Week 1: Machine Learning w/ Andrew Ng & Blockchain

January 29th, 2018

From now on, at the end of every week I will be posting what I am learning in the Coursera course and other project I sought out to learn during the previous week.

Up until now.

Since I completed the Northeastern University Level – Data Analytics program I’ve scrambled around the internet for various ways to learn Machine Learning. Chris Albon’s Machine Learning Flashcards were a great start as I’m still working through all 300 of them. I’ve taken a few Data Camp and Data Quest courses on Python for Data Science, and I’ve competed in a few Kaggle Competitions, my code for these I have posted on my GitHub.

The struggles I have faced are:

  • The more I learn, the more I realize how much more there is to learn, in a way its motivating to me.
  • It’s easier to learn through real life applications you are interested in, but finding  figuring out how to start is the hardest part.
  • Coming from a non-programming or computer science background, people often don’t take you very seriously

Coursera:

This week I started the above Coursera course with Andrew Ng.

Week one consisted a lot of the basics that I have grappled with in my scrambling to learn more so far already. Here is what I learned this week

Machine Learning:

  • “A field of study that gives computers the ability to learn w/o being programmed” – Arthur Sammuel
  • “A computer program is said to learn from experience E w/ respect to some class of tasks T and performance measure P, if its performance measure at tasks in T, as measured by P, improves with experience E” – Tom Mitchell

There are two main types of Machine Learning Algorithms

  • Supervised Learning (* the most used): is where “right answers” are given and the algorithm tries to create a best fit line based on inputs.
    • Regression: Predict continuous valued output
    • Classificaiton: Discrete valued output

ml.png

  • Unsupervised Learning: is where the computer doesn’t know the output but tries to cluster points together to create some kind of prediction
    • examples:
      • given a set of news articles, group them into set of articles of the same story
      • given a data base of customer data, automatically discover market segments and group customers into different market segments
    • Clustering: Grouping by relativity
    • non-clustering: find structure in chaotic environment
      • ie: Amazon Alexa while music is play

Model Representation:

  • Supervised Learning: given the “right answer” for each example in data
    • A training set will create a learning algorithm to apply to a test set that doesn’t have the “right answer” and will predict these outputs.
  • Regression Problem: predict real-valued output
    • m = number of training examples
    • x’s = input variables / features
    • y’s = output / target variable
    • (x,y) = one training example
    • (x(i),y(i))= ith training example

hΘ(x) = Θ(0)(1)

Cost Function:

Θi‘s: Parameters
Idea: to choose Θ 01 so that hΘ(x) is close to y for our training examples (x,y)

minimize the cost function J(Θ 01) = /2m ∑ (hΘ(x(i))-y(i))2

Gradient Descent:

  • Θj : = Θj – ∝(ª/(ªΘj) J(Θ 01) ; for j = 0 and j = 1
    • ∝ = learning rate
      • larger # = big steps down hill
        • quick but could overshoot minimum and fail to converge or diverge
      • smaller # = smaller steps down hill
        • gradient descent is slow
      • * when updating Θj
      • As we approach a local minimum the |slope of the tangent| gets smaller, thus the gradient descent (learning rate[ ∝]) will automatically take smaller steps
    • ª/(ªΘj) =  derivative of the cost function with respect to Θj
  • Gradient Descent scaled better to large data sets

Matrices and Vectors:

  • Matrix: a rectangular array of numbers
  • Dimension of matrix: # of rows x # of columns
  • Vector: an n x 1 matrix

Matrix Addition

Scalar Multiplication

Matrix Vector Multiplication

Matrix Matrix Multiplication

Matrix Inverse Properties

Matrix Transpose

** I do not the images explaining the concepts above **

Blockchain

Over this last week I also learned from @ECOMUNSING’s blog how to code my own Blockchain using the Python programming language. What I learned is:

Blockchain: an immutable, sequential chain of record called blocks. Blocks can contain transactions, files, or any data you would like, as long as they are chained together using Hashes

  • Hashes: a function that takes in input value and from input value, creates an output value deterministic of the input value.

I didn’t program a blockchain as complex as Bitcoin or Ethereum but this is essentially what their Validation function does

Bitcoin Validation fn: checks the input values are valid unspent transactions outputs(UTXO’s) that the outputs are greater than input and keys used for signiture are valid.

Ethereum Validation fn: Checks Smart Contracts were faithfully executed and respected gas limits

  • Smart Contracts: (Aka Crypto Contracts) directly controls transfer of a digital currency or assets between parties under specific conditions. Defines rules and penalties around an agreement in the same way a traditional contract, but automatically enforces them. Does so by taking info in as input and exception actions required by contractual clauses.

Genesis Block: is the first block in the system. Because it is not linked to any prior block (like the rest are required so that no value is neither created nor destroyed) it is treated differently and can arbitrarily set the system Account Balance.

Through coding this blockchain I got a deeper understanding of not just how these Blocks are created, but also other ways blockchain can be applied to things other than just creating a crypto currency. A decentralized ledger could serve many industries and could potentially impact every single market that we know today.

Through working through this, I also was able to practice and improve my understanding of python programming syntax…

MUCH MORE TO LEARN… More Updates Next Week!

 

 

 

 

 

 

 

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Astrophysics for people in a Hurry & The Four

tldr; Great books, read them: Astrophysics for People in a Hurry if don’t know what a Pulsar is. The Four if you want to understand what the biggest players in tech are all about and you plan to work in business in the next 20 years.

Astrophysics for People in a Hurry

The last book that I read in 2017 was Astrophysics for People in a Hurry by, Neil DeGrasse Tyson.

I knew the universe was enormous but the facts of this book were humbling. Reading this book right after Homo Deus was fascinating. In a way I read where the future of Intelligent Civilization will go from where we have been in Homo Deus, and then read about how not just intelligent civilization but the universe even came to be, in AFPIAH. Then to be able to learn about all the technological advancements that help us understand physics and not understand in Dark Matter, Dark Energy, and Gravity.

Neil DeGrasse Tyson really does a great job in just 208 pages describing and articulating on the wonders of how we even came to be, on a tiny planet, in the milky way, in this ever expanding universe. The most fascinating parts of the book to me were when he would describe the things we don’t understand, but how not being able to understand things like Dark Matter and energy, give us a better understanding of the universe as a whole.

Facts like, if it weren’t for Jupiter and its immense gravitational pull, pulling asteroids (and asteroid belts) into its atmosphere, earth would have been destroyed 400 million years ago, and intelligent life as we know it would have never existed. Einstein’s equation of general relativity and discovery of lambda, which allows us to measure dark matter even though we don’t know what it is. The order of the most abundant elements in the universe (1. Hydrogen, 2. Helium, 3. Oxygen). And how “there are more stars than words and sounds ever uttered by all the humans who ever lived.”

“We are stardust brought to life, then empowered by the universe to figure itself out – and we have only just begun.”

Must read for everyone, truly humbling and brings out real curiosity.

 

The Four

The first book that I read in 2018 was The Four by Scott Galloway. “The Four” refers to the biggest technology companies in Apple, Facebook, Google, and Amazon. The first few chapters dive into each of these respective companies, their technologies, how they came to be, and what they are all about. Data, data, data; I am fascinated by technology and data (if you didn’t know this already) as I finished this book in 4 days.

What all these companies have in common is they go after our instincts and utilize our wants and needs against us.

Google – Information

Google gives us instant information that appeals to us that want to know everything. It takes away the necessity to memorize or know everything as it is all one search away. The power this gives Google is every time we search something it understands us better. Not only does Google (God) know everything about anything, it also knows us better and is able to sell this knowledge to companies that want to reach their target customers, which Google knows better than them.

Amazon – Hunter Gatherer

Amazon gives us the ability to have anything we want in 2 days or less for free. Before Amazon, supply chain and logistics were inadequate, costing an arm and a leg to get something by next week. Amazon’s ability to collect all this data on things we want and get it to us within 2 days (instant gratification) builds up its reputable and trust worthy company out there. With the acquisition of Whole Foods, Amazon see’s the need for a store front to build and establish a customer experience, Whole Foods could also act as fulfillment center’s where customer’s could pick up and drop off their products, drastically decreasing logistics. Drone delivery on the horizon, oh and look out UPS, DHL, and FedEx. Basically, Amazon is and will thrive on our primal instincts to have as much as possible, because having too much just means you waste it, but having to little means you die.

Facebook – Love

Amazon has 1 Billion Daily active users and 2 Billion users. WOAH. Everyone is on Facebook as it thrives off the human want for connection and communication. You can see what your cousins-boyfriends-sister is doing on the social media. Not only that but with Facebook’s algorithm, they can provide companies with very accurate advertisements to their target customers… for a price of course. Facebook knows you better than your spouse after 100 likes…

Apple – Sex

Human nature wants us to mate with the most dexterous partner out there. Apple is a luxury brand more than a tech company. Owning a Apple product is a social statement; in owning a Mac it makes you more attractive and desirable than someone who doesn’t. Apple went from being a technology company from when it constantly kept trying to one up their previous product, to a luxury brand now that everyone wants the latest and greatest (sexiest), Apple device. Apple has so much cash since its margins are more than any other company on the planet and they sell so many.  Woz doesn’t get enough credit for what he really did for Apple.

What Makes All these Companies a Trillion Dollar company?

The “T Algorithm”:

  1. Product Differentiation (Unique product thats better than predecessor)
  2. Visionary Capital (Ability to spend money now to make money later)
  3. Global Reach (Be able to reach customers around the world)
  4. Likability
  5. Vertical Integration (Control distribution, no Macy’s, Nordstrom,)
  6. AI
  7. Accelerant (Attract top talent)
  8. Geography (Proximity top engineering university)

The 5th horseman?

Alibaba – No likability, since data privacy concerns are huge problem”

Tesla – Not yet a global firm, however could be more sex appeal brand than Apple [I can afford this car, it looks great, and I care about the environment.]

Uber – No likability. 4,000 mployees make $70 Billion, where as its 80,000 drivers make $7.75/ hr.

Walmart – No likability and access to visionary capital — Lost Macy’s size amount of earnings when decided to increase CapEx.

Microsoft – The OG horseman — Window’s phone lead to the demise. The acquisition of LinkedIn was good, but it is not a better social site than Facebook, and doesn’t have the visionary capital that Amazon has.

Airbnb – Airbnb could be the next horsemen, as it doesn’t have competitors like it with the reputation it does, and it excels in Global reach. Airbnb could have greater market cap than Airbnb by end of 2018.

IBM – Watson, enough said. However, lacks being an accelerant.

Verizon, AT&T, Comcast, Time Warner – “If you own the pipes on which the world’s data travels, you are always going to be important, highly profitable, and very big.” However, they can’t go global because US doesn’t want other countries listening to our phone calls.

Startups – Countless start ups out there in garages and dorm rooms, the next may not even be known of.

How to make it in the Digital Age:

  • Emotional Maturity, self-awareness, self-regulation, motivation, empathy, and social skills.
  • “70% of high-school valedictorians [are] female, the future really is women.
  • Be curious: ask, “What if we did it this way?”
  • Play offense: offer one more deliverable or idea than asked of you.
  • Take ownership: be detail obsessed
  • Smart is sexy
  • Go to college
  • “A decent proxy for the twenty-something’s success will be their geographic trajectory. How long did it take them to get to the biggest city in their country, then to the biggest on the continent? The strongest signal of success will likely be those who moved to global economic capitals, the supercities, versus those who stayed in the relative hinterlands.”

The Four employ 418,000 combined, equivalent to the size Minneapolis, with the combined market cap of $2.3 Trillion. The combined market cap of these four firms is equivalent to the GDP of the developed nation, France, home to 67 million citizens.

 

 

The Information Age

“What will happen to the job market once artificial intelligence outperforms humans in most cognitive tasks? What will be the political impact of a massive new class of economically useless people? What will happen to relationships, families and pension funds when nanotechnology and regenerative medicine turn eighty into the new fifty? What will happen to human society when biotechnology enables us to have designer babies, and to open unprecedented gaps between rich and poor?” (Harari).

Through my Data Science internship with Ohuku AI, a company creating a biologically inspired Artificial Intelligence (AI) assisted simulation and visualization software, I have learned that the rise of data and AI is just getting started. In today’s society, data is knowledge and is more valuable to this century than oil was to the previous, yet there are still so many more ways we can collect, use, and implement data. In the biggest companies in the economy today, data and machine learning play an essential role in the success of these companies. However, Facebook, Amazon, Netflix, and Google (FANG) are missing out on top talent because these individuals opt to work for startups instead as per recode article published on November 30th, 2017 by, David Saad. A decade ago top talent was choosing these technology companies over Goldman Sachs; the financial crisis of 2008, that caused massive layoffs in the sector, might have played a part in unappealing job seekers. I believe that the reason good technical minds are spreading out to startups is that they are able to see other applications and opportunities for data in other industries that FANG is not in. These risk-loving individuals see potential to successfully create more technologies that benefit society as a whole, and make a larger impact immediately although success is not a given. Compared to if they were to accept the more risk averse option, being a role at a FANG company. “History is often shaped by small groups of forward-looking innovators rather than by backward-looking masses” (Harari). As more technologies are created in all sectors of the economy (health, education, industrial, energy, agriculture, etc.) to collect respective data, the benefits will create a society almost unrecognizable by today’s standards in all aspects of life.

To supplement my internship, I wanted to read two books. The Big Short: Inside the Doomsday Machine by Michael Lewis and Homo Deus by Yuval Noah Harari; each giving me insight into the beginning of the information age, a look into what technology has created in the present, and what these technologies will develop into in the future. After reading The Big Short, I was able to see that the financial crisis was a result of the market for Mortgage Backed Securities (MBS) and Collateralized Debt Obligation’s (CDO) becoming larger than the housing market it-self by almost twenty-fold. This created a demand for Subprime Mortgage Loans (high interest-rate loans for people with low credit scores) which was a good idea until the whole system lost its integrity. Once there was a massive amount of Subprime Mortgage Loans owned by people who weren’t able to make payments, MBS’s defaulted, the housing market crashed. Every single bank that bet neither of these would never happen by investing in them, lost massive amounts of money. One of the biggest investment funds, Bear Stearns, failed as a result of this crisis. This was an example of a potential use for Ohuku AI’s software in history. The software would have been able to create a simulation of housing markets that was tied into the financial market, credit scores, mortgage ratings, and it would have given us a real-time visual to see that the numbers weren’t adding up. Ultimately being able to understand the crisis on the horizon, just as Michael Burry, the hedge fund manager for Scion Capital did.

Today there are algorithms running our money that react to social trends, financial data, or anything related to a company or stock. For example, on April 23, 2013 Syrian hackers broke into Associated Press’s twitter account and said that the White House had been attacked and that President Obama was hurt. Trading algorithms that monitor newsfeeds reacted and started selling stocks that resulted in the Dow Jones going into a free fall losing 150 points in sixty seconds. Three minutes later Associated Press was able to clarify that the tweet was a hoax and the algorithms reverted. Within 3 minutes of the clarifying tweet, the Dow Jones had recuperated all of its losses. This is a result of super-fast computer programs running our money; nearly instantaneous transactions whereas there are no way humans would have been able to react that much and that fast. The “fake-news” on Twitter resulted in the above phenomenon now known as a “flash-crash”. Both “fake-news” and “flash-crashes” continue to present a threat as more and more people try to exploit the growing number of algorithms used daily.

Homo Deus touches on all of the technologies I am interested in, brought concepts to my attention that I had never thought of, and as a result is one of my favorite books I have read. The book starts off by explaining how Homo Sapiens (“wise man”) have evolved overtime. Only the genes that have helped the species survive are passed on to the next generation; attributes humans have today are a direct representation of specific events in history. Homo Sapiens have evolved from barely surviving to “[f]or the first time in history, more people die today from eating too much than from eating too little; more people did for old age than from infectious disease; and more people commit suicide than are killed by soldiers, terrorists and criminals combined” (Harari). Harari proposes that Homo Sapiens will push to overcome death next and push towards immortality creating Homo Deus (meaning “Man God”). Experts believe that humans will be able to achieve this anywhere between 2100-2200. Death is a technical problem and thus can be solved by figuring out the technicalities that cause mortality. For example, in 2012 Google launched a sub-company, Calico, whose stated mission is ‘to solve death’. Eventually, once we are able cure the diseased, healthy people can supplement themselves, giving them “god-like powers”. For example, Scientist are confident they will find a cure for Alzheimer’s, a brain disease that causes people to lose their memory, thanks to data found from ample neurological studies going on presently. Once this cure is found, not only will the diseased become healthy, but perfectly healthy humans could supplement their memory. “With the help of biotechnology and computer algorithms, [we] will be able to shape our bodies, brains and minds, and to create entire virtual worlds complete with hells and heavens” (Harari).

Of course immortality is appealing, but without extreme innovation and implementation, it is a long shot. However, it is evident that life expectancy is and has been increasing over the years and a more realistic goal to live to 125 – 150 years. There will be many positive outcomes from longer lives as people will accomplish more, and see their families grow. Productivity of the economy will grow as people are in the workforce longer and overall quality of life will increase… or will it? Currently social security, 401k’s, and health care programs are not set up to support people to live this long; these systems must adapt or else they risk defaulting and causing another great economic collapse. This setback would negatively impact technological advancements that allowed people to live longer. It could consequently cause unemployment rate to increase, and for every 1% increase in unemployment, 40,000 people die” (Lewis). A system that is meant to increase life expectancy may ironically be the cause of an economics depression and thousands of deaths. If the system and economy are able to overcome this dramatic increase in life expectancy, 80 will become the new 50 and people will continue working until their 90’s to be able to afford living that long. The impact I see Ohuku AI making on this would be creating a simulation that takes into account all of these variables and creates a visual representation of these impacts. The key would be to collect necessary data (mainly time-series) for all the variables, create biological representation that is easily digestible to the common eye, and then forecast a result.

As people live longer and the population continues to grow, the amount of resources we consume will increase which is problematic because humans are already consuming at a faster rate than the ecological system is able to repair itself. Can we really afford and expect to survive on a planet that is dying? “We trust nanotechnology, genetic engineering and artificial intelligence to revolutionize production… and to open whole new sections in our every-expanding supermarkets… The real nemesis of the modern economy is ecological collapse” (Harari). Although 97% of climate scientists agree that global warming is occurring, it remains one of the most controversial topics in society today. Mainly because businesses believe the only way to decrease their impact is to decrease their productivity and output. However, new technologies such as Internet of Things (IoT), Blockchain, Green Energy, and Biomanufacturing materials are all technological innovations that could not just benefit the ecosystem, but the efficiencies of businesses as well.

“The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction” (Rouse). IoT can track data from anything connected to wifi, which in today’s society is nearly everything. This could be human wearables that track and monitor biometric data, a biochip transponder in an animal that tracks its eating habits and location, or an autonomous vehicle that can communicate with other autonomous vehicles. By collecting machine-generated, real-time data to be analyzed for insights, that will drive major improvements and innovations in all areas of the economy. All of this data collected in the IoT can be stored in a decentralized ledger known as Blockchain.

In Blockchain, data is time stamped, which makes it easy to create time series data. This is beneficial because every transaction is connected and hacks become less of a threat. This is because if someone wanted to hack the Blockchain they would have to change every single transaction that was connected to the amount they would want to take, which would be time consuming and virtually impossible because at least one of millions of people on the Blockchain would notice. This is a benefit realized because, as a decentralized ledger that is governened by the Blockchain community, no one entity has control over the price or value of the currency. Another benefit is to be able to see every transaction in one time stamped ledger. One usage for this would be in the energy market. One could see how much energy someone used, from what sources, when they used it, and how much it cost them, making for a more transparent economy allowing us to hold people and corporations accountable for their consumption footprint.

The cost of green energy has quickly been declining to be almost on par with the price of oil and coal. Companies like Tesla, BMW, Nissan, and many more are not just paving the way for battery development for electric cars but also in creating batteries for energy storage at the industrial and residential levels. This term, I was fortunate to also have a Research Analyst internship with R2B Microgrid Solutions where we consult business entities across the country on green energy generation and store services. Through this internship I was able to learn what goes into a “green grid” and the potential we have to power our whole country with it. A problem arises for green energy generation technologies (such as solar and wind) when the source (sun and/or wind) is not present. That is where the microgrid battery adds value. It allows a facility to run on stored energy which was collected during time when excess energy was generated. Another potential for green energy is in storing data. Data centers requires a lot of energy to power and keep them cool which is why big data companies have data centers in states with low geothermal energy costs (i.e. places with lots of rivers). The optimal place to locate a data center to maximize energy and minimize cost can be found using geospatial data. Geospatial data is “information about a physical object that can be represented by numerical values in a geographic coordinate system” (Fontecchio). So basically, you would be using data, to generate data, to store data, and as this continues on we are going to need to develop more efficient technologies by using more data. The use and benefits of data are cyclical in terms of green energy. Data is used to generate more data, to store that data, and then to develop more efficient technologies.

Green energy could work in solving global warming through the elimination of carbon emissions, but it does not solve the monstrosity of extracting and depleting resources or the negative environmental impact of food. Livestock farming produces from 20% – 50% of all manmade greenhouse gas emissions (Green Eatz). Luckily scientists have been researching ways to improve the production of meats other materials through biomanufacturing. Biomanufacturing is a type of manufacturing or biotechnology that utilizes biological systems to produce commercially important biomaterials and biomolecules for use in medicines, food and beverage processing, and industrial applications (Wikipedia). Scientist can now use a culture of cells to create more cells that result in the production of more particular bio product. (i.e. meat, paper, leather). This may lead to the extinction of farming or mining for resources. However, as a result, a controversial development of this technology has aroused which allows scientists to create “designer babies” through Bioengineering.

There is enough data on DNA, genes, and traits to essentially design humans with superior, “super human” like qualities. A benefit could be the elimination of harmful genes that lead to diseases or deformities before a human is even born. This leads to a lower infant immortality rate, but with a major caveat; only the rich would be able to afford this. This process would allow for the wealthy to create and give their babies desirable traits that would help them succeed over the less fortunate. For example, a wealthy family could design their baby to have a great memory and without genes that lead to addiction, whereas a less fortunate baby is still at risk of addiction and not succeeding in school because his family did not have the opportunity to alter his genes. This would contribute to a severe wealth gap as the wealthy engineer themselves to be superior across generations and the less fortunate are stuck in a poverty trap.

The most astonishing concept I got from Homo Deus was the possibility of an AI chip plugging into a human’s brain and connecting to the IoT. Think about a time when you couldn’t remember something, or a time when you couldn’t solve a math problem. This would never happen again if this were to become a reality. On a more extreme level, one would know what to eat and how to exercise to remain healthy, how long it would take you somewhere, what stock to invest in or short, and the weather anywhere around the globe or in theory even on another planet. Any information you would need to know available instantly.

Last but not least, how at risk are jobs to being taken over in just the next 20 years? ‘The Future of Employment’ says “47% of US jobs are at high risk”, most being low skill jobs (Frey).

My internships have taught me that by the time I turn forty the world will be a completely different place. Our doctors may be robots with eight arms controlled by a person halfway across the globe doing seven different surgeries at once connected by IoT, in a hospital that I rode to in my autonomous car to. I may get my organic groceries delivered to me by a drone, five minutes after I ordered them. Even the steak I eat at a restaurant could be curated in a petri dish in the kitchen, which I purchased with a cryptocurrency stored on the Blockchain. The consequence of all of these innovations is “47% of US jobs are at high risk” of being replaced by computers and algorithms in just 20 years (Frey). With this amount of jobs at risk, what jobs will be left?  I have no idea. However, there are many predictions out there ranging from: a society where no one holds a role but benefits from AI, to humans adapting and creating roles that are supplemented and coexist alongside AI. I do know that the demand and potential to collect and analyze data is limitless, as are the applications. As someone who is enthusiastic about problem solving and data, these possibilities are intriguing. Thanks to my Economics Degree, internships, and ambition to learn analytical skills on my own, I have begun my journey into analytics and data science. This is a quest that will continually push me to learn new and more skills that I can apply to solving real world problems, create algorithms that understand us better than we do, and push us to the next stages of evolution.

Works Cited

“Food’s Carbon Footprint.” Green Eatz

Frey, Carl Benedikt, and Michael A. Osborne. “The Future of Employment: How Susceptible Are Jobs to Computerisation?” Technological Forecasting and Social Change, Elsevier, 2017.

Harari, Yuval Noah. Homo Deus: A Brief History of Tomorrow. Vintage, 2017.

Lewis, Michael. The Big Short. Regency, 2015.

Fontecchio, Mark. “What Is Spatial Data? – Definition from WhatIs.com.” SearchSQLServer, Aug. 2013.

Rouse, Margaret. “What Is Internet of Things (IoT)? – Definition from WhatIs.com.” IoT Agenda, June 2016.

Saad, David. “Forget Facebook, Amazon or Google. Up-and-Coming Top Tech Talent Is Opting for Startups.” Recode, Recode, 30 Nov. 2017.

 

Edited by, Clarissa Tolan

Integrity – The Matheny Manifesto and The Big Short

I recently finished two books:

The Matheny Manifesto by, Mike Matheny with Jerry B. Jenkins

This book was a great read for all sports parents and anyone who wants a lesson on leadership and what it takes to win the small battles everyday. Mike’s biography was about his child hood, to college, minor, and eventual major league career, how he struggled and the things he really valued in life. The things he tries to instill in himself, young and professional players, and his family are all built on integrity, strength, the bigger picture, and what a good leader focus’s on.

Most of the book is about how youth sports is for the kids, that their development is the most important thing. Being able to play the game the right and respectable way. Parents need not to be involved in the games, let the coach, coach, “The Coach is always right”, and that the umpires will often be wrong but it is important to remember that they are volunteers are important concepts for any youth league parent to understand.

Mike touches on the concept of Servant Leadership and how it is a dichotomy, “Which are you a servant or a leader.” To Mike, the best leaders serve those underneath them and encourage them to excel, they hold their values true to them, they work hard and keep their goals in eyesight, and above all, do it all with integrity. “Nothing worth doing is easy.”

I would recommend this book to a sports parent, anyone interested in learning leadership qualities, and those, like myself who are huge fans of baseball, and Mike Matheny, who are interested in how Mike became the youngest Manager in baseball and how he did it and continues to coach with integrity and continued success despite preaching a team first mentallity.

The Big Short: Inside the Doomsday Machine – by, Michael Lewis

The common theme I found between these two books was the concept of integrity, and while the previous book I reviewed was about how to have it, hold it, and instill it in young men and women at a young age, these book was about the lack-there-of in business leadership positions.

The Mortgage Backed Security System was a great concept at first. These are loans that are made to people to buy a house that are backed by the value of the actual house, thus if the loan were not to be paid, the house would be taken by the bank. In theory, this worked until they started giving out Subprime MBS’s to people who obviously could not pay back these loans. They started doing this because it wasn’t so much the housing market that was bringing in all the value but rather the market for Collateralized Debt Obligations or Collateralized Mortgage Obligations  (A bunch of MBS’s and other debts grouped together and sold to another bank at a premium). This wasn’t scene as a problem because the housing market was seen as strong back then and the theory of “Who doesn’t pay their mortgage.” Once a few investors realized that the CDO/CMO and MBS market was bigger than the housing market by near 20x because of people lacking integrity and giving all these subprime MBS’s a AAA rating (the highest rating = the most like to get paid off and being able to sell these at higher prices to other banks who would buy them). There was even a system created called Synthetic CDO’s that were being sold from bank to bank to bank to bank with low risk because the Synthetic CDO’s were considered “Diversified” and sure pay off, but little did they know that these AAA rated Synthetic CDO’s were soon to Default.

A few smart groups of people shorted the housing market and made money but regretfully, they just benefitted from the stupidity and lack of integrity of the banks and the system. All of this lead to the housing market crash of 2008.

My Next books:

  • Homo Deus: A History of Tomorrow – by, Yuval Noah Harari
  • The Master Algorithm: How the Quest for the Ultimate Learning Machine will Remake Our World – by, Pedro Domingos

The Potential of Green Energy

Non-Renewables

According to the U.S. Energy Information Administration, 64.2% of the Nation’s energy sources are from Natural Gas (33.8%) and Coal (30.4%). While the earlier burns clean, it is still a resource we do not have an abundance of on the planet, and the latter is very toxic and we don’t have an infinite supply of either.

Renewables

In 2016, renewables only account for 14.9% of the Nation’s Energy sources and solar (.9%) doesn’t even account for 1%, equivalent to 9,500,000 kwh, the potential for growth in solar energy is astronomical. The average gross cost per watt for solar panel installation has already decreased 19.3% in the last 3 years, with the number of solar installations increasing 95% in 2016 alone!

solar installations

Source: GTM Research / SEIA U.S. Solar Market Insight report

To better fully understand what I am trying to get across, here is a graph to represent how much price per watt of solar has decreased compared to the increase in the number solar panel installations. Keep in mind that this graph only represents up to 2015 and with the 95% increase of installations and a 9.2% decrease in price in 2016 alone (in the first half of 2017 the price of solar decreased 5.7%). However, this 95% increase only made solar energy in the U.S. amount to .9% of the Nation’s Energy source…

solar_price-installations

Source: https://cleantechnica.com/2016/02/12/is-this-the-best-solar-chart-yet/

With wide spread application and acceptance of solar panels, and some way to store or share excess energy amongst ourselves, energy costs will no doubt, plummet. But how would this be possible?

*Hydropower 6.5%, Wind 5.6%, Biomass 1.5% , and Geothermal .4% energy sources also have had recent technological advancements recently as well.

Microgrids

A discrete energy system consisting of distributed energy sources (including demand management, storage, and generation) and loads capable of operating in parallel with, or independently from, the main power grid.

Being able to store this energy on site could have huge potential for dark times, emergencies, and for cost savings. Being able to be connected to a local grid or be serviceable outside of the grid has huge potential in cities, campus’, office buildings, etc. investing in green energy. To be able to power buildings in a black out or natural disaster, by energy your own solar panels absorbed.

Application

With the wide acceptance of electric automobiles and a widespread commitment to stop producing oil burning cars, it would be counterintuitive to burn these fuels to power these cars. By 2030, India alone plans on only producing solely electric cars, and specific car companies like GM, Ford, Tesla (obviously), NIO, Daimler, Toyota and Mazda, (Renault, Nissan, and Mitsubishi), Jaguar Land Rover, Volvo, VW Group (Volkswagen, Audi, and Porsche) , and many more have taking the commitment to create all electric vehicles real soon!

It is necessary for us to produce a big enough system to store and power an entire country and their cars on renewable resources. With solar being the most applicable and easiest way for everyday consumers and businesses to implement this renewable energy strategy. Creating energy grids and a Blockchain in order to distribute and share the necessary with everyone is the future. Imagine a system where you get money from not using all of the energy your solar panels absorbed that day. Or on the days when you need more, being able to get it from someone else who got a little more than they need, through a completely decentralized system.

This decentralized system would allow you to see how much you used and didn’t use, where you got it from, how much it cost/ or profited you. With this system, paying more upfront to have more solar panels would only benefit you in the long run.

Other Green Energy Advancements

Tesla and Cummins plan to come out with an all electric heavy duty/ freight truck. Cummins beat Tesla to the punch producing a vehicle with a short range of 100 mi, that recharges in about an hour, hoping to get it down to 20 minutes by 2020. With the short range, this truck is targeting local transportation of a 22 ton trailer, which could be applicable for local (* cue sustainability vocab) food and beverage companies.

Tesla is looking to target the regional transportation market, with a hopeful range of 200 – 300 miles.

Imagine

A time when all transportation is on the same Blockchain as energy, and with autonomous delivery by trucks and drones that can deliver everything and anything in the most energy and time efficient manner possible cutting down the impact of carbon, energy, and time in supply chain and logistics of companies. Where these trucks and drones are manufactured by robots, 3d printers, and laser cutters that are all run on renewable resources and are able to use recycled or renewable resources efficiently and recycle the rest. This is the Potential of Green Energy and technology, the future is near…

Output and Unemployment Gap

I wanted to conduct a study to see whether Output and Unemployment were correlated.

Picture1

This graph is a representation of the Output gap (Output – Potential Output) and the Unemployment Gap (Unemployment Rate – Natural Rate of Unemp.) From this graph I can guess that these two are negatively correlated.

I then ran a regression to find out how well this model is described by each other with OutputGap as the dependent variable and UnemploymentGap as the independent Variable. This is the regression I got:

Picture2

Regression Statistics
Multiple R 0.906616629
R Square 0.821953712
Adjusted R Square 0.82129913
Standard Error 1.002754174
Observations 274
ANOVA
df SS MS F Significance F
Regression 1 1262.618932 1262.618932 1255.692615 6.2946E-104
Residual 272 273.5003339 1.005515934
Total 273 1536.119266
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept -0.29633505 0.061534508 -4.815753949 2.43757E-06 -0.417479503 -0.175190597 -0.417479503 -0.175190597
X Variable 1 -1.433866901 0.040463847 -35.43575335 6.2946E-104 -1.513529042 -1.354204761 -1.513529042 -1.354204761

The goodness of fit of this regression is 82.195%. The intercept is -0.296 with the slope decreasing at a rate of 1.4338.

CONCLUSION

From this research we can conclude that holding all else constant, if unemployment rate increases, output will decrease. Vice Versa, if unemployment rate decreases, output will increase.

Although this is true, it is impossible for the unemployment rate to be 0% as there will always be people looking for work in the United States. It is something to not that, today the Unemployment rate is below the Natural Level, which is usually around 5% and output has been on the rise.

** Data Sources: Fred St. Louis

https://fred.stlouisfed.org/

New Jersey Opioid Death Analysis

I have done an analysis on the Opioid Death Toll in New Jersey from the years 1999 – 2016, although the 2016 data was only half complete and only had 6 months worth of data. I took the mean of the estimates given in the data for the total deaths as well as heroin and fentanyl related deaths.

NJ_Deaths

* Source of Data: Data.World

From this graph we can see that opioid death’s weren’t categorized by substance until 2004 when heroin deaths starting to be counted. I don’t believe that dip in total deaths in 2004 to be relevant, I think the count just got more accurate with the inclusion of specific heroin deaths being taken into account. Secondly, we can see that opioid death was decreasing until 2008 and heroin related deaths were decreasing until about 2010 when fentanyl was introduced. The giant increase in opioid deaths is due to fentanyl being much stronger and deadlier than heroine. In just about 5 years fentanyl related deaths has almost matched heroin related deaths.

2017 Heroin death forecast: 993 deaths

2017 Fentanyl death forecast: 912 deaths

This makes sense because fentanyl is cheaper and more powerful then heroin so intuitively more opioid addicts will switch from heroin to Fentanyl resulting in more fentanyl overdoses.

** RStudio

Summer Reading

THE SUBTLE ART OF NOT GIVING A F***

By: Mark Manson

A very blunt book by Mark Manson about how he has implemented not giving a F*** in his daily life and advice on how to live similarly. The common theme I found in this book was that you just really need to understand what you value most (or give the most F***’s about) and prioritizing them.

Stealing Fire: How Silicon Valley, the Navy SEALs, and Maverick Scientists Are Revolutionizing the Way We Live and Work

By Steven Kotler

This Book was about altered states of the mind, such as Ecstasis, and how they can supplement our daily lives. This book provides examples and research from business leaders, Navy Seals, psychologists, neurologists, chemists, pharmacologists, and action sports athletes in order to understand how to efficiently access ecstasis, or the feeling of flow you get in this altered state.

Key’s to Ecstasis

  • STER –
    • Selflessness
    • Timelessness
    • Effortlessness
    • Richness
  • Warnings
    • It’s not all about you
    • It’s not all about now
    • Don’t be a bliss Junkie
    • Don’t dive too deep
  • Value = Time * Reward/Risk

Favorite Quote:

  • “Experiencing the [STER] (See Above) of nonordinary states of consciousness can accelerate learning, facilitate healing, and provide measurable impact in our lives and work. But we have to revise our tactics to make the most of these advantages.” (220)

Conclusion

These two books had a lot more in common that I thought they would at first. Value was a huge concept in both books that really brought the two together. From TSAoNGaF, prioritizing the things you value was essential to figuring out that it was ok not to be ok some times. That everything is not going to be fine and dandy all the time, that it is in these less than great times where we are motivated to go to great lengths and reevaluate our priority values. Similary, in Stealing Fire, value is essential to obtaining Ecstasis. Once you find this state it is essential to balance it, and stay grounded to make sure you do not become a bliss junkie. Ecstasis is not obtainable all the time and like in TSAoNGaF, being ok with this is critical to it being sustainable.

West Coast Biased

The Golden State Warriors:
Yesterday, the Golden State Warriors clinched their 2nd NBA Finals in 3 straight appearances against the likes of Lebron James, arguably the best and most physically adept player to ever play the game. With no proof of slowing down in the near future, the word “dynasty” is starting to be thrown around when this team is mentioned. Before this last year they were arguably the best team ever, posting a regular season record amount of wins: 73-9, all without Kevin Durant. Despite how you feel about this move, you can’t take away that Kevin Durant is now a NBA champion and Finals MVP

The Oakland Raiders:
13 straight seasons without a winning record… arguable the most unattractive and worst team in all of the NFL through the stretch of 2003-2015 seasons. 2016 was a breakout year with young talent and hard work paying off to the likes of the young trio of Derek Carr, Amari Cooper, and Khalil Mack. A breakout year so attractive to other players it even brought Marshawn Lynch out of retirement to play for his favorite team from his childhood! Marshawn grew up in Oakland, California and attended Cal Berkeley before entering the NFL. Hate on them all you want but you cannot deny that this young team is good, and exciting for football; the future looks bright for the Raiders no matter where they will be. I know many are mad about the move to Las Vegas in 2018, but hopefully they can bring the Bay a SuperBowl before they move out to add to the 2 NBA Championships, since 2015, and 3 World Series titles we have, since 2010.

The San Francisco Giants and the Oakland Athletics:
Talk about Dynasty’s The san Francisco Giants have 3 World Series titles in the last 6 MLB season. But the whole hook, line, and sinker for this post is with the Oakland Athletics. This is where I can connect the winning sports culture (LOL not the A’s recently), with the innovative, tech culture of the Bay Area with MoneyBall. The Oakland A’s used statistics to win baseball games in 2002; which they ended up recording the second longest win streak in MLB history with 20 wins (21 wins is record by the 1935 Chicago Cubs). Moneyball was the theory that on-base percentage was not as highly touted as sluggers, which they believed should have been. And therefore exploited it, the rest is history.

TECH:
Arguably changing everyday life as we know it with: Facebook, Google, Apple, Stanford, Berkely, etc. It’s hard to argue against the fact that Silicon Valley is the most innovative, transparent, forward thinking, accepting, and overall smartest places on the planet. From companies creating social media, to artificial intelligence, venture capitalism, medicine, cryptocurrencies, and many many more things. Many of these things due to using programing and statistics on big data, creating Artificial Intelligence, Machine Learning, and Data Engineering to name a few. Others due to extenuating research in medical fields and market demand. Many of these successes I would say are due to the diversity and the ever increasing cost of living which creates greater competition to survive and thrive in the area. This creates drive in person to want to be apart of something exciting and monumental that is going right next door.

Sources:
Warriors(Kevin Durant) pic: http://miami.cbslocal.com/photo-galleries/2017/06/13/2017-nba-champions-golden-state-warriors/
Marshawn pic: http://www.thesportsstance.com/2017/03/marshawn-lynch-going-beast-mode-in-bay.html
MoneyBall pic: http://blogs.bloomsd.k12.pa.us/17nicholasm/2015/11/03/ar-blog-moneyball/
Tech pic: https://blog.sethgillespie.com/2014/03/11/in-the-heart-of-silicon-valley/

Sustainability of Cryptocurrency and Blockchain

Materials:

Credit Cards:

Credit cards are made from polyvinyl chloride (PVC) which is oil based and toxic. The production of credit card requires 45,000 barrels of oil, which is a drop in the bucket compared to the 20 million barrels of oiled consumed on a daily basis. However, not included in these 45,000 barrels are gift cards which are made from PVC as well. These credit cards are made to last 20 years but expire within 2 to 4 years requiring individuals to get new ones and whether they are recycled after is hard to determine since they are often destroyed because they contain valuable information on them.

Cash:

Cash is made from Cotton and Nylon. From the last post on textiles, I described how many chemical pesticides, insecticides, and water usage goes into the production of cotton. Cash lasts on average approximately 16 months, then the bill is too worn to use, thus more is to be made and put into circulation. When it is taken out of circulation it ends up in a landfill as cotton cannot be recycled and Nylon, which is oil based, is very hard and expensive to recycle.

Smartphones:

Smartphones are made from many types of recyclable metal and polymers. The performance of smartphones last ~ 4.7 years before performance is noticeably worse and become obsolete. The lifespan of smartphones is longer than cash and is recyclable at the grave; the materials in smartphones can be recycled to make next generations of smartphones.

Smartphones are multifaceted and serve huge purposes to a persons daily life; today in 2017, smartphones have become crucial to the productivity of a persons day, average person spends over 4 hours on their phone a day. Because smartphones play such a big part of everyday life they are always being used and secured so that very rarely are they misplaced or stolen. Losing a cellphone is way more detrimental then losing a $5 dollar bill, hell, even $100 dollar bill.

Wrap up:

Credit cards and cash are just another thing someone has to cary on them, that ultimately serve the same purpose. If, this one purpose were to be consolidated onto a device that already serves many purposes in our daily life, not only would less materials be used, but also everything we need would now be all in one place, efficiency. Now the materials that are used in smartphones serve a bigger purpose and the impact that they have now are smaller due to the increase in productivity and purpose.

  • Security (Not sustainability related by important to personal interest)

Credit card fraud is currently at an all time high, and cash, as a physical asset, is stolen on a daily basis. Electronic payment systems, such as Apple Pay and PayPal, are even more secure than the “chip and pin” payment system that credit card companies are telling us are more secure than the older “swipe and pin” system.

So what you’re saying is we should just transfer from physical currency to a electronic currency?

Yes. But I haven’t even explained the benefits and opportunities of cryptocurrency yet.

Cryptocurrency:

Cryptocurrency is a global decentralized database that tracks and records transactions and data. There is no paper, no materials used besides in the technology we already use for many other purposes on a daily basis, and no middle man, such as a centralized bank or government, regulating the currency. Cryptocurrency such as Bitcoin and Ethereum are regulated by an open source creative code that secures the transactions by tracking every single transaction (and other data in the case of Ethereum). It’s security is behind this creative code in Blockchain technology that could only be hacked if someone were to rewrite the entire code for a trail of currency in broad daylight, which is ultimately impossible. The three cryptocurrencies that are playing a big role and gaining traction are Bitcoin, Ethereum, and Litecoin.

So whats the difference between an electronic currency and cryptocurrency?

BIG DATA!!!

Bigger Picture:

Ya cutting out the material’s of cash and credit cards would be great, but ultimately this impact is very minuscule to what an economy run on blockchain/ cryptocurrency technology could do and look like. We are currently in the information age, more specifically in the big data age, which is why cryptocurrency could become so valuable and sustainable in the long run. Data is currently more valuable than oil. Imagine a completely transparent economy, one where you can see who purchased what from where, and is using what technology at the cost of what. For example, being able to find out whether the company you purchased your “Green” water bottle from, actually got its materials from a sustainable source, that efficiently and effectively produced this material in the most energy and least polluting way possible. This in-depth understanding and transparency of industries is not do able with the current cash and banking system, but with a globally accepted cryptocurrency, this is more than possible.

Cryptocurrency is currently thriving due to the distrust in industries, organizations, and government. Cryptocurrency would bring transparency to consumers giving them the opportunities to see and choose from the most ethical and responsible companies and industries globally. This demand for more ethical and responsible products would then bring in more competitors into the market, driving down the prices altogether and increasing the supply of more efficient and sustainable products.

transparency = Trust + Credibility => Sustainable purchases and demand for ethical companies globally

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Sustainable Planet

Other Sources: