Austinology by LASA Ezine

Table of Contents About the Editors

AI: The Good, the Bad and the Ugly

The Inner Workings of a Chatbot

The Game Beyond the Code

The Process of Making a Game

East Austin Tech Invasion

History of Big Tech Moves to Austin

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3-4 5-8 9-10 11-14 15-16 17-20 21-22

Meet The Authors Gabriel Gallegos This is Gabriel, he has a lot of friends and enjoys playing ultimate frisbee for his school team. Though hes not very good, he is improving. Gabriel also enjoys playing video games in his free time, sometimes a little too much. His grades are good enough, and fortunately he hasnt been removed from the school yet. Gabriels favorite class is World Geo and his least favorite class is AP Spanish. Even with all the homework, assignments, and tests he gets, he still loves being a student at LASA.

Cole Henson This is the legend himself, Cole. He attends LASA at day to become smart but lives on a boat in the amazon rainforest. Cole is very good at writing Autobiographies and he is a better student then Gabriel. Cole has as few friends as he can without being asked questions about his mental health. Cole’s favorite subject is world geography because Cole is crazy. He enjoys playing in school band and is a mediocre clarinet player.

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Kent Smith This is the Rubik’s cube god, Kent. His fastest time is 9 seconds and he can also solve a cube one handed. Kent likes playing video games and coding in his free time, and sometimes participates in birdwatching. He plays piano and the EWI. (electronic wind instrument) His favorite class is AP Computer Science because he is an absolute nerd. He also plays tetris in class sometimes and Gabriel and Cole thinks he is really good (hes not). He made the cover for this magazine and he has performed the haywyrmabaueieoqiqiopppqo skip 10 times (look it up PLEASE).

Kiri Orr This is Kiri. She is a member of the LASA Raptor Band. She plays alto saxophone, bass, baritone saxophone and is also a member of stage band. Her favorite class is band class. Like seriously, her entire existence revolves around it.

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AI: The Good, The Bad and The Ugly A deep dive into the pros and cons of using Artificial Intelligence By Kent Smith

I has taken many forms; self driving cars, manufacturing robots, security systems and the personal assistant sitting in your phone right now. Most people would consider AI to be a purely positive innovation, but not many know the negative consequences that follow misuse of AI. Artificial Intelligence and Machine Learning is a powerful tool that has taken large technological advances over the past few decades. Researchers around the world are improving on AI systems, and using machines to learn new things. As a result of the research, many companies in a variety of fields are using Machine Learning to help them succeed. Some fear that Artificial

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Intelligence will take over the world, but AI researcher Jonathan Mugan thinks that machines could help us answer the questions of life. “There’s a lot of scientiﬁc mysteries, you know, we don’t know what happened before the big bang, or all that kind of crazy stuff,” Mugan said. “It would be nice if there was a machine that could learn that kind of thing, or infer models about that, and then explain it to us.”

In order to have a " "model, you need some sort of rich training, like the way we learn. - Jonathan Mugan

Artiﬁcial Intelligence can be used in all sorts of subjects, It’s adaptiveness is one of the reasons why it has become so popular. For example, at

UT, people are using Machine Learning to analyze planets, according to UT Professor Dana Ballard. “You can find stars that have solar systems, and everybody wants to know if they can support life,” said Ballard. “The idea is that the planets have to be in this sweet spot, not too cold or too hot. So, we use machine learning and you can get some good guesses about the probability that you’d find so many planets with the right properties.” Machine Learning can also be used in places you wouldn’t expect, like industrial industries. For example, a fiberglass manufacturer, who was helped by Michael Grant, the VP of services at Anaconda, a company that distributes tools for AI and

machine learning, uses AI to help their business. “We worked with a company who makes fiberglass insulation,” Grant said. “First of all, they make more money, because their factories are more efficient, so that’s great. But also, fiberglass insulation helps save the planet, right?” AI can also be used for fun too, from chatbots to a robot that can play chess. Ballard, who plays Go, an abstract strategy board game for two

players in which the aim is to surround more territory than the opponent, says that a computer is the best Go player. “I play Go, and of course, the computer plays the best Go,” Ballard added. “The reason they got to be the best, the computers, is because machine learning came up with a probability search technique that’s brand new, and you can look down huge search spaces now.”

Even with the number of companies using AI increasing quickly, Grant thinks that AI could be integrated into even more areas. “I think that the more that we the toolmakers can help make it easier for people to try, then the more that’s going to happen,” Grant explained. “So yeah, there’s still a lot of places where machine learning can be applied. Absolutely.”

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There are many reasons why people begin researching AI, but many start by studying the brain. Mugan, for example, said that his research with AI was sparked by his interest in the human brain. “Well, I used to be interested in how the human brain worked, and it turns out that we don’t really know,” Mugan explained. “I thought a different way to go about it would be to try and build a brain, and so AI seemed like a way to do that.” It turns out there are a lot of parallels between the brain and Artiﬁcial Intelligence, and many researchers apply concepts between them.

see like a human, and so we could answer kinds of questions that you couldn’t before.” Ballard explains.

They can learn a " tremendous amount about"

“If you ﬁnd ways to understand how the brain works, and they’re way more intelligent than robots, then maybe some of those methods will translate onto the robot,” Ballard said.

you without having to listen to you, you know? - Michael Grant

Unfortunately, there are some glaring problems with Artiﬁcial Intelligence though, Some machines are built to try and many people don’t realize this. Grant says that and understand how humans work too. Ballard built a robot some people use AI without sufﬁcient knowledge. that would mimic human eye movements, or saccades, “There are AI systems that which helped develop a were built in good faith; deeper understanding of people trying to do good human perception. things for their company or for the world, but because “We had a robot that could they forgot important

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nuances of the modeling process, it had some bad consequences,” Grant explained. Grant also describes an example of the bad consequence resulting from unintentional misuse of a machine learning system with a facial recognition system. “For example, if I’m a bright eyed, ambitious AI engineer, and I want to build a facial recognition system,” Grant added. “If I happen to feed a bunch of light skinned faces into my training algorithm, then that algorithm is not going to do a very good job of detecting dark skinned faces.” Grant also says that some people, with a set of data, jump to AI as their solution too quickly, when human

analysis would be easier or more beneﬁcial. “I think that a lot of people believe that AI is magic, or something so powerful that it’s almost like magic,” Grant explained. “So people who don’t spend a lot of time doing it will often tell their data scientists or their engineers, hey, go do deep learning on this data and tell us what happens, without any real understanding of how useful or practical that’s going to be.” The ethics of AI are also a

hotly debated topic. Using AI, there could be ways to uncover an anonymous person’s identity online. “Privacy issues, for example. I could use AI to mine publicly available data sources, and identify people from their anonymous data that’s out on the internet,” Grant added. “I could combine a bunch of datasets together, and I could ﬁgure out things about a person that they didn’t intend for me to know.”

more advanced, hopefully it gets applied in more places to help the world. Problems will arise, but more people should become aware of the problems that come with AI and develop solutions, and I hope that ethics will be kept in mind when developing new, powerful systems.

The End

As AI gets inevitably

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The Innerworkings of a Chatbot By Kent Smith

Making A Neural Network First, what is a Neural Network? A neural network is a computational representation of the brain. A complicated network holds many nodes. These nodes are similar to neurons in the human brain, they take in some inputs, process it, and give out an output. Stringing these nodes togethor will form your network and will produce the final output.

Hello!

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Training Your Neural Network Training a neural network is the process of giving your neural network a bunch of data, so it can base it's calculations off of something. Training a neural network will teach it how to give an output based on the data you give as a base. For a chatbot, the neural network will require a ton of text, so it can generate more natural sounding sentences based on the text. The more data it gets, the more on-topic and natural the text will sound. The problem with training an AI like this is that it will never know the rich meaning behind a word, only the words are that are likely to go near it.

Creating The Response When creating a responce to a message, the neural network will calculate a probability for the next word in the sentence. The probablity is affected by weights which are decided based on the data the network is trained with. The network will then pick the word with the highest probability and add it to the sentence. It will repeat these steps while updating the weights each time, until the sentence is complete.

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GAME assert ( ( Hodor ) ) : "Nothing burns like the cold." def TABLE[( ( -Hodor - -Ygritte + COLS ) )][k] { -Ygritte; dog += 620.248 * --mislead(( -59 / -mislead(Jon) - COLS - -Stark ),--Hodor protect(Hodor,TABLE[Stark][TABLE[-0.48][--86]],-15)) - Jon; Ygritte *= mislead()

} def TABLE[1][j] {

} else {

if(-1){ if(dog){ -betray(dog - 710.1,ROWS,ROWS); if(COLS){ -( -580.823 ); Arya *= betray(ROWS) * TABLE[ROWS][Stark]; Arya /= 280.414

-ROWS; betray() } } } else { -40 + TABLE[80][( TABLE[Hodor + x][COLS] )] + -protect(rule(-COLS,-( -dog / ROWS / ( protect(-64,rule(COLS,TABLE[-x][rule(-37 * mislead(protect()) / ( ( ( TABLE[-COLS * -mislead(-( ( 36 ) ),ROWS,COLS)][-1] * --destroy(TABLE[protect(-x,Hodor,-0.49)][Hodor],COLS) / ( TABLE[mislead(( TABLE[TABLE[-foo(Arya - ( ( ( 57 ) ) ))][TABLE[--0.43 / -( ( bar(-0.65,( 0.7 ),Ygritte) ) COLS )][( 0.04 )]]][( Ygritte )] ))][-Jon] ) ) ) ) + -Hodor / -TABLE[( foo(betray(TABLE[x][-1] / y,Jon)) )][-COLS],-26)])) ) )) + -( ( 68 ) - 310.651 ),0.84 * destroy(foo(destroy(protect(protect() / ( Stark - -( -Sansa ) ),-85 * ROWS,destroy(foo(destroy(COLS / --ROWS / TABLE[TABLE[COLS + protect(TABLE[-( -1 )][-COLS],x,rule(( ROWS ),( foo() ),TABLE[TABLE[protect(Hodor)][foo(-99,TABLE[( 810.1 )][bar(Sansa,-betray(Jon)) / bar(foo(x,ROWS))] / COLS) / -( foo(-TABLE[protect(ROWS,mislead(Hodor))][-0.78 / COLS],TABLE[( -33 ) / ( ( betray(TABLE[bar(-Jon / x / -Hodor) * -( 57 ) * -0.37][( destroy(1,--( mislead(TABLE[-18][Jon]) * TABLE[COLS][( -1 * x ) + 1] / ( betray(( TABLE[y / dog * -1][betray(TABLE[( bar(( 0.59 ),( -1 - ( ( ( Jon ) ) ) )) )][----ROWS],Jon,( protect(destroy() + -TABLE[( bar(( -1 )) )][COLS] - Hodor,COLS) ))] )) ) ),-COLS) / Stark / rule(Sansa - COLS) )]) ) )][Hodor]) )] / COLS][-protect(Arya * ( -COLS + --( COLS ) / -Hodor / y + ( TABLE[( 0.09 )][-bar(( Stark - 22 * Stark / COLS ),-63 + ( betray(rule()) )) - -1] ) - destroy(protect(),-( -23 )) )) - Hodor]))][rule()]][( -0.63 )],-1),14),betray(-0.31 - COLS,-ROWS))),ROWS,Sansa))),destroy(foo(( destroy(-1,foo(1)) / --( Sansa ) ) / 28,Hodor,x),--0.95,-0.75 * bar(COLS)) * -bar()) } } var Sansa = Jon def rule(x,Ygritte){ } var Jon = TABLE[-( ( 310.989 ) )][--( --( --foo(( COLS ) * Hodor) ) )] def mislead(x,x){ if(y){ Ygritte /= ROWS

};

if(( destroy(1) )){ destroy(protect(-rule(( ( y ) + --COLS ),TABLE[betray()][COLS]),-( ( -Stark ) ) / COLS / Hodor + 28 / COLS,--x + Stark)); ( destroy(-630.6987,( TABLE[bar(-TABLE[( -930.874 * TABLE[betray(-0.03) + ----rule(protect(--0.81,Stark),COLS) / betray(( --0.14 ),-bar(bar(),-protect(94,TABLE[foo(rule(-440.603),TABLE[( 75 )][TABLE[protect()][-rule(--(

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The Game Beyond The Code The required process by which your favorite video games come to be.

Over the past 2 weeks I interviewed three different people that work in the game industry through online video calls. They each have different professions, skills, and experience. The first game designer is Michael Daubert, and he has been a game designer, CEO, and CCO of Phaser Lock Interactive for 20 years, and specializes in VR game deisgn. The second person is Ethan Caraway, the founder of Flight Paper Studio and having 9 years of experience with game design. Finally, Michael Wells; he programs and develops games requested by clients. As you press play on the screen of your favorite video game, you think to yourself “What is the story behind this game and what was the process of its creation?” All these years of video games and people have been taking them for granted, not realizing the effort and time that was put into the game purely for your and others enjoyment. According to Ethan Caraway, “The first thing that you’ll learn whenever you start working on video games, is that video games take a lot of people to work on.”

small team is programming and designing, but he also works with character and environmental artists, musicians, sound engineers, and many other employees, each with their own set of unique talents. Typically, popular games like the sandbox, survival game, Minecraft, required around 500 developers to create. Even small games like Titanfall still had 90 people working on the game. Knowing this, Caraway and other game developers say that there is a general process in making video games.

vvv

“Try starting with a plan. And I mean, down to the absolute smallest, most insignificant little detail,” -Ethan

His role within his #

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Michael Daubert says that game design starts from the early concept side, which the game experts call a GDD (Game Design Document). A GDD is where you write a high-level vision of what the game is going to be. In this document you call out all the different systems, like the different technical needs or the art needs that you're going to require. In the beginning, this document is usually really thick, ranging from 100 to 200 pages, and it keeps growing. Once completed you go and present in front of a publisher to raise money. The next step is finding the game loop which is a short description of the loop that the player will go through from the very beginning to the completion side. If you think of any game that you have ever played, whether it has a complicated or basic plot, as long as it has a beginning and an end, chances

are it will have a game loop. The following step is called the fun factor. At this point in the process you as a game designer should be asking if the game is fun or not.

Daubert. A vertical slice is a demo that shows what the player will experience in the full version of the game.

You continue asking yourself these questions, correcting and improving your game, assuring that every moment in the game you are experienceing something new that is entertaining until you reach the completeion side of it. Next, you create the Vertical Slice, says Michael

“The vertical slice contains one visual experience for every element in the game,” adds Michael Wells.

“Ask yourself, is the game fun yet, if not, how will you make it fun” - Michael Daubert #

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After that, Michael Daubert explains that the next step of the video game process is creating the proof of concept. The proof of concept is a complicated description of the game that you are making. This explains how the game functions, how the ai functions, and other mechanical parts of the game. Michael Wells says to “Ignore the artistic side of the design, since the proof of concept is purely made to show the product to a company.”

would be the greatest job ever. You get to play video games and get paid for it. No, what QA does is they jump in and they play the game until they find a bug. Then they have to play the game three times.” On top of that they have to find the bug over and over and over to try to reproduce the bug, document the proof and send it back to the game developers. After that, they wait for the programmer and then they keep playing, writing down and sending back all the bugs they find.

Unfortunately, it is impossible to eliminate all possible bugs due to all of the possible issues that each factor and element of the game could have. “It is impossible to have no bugs. The great thing about game development is there’s so many moving pieces.” says Michael Daubert. Finally, the launch of the game, where it is released to the public. In Michael Dauberts words, “Fingers crossed, it’s a successful launch.”

Once finished with the proof of concept, the alpha stage begins. The alpha stage is where all the questions are being answered at the alpha level. This is where the publisher, or anyone should be able to walk in, play the game, understand the game, and see where it’s going visually. It’s not all complete yet, but they should understand and all systems should be defined already. Once you have finished the overall game, the beta stage commences. In this stage, an important job in the video game industry steps in, the QA. In Michael’s words, “QA, you would think, #

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The Process of Game design document (GDD) Is a document that contains your entire plan about the game you envision like what the game is going to be about, how will you win, what is the main objective, what will be in the game, etc. (100-200 pages)

Game Loop This is a short description talking about the full circle the player is going to go through from the very beginning to the completion side of it (1 page)

Fun Factor

This is an evaluation where you look at the game and ask yourself is it fun and if not how will you turn it into the game you want it to be

Vertical Slice This is where you will create a playable demo for the company that demonstrates what the player will experience in the game Austinology - 15

Making A Game Proof Of Concept (POC) This is the product concept. This is made to sell your video game product to a company(What is the ai in the game, what are the particles like, how does the game function)? ignore the artistic side of the design

Alpha Stage This is when all the questions are answered and the system of the game is already defined. At this point, anybody should be able to play the game, understand the game, and see where its visually going.

Beta Stage

This is where your games content is complete and people play your game and give you feedback. Also, QA plays your game and reports bugs that are found by being played hundreds of times, then these bugs are solved by the programmers and designers

Launch

This is where you make the game available to consumers and release it into the public

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East Austin Tech Invasion How large migrations of tech workers force people out of their homes Cole Henson

Austin Skyline

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Imagine starting a family in Austin, living out the American dream by buying a house you can reasonably afford. Then 40 years later being forced to leave your home because you can’t afford to pay your property taxes. This is unfortunately the situation many residents of East Austin are going through. These residents bought homes that weren’t too expensive for them but they can’t pay their taxes because of factors they cannot control. In recent years, many large tech companies such as Oracle, Apple, and Google are establishing large workforces in Austin. This migration of well paid workers to Austin has spiked house prices. In many cases, these high paid workers have the resources to pay $100,000 over the market price of a house. As of August 24, 2021, the median home price in Austin has reached $575,000. High local property taxes combined with this sharp increase in home value has led many to be forced out of their homes because they cannot afford to live there.

One of the main reasons the house prices are so high is because many people move to Austin to work for large tech companies who pay them well above the median in the United States. Another major reason housing prices in Austin have risen concerns the pandemic, since the number of people working labour jobs during the pandemic sharply declined, fewer houses were built. This increase in housing prices has led many to be displaced from their homes and

gentrification. The process by which poor urban areas are changed by rich people moving in, is now one of the most prominent issues for longtime residents of east Austin. To combat displacement as a result of gentrification, many measures have been put in place by nonprofits and the government. One example of this is the Uprooted project.

House built on unstable foundations collapses

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The Uprooted project has been one of many efforts to prevent displacement, one of these efforts has been by Raul Alvarez, who was elected to the city council in 2000 and was reelected in 2003. Raul Alvarez now serves as the executive director of the Community Advancement network where he works with clients to help them continue living in their current residencies. Although the city of Austin funded the Uprooted project study, Alvarez doesn’t think they are doing enough as he said, “there really “I don’t think anyone is no other entity in the whole city that provides on the city council was taxpayer assistance.” This is surprising because against doing the study, assisting people with their and people thought that property taxes is one of the more straightforward ways to help them. it would be a good idea Unfortunately, many to have some sort of sys- people do not get help and are forced to leave the tematic analysis of gentri- city. The recent pandemic has led many to lose fication, because it gets their jobs and unable to pay their bills. Along talked about so much” with a decrease in the labour force caused by -Raul Alvarez COVID-19 and a decrease The Uprooted project is a study by Heather K. Way, Elizabeth Mueller, and Jake Wegmann, all of whom currently work at the University of Texas in Austin. The project’s goal is to collect data about the current areas where gentrification is most prevalent and provide tools to combat gentrification for the government and local communities. As Jacob Wegmann, one of the people working on the study explains the reasoning behind the funding of the project,

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of immigration, the large number of people working in tech relative to those working in labour jobs is high, as Jacob Wegmann, a professor at UT who worked on the uprooted project, which is a study of gentrification in Austin, put it, “we have this narrative that Austin’s booming, and everyone who’s moving here is a rich, you know, tech bro from the Bay Area, who’s going to get paid 120,000 bucks a year to work at Google,” he also says, “a lot of low wage workers moved to Austin because low wage jobs get created at the same time that high wage jobs do, but those people come here and they compete in a very tough housing market that’s pretty unforgiving for them.” In short, this means most of the people who can afford to live near central Austin, work in the tech industry. The issue here is that lower income workers who build houses live further from their work, so fewer people are able to work these jobs compared to the greater population. Amidst all the heat of the Austin housing

market, many investments have actually become less lucrative. As Leslie Kasen, a realtor who lives and works in the Austin metropolitan area says, “when home prices go up, where the median home price is 350,000 or 400,000. But if you buy it, you can only get $2,000 for rent, the money that they’re getting every month isn’t enough of a return on the down payment that they put in.” Although fewer people are investing in houses, the total demand in Austin continues to grow because the number of people moving to Austin greatly outweighs the decrease in investment. Hopefully, over time policy will be put in place by the city council to reduce the negative effects of gentrification. If poor populations continue to be forced out of Austin, there will be nobody to do the labour intensive jobs

that don’t pay well but are necessary. A large aspect of the culture in Austin will also leave with the displaced people and Austin will become arguably worse to live in as costs of living go up. To put it simply, the people who are moving in for tech jobs need the people in East Austin just as much as many of the people in East “We’re supposed to be Austin need support to ok that the amerincan stay in their homes.

dream is shattered for them?” -Raul Avarez

“Austin - East Austin: Mural at Cisco’s” by wallyg is licensed with CC BY-NC-ND 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/2.0/

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History of

Big Tech Moves to Austin

D EL L

Dell Round rock office

Texas Instruments moves to austin Texas Instruments now employs 385 perminant workers but once employed 5,000 people in austin

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1967

1963

IMB moves its Selectric typewriter facility to Austin

IMB employs 6,000 people in austin

Oil prices fall and Austin moves towards tech in an effort to avoid being deendent on oil

1980 1984 1974 Dell is founded in

Moterola moves to austin Moterola now employs 141 people

Austin. Dell now employs 13,000 people in austin

Information from Austin Chmaber of commerce and various company press releases

Apple moves to austin Apple’s campus in austin where ground was broken in 2019 will employ 5,000 people in austin. Right now there are 7,000 Apple employees in austin

Oracle announced it will move its headquarters to austin. Right now Oracle employs 2,500 people in austin, but after the headquarter move the company could employ 10,00 people in Austin.

1992

2016 NXP Superconductors moves to austin NXP now employs 4,000 people in austin

2020 Austinonlogy - 22 2021

Tesla announces it will move its headquarter to Austin and will build a factory in Austin. So far tesla has created 5,000 new jobs in austin.