Note: This is a very opinionated post. Research for this writing is mostly centered around reading other’s opinion pieces and historical storytelling, I’ll link throughout. I will attempt to convey my evidence and recommendations with all four of Aristotle’s modes of persuasion. As with many of my other posts this is specifically about the USA. These issues are felt worldwide, however different in their ways. Since I know this place the best, and it’s a prime example, we are going to use America.
Side Note: I have been super bust applying and interviewing with companies, which is why this post took so long to make. Hopefully the next one comes quicker.
Summary and Outline
Racism and sexism are bad for society, plain and simple. They create and perpetrate misunderstandings which have historically lead to conflict, sometimes violent. Even if the institutionalized manifestations of racism and sexism are ousted, it’s still alive and well in persons and communities. Those people who choose to subscribe to a hateful mentality that blankets judgment based solely on characteristics determined pre-birth are bad people. It divides us unfairly, and more often than not the people who are oppressed develop a hatred for the class perpetrating the oppression. (Understandably)
This doesn’t have to be the case. We as a society can address this issue head on and change the future of humanity to eliminate hatred forever, if we wanted to. The problem is, for generations on generations the people with the most power to attack the problem were the main perpetrators. They successfully institutionalized inequality, with the power always given disproportionately to white men.
I often feel guilty being born a privileged white male in America. I wish I understood what it would be like to live in another’s life, how would something I can’t control, affect me so negatively so often? The least I can do is ask questions, listen, try to understand, then advocate and educate for the underrepresented.
The reason I am so decidedly against these unfair and often hateful ideologies is because I’ve seen the results, albeit second hand. Technically, I was raised in an area where I was statistically a racial minority. I had strong female influences in my early life through my two sisters and caring mother as well as consistently solid male influences through my father and friends. My experiences were relatively diversified, and I quickly Iearned there’s no difference between you and I. If you are a living being, you yearn for love. Your job as a living human is to give that love to your fellow creatures. Because, “Service to others is the rent you pay for your room on Earth.” – Muhammad Ali
It’s time for us all to start paying our full rent. Alas, with every large societal change, it will take the efforts of many … and a generation of cooperation.
Outline:
I will of course recommend solutions [Action], but first I find it important to understand how we got to this situation in the first place. Skip around if you’d like.
Summary and Outline
Brief History of Women’s and Minority’s Rights
Evolutionary Byproduct Theory
Necessity/Availability of Worldly Understanding
Masculinity Complex
Action
Concluding Thoughts
Brief History of Women’s and Minority’s Rights
There are four main pieces of legislation that enable women and minorities to participate in government, namely the 14th, 15th, and 19th amendments as well as the Voting Rights Act of 1965. For those who don’t know how important these are let me remind you. After the Civil War, you know, that 1861-1865 bloodbath fought over slavery expansion into the west, yeah, the south had to agree to a few reconstruction amendments in order to be represented in Congress again. These amendments abolished slavery, guaranteed citizenship and equal protection of rights, and voting rights for non-whites. Women gained their right vote nationally later in 1920 thanks to the 19th amendment, and all the women and men who fought for the cause. Then finally, not until 100 years post Civil War did the federal government truly guarantee equal rights for voting, outlawing unfair Jim Crow Laws, with the Voting Rights Act. Now they are equal in the eyes of the law, right?
Sure, but oppression is still rampant, equal treatment goes far past the ability to vote, go to school, work, and pay taxes. In order to understand why, it’s important to look even further back, to the origins of our nation.
The Atlantic Slave Trade wasn’t originally racially charged. It was a byproduct of imperial capitalism. European’s created a high demand for the goods being created in the colonies. To keep up with demands and ensure the fattest financial returns – sorry, to ensure “growth”– the new American capitalists needed a large, yet affordable workforce. They tried enslaving natives, but they were either too proud, too dangerous, or would die to malaria. So, why not just ship in laborers from the already well established slave trade market in Africa? They had developed immunity to malaria, a major obstacle encountered in these slavery plantations a.k.a. labor camps. Poor whites saw their job opportunities diminish due to this process and resented African-American slaves, even after they got better jobs in the city. Rich whites needed to display their dominance in order to control their completely involuntary, outnumbering workforce. The owners were in constant fear of a violent slave uprising, so they institutionalized racism. After generations of this perpetual hatred and oppression, racism was ingrained in every white person, obvious hyperbole. Most whites were taught from a very early age that they were God’s children, and that black kids were destined to hell’s fury. They were told that they were a lesser species. This was accepted as truth and has now lived on in some form or another for generations.
Women have an even longer history of inequality that seems more ingrained in our society. Which is why it’s for sure hardest to be a non-white female. This I believe stems from a same sort of institutionalized patriarchy, just one that manifested in human society far earlier on. It’s cultural characteristics vs. a biological separation. Both critical for a healthy society’s survival, and elements of evolution, but one is recognizable before widespread multi-cultural understanding. Which I will talk more about in the next section.
Evolutionary Byproduct Theory
Two words, in-groups and out-groups. Evolutionary psychology tells us that we have always wanted to classify the people around us. There’s a reason why we are naturally prone to gossip: we want to know what type of person other people are. We tend to classify people by many things like economic class, culture, race, religion, and sex. All of us belong to a particular group in each of these classifications. And we naturally associate and advocate for those groups. Many groups form coalitions that sponsor negative rhetoric and action toward others. These often negative associations for people of a different class leads to the philosophy of in-groups and out-groups.
This is a mentality that seems universal to all creatures of evolution. Other species utilize different forms of class structure, most often seen in the separation in labor based on sex. For some reason, just about every culture in human history has exemplified some sort of patriarchal structure. However separate the workload, is it equal? I’d wager no. This is an idea I’ve grappled with for much of the past month, brought about by a few questions I had: When did motherhood stop being considered a better gig than practically anything else? Was it ever considered equal in the eyes of society?
Can we graduate ourselves out of this deeply ingrained separation of power? It seems like the only way we can, is to collectively realize the fact that we are developing a society who’s needs of gender roles are changing. Valuation of a person’s time spent contributing to society should not be judged with such narrow terms as often is done with money, rather by the happiness and fulfillment of the person. I stray away from evaluating a person’s value based on the number of other’s they impact, quality over quantity is a hard argument to fight sometimes. We are changing our own futures now, increasing our reach and speeding past evolution’s hand.
I believe this understanding and change is already in it’s early stages. Equality will grow due to the wide accessibility and understanding of out-group perspectives.
Necessity/Availability of World Understanding
Gaining a well-rounded world view goes a long way to increase the diversity of perceived in-groups. The environment you get raised in, that is the people you learn from and associate with, greatly impact what you think is normal. Growing up well attuned to the differences exemplified across the globe gives the best chance at understanding the myriad of cultures and customs. Experience fuels decision making. The larger breadth of experience, the greater acceptance of others. How romantic.
In recent times, due to the large accessibility of internet communications across the globe, people have been learning more and more about people completely unlike themselves. Honestly, since the era of television people have been taking note of the apparent different lifestyles – domestic and international. I firmly believe that the solution to these problems can come from improved and encouraged access to a wide range of diverse publications on the web. Not just written stories, but audio and video alike. Someday we will have sophisticated enough virtual reality to experience life from a truly alternate point of view. Want to see the affects of racism? -> Enter the perspective of a black guy at a traffic stop. Want to feel sexism? -> Enter the perspective of a woman in a board meeting getting shutdown by her male colleagues.
Access to these advanced technologies may not be widely available, but what should be is the core internet technology. I believe the web of knowledge we humans have created and called the internet should be free and accessible to all people as a right. I often say that people have a right to pursue knowledge, not always attain it depending on classification, but pursue it regardless.
However technology can sometimes foster exclusivity, certain groups will only continue the hate online. This may discourage people from change, but I have faith in humanity that the majority of users would see past the malice. This also has a lot to do with proper education and instruction detailing the difference between well-intentioned and evil user-group.
Masculinity Complex
America has a truly ill conceived ideology of the way a man is supposed to act. These masculinity notions are passed down from generation to generation through fable and fiction. Sayings like “be a man” carry with them connotations that often encourage belittlement of alternate viewpoints. It’s truly no wonder that so many men are being publicly scorned for their sexually heinous actions. These men have been indoctrinated into a society that reaffirms their masculine superiority. In university, I always found it so appalling when guys would congratulate other men on their hookups whereas women would sometimes shame each other for a similar action. Neither group is always correct in their judgment, but the disconnect was apparent. Men are revered by other men who admire their sexual prowess. Yet little is asked of them as to how they managed their fortune, and as we’ve found out time and time again it has a lot to do with force and coercion rather than mutual agreeance.
This problem is rooted deeply in american society. I believe it stems from a sort of reinforced entitlement. The same entitlement that has perpetrated the hateful racism by white people for decades. American culture encourages objectification of women through venues such as: pageantry, fashion/modeling, pornography, and even hooters. These are a few choice examples, but others can be drawn from historical portrayal of flight attendants or secretaries. Even war contributes to this complex, after all, conscription (the draft/selective service) is sexist.
The fact that we as a society have yet to graduate beyond this masculinity complex is not entirely surprising. But unlike the changes necessary to eradicate sexism and racism, this complex must go first. It governs much of the hate that has informally matured over the years. This can be solved with desire and understanding, just like the rest.
Action
As already mentioned a major influence of change is the diversity of an individual’s in-group. This can be increased in many ways, but the easiest by far is with the use of communication technology. Technology meant to share perspectives should be utilized in education from a young age. Alongside factual history and science education, children will be able to feel truly a part of the global society. In the process, they understand the struggle of out-group members. Coming to an understanding: that everyone is just trying their best with what they got. This greatly alleviates pressure from these negative ideologies. We as a society have seemingly outpaced evolution with our technologies. Once we all realize this, equal treatment of individuals will be obvious. Race and sex are pointless measures of capability.
On the recent subject of sexual misconduct: I personally believe that the public shaming of individuals based on allegations alone are hazardous to a healthy state with due process. But when a man has the decency enough to admit his wrong doings, he should be forgiven somewhat by society. He may not be able to do what he used to, but at least he was somewhat a man in the end – honest. Other individuals with racking allegations who deny wrongful action even when there is obvious evidence against them, are not very decent people.
Besides the most positive generational advice of educating the youth, the society must also educate the white men. White men need to increase their understanding then inspire and take positive action through education or advocacy. After all most of the burden is created by us. We should stand up for the underrepresented and disrespected. And always try to be models of sincerity and respect whichever way you choose to show it.
Lastly, it may be helpful to have powerful leaders stand as the head advocate to inspire more action and discussion about the issues at hand. I understand if you don’t want to get threatened or shot, but it may help morale and suppress the discouragement felt at times.
Conclusion
The sooner we act the better. This will undoubtedly take another generation to become noticeably better if we start now. Of course this is never going to go away completely, but we can try our hardest to not perpetrate the oppression. There are many resources online already available to educate people on all of these issues. Diversity and equality are the most important attributes to modern day society, we all need to realize that to overcome the barriers. Luckily a lot is being done right now, but more is on the way. So stop hurting and start helping.
My two favorite things in the whole world, Artificial Intelligence and Government! What is their value, and what sort of interdisciplinary cooperation is necessary to make the world a better place for everybody? I decided to write this now because of a talk Elon Musk gave to a set of America’s Governors where he discussed his business’s plans, the global energy crisis, and the dangers of AI, here is the video. He mentioned again the need for a regulatory agency, or something similar, to ensure the creation of benevolent human-value aligned AI. Which is something I firmly believe is necessary and even want to make a career out of. In this piece I will highlight major advancements in technology and propose significant changes to the way we humans view public policy and governance. There are four sections: AI, Government, AI Helping Government, Government Helping AI. Some definitions and clarifications before I begin:
AI is any intelligent software and/or hardware combination that operates somewhere on the scale of cognition between dust and humans but is not a living being.
Government is in most cases meant to be the USA federal government, unless further specified. State institutions are often very similar in structure and thus can stand to benefit from the same recommendations.
I’m not an expert in either of these fields yet, so read with scrutinous eyes. My opinion is not just my own, but is mine to own.
Let’s dive in!
Stolen From: http://vergys.com/
AI:
Known to many as HAL 9000, Skynet, or Watson, artificial agents are forced to live with the cheesy names we humans give them long after their heyday. Now, two of those AI’s mentioned are fictional, the same two are also malevolent. The third, Watson, is real, and has already started saving lives, literally. The research and development of intelligent systems and algorithms has always been shrouded by public misunderstanding, mostly due to Hollywood’s sensationalism of terror inducing AI. Granted, many scenarios portrayed in film and television are not incredibly far fetched, but they are still too shaky a forecast of what is to come. One thing is for sure, the general populous needs to learn more about the improvements and breakthroughs happening today in artificial intelligence. Their livelihood could potentially be effected by a sweeping wave of new innovations targeting the most automatable parts of civilian life. As a paranoid silicon valley engineer once quoted, “I don’t know of a single job that doesn’t have some Y Combinator startup desperately trying to code it out of existence.” To understand the state of affairs, it’s important to break down the large field of AI and examine each subset’s value and momentum.
A good breakdown of the major components that make up today’s AI is provided by an article by Deloitte on AI’s potential impact on government, posted here. They are:
Expert/Rule-Based Systems
Natural Language Processing (Speech Recognition/Machine Translation)
Computer Vision
Robotics
Machine Learning
Of course, there is often multiple pieces used at once by any one project, but these are the pieces themselves. I’ll mention briefly the importance of each and what is happening to develop them further.
Also, this has nothing to do with what is commonly referred to as Artificial General Intelligence or Artificial Super Intelligence, since those are theoretical states of intelligence. Hypothesizing the future state of a budding technology is both naive and dangerous. I present only information that is either in development currently or already created. However, this is not to say that the development of intelligent, potentially self-replicating software isn’t an incredible technology, it is and may be a top 5 human invention. Every powerful technology has its even share of potential harm and benefit. You just have to try to realize the full impact an enabling development like that has, then try to guarantee the maximum positive impact in reality.
So let’s talk about modern day AI.
Expert Systems:
Our knowledge of automated systems is almost entirely due to expert systems. In the early days of AI research many scientists thought they could create algorithms that worked in a specific domain by coding in a large breadth of expert knowledge. This was just before the second AI winter, a period of time where funding dropped off due to a lack of commercial viability. At the time (late 80’s) over 1 billion dollars was being invested into the promise that AI would be “solved” within a generation. These systems were not a complete bust, many just couldn’t process data quick enough to give a valuable answer. Others simply didn’t have enough knowledge programmed in. Most chat bots or automated telephone system are an example of these expert systems in service. Not many people enjoy a robot picking up the phone instead of a human being, but the fact of the matter is that millions of man hours and company dollars are saved every year because of these programs. So however unaesthetic these systems are, they demand respect because as much as you hate having to click through to a get a human assistant, you probably hate more having to wait on hold for hours.
Natural Language Processing (NLP):
Without a doubt, the most important human invention of all time was one of the first, language. When you consider language a man-made technology, it’s obvious how much you take it for granted. Being able to efficiently communicate ideas to each other is what fostered the growth of communities and enabled us as a species to speed up our own evolution. Written and spoken language set us apart from any other animal. All technological advancement ever would not have been possible without the verbalization of ideas, and then the passing along of those ideas to others in the form of writing. Now, thousands of years later, it seems we are finally going full circle with the mission of teaching computers how to communicate like humans. We are trying to teach a man-made technology how to use another man-made technology. NLP, generally, is the attempt to understand the rules and norms of natural human language. There are two main sub-fields: speech recognition and machine translation.
Speech recognition is the transcribing of spoken language to written language. The other way around isn’t as hard since almost all the rules for speech are written down for different languages. Speech recognition is difficult because humans often speak with ambiguity intentionally, meaning of words are implied with context. Context is never easy for computers to understand. Take sarcasm for instance, software is super great at understanding sarcasm. (lol jk)
Machine translation is the science of taking a set of text in one language and turning it into the same text in a different language. For small string lengths this is not as challenging, but holding context after translation of a large document is near impossible. Methods for these NLP tasks has changed over the years, originally using a bottom up approach of breaking down language into sounds and smaller sub-word structures then building sentences and paragraphs from those sounds or words. Nowadays, a lot of this is done with the help of machine learning off of human transcribed/translated data. This research is fairly dominated by Google, since a Google search really is just understanding a natural language query and returning the most desired information. Plus Google Translate is the most used translation service on the web. Recent developments are only a few months old, and their algorithms are even creating a new language to help understand them all?
Computer Vision:
Similar to how NLP is focused on hearing and understanding human ideas, computer vision is focused on seeing and understanding the world around us. This, also like NLP, relies heavily on machine learning for progress. Some older techniques utilized multiple types of cameras and sensors, but there is a big move in industry towards only using regular 3-color photographs. The idea that many researchers have in their head is, “If the brain and eyes can do it with lenses, rods, cones, and some back-end processing, so can we.” (sorta) Take for instance autonomous vehicles, in the beginning of their development, many companies achieved the feat with an array of LIDAR and motion tracking cameras. Now, many of the forerunners are using only cameras and learning off that data alone. Facebook has one of the largest steaks in this technology because it’s widely used as a image sharing platform (especially with the purchase of Instagram). They implement facial recognition software to auto tag your friends and you in pictures, and now are moving toward even more robust capabilities. Soon, labeling all sorts of objects in moving images will be possible. Thanks to the incredible ground work done at Stanford on a project called ImageNet, Facebook is leading a computer vision revolution.
Robotics:
This is truly where AI becomes visual. Advances in robot manufacturing to create human-like bodies for these intelligent systems may be a large step in the wrong direction, but not many people really want that anyway. Currently the cutting edge of AI and robotics is taking place on the eastern hemisphere. As western cultures continue to be timid accepting robots into daily life, countries like Japan are welcoming them. All over the developed world more people are getting older and living longer. The average age of many countries is trending up, this means that there is going to be a higher demand for care-givers for old people. In places where immigration is not an option, the only answer is robotics. Autonomous cars can also be considered robots. I don’t pay much attention to robotics because I think the most important AI will be almost entirely software based and on devices whose primary use is shared with the AI. Like phones.
Stolen From: https://www.scienceabc.com
Machine Learning:
Without a doubt machine learning will revolutionize the way we view intelligence and the way we think about learning. This sub-field can be broken down into smaller subsets: Supervised, Unsupervised, and Reinforcement Learning. Each of these has their applications, whether it’s grouping similar things (clustering), or classifying objects (classification), machine learning is all about taking data and recognizing patterns. If the data is labeled, the algorithm has some insight to learn off of. If there are no labels, the algorithm seeks to gain insight from realizing all examples. What I think is the most potentially groundbreaking sub-field is deep reinforcement learning. This is where an artificial neural network is trained on live streaming data and is tasked with some optimization function. When you think about human learning abstractly, this seems nearly exactly the same. Advancements in this field are shared by almost all tech companies since the boom of deep learning in 2016. But the company with the most gusto right now would probably be Deepmind, since they started the craze with AlphaGo. These people are pushing to create AGI, artificial general intelligence, and they are very clearly on the right path.
AI scientists are feverishly innovating, trying to build on past inventions to create new marketable algorithms. Different companies are interested in similar tools, so there is some communication, but not much. Some companies are starting to request patents on certain machine learning technologies. I’m all for science and the discovery of new tools and technologies, but there seems to be something missing, right . . . ethics. There are many engineers working very hard to create incredible systems, so many that the philosophers are having a hard time keeping up.
AI Ethics:
There are many non-profit organisations working toward the goal of formalizing AI research and creating a set of standards to govern development. There are a lot more thorny problems when it comes to the development of AI software, many of them centering around what sort of minimizing/maximizing function is morally right and aligned with human values. But much of the AI ethics discussion is also dominated by the idea of mass human unemployment. I’ll talk more about this is a later section, but let me just say this now, we as a society need to talk about what is right and wrong when it comes to introducing a new technology to this world. AI has the potential to both destroy and beautify this universe, the more people dedicated to finding how each scenario plays out the better.
Government:
Never before has American government been so broken, corrupt, and fraudulent. It’s time for a new new deal. There needs to be a huge focus shift. Right now, the administration and congress are more focused on increasing the private sector’s good, than the public sector. Our president would rather guarantee the success of a wealthy coal CEO than the livelihood of millions of unborn children. And our congress would rather fill the pockets of their allies, than develop law for the public good. Money speaks many volumes higher than sense does in DC. And all trust in the system and its contributors has been dropping significantly since January 2017. The american people were desperate for change, sick of politician’s lies and failed promises, but in the end were all conned. Both parties are to blame, the DNC ruined their chances when they unjustly snuffed out Bernie, and the GOP should have never given Trump the light of day. Now everyone suffers, everyone but the already rich. Wealth disparity will only get worse in a Trump America.
Alright, rant aside government has a couple main functions that everybody agrees on. Government is needed to defend and protect its citizens, create fair laws and just punishments, and foster the community of a nation by investing in transportation, education, agriculture, science, and many more. The reason why every kid growing up in the 60’s and 70’s wanted to be an astronaut was because we put a man on the moon. Government should fund inspiring sciences, it’s odd to think about, but wouldn’t it be nice if our government also provided motivation for young people? Kids nowadays don’t dream as big because they are not exposed on a grand scale to anything other than celebrities and pro atheletes. But why does everything seem so broken anyway? Lets explore that question.
Misrepresentation:
Probably the most obvious reason for the apparent disconnect between government officials and their constituents is misrepresentation. Our 115th US congress, the 535 elected officials that are supposed to represent the over 330 million people living in America, are not like us. Lets just look at gender, race, and age for a moment. Here are Congress’ stats: 80% male, 20% female, 7% Hispanic, 9% African-American, 3% Asian-American, 81% white, average age: 58 years old. Here are America’s stats: 50% male, 50% female, 16% Hispanic, 12% African-American, 5% Asian-American, 64% white, average age: 38 years old. If those numbers aren’t enough here are the numbers on wealth: members of congress average net worth is just over 1 million dollars when the average for american households is around 60 thousand dollars. These are only a few statistics showing the lack of diversity and misrepresentation, others such as religion and education draw a larger divide. I think it is important to have wise individuals creating laws for a society, but those wise individuals need to be nested in reality. Perfect democratic representation is probably impossible, but what we have is nowhere even close.
Agency Expansion:
The framers of the constitution would probably not be super stoked about how the government is looking nowadays. Separation of powers is shot. Everybody complains about bureaucratic red-tape and how slow things move in government, but it’s no surprise when you look at how complicated the agency landscape is. I mean, have you ever even heard of Federal Retirement Thrift Investment Board? The president has more power than ever before, having the capability to nominate/replace individuals for all of these major government rolls. If a president were truly corrupt, they would just nominate all their friends to these rolls once elected, or even worse the people who donated the most during the election. Oh wait, shit. I’m not saving that all government agencies are a bad thing, but I do think that there is a lot of fat to be trimmed in the administration and its agencies. The power that they have is derived from the executive branch, but the executive branch should have the least power.
Stolen From: https://www.frtib.gov/
Spending:
One of the most difficult things that our government has to do is figure out who gets what share of the taxes. Luckily the congress can’t raise their own paychecks easily, but they do need to decide who gets what. Remember when the government shutdown and everybody lost their mind in 2013? That’s because they couldn’t agree to where the money went (house republicans hated ACA). They couldn’t vote on an fair budget. As hard as I know this process to be, I think it could be made significantly easier if one thing changed: de-funding defense. Every year the “defense” budget increases. America is a country that was built off of conflict, the only reason why we are considered a world power is because we didn’t go bankrupt fighting the Nazi’s on our own turf. Ever since the end of WWII and the dissolution of Secretary of War into the Secretary of Defense, money flow has increased. Over half of the US federal discretionary spending is spent on the military, more than education, agriculture, science, technology, energy and transportation combined. NASA and the EPA are being decimated under the Trump Administration. The majority of taxpayer dollars goes into programs like medicare, medicaid and social security, I recognize, but those reapportionment’s deserve another conversation entirely. Seriously, when 17 billion dollars is less than a percent of the US budget, I’m sure the Military can do without its total of 13%.
Reactivity:
The final point I wanted to bring up when talking about the functionality of the american federal system is the way it makes change. For all of it’s history, the government has enacted a majority of reactive policies. For instance, the Federal Aviation Administration didn’t exist until over 50 years after the first manned flight. (However there was small regulation committee created in the late 20s) It’s practically impossible for the federal judiciary to act proactively since they can only make rulings on cases that are brought to them. The congress and executive branches have more potential to pass and influence proactive legislature. The reason why I am bringing this up is simple, for government to stay useful, they need to be adaptable. In this age of incredibly fast technological development the american government has had to start acting proactively to maintain it’s competitive advantage over adversaries. I mean, take even the space race and nuclear arms race for instance. Nowadays most of government proactivity is seen in cyber warfare. Since before NATO added cyber to the domains of war America has been attacking and defending itself from cyber attacks. Many times throughout America’s history there has been a necessity to use or regulate a technology that is in it’s early stages of development. The ability to recognize this before full development has always given America an advantage. Technology isn’t the only thing that is moving faster, all of human life seems to be speeding up. So either the government accelerates its functions tremendously, or they start devoting more time to creating more proactive policy, or both hopefully. Technologies like renewable energy, advanced bio-engineering, and AI need proactive policy, whether that means grants or regulations depends on the benefits and dangers.
AI Helping Government:
Both state and national government could use some work when it comes to efficiency. In the Deloitte article I linked earlier, the one that highlighted the different genre’s of modern day AI, they talk at length of ways we could utilize software to speed up government processes. Not only could many processes be sped up with new technology, but many services could be partially automated with already built technology. There has been a trend in the private sector that sees all sorts of different companies transitioning towards a more tech dense environment modelling the numerous software companies that have utilized their computer science and business expertise to create the most efficient workplace possible. The government should do the same. One of Deloitte’s studies followed a child protection service’s employee and found out that more than a third of their day was spent just on paperwork. Having an AI-augmented government would speed up processes and save the taxpayer money.
This may be the the only positive note coming out of the woodwork of the Trump Administration, the new formation of the American Technology Council. This council is made up of some of the most famous Silicon Valley CEOs with the mission of revolutionizing the government’s information technology and cybersecurity. But it’s going to take a lot more than just the top 20 tech CEOs to take care of such a huge problem. There needs to be a full agency or department dedicated to exploring each federal position and whether or not it would be viable, valuable, and vital to automate a portion of it. This agency would not be viewed as the bad guys, they would not be taking people’s jobs away. In fact, they would be seeking solutions to problems that these workers have voiced for years. They would be giving them their job back in a sense, because most jobs would have more time to dedicate to “mission-aligned” tasks.
GovTech Singapore:
Only one nation has an agency dedicated to bringing it’s government into the information age: Singapore. Here’s a quote from the man in charge of the agency, “How do we look at every aspect of our lives and our public sector services and make sure that where there’s an opportunity for that service, product, or experience to be enabled by technology we do so maximally. Where there’s an opportunity to reduce friction and increase efficiency, we do so maximally.” I love this quote, spoken like a true scientist. Singapore is a small city-state-nation near Malaysia and Indonesia, and have only been a country for a little over 50 years. But just in that small amount of time they have transitioned from an undeveloped nation into one of the world leaders in education, health care, and quality of life. They even just passed America on the UN Human Development Index. But how could they do all this in such a short amount of time? Some experts say it’s the pride of the people, others say it’s due to the increasing acceptance and use of technology. While I’m sure it’s a combination of the two, I care more about how they used technology to spring forward past many nations 10 times as old. How did they make this transition in one generation? By caring about the kids. The government has invested large sums into teaching young children the important skills they need for a successful life in the 20th century. They learn sequencing and coding from preschool on, with the curriculum getting more and more complex as they grow older. Even more so, they consider immediate student feedback. If Singapore is different from every other nation because of one thing, it would be their insentient desire to improve the living environment of generations to come. By creating this agency, Singapore hopes to bring positive data analytics and information processing automation to their public sector employees. Not only improving the quality of life of the workers, but also all of the citizens that they interface with. Then they instill their future leaders with the knowledge they need to continue to improve the society. Doesn’t sound that hard right? Well, Singapore only has 5.5 million inhabitants, so there are some scaling problems if adapted to America.
Federal Technology Agency:
I propose that a new agency be created under the president, this agency will be tasked with a similar mission as GovTech in Singapore: to maximize efficiency in government jobs with the proper implementation of technology. This agency would report to the president on the current state of every other department’s information technology landscape, and where improvement can be made. This agency can then recommend, to other agencies, actions that can be taken to reduce friction in operations and alleviate stress on behalf of the workers. More over, the Federal Technology Agency would work alongside other government technology organizations such as the OSTP to research further advancements in technology and promote development competition. This agency will use it’s budget to pay its workers, and auction contracts to companies that can create and maintain technologies devoted to augmenting government processes. Similar to what this company does. We as a society are entering a new digital age, one where information is bountiful but often neglected. If not neglected we as a people can transcend the drudgery of data and work with what we are naturally better at understanding, people. This agency would create a metric calculating potential technological impact to determine which department or agency needs the most improvement. From there, they can start working on how to relieve pressure. This would be a medium to large agency, comprising of executives, managers, automation engineers, economists, human resource managers, and many other fields so that each department scrutinized by the agency would have a fair judgement on what could improve. The short term goal of the agency would be to revamp the government’s IT. The long term goal would be to aide research and development of better, more augmenting technologies. Someday, no one will have to work, but until that day let’s make everybody’s job easier and more fun. Not to mention the amount of money this agency would be saving all of the taxpayers.
Government Helping AI:
For the content you probably skipped all the other stuff to read, you first have to read this: government needs AI to work more efficiently, but government also needs to control the future development of AI. When I say control, I don’t mean that we need another Manhattan Project for AI. First and foremost, artificial intelligence should never be utilized as a weapon, it may augment warfare, but never used directly as a weapon. There are many fundamental problems with artificially intelligent systems, questions concerning who has control and what are good motives, that haven’t been answered correctly yet. Of course this is a new field, but there is a huge problem with this field: money. The problem isn’t a lack of money, but the over incentivization of money. Companies like Google and Amazon are duking it out right now trying to create even more cutting edge software to outperform competitors. As much as I am a capitalist, I’m also a realist, and that sort of fuel for development could lead to unforeseen accidents. You’d think after the 2010 high frequency trading crash everyone would realize that making AI compete against each other is a recipe for disaster. But just like how the mutual funds kept competing, these tech companies will too: because it’s a business. There are two ways the federal government can help the development of benevolent AI: grants and regulations.
Grants:
A lot of the funding for artificial intelligence development comes from the already wealthy silicon valley CEOs. Those who see its value and understand the power that comes with it. This needs to change. As everybody in the industry has noted, research is moving from academia to the private sector. This to me has a lot more negative affects than positive ones. Academia research has always been more transparent than private company research. When the secrets have a dollar sign attacked to it, they are much more important to keep. But that is the opposite of what we need when we are moving forward with the develop of AI. I propose that the american government greatly increase the budget for science and technology research and use it to not only contract companies to build software for the government, but also provide more grants to promising AI research. This could be done under the umbrella of the previously proposed new Federal Technology Agency. With the money also comes an understanding that the government wants to know what you’re up to. These grants would not just be buying a ticket to the magic show, but also a behind the scenes VIP explanation of all the tricks done. The agency should have the expertise available to understand the magic and relay that information to others and make the developments understandable to all. Grants would be given to research groups in both academia and the private sector. These grants will help the research organization hire more talent, and motivate the completion of projects. It helps the government by providing the agency with much needed valuation of progress. The agency will already have a basic road map of where it wants research to go and where not. If any grant recipient starts along the wrong path, the government will know about it sooner rather than later, or at least before it’s too late. Currently the only funding for research given out by the federal government is under the National Institutes of Health and the National Science Foundation. There needs to be more than just grants.gov for us as a nation to improve faster, and we need to keep up.
Regulation:
Nobody likes to be controlled, I get that, but nobody wants to die to nuclear fallout or an avoidable plane crash. Consumers expect their products and services to be safe. So when a company lets them down, it really hurts. As many people, perhaps, already have a unfair negative outlook on AI, making sure that companies deploying artificial agents do so safely and wisely is of the government’s utmost concern. No technology or research needs to be stifled when good regulation is done. If anything, regulation should focus organizations on what is the fair way of operating. Regulation is mainly necessary in the field of AI because of the danger it poses to the general public. The danger is not yet realized by the public, and even worse the engineers. Software is biased, because humans are biased. It’s going to take a lot of work to insure the creation of an unbiased, secure system. Organizations like the Future of Life Institute are actively thinking about how we as a society can promote the development of beneficial AI. They recently held a conference of AI research elites that had a goal of creating a list of “must-follow” principles voted yes on by at least 90% of the attendance. This list can be found here. It outlines three areas of principles: research issues, ethics and values, and long-term issues. I encourage you all to read them carefully because they are obviously well thought out. Each is very important, but I choose to focus on numbers 3, 4, and 5. Together these principles say that there should be a positive communication channel between government and researchers as well as between all research organizations. Most importantly it says that these research and development organizations should avoid an AI arms race. Number 5 references the avoidance of cutting corner on safety standards but fails to recognize any agreed upon set of standards. Hmm, sounds like everybody wants a set of standards to hold each other accountable to, I wonder who would make that? Oh right, the government. These scientists want the government’s help formulating a set of AI research and development standards. So we should do it. Who does it? Maybe it be under the Federal Technology Agency as a Technology Standards Committee. Regardless, the first step for regulators of this industry would be to gain useful insights into the current developments happening around the globe and attempt to measure the rate of advancement. Then and only then can a set of standards be made, and not without the help of all the current researchers. Ideally, whoever is creating these standards has had numerous years of experience in industry designing these systems. Alongside the creation of standards is the creation of enforcement policy. Whether it be the pulling of grant money or, even more extreme, the shutting down of a project or company, this regulatory agency must have the power to resign any project that it deems potentially a great hazard to human life. This would be a tough decision to make by any regard, but having some metric for how positively human-aligned a program is without a doubt would be vital to this survey. Many steps need to be taken to insure public protection from security threats and potential accidents brought about by the uncareful development of AI. With a government agency dedicated to this subject, both policy makers and research scientists can be aware of the paths of AI and their entailed dangers/benefits.
Conclusion:
So, AI is advancing without any signs of stopping, government is bogged down with paperwork and focusing on the wrong things to advance, and no one in government seems to be doing anything about either. The Obama administration at least made a report on the subject, but the few steps he took forward towards positive technology-policy relationships have all but been taken back by the new administration. The government needs to re-imagine the way they do their job, and they need to be comfortable allowing technology to augment their professions. Because with the size of our population, these clerical problems are only going to compound. A new agency needs to be created with the task of providing government with viable and valuable answers to the increasing need for more technology in the workplace. Also, the government needs to do more work to progress the cutting edge of AI technology through grants and regulation. With this sort of agency, billions of taxpayer’s dollars would be saved in man hours, and the potential harms of revolutionary technology like AI can be managed securely. These propositions that I’ve outlined could be realized differently in government. Perhaps instead of an entirely new organization being created, a large subset of an already created agency can be devoted to these practices. But I think it’s fair to say that such an organization, one that both seeks to improve government operations and provide grants and regulations to emerging technologies, would be quite large and in need of a good portion of the US federal budget (% 0.5 maybe). Having those qualities make me want to envision it as a full fledged agency.
I have always been interested in the idea of value, asking questions like, “Does anything have an objective value? Are we born with a set of values that are tied to survival? Why do people’s values hardly ever align?” There are two definitions to the word “value” in those questions, one is the literal assessment of worth, and the other is the subjective desires of an individual. Both link to worth, so I often find myself wondering what has worth to me. I plan on writing many pieces on what I think has worth and ultimately defining my values. I still seek out an objective answer to “What has worth?”, but so far I have been unsuccessful in my self-inquisitions.
Education:
Education is something that I think may have the most objective value, I often say, “Knowing is better than not knowing.” I bet many people have examples of when that’s not true. From my perspective as a 21 year old, I’ve spent practically my entire life in school working hard to get an education. If I had to subscribe to a philosophy I would most likely choose pragmatism, following many teachings of the great American Philosopher, John Dewey. Learning is not only necessary in survival, but imperative in self-growth. This post is dedicated to my experiences in university and what they are worth to me. For clarification, I will be mostly speaking on the actual learning material I garnered knowing fully that college isn’t all about books. I will pepper in life lessons learned outside of class to showcase my college experience when not studying.
University:
University is not right for everybody, but it was for me. I already had a strong skill set in computer networking and hardware from my time spent at the technical high school I attended alongside my public school. (Photo for my own personal embarrassment) With this skill set I could have been some Best Buy Geek Squad worker, or further my knowledge and study computers in college. I wanted to help people, and I knew I would be able to help more people if I got a degree. Plus my mom sorta made me. So with little clue of what each major meant and specialized in I chose to study Computer Engineering, a decision I’ll comment more on later. As far as where I wanted to study, the options were slim. I needed somewhere cheap, in California, and top of the nation for computer engineering. The most logical fit was Cal Poly SLO, and somehow they let me in. What I wanted most out of the next four years was to find a purposeful career path. Now that I am near graduation I have a very good idea of what I want to spend my life doing, so I’d say it was a success.
Cal Poly:
California Polytechnic State University, San Luis Obispo commonly known as Cal Poly SLO is undoubtedly a unique university. Consistently ranked among the top engineering universities in the country, this campus has everything and more to offer its students. The school has a wide range of diverse opinions and interesting people, with 6 colleges, over 60 majors and more than 300 clubs. I spent my extra time getting deeply involved in the nationally recognized orientation program, founding a social fraternity, and holding an on campus IT job for three years. Something important to note before I go into detail about what I learned here is that Cal Poly is on the quarter system. That means that each class is ten weeks long and there are four periods of instruction (including summer). Some say its not enough time to internalize all the important information, I say learn fast or die trying. I guess it’s no surprise that many die trying, the university has a 40% four year graduation rate (and that’s for all majors). Engineering, let alone computer engineering are much lower. The reason why I am graduating on time is because I spent every summer in summer school and had AP credits transfer, in the end I had 28 units completed outside of Cal Poly. That’s about two quarter’s worth of classes, which freed up my schedule a little each quarter. Not to mention I gamed the system for a while by sitting on a “presidential committee” that gave me priority registration for a year. The biggest complaint among students is the congestion of registration and lack of offered courses. The computer science department has been fighting for more faculty for a while, but teachers get paid terribly and engineers that want to teach are hard to come by. With all that aside, I learned a lot. I can confidently say I know how computers work, from transistors to high level programming. I will do my best to explain my education in the following paragraphs.
Freshman Year:
Fall 2013:
Coming to college with little to no coding experience was daunting because as I soon found out many students had already taken AP Comp Sci in high school (too bad it wasn’t offered for me – if only uniform education was a thing). My first quarter I got introduced to code with CPE 123 – Intro to Computing: Computational Art. The class taught a sort of baby Java called Processing where we designed basic figures with simple geometric shapes, like penguins and fish. For my final project I got carried by a coding wiz named Christopher who taught me my first important lesson. Just because you aren’t as good a coder, doesn’t mean you can’t help. We built a fun interactive game that opened a chest and shot out fireworks on a mouse click.
Winter 2014:
My first real coding class came in the form of CPE 101 – Fundamentals of Computer Science 1. It was C. This class taught the basics, conditionals, loops, functions. I don;t remember any specific projects, but I remembering thinking, “Code is magic, but I hate having to type semicolon at the end of every line”. This class got changed when I was a Junior to python, probably would have thought the same thing, but instead said, “I hate having to indent everything perfectly”. Either way, it was a proper introduction to a classic language and many classes down the line were in C so I appreciated the intro. The other CPE class I took was CPE 141 – Discrete Structures, this was all about ways to store data. It went well with the first intro class because it showcases things like arrays, and how data moved around the computer.
Spring 2014:
Like many classes in my major, they were apart of a series of courses. What came after CPE 101 was CPE 102 – Fundamentals 2. However, this was in Java, for whatever reason. This course taught more advanced data structures like linked lists and basic algorithms like search. Up until this point all I knew was imperative and functional programming. With this class I was introduced to object oriented. I don’t know if I skipped a class or something, but to this day OO is weird to me even if it is most logical and explicit. This quarter also marked the beginning of the electrical engineering courses. EE 112 – Electric Circuit Analysis 1 was all about Ohms Law basically. It taught what current was, how voltage controlled it and how resistance managed it. This is where the CPE’s started wishing they were CSC’s. I was different, I enjoyed the math and was curious about electricity so I had fun.
Sophomore Year:
Fall 2014:
For CPE’s sophomore year and junior year are by far the toughest. There are many pivotal classes that make or break your career. If you fail anything, you will be held another year. It was also difficult because each student was required to take many other difficult courses outside the major like physics and linear algebra. My first quarter I was in CPE 103 – Fundamentals 3, CPE 133 – Digital Design, and EE 211 – Electric Circuit Analysis 2. 103 was also in Java and went over more complex algorithms and data structures like hash maps and sorting. 133 was the first real computer engineering class where we learned assembly code and what was going on under the hood with C. I really enjoyed this as I got to see what the machine sees, how the CPU runs instructions. We also worked with FPGA’s like the Nexys 2 board and learned the basis to VHDL. EE 211 was tough, it was all about op amps, RLC circuits, and phasors. Capacitors and inductors were now about of my circuit toolkit. This was about the time were things got really confusing. Like when you do a lot of multiplication and division, addition and subtraction start to seem weird. I found it difficult to visualize how the electrons were reacting to these new elements, so it was then difficult to understand the basics as well. Plus, mesh analysis and superposition are freaky.
Winter 2015:
I took it easy this quarter, I was worried about my GPA and wanted to keep above a 3.0. I took only one major course: CPE 233, the follow up to 133. This class was a bit more fun than the last, because we got to chose our project. We got to build a CPU with assembly and VHDL. It was complete with adders, registers, and stack. The final project my group member and I got to experiment with VGA and designed a tomogachi knock-off with a feed and kill switch. It was actually amazing.
Spring 2015:
Another more relax quarter, because at this time I was sitting on the exec board of my fraternity and a team member for orientation. I took the last circuit analysis class EE 212. Finally the whole picture was revealed to us, AC power did exist and it was super cool. Transformers were added to our toolkit and a new domain was understood, frequency response. It was interesting and enlightening to grasp the relation between magnetism and electricity more fully. Both are incredible forces in our universe, that are very much related. I also took a modern physics class, and learned E=mc^2! Quantum physics isn’t that bad! What is hard is grasping that energy is a wave and a particle always, kinda?
Junior Year:
Fall 2015:
This was my hardest quarter at Cal Poly. The hardest EE class and hardest CSC class both in the same quarter, what was I thinking? CPE 357 – Systems Programming, this class is notorious for failure, and it was a prerequisite for practically all upper division courses. It had been awhile since I programmed in C, so when this class came around I was blindsided. 3-5 labs a week, 3 huge projects, 6 midterms and a final. This class challenged me beyond any other to date. But after, I knew I could program, there was no doubt in my mind. This also introduced me to a terrible pain I later coined as code migraines. No special project, just constant code. EE 306 – Semiconductors was all about NMOS/PMOS dioeds, transistors, and BJTs. So much crammed into one class, not to mention the hardest EE professor who made my cry in office hours. I appreciated learning how doping works, but really didn’t appreciate learning about technology that was obsolete. Finally I had EE 228 – Continuous Time Signals and Systems, which was all about the frequency and time domains, and a bit about foureir transforms. What I learned most from this class is how a function is just a black box with an input an an output.
Winter 2016:
My last EE course, thank god. EE 307 was amazing, in it I learned about building all sorts of gates/adders/multipliers with transistors. This really wrapped up electrical engineering for me because I could truly understand how current made decisions in a transistor network. Realizing the massive scale of these networks in our everyday devices was eye opening. Moore’s Law was shown to me and I was skeptical and bewildered. I was also really glad to not have to draw any more circuits, because straight lines aren’t my thing. I also took CPE 315 – Computer Architecture this quarter which taught me about some important features to computers. With this class I found out I loved computer history and started reading about John von Neumann. It;s incredible to think that 99% of computers out in the world follow the same style of architecture. I also took technical writing and stats, which I loved and wanted to do more of after school.
Spring 2016:
My first really fun quarter! I took CPE 329 – Microprocessor Based Design, where we wrote C to control a small board. This class was great because of the free form final project. My partner and I chose to build a heartbeat sensor utilizing a photosensitive diode and bright LED. CPE 453 – Operating Systems was the second of the “big three” 357 being the first. This class went over every major OS related subject, from scheduling to file systems. The final project had us create a mini file system similar to Unix, the code ended up close to 2,500 lines all in C. I respect the early programmers so much after this class. It helped me realize just how much code is out there in the world, and somebody had to write it. Finally, the most pivotal class in my college career: CPE 480 – AI. I learned so much in and out of the classroom for this course, read numerous books/research papers and watched many presentations. This marks the period in my life where I finally decided what I wanted to get in to. The project I worked on was a machine learning model to predict stock valuations using twitter sentiment data. This was only to be the beginning. I also took a social ethics course with peaked my interest because AI has many social implications that I hope to solve in my lifetime.
Senior Year:
Fall 2016:
Senior year, the final stretch, with an end in sight I wanted to utilize my time looking into what I would do after university. I took CPE 349 – Algorithms, which was a bit of review for me, but allowed me to practice my python skills, which until them were terrible. Looking back on the programs I wrote I see how crappy they were, but you have to start somewhere. CPE 365 – Databases was the second technical elective out of three that I chose. It was all SQL all day, just like 357 this course had an excessive amount of work. But at least now I can say I know SQL like the back of my hand. For the final project I built football statistics app utilizing JDBC. This quarter also marked the beginning of Capstone, a 2 quarter long project based course. I was apart of the group that was tasked with redesigning an outdated home inspection app. I was the program manager and led the team through the process of prototyping and initial coding for the iPad app.
Winter 2017:
The second quarter of Capstone was in full swing and many of my weekends were spent in the computer lab programming in XCode. I learned that planning is the most important part of any coding project. I learned that by not finishing the project in the amount of time we expected. I guess I also learned to manage my expectations. Lastly, I learned that college is full of busy students and not everybody is somebody you can rely on. The last of the “big three” was also this quarter, CPE 464 – Networks. I loved this class because it was all about the internet! TCP/IP protocol stack was the name of the game. I build my own messaging app and even my own ftp server, from scratch. This was the class where I wrote the most lines by myself for one program, around 1800. I also started my senior project, which is another two quarter long class. I decided on building a machine learning and computer vision slither.io bot.
Spring 2017:
My last technical elective was spent on CPE 466 – Data Mining. So far We have surveyed basic rules mining from web data and supervised/unsupervised learning. Pretty much all major coding is in python (some SQL), and I am finally getting good at using python. I’m also finishing up my senior project.
Conclusions:
Cal Poly has given me so much knowledge, most of which I could never have even thought of learning. I’m very proud of my education, and am hopeful to learn even more in my future. My university career has given me passion and guidance to follow my dreams of programming AI applications. If I learned anything it’s this: “Life is full of things to learn, never be complacent, always improve and always strive to be better. If you try everyday to grow your skill set and understanding of the world, you will die happy.” It may seem morbid, but to me it’s just realistic and simple.
