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” SearchSQLServer, Aug. 2013.

Rouse, Margaret. “What Is Internet of Things (IoT)? – Definition from” 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


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