough to eat good food very often. By extension, how often do I find myself saying the equivalent of “Mmm Yummy” when I enjoy the many other gifts – good health, wonderful relationships, this awesome internet, and dedicated colleagues and teams?

I’m vividly reminded of a conversation when a few of us were walking toward a football game a few years back. We saw an extremely fit woman on what looked like a long run and someone remarked – “Wonder why someone that fit needs to run?” Someone else immediately quipped – “It is thanks to these runs that she is extremely fit.”

Similarly, it is tempting for me to draw the conclusion that she says “Mmm Yummy” because she’s happy. In reality, though, it is not happy people who are thankful. It is thankful people who are happy.

Getting feedback on your regular meeting

If you can avoid your regular meeting, cancel it. This is particularly true if your regular meeting has just become a status update meeting.

How to tell if you need feedback: If >10% of the attendees are typing away on their laptops instead of paying attention, you likely have an ineffective meeting on your hands.

How to ask for feedback: “What will it take for this meeting to get 100% engagement from everyone present?”

Typical causes of meeting failure: Lack of alignment around purpose and lack of preparation and follow up.

While it is easy to diss regular meetings, it is also important to remember that a thoughtful and well run regular meeting can be a powerful driver of context, learning, and belonging within a team.

The commencement speech problem

Many of us picture commencement speakers giving variations of the “Take more risks, work hard, do good” speech. The good news is that the proportion of speeches that contain such advice seems to be going down. Understanding why is useful for all of us as we often end up giving others advice from time-to-time.

The trouble with generic advice is that it doesn’t work for a large group of people. Some people need to take more risks while others don’t. Some folks need to work harder to earn their privilege while others need to be careful about avoiding burn out. Such advice is easy to give – but is generally flawed because it is either self serving (quit college and start companies so I can invest in the best of them) or designed for people similar to the advice giver.

While the best advice is given once you understand a person and their proclivities, that doesn’t scale. The better approach, then, is to focus on principles. For example, a career principle might be to – invest in understanding yourself and use that understanding to make better decisions and develop good judgment.

The challenge with principles is that getting to them takes considerable thought – the sort that should be a pre-requisite to giving advice.

(H/T Julia Galef, Shripriya Mahesh for notes/discussions on this)

Throwing money at problems

In the past 2 seasons, Jose Mourinho, the manager of Manchester United Football Club, has spent upwards of $400M in recruiting new players. And, his predecessors had spent an additional $400M over 3 seasons (take a moment to let those numbers sink in). But, he wanted to set things right.

While there has undoubtedly been some progress in the past two seasons, watching the team play has often been a joyless affair of late. A recent article described this well – If a team reflects the personality of its manager, then United need help because Mourinho’s demeanor and personality since arriving at Old Trafford has been anything but the bold, courageous and charismatic that the club demands. It has been downright miserable and tetchy.

After a relatively mediocre season, he has reportedly asked for an additional $250M to spend over the summer. His response is simply to throw more money at the problem to make it go away.

However, money doesn’t make all problems go away. Having a certain amount can help with a few problems, sure. But, throwing money at your marriage doesn’t a happy marriage make. And, good luck trying to spend your way into happiness.

And, more importantly, money can never be a substitute for good leadership and a great attitude. Some of the best funded teams fail because they approach problems with poor intent and attitude.

Improving our attitude remains one of the best ways to improve our performances.

Optimal Stopping

We were trying to sell an old car recently and had placed it in a used car parking lot. These lots charge a monthly rent. So, you are incentivized to sell your vehicle within the first month. The question for us, then, – when is the optimal time to sell? For example, do we hold out till the end of the month and wait for the best offer?

Luckily, mathematics has a solution for us. The optimal time to stop is at 37%. If you have a 100 candidates for your next role, the most optimal way to make a decision on the best candidate is to reject the first 37 and then pick the first of the next few that is better than the first 37. Essentially, the algorithm suggests we use the first 37 to calibrate.

Optimal stopping can be extended to time as well. In this case, we had 30 days to sell and 37% of 30 days is 11.1 days. By that logic, we would hold out for the first 11 days and then sell – assuming a half decent offer comes along. Our first offer came after 14 days and we sold for a price that was eventually slightly lesser than we’d planned for. But, we had no regrets because math told us that we’d made the optimal decision.

Algorithms like optimal stopping are likely the future of psychology and behavioral economics. Optimal stopping can be applied to choosing a restaurant, a spouse, and while buying a house. As we learn about our fallibility in making decisions, we can use algorithms like this one to get better at making decisions.

(H/T: Algorithms to Live By – Brian Christian and Tom Griffiths)