wrong tool

You are finite. Zathras is finite. This is wrong tool.

Iranians of note

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Just to name a few notable Iranian-Americans who are REALLY MAKING AMERICA GREAT!
#dontbeignorant #geteducated #lettheworldknow #iraniansarenotterrorist #nobannowall #sickofpolitics #peaceandlove

• Salar Kamangar, CEO of YouTube, VP of Google’s web applications
• Pierre Omidyar, Founder of e-Bay
• Dara Khosrowshahi, President and CEO of Expedia, Inc.
• Sean Rad, Founder & CEO of Tinder
• Farzad Nazem, CTO of Yahoo!
• Ali Rowghani, COO of Twitter
• Ali Partovi & Mehdi Partovi, Founders of Code.org
• Omid Kordestani, Senior Vice President of Google
• Hamid Akhavan, CEO of Siemens Enterprise Communications
• Arash Ferdowsi, Co-Founder & CTO of Dropbox
• Goldy Kamali, Founder & CEO of FedScoop
• Dr Firouz Naderi, NASA director of Mars project
• Lotfi A. Zadeh, mathematician at the University of California, Berkeley and Father of Fuzzy Logic
• Gholam A. Peyman, Inventor of LASIK eye surgery
• Anousheh Ansari, the world’s first female space tourist, co-founder and chairman of Prodea Systems, Inc., co-founder and former CEO of Telecom Technologies, Inc. (TTI)
• Mark Zandi, economist and co-founder of Economy.com.
• Christiane Amanpour, anchor of ABC Sunday morning political affairs program, former CNN chief international correspondent
• Shahram Dabiri, video game producer, lead producer of World of Warcraft
• Davar Ardalan, NPR producer of Tell Me More
• Azita Raji, United States Ambassador to Sweden
• Leila Vaziri, The current world record holder of the 50 m women’s backstroke
• Andre Agassi, professional Tennis player
• Cyrus Habib, 16th Lieutenant Governor of Washington, first and so far only Iranian-American elected to state office
• Sina Tamaddon, Senior Vice President of Applications for Apple Computer
• Hamid Dabashi, Professor of Iranian Studies and Comparative Literature at Columbia University in New York City
• Pardis Sabeti, world-renowned computational geneticist, Associate Professor at Harvard University
• Homayoun Seraji, Senior Research Scientist, Jet Propulsion Laboratory
• Nouriel Roubini, one of the leading economists of our age, professor of economics at the Stern School of Business, New York University and chairman of RGE Monitor
• Ghavam Shahidi, IBM Fellow, Director of Silicon Technology
• Babak Hassibi, Gordon M. Binder/AMGEN Professor of Electrical Engineering, Caltech
• Payam Heydari, Professor of Electrical Engineering, University of California, Irvine
• Hamid Jafarkhani, leading communication theorist University of California, Irvine
• Ali Khademhosseini, Associate Professor at Harvard Medical School,
• Abbas Milani, Director of Iranian Studies Program, Stanford University
• Ray Aghayan, Emmy Award winning costume designer
• Shohreh Aghdashloo Academy Award-nominated film/television actress
• Mayor Jimmy Delshad, Mayor of Beverly Hills, California
• Ross Mirkarimi, Former Member of San Francisco City Council and current San Francisco Sheriff
• Shayan Modarres – Civil Rights Lawyer and Activist, 2014 Democratic primary candidate for the U.S. House from the 10th district of Florida
• Faryar Shirzad, former Deputy National Security Advisor and White House Deputy Assistant for International Economic Affairs to President George W. Bush
• Maz Jobrani, comedian and actor
• Max Amini, comedian and actor
• Antonio Esfandiari OFFICIAL FAN PAGE, champion poker player

Iranian-Americans Reported Among Most Highly Educated in U.S.
Iranian-Americans also contribute substantially to the U.S. economy

http://iipdigital.usembassy.gov/st/english/article/2004/01/20040113191603atarukp0.6147425.html#axzz4X5qoxG00

https://en.wikipedia.org/wiki/List_of_Iranian_Americans

http://www.forbes.com/sites/elizabethmacbride/2015/12/20/100-influential-iranian-americans-in-silicon-valley-and-beyond/#4d10467b4e52

http://www.ranker.com/list/notable-iranian-americans/famous-iranians

Click to access Factsheet.pdf

When you sit and wonder who these people are, read this list.

You don’t care enough, and other failures in leadership

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Twice in my career, have I sat in a meeting where an executive has harangued his employees about not caring as much as he did.

And, both times, the people who cared the most handed in their resignations within hours.

The executive was furious that the employees were not as concerned as he was. The point he was trying to make was that the employee should care about the business beyond the extrinsic rewards that the company provided. That this company, this employment opportunity was more than a job. The executive was frustrated that he was working with people who didn’t feel as connected to the mission as he did.

And in many ways, the minute the executive said that he had also admitted that had failed as a leader. If you feel the need to call out your team, a team you assembled then you failed.

And it got me thinking about caring.

As a leader, why people do the things they do is imperative so you can motivate them. Everyone’s motivations are different. Some people believe in the mission, some people do it for the money, some do it for the commute, and some do it for the sheer joy of doing it.

Your job is to figure out what motivates them and make sure that you align their rewards with their motivations.

Your job as a leader is to connect people to the mission every single day.

Your job as a leader is to connect people’s motivations to the mission every single day.

And the minute that connection breaks, you failed to do your job.

And I get why the executive felt frustrated. He had failed as a leader, and it was evident. And he was vocalizing his frustration at being unable to connect the company mission to the employee’s motivations. And he was taking it out on his team.

When that happens, and it will, what I found works better is taking a deep breath, and then asking your team what’s wrong. Ask your team why the team does not feel connected to the mission. Ask the team what all need to feel connected. Speak 1×1 with each key member and understand their needs.

Leadership is hard, and we can fail at being leaders, and when we fail our job as leaders is to recognize that and do the hard work to be great leaders again.

 

 

The problem with the mac

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Over the last several years, I have two discrete sets of workflows:

  1. Kostadis, the developer who wants a full Linux experience
  2. Kostadis, the guy who interacts with product managers, engineering managers and business leaders who require a complete Windows experience.

When IBM used to make the Think Pad, the solution was obvious: use VMware Workstation to create a Linux VM.

However, after IBM sold the ThinkPad to Lenovo, and Lenovo couldn’t retain the same quality, and the improvements of the Mac made the Mac an attractive compromise.

You could use a Mac and use Windows software like Outlook, while simultaneously having a native Unix development experience without dealing with the complexity of virtual machines.

The experience wasn’t Linux, and the quirkiness of Mac OS made things annoying, and yet it was close enough.

At some point in time, pre-Nadella, the crappiness of the Windows software on the Mac made a choice painful.  And at some point, the pain was significant enough to cause me to switch back to Windows.

A few months with the best Dell and Lenovo had to offer, and that transition lasted less than a year.

And after the utter underwhelming release of the latest Mac hardware, the opportunity to check out Windows hardware became an option.

And so I looked at what IT had to offer and discovered the Dell Precision 5510. The power of a modern PC coupled with improvements in virtualization software has meant that the overall value proposition of the PC + VMware + Linux to be superior to the Mac + Crappy Microsoft Apps + Not Quite Linux or Mac + VMware w Linux and Vmware with Windows or some flavor of those.

 

 

My Drow are Albino

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I’m Greek. And I grew up in Montreal and Athens.

Growing up, I knew one black kid. And he was my first friend. And slavery was always bad.

And I was a huge Dungeons and Dragons fan.

As a Greek and kid who did well in science, I knew that if you stayed in the sun, you were this color

And if you stayed out of the sun, you were this color.

And so it was very confusing why the elves that were good and lived in the sun were this color:

And the evil elves that lived underground were this color: 

For a Greek kid, that had no understanding of the evil racial history of the United States, this was very confusing.

If you lived in the sun, you were tanned. People who were white and did not tan were this color:

As I understood the world, people who lived in sunny climates tanned and were olive skinned, or darker. People who lived in climates in climates that were not sunny were very white and turned bright red.

And I stopped playing Dungeons and Dragons after I left Brown University. And I forgot about the Drow and their peculiar skin color.

And I didn’t give it a moment’s notice until I started playing with my six-year-old son. And he asked what a Drow is? And I said, well there are two kinds of elves. The good elves that are white and the evil elves that live underground and...

And I stopped talking. What was a mystery as a child, was sadly so clear as an adult. Of course, the good guys were white, and the bad guys were black.

And the blatant racism in the description of the Drow and Elves was evident.

And so I told Nick, a different story. The Drow because they live underground, are albino white and the elves that live above ground are olive-skinned and tanned.

 

 

Automation of Mathematics

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Many moons ago, I read a book about Admiral Pointdexter, and in this book, there was a reference to his Ph.D. in physics. What struck me was that the Ph. D. was a computation. He did the work of a computer.

And then this article popped up:

All The Mathematical Methods I Learned In My University Math Degree Became Obsolete In My Lifetime

Dr. Devlin began his career being a computer. And when calculators and the computers and then the cloud emerged, his ability to be a computer was displaced with ever increasingly sophisticated and faster computers.

What to do then:

So what, then, remains in mathematics that people need to master? The answer is the set of skills required to make effective use of those powerful new (procedural) mathematical tools we can access from our smartphone. Whereas it used to be the case that humans had to master the computational skills required to carry out various mathematical procedures (adding and multiplying numbers, inverting matrices, solving polynomial equations, differentiating analytic functions, solving differential equations, etc.), what is required today is a sufficiently deep understanding of all those procedures, and the underlying concepts they are built on, in order to know when, and how, to use those digitally-implemented tools effectively, productively, and safely.

In short, jobs that rely on the ability to execute repetitive tasks without understanding are going away to be replaced with jobs that require adaptability and are non-repetitive.

The downside to these new jobs is that their outcome and payout is less predictable.

The other downside to those new jobs is that they are not the old ones.

And the final downside is that the skills necessary to do the new jobs are different from the old ones.

And the real foundational challenge is that we are preparing our children in our schools for the old world order.

We are like a company caught in a huge disruption. On the one hand, the old business pays but is going away, and the new one is too small.

And the next 20 to 30 years will be gut-wrenching. What the Trump voters experienced, will be experienced across every form of human endeavor. If your job is to fit into a machine, the machine will replace you. If your job is to figure out what tools to use or how to invent new machines, then there is a place for you.

Teaching kids to find the white space is the only important thing that we should be teaching them.

 

 

The correlated risk of the valley

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The past eight years have been great for the Valley. Before 2008, the valley built technology for large corporations that in turn would use the technology to optimize their businesses. Now the valley is creating new businesses that happen to use technology.

In short order, we overturned the TAXI industry, created the first new car company of note, transformed how we interact with each other, radically transformed how content gets created and delivered, transformed food delivery, are disrupting pay-day loans, and the list goes on.

At the heart of the business models is an understanding of how people interacting with intelligent machines can efficiently deliver services that in the past were too costly to provide.

We have gone from being the disruptors to becoming mainstream.

When Mark Pincus used analytics to help create Zynga, the gaming industry puked all over us. Now, every single game company uses some amount of data analytics to optimize their games.

And that got me thinking.

We have created a bland uniformity in our corporate structure. Our companies look the same, have the same kind of people in it, are structured the same and are leveraging the same kind of technology.

Our venture capitalists are pursuing the same sort of risk mitigation strategies. Distributing their bets across as many good deals as they can find. And yet, the underlying technology structure of most of those bets is similar.

The last time this kind of thing happened was in the banking crisis of 2008 when every single banking company was pursuing the same business strategy leveraging the same algorithms to reduce risk and as a result exposing themselves to the same underlying catastrophic risk.

And startups are doubling down on the intelligent machine model. For example, Zappos is trying to fix human interaction. The Zappos solution is to seek to replace the ambiguity of human relationships with the structure of software systems.

One of my favorite thinkers is Nassim Taleb. His books are difficult to read. And yet he makes a profound point. The more you try and avoid risk, the more robust you make a system, the more fragile it becomes because any remaining weaknesses will obliterate everything.

In our case, the valley is trying to de-risk human decision making using intelligent machines.

There is too much sameness, too much of the same kind of operating model.

And when you see this amount of similarity, you know that this entire world will get disrupted somehow.

My belief is that the limits of intelligent machines are poorly understood. And the faith in the power of those tools will lead to massive amounts of correlated failure. The failures will occur simultaneously because of the sameness. And the effect will be a broad-based failure.

The companies that do disrupt the current valley will be those that understand the limits of machine learning and figure out how to use the human brain, not to make the algorithm more efficient, but to enable the human brain to do things it could not.

What that thing is, is unknown and the timing of the disruption is also unknown. The only thing I am certain of is that both will happen.

 

 

Normalizing Horror and Engineering Ethics

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I am reading a fascinating book these days, titled: Enhanced Interrogation: Inside the Minds of and Motives of the Islamic Terrorists Trying To Destroy America

The author describes his work in using torture, euphemistically called Enhanced Interrogation Techniques (or EIT) to fight the war on terror.

He not only used EIT, but he also invented many of the procedures and protocols.

And in many ways, he was very successful. EIT works.

The distinction between torture and EIT, of course, is perhaps a matter of perspective. James used techniques that were more effective and efficient. And the goal wasn’t pain; the goal was to break down the resistance of the evil terrorist.

Using torture (aka EIT), James was able to break people who otherwise were incredibly stubborn and difficult to break.

What is fascinating is the realization that the two inventors of new efficient torture or Enhanced Interrogation Techniques, felt trapped.

On the one hand, they were the experts and could do a great job that balanced the need to extract information and the need to be brutal and on the other the realization that if they didn’t participate innocent people would die and fewer expert torturers would be used.

Repeatedly in the book they try and explain their moral dilemmas and their personal repulsion to the whole activity and their attempt to figure out where exactly was this line they felt they may be crossing.

Reading the book, made me think of the morality of expert advice. Were these torturers making a moral choice?

Is the defense – well the alternative would have been worse a good one?

As technologists, increasingly we will be asked to do evil things. And is the defense that the alternative is worse, defense?

As a student of the second world war, if Albert Speer had not helped Nazi Germany the war would have ended sooner. If Field Marshall Gerd von Rundstedt had not been as effective a defender of Germany, fewer lives would have been lost.

In short, less evil is still evil.

To quote Gandhi, the only moral response to evil is non-co-operation. Mitigating evil, will not make the evil less evil.

I hope never to have this kind of moral quandary.

What I do know is that as more and more of evil will require engineering, we all have to ask ourselves, is it better to mitigate evil or not to do it?

And I hope, and I pray that we all choose the only moral choice, there is no mitigation of evil, there is only non-cooperation.

The archer and the gun and misunderstood impact of automation

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Last night I went to a great burger place in Arnold, called The Giant Burger. And I sat there waiting for my burger to arrive, I had a chance to reflect on the impact of automation.

The Giant Burger is not a fast place. It’s a place for great food. Not a place for getting great food fast. The reason is one of the employees will carefully assemble each burger to order. And because orders are big, and because she is one person, orders come out at about the rate of six per hour.

And as I was staring at her and thinking about machines, I was wondering what do machines do?

What machines do isn’t replace human beings. What they do is make less skilled workers more skilled.

Consider in the middle ages the archer. Being an archer requires a lot of skill and practice. You had to train from a young age and continuously hone your craft. In some sense, you could argue that archers were the artisans of war.

And then the gun showed up. And it wasn’t more reliable and more efficient than the original long-bow, but you could find 50 people hand them 50 fifty muskets and do almost as much damage as the archer.

In short, the gun made large armies of archers possible by reducing the skill requirement.

And that happens over and over and over again.

Look at the modern military drone. I can’t fly an F16 because I am too old, too tall and too fat. I could fly a drone. And there are more middle-aged fat guys than there are highly trained fighter pilots.

And so what happened?

We have drones all over the world killing random terrorists because we can have armies of fat people sitting in rooms flying a robot.

We have more people killed from the air than at any time since the Gulf War, and not a single pilot has done the kill.

Or look at the DaVinci system for surgery. To date, surgery was about skill. Surgeons were more athletes than scientists. With DaVinci, the skill necessary to do surgery will decline over time.

What automation does, what machines do, is they reduce the value of specialized skills and democratize those skills. And in the process make the value of the human labor declines because the number of people who can do the task increases, thereby reducing salaries.

And now software is making it worse. In the past, upgrades required new physical systems, now with software we can upgrade existing systems in place. And because of how electronics work, we can improve the intelligence of systems at the rate of 2x every 18 months.

And where it gets interesting is that in the past before software, mechanical systems had to be carefully engineered. For example, a mechanical lever has less tolerance than a computerized control system that can make micro-adjustments very quickly.

In short, we can innovate faster and cheaper than ever before in creating machines that make anybody be able to do anything.

Automation isn’t about replacing people; it’s about eliminating the need for skill and with that we remove the value of training and with that, we replace the highly trained archer with conscripts.

Which begets the obvious question:

So what?

Given that the value of skill is declining faster and faster, then that implies that the value of most human labor is decreasing, and therefore the per-unit cost of paying someone to do the job is below what people would accept.

And so when we say: Automation is killing jobs, what we are saying is that automation is causing the price we are willing to pay for humans to do jobs is decreasing.

And then we get to the policy prescriptions.

1. Some kind of universal income

One approach is to realize that there is a net surplus labor force at the current labor price, a price artificially kept high because of the minimum wage, medicare, food stamps, etc. And recognize that that group of people is going to have to die off, or leave the country for the surplus to get eliminated and in the meantime continue to extend those benefits including something like a universal income.

The problem is that that group of unemployable people is going to expand over time.

And the other problem is that there will be an increasingly shrinking set of people who will subsidize the lives of those whose skills have no value at the current price.

2. Make human labor competitive by retraining

This approach recognizes that it takes some time to build computer systems that can replace all skills and that the computer systems themselves need human operators. And so we continuously retrain people.

The challenge is that during retraining people are not employed and post-retraining the value of the labor is low. And so humans continue to experience points in time where they make less money and don’t have access to a stable income.

This also has the problem that the cost of the training has to be covered. And the folks who are making money will resent that their money is helping other people.

3. Make human labor competitive by lowering price and over time increase the price by reducing the number of people in the labor supply.

Another policy prescription is to cut those benefits such that the surplus labor becomes competitive with machines at a much lower price point, and then rely on other policies to cause the labor pool to shrink over time.

For example, a starving man will work for less than $7.25.

Cut his medical coverage, and a sick person will die off quickly.

Cut off his social security, and when he is too old to work, he will die of hunger and illness.

Restrict immigration and the number of people who enter the country will decrease over time.

The net effect will be that surplus labor will decline over time. In the short term there will be some pain, but in the long run, this will work out.

In the press, there is some discussion of the heartlessness of the tech industry because we create the machines that displace skill.

Tech is amoral. Our policy prescriptions are moral. If you are outraged with the outcomes of an amoral device, go ask yourself what policy prescriptions do you favor?

 

 

 

 

AWS and the automation of retail

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I as noodling on how automation was affecting industries. And I was also noodling about cloud in my role at VMware.

And that got me thinking about what is going on with retail because it is the Christmas season.

Amazon is forcibly re-engineering the entire retail supply chain to be digital.

You use a mobile device to find and then buy stuff. If your business doesn’t have a mobile presence, your business is not reaching a staggering number of customers.

The change from brick-and-mortar to digital interaction is so huge that it’s got its own name: Digital Transformation.

Then this got me thinking about, how does this affect society?

The computers sitting in the cloud are doing the job of the retail employee who would help you find stuff, and then ring you up at the register.

 

This retail season, I spent a lot of time thinking about the macro of the cloud. And I realized that the macro of the cloud is that anyone in the retail industry is moving to a cloud service model because they need a peek burst capacity. During the gift-giving season, retail makes more money and employs more people than at any point in time. And the total number of people they require during the low retail season is significantly less.

And the computing capacity required during the low retail season is significantly lower. And since the fixed cost of peek burst capacity is very high, it makes a lot of sense to spin up capacity on demand in the cloud.

And that got me thinking – what happened before?

And the answer is what we used to call seasonal hiring.

And if I was right then the impact of automation on seasonal hiring should already be visible in hiring patterns.

And lo and behold:

http://www.retaildive.com/news/bucking-trend-jc-penney-hiring-many-more-seasonal-workers/426625/

Last year’s job gains were 1.4 percent lower than 2014 figures, according to employment data from the Bureau of Labor Statistics cited by Challenger, Gray & Christmas. “We continue to move from brick-and-mortar toward click-and-order,” Challenger, Gray & Christmas CEO John A. Challenger said in a statement. “But even in the internet era of holiday shopping that means that brick-and-mortar fulfillment facilities need seasonal workers.”

 

 

God is alive, and scientists resurrected him – Nietzsche.

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My dad’s a scientist. Apparently a well known one. To me, he is just my dad.

And I suppose, because of him, I acquired a healthy respect for reason. And science.

And then this bullshit happens:

In the 1960s, the sugar industry funded research that downplayed the risks of sugar and highlighted the hazards of fat, according to a newly published article in JAMA Internal Medicine.

The article draws on internal documents to show that an industry group called the Sugar Research Foundation wanted to “refute” concerns about sugar’s possible role in heart disease. The SRF then sponsored research by Harvard scientists that did just that. The result was published in the New England Journal of Medicine in 1967, with no disclosure of the sugar industry funding.

Sugar Shocked? The Rest Of Food Industry Pays For Lots Of Research, Too

The sugar-funded project in question was a literature review, examining a variety of studies and experiments. It suggested there were major problems with all the studies that implicated sugar, and concluded that cutting fat out of American diets was the best way to address coronary heart disease.

The article got me curious. And so I asked a friend of mine who studies the scientific process what he thought:

Diet effects, especially for single nutrients, such as sugar or types of fat are so difficult to establish with observational studies that basically what you see published in the literature and then further cherry picked by media is the algebraic sum of all the ridiculous opinions of opinionated professors plus all the bribery inputs of the industry.

My friends happen to be the set of people who recognize that global warming is a fact. My cousin happens to study some of the phenomena for NASA.

And my friends and family wonder why so many people choose not to believe or listen.

And it’s because of this bullshit. When scientists decide to sell out to advance their agendas or buy a new car, science is the victim. And science and reason are the only things that can save humanity. And we’ve managed to kill science.

Scientists took advantage of the age of reason, of our need to believe in rational gods, to lie to us for their petty interests. And now we are reaping the whirlwind.

Why should we trust anything they say?

If Nietzsche were around his new tag line would be “Science is Dead.” God, on the other hand representing the age of faith and lack of reason, he would note is very much alive.