Reinvented Magazine Issue No. 18

TeamCredits

EDITOR-IN-CHIEF

Caeley Looney

MARKETING

Social Media Manager:

Asia Fee

Varija Mehta

Anika Garg

Chantelle Faria

Anvi Kumar

Ramya Kotagiri

DEVELOPMENT

Development Director: Palak Mehta

Ankita Sundar

Avantika Samanta

Risha Bhat

Sarasi Rout

Siya Goyal

WRITING

Writing Officer:

Aparna Rajesh

Writing Director: Miranda Mogan

Onboarding Manager:

Madeline Day

Guest Writer Logistics Manager: Yashvi Patel

Staff Writers:

Emily Zakkak

Isabel Powell

JoCee Holladay

Madeline Day

Rachel Lynn Mason

Sophia Tang

Andrea Gajic

Hanna Gabrielle Bidon

Suhani Modha

Angela Tian

Mariah Lewis

Zoha Ali Khan

Chloe Drieu

Proofreaders:

Grace Pfohl

Lavanya Sharma

Madeleine Bloomer

ISSN: 2770-6672

FINANCE

Finance Manager: Niyati Patel

PUBLISHING

Publishing Officer: Laura Peckyno

OPERATIONS

COO:

Yessenia Lopez

Volunteer Director: Shayna Weinstein

Sharafa Mohammed

VOLUNTEERS

Volunteer Manager:

Allison Maurice

Avika Patel

Nithya Parepally

Sindhu Sivasankar

ADVISORY BOARD

Casey Fiesler

Ruthe Farmer

Karen Peterson

Jannie Fernandez

Raquel Romano

Melissa Jones

Divyansh Kaushik

CREATIVE

Magazine Director: Abigail Muth

Branding Director: Megan Jacob

Creative Logistics Manager: Abhirami Rajesh

Amy Loveless

Asher Below

Daniela Freire

Dima Hamza

Faith Lam

Jolie Miller

Maria Fernanda Sagastume

Reyna Morales Lumagui

Rimi Chakravarti

Sydney Bowler

Mridula Srinivas

EDITOR EDITOR LETTER FROM THE

Dear Readers,

Firstly, congrats on surviving the summer! I moved to Texas a little over a year ago, and, somehow, the summers here are even hotter than the summers I experienced in Florida so I’m hoping you all were able to enjoy the time off and stay cool! Secondly, welcome back to school! While back- to- school season was never really my favorite time of the year, it does mean that a brand new issue of Reinvented Magazine is hot off the press and ready for you to read!

This issue is focused all around one of the hottest topics of the year: artificial intelligence. Our team has seen countless questions from students surrounding AI – how does it work? How do we monitor and police it? How do we evaluate the ethics of AI? All of these questions, and more, will be answered throughout the pages of this issue.

Our team worked alongside an incredible group of advisors to bring this issue to life; all of which are making significant contributions within various avenues of AI and STEM. With their help, we were able to not only ensure our content was accurate and informative, but also secured some really insightful interviews with researchers, policymakers, and AI startup founders! I want to give a huge thanks to: Ruthe Farmer, Karen Peterson, Jannie Fernandez, Melissa Jones, Casey Fiesler, Divyansh Kaushik, and Raquel Romano for making this AI- themed issue a reality for all of our awesome readers!

Finally, I’m so excited to share that our 3rd annual Space Gala is coming up this November! We will be welcoming 300 guests back to the rocket garden at Kennedy Space Center on Saturday, November 9th, 2024, where the rockets will be pink and the fashion even more awesome than years before! If you ’ re in the area, or looking to travel, I hope to see you all there as we celebrate the amazing accomplishments of women in STEM this year!

To Infinity & Beyond,

Want to get in touch with Caeley and the Reinvented team?

Drop us a line at the address below!

Reinvented Inc. PO Box 236155

Cocoa, FL 32923

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Our One-for-One Program ensures that for every one magazine bought, one magazine will be donated to a local library, school, or girls club!

AI TERMINOLOGY, HISTORY, AND WHERE YOU CAN FIND IT

BY CHLOE DRIEU

In today’s rapidly evolving world, Artificial Intelligence (AI) is making big waves and getting a lot of buzz, both good and bad. It’s not just about robots–AI is transforming industries and changing the way we live and work. From healthcare to self-driving cars, AI is everywhere, and that is why it’s so important we understand the fundamentals of this revolutionary technology.

Terminology

AI is a type of software system that simulates human intelligence processes, such as learning, reasoning, and self-correction. We have been living with AI for quite some time email spam filters, search engines, and voiceactivated virtual assistants like Apple’s Siri or Amazon’s Alexa are examples of them. However, the landscape has recently shifted with the advent of Generative AI.

The recent onset of Generative AI software such as ChatGPT, Google’s Gemini, and Microsoft’s Copilot, to name a few, has brought on a new surge of interest in AI and its power Generative AI can create text, images, video, audio, and synthetic data Embodied AI represents a distinct avenue where AI takes a physical embodiment, such as robots or self-driving cars, addressing different goals and applications compared to non-embodied AI

AI is an extremely intricate technology with numerous

parts that help it function as a whole. Here are the key terms that you should know in order to understand the fundamentals of how AI works:

Machine Learning (ML)

A subset of AI that builds systems by learning from data to improve their performance over time without being explicitly programmed for each task. It enables systems to learn from data.

Algorithm

A set of rules or instructions designed to solve a problem or perform a computation In the context of AI, algorithms process data, learn from it, and make decisions

Neural Network

A series of algorithms that mimic the operations of a human brain with interconnected neurons, also known as nodes, to find patterns and relationships between vast amounts of data.

Deep Learning

A subset of ML that uses neural networks with many layers (hence "deep") to analyze complex data patterns. It is

inspired by the structure and function of the human brain's neural networks.

Large Language Model (LLM)

Deep learning algorithms that can understand, summarize, generate, and predict new content.

Natural Language Processing (NLP)

A key component of AI focusing on computer and human interactions through natural language The ultimate goal of NLP is to enable computers to understand, interpret, and generate human language

Supervised Learning

An ML task where a model is trained on labeled data, meaning that each training example is paired with an output label, and the goal is to predict inputs into outputs.

Unsupervised Learning

An ML task where a model is trained on unlabeled data, and the system tries to learn patterns and the structure from the data.

Reinforcement Learning

An area of ML where an agent learns to behave in an environment by performing certain actions and observing the rewards/results of those actions.

Big Data

Immense data sets that are analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior

Computer Vision

A field of AI that trains computers to interpret and understand the visual world, much like our eyes It is used to identify and classify objects in images and videos.

Hallucination

An AI-produced error that may be due to insufficient AI training data, biases during training, or incorrect assumptions.

A Brief History of AI

It all began in the mid-20th century when philosophers and mathematicians started laying the foundations of AI. A British mathematician and computer scientist by the name of Alan Turing made a groundbreaking contribution in 1936 with his proposal of the Turing Machine, a hypothetical machine that could perform any computation that can be algorithmically described

In the subsequent decade, an academic field centered on machine-based cognition began to take shape. It was at the Dartmouth Conference in 1956 that John McCarthy, an American computer and cognitive scientist, introduced the term “artificial intelligence.” This same year, the development of this technology reached an important milestone: the creation of the first AI program by scientists Allen Newell, Herbert A. Simon, and Cliff Shaw. This software, which they called the “Logic Theorist,” was able to autonomously reason and prove simple mathematical theorems In 1957, they enhanced their earlier creation and produced the “General Problem Solver,” a machine that serves as a universal problemsolving machine Unlike the previous Logic Theorist

project, it employs a means-ends analytical methodology.

Fast forward to the 1980s, the period known as the “First AI Boom,” when AI saw a significant shift with the rise of expert systems Scientists like John Hopfield and David Rumelhart contributed heavily to neural networks and the early foundations of deep learning, allowing AI to emulate the decision-making capabilities of human experts in specific domains This period marked immense progress in knowledge representation within computer systems and reasoning process simulations to arrive at intelligent conclusions. After these rapid advancements, there was a period known as an “AI winter.” There was a decline in excitement surrounding AI research and development due to overhyped expectations, technical limitations, reduced funding, and a lack of demonstrable success in AI applications. This resulted in limited advancements in AI capabilities.

In the following decades, from 1993 through the early 2000s, AI experienced another wave of development, largely as a result of advancements in machine learning

techniques Support vector machines and neural networks became pivotal technologies, enabling AI to enter diverse domains such as data mining and medical diagnostics These technologies allowed computers to learn from data and perform tasks in domains previously thought to be exclusive to humans

Moving into the 2010s and beyond, AI has reached unprecedented levels of integration into everyday life. Deep learning and big data dominate this era. Breakthroughs in computational power and algorithmic efficiency have fueled the advancements in crucial areas, like image and speech recognition, NLP, and the development of autonomous vehicles. AI has become more accessible and impactful across various industries, transforming how we interact with technology and perceive its role in society

Where can you find AI?

Everywhere Or, at least, that's the direction we ' re heading in

AI has become ubiquitous in our daily lives, enhancing convenience and efficiency across various domains From the AI-powered personal assistants on our smartphones that manage schedules and offer tailored recommendations to the recent development of autonomous vehicles set to transform transportation, its impact has proven to be profound.

In e-commerce, AI algorithms analyze consumer behavior in order to provide personalized product recommendations and optimize inventory management. Not only does this technology enhance user experience, but it also boosts sales and operational efficiency for businesses worldwide

Healthcare has also seen significant developments thanks to AI, with applications ranging from diagnosing diseases through medical imaging analysis to tracking and predicting the spread of deadly illnesses like COVID-19 AI's ability to sift through vast amounts of data quickly and accurately has proven invaluable in research and clinical settings.

AI plays an important role in cybersecurity by identifying and mitigating cyber threats It detects unusual patterns and behaviors in network activities This proactive approach assists organizations in defending against evolving threats and safeguarding sensitive data

Our cities are becoming smarter as a result of AI-driven solutions, like smart thermostats, that optimize energy usage in buildings and improve traffic management to reduce pollution and congestion. These advancements aim to create more sustainable urban environments while enhancing the quality of life.

Even simple everyday tasks, ranging from language translation to navigation, have been revolutionized by AI. It has made communication more seamless and travel more efficient than ever before AI-powered language translation tools can now interpret spoken and written text more accurately, ultimately breaking down language barriers in global communications

As AI continues to evolve and integrate into more facets of our lives, its transformative potential remains vast, promising further advancements that will shape the future of technology and society.

ZOELOFGREN JESSIESMITH

EVERY CHANGE DAY MAKERS

SHUCHIGROVER

BCAVELLO

BEYOND THE FRONTIER:

How

Representative Zoe

Lofgren Shapes Tech Research and Empowers

STEM Education

BY MADELINE DAY

From the origins of Google in the 1990s to the recent achievement of fusion at the Livermore National Laboratory in 2022, California congresswoman Zoe Lofgren has witnessed–and supported–the defining technological breakthroughs of recent decades. Through her work in Congress, she’s been able to address issues such as clean energy, STEM equity, and artificial intelligence (AI) innovation. In doing so, she’s made both her community and the world beyond a better place.

From Student to Service:

Having grown up in Palo Alto, California, Congresswoman Lofgren recalls the city’s early days–and her childhood–with fondness “My dad was a beer truck driver, and my mother was a cook and secretary,” she notes “I grew up in a regular home ”

Lofgren first encountered the world of politics and government during her childhood. She remembers attending Democratic Party meetings with her parents and walking precincts to support local candidates. There, Lofgren began to understand the important role that citizens and voters play in government.

Lofgren went on to attend Stanford University, earning a degree in political science There, she recounts her exposure to a diverse range of academic fields and disciplines–from meeting students paving the way in genetics to using business machines in early computing courses After graduation, Lofgren moved to Washington, D C , where she joined Congressman Don Edwards’ staff. She worked there for almost a decade, contributing to work on the Equal Rights Amendment and navigating through President Nixon’s impeachment while earning her law degree from Santa Clara University.

Upon her return to California, teachers recruited Lofgren to run for the local community college board of supervisors. She remembers how “ a lot of people did not think [she] was going to win ” However, Lofgren did win that first race, and at that point, she knew she wanted a life in public service– “I wanted to make change, and I wanted to make a difference ” After Congressman Edwards retired in 1994, Lofgren decided to run for office “I was the underdog,” Lofgren recalls She still keeps

The headline? “Lofgren

in a surprising upset.”

Science, Space, and Technology:

Since the beginning of her career in office, Lofgren has been on the House Committee on Science, Space, and Technology. She is currently the committee’s ranking member–the most senior member from the minority party. Though Congress can be partisan, Lofgren notes that what unites Science Committee members is the common goal of advancing science and a “shared desire to get things done ” The committee has a broad jurisdiction, overseeing organizations from NASA to the Environmental Protection Agency to the National Science Foundation Lofgren is proud of the issues they

the newspaper clipping from that first election in her office today.

wins

oversee and highlights how “[they] cover issues from outer space to the sea floor ”

Through her role as a Science Committee member, Lofgren has been able to support key innovations in AI, fusion, and computing. For example, the Science Committee passed the National AI Initiative Act in 2020, which supported the National Institute of Standards and Technology’s development of a risk-based framework for AI. The 2023 Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence has since incorporated the framework. The committee also worked on the Creating Helpful Incentives to Produce Semiconductors (CHIPS) and Science Act, which provides federal funding and support for semiconductor research Lofgren notes that funding and policy are crucial because “AI development is rapid and will not stop ” As a committee member, she’s finding ways to “advance the good things about AI…how we can use this to the benefit of mankind while avoiding disruptions or threats.” Lofgren is optimistic about the use of AI as a tool to improve society, from large language learning efforts to improving diagnostic capabilities in medicine.

Lofgren has also played an essential role in the committee’s support for fusion research Nuclear fusion is a clean and renewable energy source that could play a crucial role in combating climate change As such, Lofgren believes that “fusion is something that ought to be paid attention to ” In recent years, the Science Committee authorized one billion dollars in funding for fusion research and facilitated partnerships between the public and private sectors to accelerate progress. In 2022, the Lawrence Livermore National Laboratories achieved fusion ignition, a breakthrough in

clean energy development “We’re meeting milestones,” notes Lofgren She’s excited that fusion creates countless possibilities– “in the 1990s, fusion was a far-away thought Now, we may be able to provide fusion energy onto the grid.”

Representing Silicon Valley in Congress and serving on the Science Committee has given Lofgren a unique firsthand experience in technology developments. She remembers an interaction with a Palo Alto startup in the 1990s when one of her engineer friends had invited her to visit his new venture in a one-room garage: “That venture turned out to become Google ”

Lofgren reflects on how “ much has changed in technology since [she] was a girl ” For example, “Now there’s a whole variety of industries, from social media to chips, to AI, and quantum computing.” Lofgren aims to continue supporting innovation and science within the government.

Empowerment and access:

Lofgren’s career has not come without challenges. She admits that “when [she] was first running for office, there was still skepticism about female candidates.” Lofgren believes that the people who supported her allowed her to overcome such obstacles and continue to make changes As such, Lofgren fervently supports STEM education initiatives that aim to help young people overcome barriers She supports initiatives “reaching out to girls, making sure they know that they can go into science ” Lofgren is a proponent of increasing access to STEM education because “ we can’t lose brilliant minds of science.” She hopes that young people know they can have an impact “ everyone can make a difference in the world.”

Jessie J. Smith: Championing Ethical AI and Machine Learning

BY ANDREA GAJIC

When people think about Artificial Intelligence (AI) and Machine Learning (ML), the possibilities first come to mind. From writing essays faster to predicting what the next hit song will be, people are eager to learn how to use AI and ML. However, very few people pause to think about the ethical problems that arise with this new technology; enter Jessie J. Smith.

Her Journey

Jessie Smith started her journey the same way many future technologists do - learning how to code during high school and being inspired to continue. She had every intention to pursue a career in website development after her college graduation, but in her fourth year, she realized that she still had more to learn and chose to pursue a Ph D in Information Science Her inspiration? A computing ethics course taken in tandem with data science That semester, during her undergraduate degree, she realized she “should have been learning more about the impacts of machine learning while [she] was being taught to create it ” This key realization drove her to focus her Ph D strides on exploring how AI and ML technologies could further improve people’s lives rather than harm them, even if the harm is not direct.

In academia, she focuses on theoretical work that can later influence better practices for ML design and can help improve the fairness of the platforms while reducing the harm they may cause. With public scholarship, she works to educate people on how technology is impacting society and how technology design can be made more ethical. She works to accomplish this through blog posts on Medium and even had a YouTube channel for a time where she looked at “tech ethics dilemmas through the lens of science fiction movies ”

The Radical AI Podcast

Jessie doesn’t only focus on academia., She also had the opportunity to be the co-host and co-creator of the Radical AI Podcast, which actively recorded for several years and drew thousands of listeners. The podcast started out as a small-scale project intended to fill a gap they saw - that “there was not a well-known platform for discussions about responsible technology design and the HCI (HumanComputer Interaction) research discipline ” Its ultimate goal was to allow people to have discussions about AI ethics, mainly because those topics are usually neglected in conversations about technology innovation

Despite its humble start, with interviews with fellow PhD students and shared with local communities, it quickly rose in popularity. With the COVID-19 pandemic happening soon after its creation, more people began working remotely than before and became available for remote interviews. For the team, that was the chance of a lifetime, and they were “able to have amazing conversations with some of [their] favorite researchers from industry and academia.” Jessie notes that she “feels incredibly grateful for the luck that we ran into while making that podcast, for all of the amazing scholars and researchers who took the time to chat with us, and for the incredible community that was formed around it ”

If someone could listen to only one of the podcast episodes, Jessie recommends that they listen to “The Limitations of ChatGPT” with Emily Bender and Casey Fiesler. ChatGPT is a tool that is increasing in popularity each day. People use it for everything, from help writing essays to getting information quickly to solving homework problems. ChatGPT is not the only chatbot available, as many companies are adding them to their websites for customer service and more. Jessie describes that the “episode is a really good sneak peek into how those technologies actually function, what we do or don’t need to be afraid of, and how we can engage responsibly with them in a way that aligns with their intended purpose ” The episode would be an excellent way for all AI users, even if they aren’t AI professionals, to learn more about the tools they are using and gain valuable insights

Although the podcast is no longer actively being recorded, Jessie aims to keep the content available for as long as

Smith giving a lecture on tech ethics

possible Her hope is that as many people as possible in the AI ethics community can benefit from the conversations that were had on the podcast during its running course.

Future Work

Jessie’s journey won’t end with her Ph D and the Radical AI Podcast; she has big goals for the future After her dissertation, she aims to continue working with different research groups and industry practitioners to improve how ML systems are evaluated, especially because AI ethics is a very subjective area One person ’ s definition of fairness and harm may not be the same as another person ’ s and may even change in different contexts. Jessie wants to “keep helping people make informed decisions about how to translate complex philosophical ideas into actual system designs.” She also hopes to “work more with individuals and communities who are being negatively impacted by technologies to co-design ways to improve these technologies so they augment their lives (rather than exploit them).”

She has noted that there are great challenges with making ethical technologies While individuals in the industry may feel that technology should be designed ethically, companies may feel differently, and monetary incentives can counter ethics incentives To counter this, she hopes to continue to find “methods to motivate business leaders to design more ethical technologies by finding business incentives and rewards for designing AI responsibly.”

Advice

As an everyday changemaker in AI ethics, Jessie has some key advice for those who are looking to experiment with AI Her biggest suggestion at the start of that journey is to consider your own personal values She asks people to “Think about what is important to you and how you interact with technology on a daily basis. Do you care about privacy? Then, perhaps consider taking measures to make sure you are not sharing too much of your personal information online. Do you care about transparency and accountability? Look into the companies that you spend money on, the products you buy, and the apps you use, and start learning about how they function and the impact of their organizations.” Jessie also shared a good resource to

help people get involved in the A community - the FAccT conference, wh events throughout the year and more sha those who connect with them.

To those who use AI and ML in their lives, as many of us do, she suggests th be afraid to interrogate the systems tha AI is efficient and works quickly, but wrong, so asking follow-up ques checking that the output is accurate is best practice!

AI is helpful in many ways However, have unforeseen consequences “inequality of outcomes, large sc exploitation, massive environmenta spread of misinformation, and a addiction ” So, caution and careful con are critical each time you use AI!

BetheHeroinYourOwnStory:Rewriting theAINarrativewith

BCAVELLO

BY ZOHA KHAN

What does a “tech person” look like? By the end of this article, I hope you'll see that the tech person you've been searching for has been staring back at you in the mirror all along. This revelation came to me after conversing with an inspiring figure in the tech industry.

B Cavello, Director of Emerging Technologies at Aspen Digital, former IBM Watson Senior Engagement lead, and LGBTQ Out Role Model, has shown me how our relationship with tech is more human-focused than we may believe.

Embrace your inner hero:

Following their graduation from the University of Texas at Dallas, they started a nonprofit with a friend called “Phenomenon Media”. One of their notable achievements was “colorCODE”, an experimental tool designed to educate people about coding and the digital world in a fun, playful way.

By adopting a grassroots approach to digital education, Cavello uses tools like “colorCODE'' to “take the code out of the computer” and to combat the powerlessness that people often feel when confronted with niche topics like computer security and encryptions Cavello explains, “A lot of these topics made people feel way in the deep end And we really wanted to challenge the notion and say ‘ you could participate in this.’” Using a combination of interactive games and stories that encourage people to approach both technical and political issues, colorCODE demystified the world of tech in a way that “made people feel like they were the hero.”

Cavello has since continued to contribute widely to the digital community, from directing youth toward new career paths to playing a crucial role in the Senate and

You don’t have to wait until you’re officially hired as a grown-up professional to start doing things that matter. Be really open about what you’re interested in, share it with the world, sing it from the rooftops. Let people know what you care about

drafting the “Algorithmic Accountability Act of 2022”, which aimed to reduce bias in technology-driven decisionmaking. They were also a fellow of the “Assembly of Ethics and Governance in Artificial Intelligence” in 2019, a collaboration with Harvard University and MIT to tackle the ethical concerns of AI

While Cavello has already accomplished much in their career, they also taught me that, like any healthy relationship, a good relationship with tech hinges on trust Cavello remarks, “Trust really needs to be something that’s earned ” This sounds straightforward enough, but they explain that, unfortunately, when it comes to corporate digitalization, “there’s a lot of information that, for competitiveness… or other reasons, is kept a secret. And there’s a lot of newfangled tech terminology that can be used to confuse people.” Underneath all this confusion is a hint of something subtle yet harmful deception, a “legacy of the way technology has been developed, truly being kind of a race to extract as much from people and from the environment as possible.” But how do we ensure that we contribute to a potentially beautiful thing rather than allow it to take from us what we aren’t ready to give?

Putting the “I” in “AI”:

I would like to invite you to consider Cavello’s vital question: “What is our relationship with the people who are building these tools?” The good news is that all kinds of people will experience and work with AI, as so many fields will utilize it. “I think that right now, ” Cavello expands, “ we have a real opportunity to lift up voices…There’s art and culture and language and mathematics and engineering. So everyone ’ s voice is super important I think people wouldn’t feel threatened by a technology if they were the ones that built it ”

What’s interesting about Cavello’s approach to education is that it is as realistic as it is encouraging With experience, Cavello has noticed that “ one thing that technologists often do, because they’re comfortable with technology, is they try to say ‘No, no, it’s not that hard.’ and I actually think that’s a disservice. Because if you ’ re struggling with something, and someone says it’s not hard, you ’ re like, ‘Well, it’s hard for me. ’ And so one of the things that I think is really important is to honor that it is hard; it is confusing. And also, you have what it takes.”

On a final note, I asked B Cavello to offer some advice to our wonderful readers pursuing their own careers, especially in a world that demands new and evolving digital skills. Cavello offers welcome reassurance: “You don’t have to wait until you ’ re officially hired as a grown-up professional to start doing things that matter. Be really open about what you ’ re interested in, share it with the world, sing it from the rooftops. Let people know what you care about.” They believe this will help you find others who care about the same thing and build a community on mutual passion and collective support In fact, B themselves reminisced how many of their jobs were attained through referrals and suggestions, offering them opportunities they might have never known about Comfortingly, Cavello left us with a piece of advice that speaks straight to our habitual humanness. “So many of us feel like we ’ re shouting in the void, and if you can help someone feel less alone and more like they have a buddy on this journey, then you ’ re going to have a buddy too… Be openly passionate, and celebrate and support other people’s passions as well.”

Cavello

Transforming Tech Education: Shuchi Grover’s Journey from the Computer Revolution to the Age of AI

BY JOCEE HOLLADAY

1984 was a landmark year for computer science. Apple released its iconic Super Bowl commercial introducing the Macintosh, revolutionizing how technology is marketed. CD-ROMs surged in popularity, fueling the rise of multimedia. MATLAB was introduced, broadening the capabilities of computers in research. Computers transitioned from being a fringe interest to becoming a mainstream product The computer revolution began: transforming every industry, communication method, even the way humans think and evolve

Shuchi Grover graduated high school in this pivotal year, “completely oblivious” to the significance it would have on her future career. Her first encounter with computers was during a class offered by her sister’s more affluent friend, whose family owned a computer. This is where she learned the BASIC programming language. A driven student, Shuchi eventually earned degrees in both computer science and physics from Birla Institute of Technology and Science.

“During my undergrad years in my physics cohort, I was the only girl. Somehow, I recognized that this is a male-dominated field, and I’m going to make it.”

When reflecting on her experiences in each field, Shuchi highlighted the gender disparities she witnessed in them both. While women remained a minority in STEM fields, it was in her physics degree where she was the only woman versus her computer science cohort which included “four or five” other women “During my undergrad years in my physics cohort, I was the only girl Somehow, I recognized that this is a male-dominated field, and I’m going to make it ”

Since 1984, the year Shuchi entered college, the percentage of women studying computer science has declined. Per the National Center for Science and Engineering Statistics database, women accounted for 34% of computer science degrees then, but today, they represent only 19%. In contrast, the trend in physics has been the opposite In 1984, women earned 15% of physics bachelor’s degrees; today, women account for 25% of those degrees. While Shuchi was the only woman in her physics class and had female peers in computer science (albeit limited ones), a student today, nearly 50 years later, would likely experience the reverse: more women in physics classes than in computer science, and women still remain a minority in both fields.

After graduation in 1989, Shuchi began her career in software engineering, but soon after the “natural next step” in her career came about: Shuchi immigrated to the United States to pursue a master’s degree in computer science This was when she first encountered the term “AI” Surprisingly, it was an AI winter: a period marked by reduced interest, funding, and opportunities in AI. “One was hearing that AI is not going anywhere, and there aren’t any opportunities.” This starkly contrasts with today’s reality, where AI is undoubtedly experiencing a blistering hot summer. During her master’s program, Shuchi witnessed another significant shift in computing: the advent of the internet browser. She described how the internet browser transformed everything before her eyes. It was an exciting time to be in computer science!

After earning her master’s degree, Shuchi spent a few years raising her family When her youngest son turned two, she felt it was time to reenter the professional sphere However, she didn’t want to return to software engineering; she sought a new direction She enrolled for a 2nd Masters – this time in Education – at Harvard’s Technology, Innovation and Education program During this time, Shuchi volunteered on a project at MIT for the Playful Invention and Exploration Network, which combined computing and education The MIT Media Lab “cricket” (which later went on to inspire the brain of the LEGO Robotics kits) aimed to teach students about programming, but Shuchi noticed that the project’s tools and curriculum limited who could use them. “It’s interesting how the affordances of the tool guide the kinds of thinking and what you do with the tool. So when you have a robotics kit full of wheels alone, you are naturally making cars and things that move. When you add pipe cleaners, you are making little critters and flowers and butterfly wings that flap. So you ’ re making a different kind of thing, and clearly this would appeal to another group of kids too.” This observation would shape the rest of Shuchi’s career as she dedicated herself to expanding access to technology by designing inclusive educational tools and methods

She realized that, unlike her childhood, the current generation of children including her own would grow up surrounded by technology This realization prompted essential questions about how students should be taught about, and with, technology “Today we are surrounded by computing, [it’s important] for students to have a functional understanding of what this thing is, so they are always an intelligent and informed user and consumer, even if they are not directly in these fields.” Historically, computer education was reserved for graduate students, with younger students rarely being taught these skills – they were thought to be too niche or upper level. To explore these ideas further, Shuchi pursued a PhD in the Learning Sciences and Technology Design Program at Stanford focusing on learning in STEM disciplines.

“Today we are surrounded by computing, [it’s important] for students to have a functional understanding of what this thing is, so they are always an intelligent and informed user and consumer, even if they are not directly

in these fields.”

During her PhD, Shuchi learned two key principles that would guide her career in STEM education. “Learning is always contextual. This is why learning as an enterprise is so difficult. What works in one classroom may not work in another classroom, as the classroom composition is different. Students come from different backgrounds and experiences. You’re working with humans. The other thing is that learning is very social… The majority of learning is social and relationship-driven.” Shuchi believes there is currently an inflection point in education with the rise of Generative AI – for the first time, communication and interaction with learning tools feels organic and human-like with the new implementation of chatbots This, she suggests, has the potential to address the contextual and social challenges of education “This is why [AI] bears the potential to make a big difference ”

Shuchi has been a prominent voice for the "Computer Science for All" initiative, focusing on bringing computer science learning to K-12 classrooms. With the AI revolution underway, Shuchi is focused on answering the questions, "What and how do we teach AI" to younger students and what AI tools should we be building? She is dedicated to developing AI tools and curricula that include every group of students tools that build little creatures instead of just cars. In recognition of her impactful work, Shuchi was this year ’ s award recipient from the National Academies of Sciences, Engineering, and Medicine's Division of Behavioral and Social Sciences and Education, which honors a social sciences scholar each year whose work informs and impacts public policy.

Having witnessed and contributed to major technological shifts from the dawn of personal computing to the rise of internet browsers Shuchi is uniquely positioned to shape the future of technology education Her goal of creating informed and educated users helps alleviate our fears of a dystopian, AI-dominated future Instead, we can envision a future where AI is used responsibly by knowledgeable and thoughtful creators and consumers

AI Ethics

BY EMILY ZAKKAK

Artificial intelligence is gaining popularity online, and most people are quick to accept it without considering its ethical impact. Given AI’s complexity and increasing accessibility, it's becoming increasingly crucial to address the ethical issues surrounding AI.

Artificial intelligence (AI) is rapidly transforming our world, from self-driving cars to personalized recommendations on streaming platforms and intelligent assistants. With AI's ubiquity across nearly every field and aspect of our daily lives, it's imperative to examine it through an ethical lens. The accessibility of AI raises concerns about the intentions behind its use, prompting us to consider whether to embrace it or approach it cautiously

Understanding AI ethics means considering aspects such as fairness, accountability, transparency, and privacy As AI systems become more advanced and integrated into our lives, addressing these ethical concerns is essential to ensure AI aligns with human values and rights, distributing its benefits fairly and minimizing potential risks. In this article, we’ll explore AI ethics, why they matter, and how to develop responsible AI practices in your daily life.

Key Ethical Principles in AI

Fairness and Nondiscrimination

AI systems must be developed and implemented fairly, without discriminating against individuals or groups at any point in the development and implementation processes. This involves, for example, ensuring that the data used to train AI models is inclusive and free of biases that could lead to unequal outcomes such as facial recognition systems that do not work equally well for people of all races and genders

Unfortunately, there have been many failures in this regard Computer vision products have mislabeled photos in ways that demonstrate racial biases In a notable incident, Google's face recognition technology misidentified two African Americans as "gorillas," likely due to inadequacies and exclusions in the training data.

This error highlights the racial biases present in the data and emphasizes the dangers of using biased AI. Furthermore, facial recognition software has been much less reliable for Black and Asian individuals. Research by the National Institute of Standards and Technology (NIST) found that photos of Asian and Black people were 10 to 100 times more likely to be mislabeled than photos of white individuals

We must hold artificial intelligence accountable. Its designers must accept responsibility for their decisions that affect people's lives, which includes ensuring AI judgments can be challenged and corrected. For example, there must be a clear method for appealing and assessing a loan denial issued by an AI-powered loan application system

Furthermore, the necessity of accountability extends to critical uses like self-driving cars Tesla's autopilot technology has been scrutinized and legally challenged as a result of its involvement in accidents, raising questions about AI responsibility and safety in life-or-death scenarios. These occurrences illustrate the need for rigorous methods to guarantee that AI systems work fairly and safely, and for explicit processes for dealing with any failures or errors that may happen.

Accountability Transparency

Transparency in AI entails making the operations of AI systems understandable to users and stakeholders However, this is an unsolved problem in machine learning Many models, particularly deep learning ones, naturally operate as "black boxes" their decision-making processes are extremely

challenging to dissect More accurate models often sacrifice interpretability for performance, making it difficult to explain their decisions transparently

This lack of transparency presents an ethical problem Users cannot fully trust or evaluate the technology's fairness and accountability without understanding the reasoning behind an AI system's advice or choices. Addressing this issue is crucial to ensure AI systems align with human values and rights.

Confidentiality and Consent

Privacy protection is crucial with AI systems, which routinely handle enormous amounts of personal information This raises serious concerns about how the data is collected, stored, and used Strong privacy precautions are required, and people must have control over their data For example, AI systems in the healthcare business must handle sensitive patient data with extreme caution and follow tight privacy standards.

Beyond privacy, it is also important to know that much of the content used in the training of AI models was collected without consent. As a result, there have been widespread concerns about copyright infringement in AI-generated content. For example, major record companies have sued AI music businesses for copyright infringement, emphasizing the ethical and legal ramifications of exploiting copyrighted content without permission And in addition to commercial content, many more people’s social media and other online data is being used to train AI, which raises both copyright and privacy concerns that are important to address.

Non-Maleficence and Beneficence

AI systems should be created to reduce harm and maximize usefulness to society. This medical ethics-based premise highlights the significance of developing AI that enhances human wellbeing rather than causing damage. For example, AI in autonomous cars ought to put safety first and steer clear of situations that can jeopardize people's lives.

Promoting Ethical AI Practices

Gaining the trust of AI requires a multifaceted approach. Governments and corporations are constructing ethical frameworks for development that prioritize responsibility, transparency, and fairness to create AI that helps everyone, not just a few It's not only about big ideas Ethical issues must be integrated into the design process from the outset to accomplish this Developers need to be skeptical of any potential biases and consider the potential effects of AI on all of their potential users. Engaging with diverse voices and conducting thorough ethical impact assessments are crucial steps. Public engagement is just as important. We need to raise awareness about the ethical implications of AI and foster open dialogue among everyone – from tech developers to lawmakers to everyday people like you and me. Public consultations on AI regulations can help ensure that the technology we build reflects our shared values and expectations Imagine a future where AI isn't just a powerful tool but a trustworthy one We can make this a reality by working together – businesses, governments, and the public

The Future of AI Ethics

The world of AI is booming, and the ever-growing need for ethical considerations comes with it The AI ethics field is constantly evolving as new challenges and technologies emerge

Building Bridges Between Disciplines

Tackling AI ethics requires a collaborative effort We'll see increased cooperation between computer scientists, lawyers, sociologists, and even philosophers Imagine a world where ethical guidelines are built on the diverse perspectives of these brilliant minds, ensuring they're comprehensive and effective

Global Collaboration for Responsible AI

AI doesn't have borders, and neither will its impact. International cooperation will be key to ensuring responsible use worldwide. Picture countries working together to harmonize ethical standards and regulations This will help address challenges that transcend borders and promote responsible AI use globally

Lifelong Learning for an Evolving Field

The field of AI is moving at breakneck speed, so staying informed is crucial. We can expect ongoing education and awareness initiatives, which include training for AI developers, policymakers, and even the general public. We can integrate them seamlessly into AI development and use by ensuring everyone understands the ethical considerations.

Demystifying AI Decisions

AI's opacity is one of the main obstacles to trust Fortunately, explainable AI (XAI) is progressing, promising systems that can clearly explain the decisions they make and cite sources for their data. This transparency will be crucial for users to understand and ultimately trust these powerful technologies.

The future of AI ethics is about building trust By fostering collaboration, thinking globally, and prioritizing education and explainability, we can ensure that AI becomes a force for good, not just a powerful one

Q&A with

What is your origin story?

I've always loved to work and make things When I was in middle school, I'd make my own clothes, thinking maybe I'd be a fashion designer someday. Art was also my escape – I loved sitting for hours working on charcoal drawings. School? Not my strong suit. But tech? That lit me up. When touchscreens hit the scene, I couldn't afford an iPhone, but I scraped together enough for a touchscreen MP3 player. It felt like magic in my hands.

People are my fuel I thrive on teamwork and collaboration Put me in a room full of folks working towards a goal, and I'm in my element Oh, and I've got a serious case of wanderlust Risk-taking? Sign me up It's how I learn and grow

This mix of creativity, tech fascination, and love for adventure set the stage for my journey into the startup world. It taught me to see possibilities where others might not, a skill that's been crucial in building Andi.

What sparked your passion for the work you’re doing?

It hit me during a backpacking trip across Europe. I'd just finished a work year in Australia, and I was Googling my way through new cities. The search results were driving me nuts – so much junk to wade through. Meanwhile, I was living on Instagram and WhatsApp, sharing adventures and chatting with friends It got me thinking: why can't search be as easy and engaging as social media? That spark lit the fire that became Andi

The more I thought about it, the more I realized how broken search was, especially for my generation We're used to quick, visual information, but search engines were still stuck in the past of "10 blue links". I became obsessed with the idea of reinventing search for Gen Z – something that could cut through the noise and give us the answers.

How did you get to where you are in your career today?

My journey's been anything but traditional It all started with Boulder County Youth Corps when I was 14. For three summers, I worked my way up from team member to Assistant Team Leader. Judy Wolfe, the program director, became a mentor to me and instilled the importance of hard work, and time management. She also trusted me with real responsibility. It was a huge confidence boost.

As I was approaching the end of high school, I had this wild idea to save up and move to Australia for a gap year, after learning at a school fair that you could get a visa That

year pushed me so far out of my comfort zone and showed me I could make big things happen.

In Australia, I started as a construction laborer because I wanted to be a civil engineer at the time Then came a huge leap – I landed a gig working with Microsoft on their Azure Data Center It was mindblowing, seeing cutting-edge tech up close

When I came back to the US after my travels, that search engine idea wouldn't leave me alone. I was in college, studying business, when I realized I had to go all-in on this dream. So I did something terrifying – I dropped out. My family thought I was crazy, but I knew I had to try.

I poured everything into building the first version of Andi We applied to Y Combinator, the startup accelerator that's launched companies like Airbnb and Dropbox Getting in felt like winning the lottery Those three months were the most intense, educational experience of my life

After YC, things moved fast. We raised $3 million in funding, grew to over a million users, and started getting press coverage. Suddenly, I was being interviewed by major tech publications and speaking at conferences. It was surreal.

The road hasn't always been smooth. There were nights I wondered if we'd make it to today, weeks where nothing seemed to work But every time a user told us how Andi helped them, it fueled the fire to keep pushing

Today, we're not just building a product –we're on a mission to fix how people access information online It's a huge challenge, but that's what makes it exciting.

What does a typical day at work look like for you?

There's no such thing as typical in startup life, but here's a snapshot:

I'm up by 7 am, feeding my cat and grabbing some exercise Coffee is nonnegotiable On the bus to work, I tackle emails Once I'm in the office, it's go time A huge part of my job is talking to users and refining our product I might jump from a team meeting to a call with investors, then spend hours deep in product strategy.

The key is staying flexible. Some days I'm building alongside our engineer and designer; other days, I'm the face of the company at events. It's a rollercoaster, but I wouldn't have it any other way.

Since Y Combinator, I've learned the importance of focus We have a weekly allhands [meeting] where we align on our most critical goals I make sure to carve out time for deep work – whether that's analyzing user data, brainstorming new features, or working on our product roadmap

One of my favorite parts of the day is our

team lunches and dinners. It's a chance to step back from the grind and connect with the amazing people I work with. Building a startup is intense, but moments like these remind me why we're doing it.

And when I need a boost? I blast some Taylor Swift. Her lyrics and unstoppability never fail to inspire me

How are you interacting with AI in your work?

AI is the heart of what we do at Andi We're not just using off-the-shelf models – we're pushing the boundaries of what's possible in search Our team is developing proprietary AI that can understand context and nuance in ways traditional search engines can't

A big part of my job is working with our AI team to refine these models. We're constantly testing and iterating, feeding in real-world queries and fine-tuning the responses. It's like teaching a super-smart friend to help you find exactly what you need.

But AI isn't just our product – it's woven into how we work We use AI tools for everything from code generation to design work I personally use Andi for research and content generation all the time It's wild to use

something you've built and see it genuinely make you more productive.

The most exciting part is seeing how our users interact with AI. We're learning so much about how people want to access information, and it's shaping the future of our product. Every day brings new challenges and breakthroughs – it's never boring!

What do you see for the future of AI?

AI is going to supercharge human productivity It's not about replacing people –it's about enhancing what we can do I see a future where AI becomes like a personal assistant for everyone Imagine having a digital buddy that can find anything, book your appointments, and even carry out tasks for you. The key is making sure this technology serves us, not the other way around.

Through our work at Andi, we're seeing firsthand how AI can transform information access. I believe we're moving towards a world where finding answers is as natural as having a conversation. No more sifting through pages of results or battling with keyword searches

But with great power comes great responsibility. As AI becomes more integrated into our lives, we need to have serious conversations about ethics, privacy, and the societal impact. At Andi, we're committed to building AI that's transparent and puts user privacy first. I think that's the approach the whole industry needs to take.

This is why I was so honored to speak at the White House, Congress, and Senate about competition policy and regulation and to be involved in the debate with FTC, DOJ and Californian government about AI regulation It's crucial that policymakers understand both the potential and the challenges of AI as we shape its future.

What gets me most excited is AI's potential to democratize education Imagine a world where every kid has access to a personalized tutor available 24/7. AI could adapt to different learning styles, making education more inclusive and effective. It could break down language barriers, making global

But it goes beyond just learning facts. AI could help spark creativity, guide critical thinking, and even help young people discover passions they never knew they had.

At Andi, we're already seeing how AI can make information more accessible For students doing research, our AI can summarize complex topics, explain difficult concepts, and point them towards reliable sources It's like having a knowledgeable study buddy who is always ready to help

I also think AI will play a huge role in solving global challenges. From climate change to healthcare, AI can process vast amounts of data and uncover insights humans might miss. The next generation will have incredibly powerful tools at their fingertips to tackle the world's biggest problems.

Dive in! The best way to understand AI is to play with it. Check out sites like Futurepedia.io for inspiration – they showcase tons of AI tools you can experiment with. Try using AI assistants for your homework or creative projects.

But don't just consume – create! Learn some basic coding, or try prompt engineering to see how you can guide AI to do cool things The field is moving so fast, and young minds often come up with the most innovative ideas

If you're serious about a career in AI, consider joining hackathons or AI competitions. They're great for hands-on experience and networking. Look for internships at AI collaboration easier than ever.

startups, too – that's where a lot of the cutting-edge work is happening.

Remember, AI isn't just about coding. We need people who understand ethics, user experience, and the societal impact of these technologies. Your unique perspective, whatever your background, is valuable in this field

Most importantly, stay curious and critical Ask questions about how AI works and its impact on society You're the generation that will shape how we use this technology –make it count!

Anything else you want us to know?

I want young women to know that there's no one "right path" into tech I didn't graduate from a fancy university or have a computer science degree What I did have was curiosity, determination, and a willingness to learn

When I started Andi, I wasn't technical and I didn't know how to code. But I had a vision, and I wasn't afraid to ask for help. I spent countless nights learning about startups and Y Combinator, doing coding tutorials, pestering patient mentors with questions, and learning through trial and error. It was tough, but every small victory felt huge.

Y Combinator's Startup School online was a game-changer for me It's a free resource packed with knowledge from people who've built successful companies When we got into Y Combinator's main program, it was like gaining a superpower Suddenly, we had access to some of the brightest minds in tech and a community of other founders figuring things out alongside us

I've also been incredibly fortunate to have amazing mentors along the way. Jared Friedman, Surbhi Sarna, Nicolas Dessaigne, and Garry Tan at Y Combinator have been invaluable guides. Luther Lowe from YC has been a huge help with policy and regulation issues Our early advisors, Nick Chan, Tim Trumper, and David White, have provided crucial help And I can't thank Joan DeGennaro from YC Startup School enough for her support

I also want to give a massive shoutout to Ruthe Farmer from Last Mile Education Fund. She's been a big inspiration and introduced me to Caeley, the founder of Reinvented Magazine. It's people like them who are changing the face of tech and opening doors for the next generation.

Here's something I wish someone had told me earlier: imposter syndrome is real, but it doesn't define you. There were times I felt like I didn't belong in tech rooms full of seasoned engineers But I realized my different background was actually an asset It gave me a fresh perspective on problems others might overlook

I've also learned the power of community Seek out other women in tech – whether it's through local meetups, online forums, or mentorship programs. Having a support network makes all the difference.

Lastly, don't be afraid to dream big and take risks. The tech world needs diverse voices and experiences. Your unique perspective is valuable. So, if you're passionate about STEM, go for it The path might not always be clear, but that's part of the adventure You've got this!

Sponsor Reinvented

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Review our 2024 Sponsor Guide and reach out to us when you're ready to help us empower girls in STEM!

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NYBG x AI: Inspiring One Student at a Time

BY DHRUHI PATEL

Though two distinct fields of study, machine learning and botany have found common ground in an exciting new program for undergraduate students in the U S This partnership stems from a conundrum plaguing the field of botany In New York’s famous botanical gardens’ William and Lynda Steere Herbarium, a vast array of 7.8 million

plant and fungal specimens are dried and preserved for long-term study Over 123,000 plant sample images would need to be sorted into ten categories, such as herbarium specimens, drawings, and photographs, to aid in new specimen discovery and biodiversity data cultivation in the herbarium pivotal to worldwide plant conservation efforts.

Founder and CEO Dr. Judith Spitz with Break Through Tech students celebrating the completion of their program in April 2024. Photo courtesy of the New York Botanical Garden

Since classifying these specimens by hand would be immensely inefficient and require a lot of manpower, machine-learning algorithms must be developed for the task Thus, in 2021, the New York Botanical Gardens partnered with Break Through Tech AI, an organization committed to equipping a diverse range of undergraduate students with industry-focused AI/ML training, to train an initial group of 30 undergraduate students from New York City to accomplish this massive task. Now, in January 2024, this partnership has bloomed into a nationwide program with 900 students per cohort

Break Through Tech AI is part of a larger entity called Break Through Tech, which helps women and nonbinary students pursue tech and computing degrees virtually and in person with affiliate sites on college and university campuses across the United States. Break Through Tech’s heart, its national office, is located on the Cornell Tech campus in New York City, next door to its partner, New York Botanical Gardens (NYBG)

The NYBG x AI program is a ten-month rewarding adventure for its participants Students start with a summer course to introduce them to industry-relevant technical tools and let a wide range of industry professionals mentor them.

The initiative’s Career Development Mentors program asks mentors to work with students during monthly sessions to help them create high-impact resumes and GitHub profiles that help them land jobs During this process, consistent mentorship helps students overcome any setbacks It inspires them to employ their machine-learning skills creatively for the better. Hence, in the fall, apart from Career Development Mentors, special Challenge Advisors work with students to complete a real-world industry project. Then, in the spring, industry mentors are on call to dig in with students and support them on technical issues they may encounter with the Kaggle Datathon

Following the summer course, students participate in Fall and Spring Studios, where they creatively apply the tools they became familiar with during the summer. For instance, students form teams during this time to participate in Google’s Spring data science competition, the Kaggle Datathon, where they are tasked with developing a machine-learning algorithm that the NYBG Herbarium can use to sort their vast number of plant and fungi samples NYBG may use the top-performing and most innovative model(s) produced by Break Through Tech student teams to create and release a free and publicly available tool via GitHub, which will enable plant scientists across the world to run a trained model on their raw data

to filter out unwanted non-standard images, resulting in datasets that can be used to conduct important biodiversity research using machine learning.

The NYBG x AI initiative is the largest and most diverse program of its kind in the nation, and it’s free for qualifying undergraduates. Diversity is of utmost importance in the technology ind teams tend to think and act simil and creativity In fact, according t Center report, women only held 2 jobs, down from 32 percent in addresses this widening gender-ba the computing industry by p nonbinary students with industry resources to embark on the next st confidently

Isabela Sanchez Taipe, Ada Wang, three of the 900 undergraduate stu this program. Their team, formally won first place in the Kaggle D program! These three exceptiona backgrounds reflect upon their exp as revolutionary in merging clima intelligence

Taipe is an international studen cultivated her love for technology old. She grew up with limited Intern finally gained access to a compute immediately knew she had a p Currently studying computer scie Wellesley College, she looks forwar science in the future Sharing a pas with computer science, Taipe says, to teach ” Taipe recalls that her boosted her confidence in tech explains that, in retrospect, the pro participants made her feel more STEM.

Serbina is an international stud discovered her love for computer sc

at Amherst College. While conducting chemistry research, she made a short Python script that greatly sped up an experiment she performed ChatGPT, the virtual assistant and chatbot, was released the next year Serbina recalls how conjunctively these experiences exposed her to the enormous potential of computer science applications to advance science and everyday life. As a double major in computer science and chemistry, Serbina always held a passion for the intersection of technology, nature, and climate. Taipe agrees, adding that “Serbina was a passionate coding leader in her time in the competition Her passion for software engineering allowed her to thrive [and] encourage others ”

Similarly, Wang, an international student from Chengdu, na, at Wellesley College double-majoring in computer nce and economics, shares that she initially wanted to sue a humanities degree but that the projects of this gram encouraged her to explore more AI-related topics.

machine learning concepts taught in the program are h more advanced than college-level computer science ses, and they encourage students to step outside their fort zones. According to the participants, the advanced l of instruction was one of the most rewarding aspects he collaboration between NYBG and Break Through h AI. Wang explains, “The projects were very different m those in college. Studying data sets helped us develop ght into the workings of the industry! Plus, office hours e provided as a space for industry mentors to answer questions ”

se mentors poured in from a variety of organizations, uding the Botanical Gardens, Google, Cornell Tech, s Schwarzman College, and UCLA’s Samueli School of ineering. They shared a mutual dedication to nneling the skill and talent of the hundreds of ergraduate participants at every step of the program e door was left open so we can learn everything ependently, starting in the summer This greatly built confidence and resilience as engineers!” Taipe aimed.

This program provides budding software engineers with industry skill-building and networking opportunities that can be pivotal to their professional growth It helps participants develop career goals and build successful resumes

Contrary to what one might expect, this program stretches further than theory by providing industrial exposure to its participants on the intersections of botany and computer science! Serbina gratefully remembers that “in between the machine learning course and the spring competition, there was an industry project with a group I worked with a biomedical company, Nevardis I was in awe of the flexibility of the setup, and the outreach efforts by NYBG were really rewarding!”

Even though 2024’s NYBG x AI program has come to an end, its ambitious undergraduate alumni have only started their journeys in computer science.

On average, the placement rate for paid internships or jobs for NYBG x AI alumni is an astonishing 80 percent! With an elevated passion for STEM after participating in this program, these alumni proudly share their new ventures Wang is starting a research program investigating bias in machine learning programs, inspired by her experience in NY Botanical Gardens, where she learned that bias exists in massive data sets. Serbina is currently working on her thesis on AI. Taipe has landed an internship at Yonsei University allowing her to travel to South Korea, for which she will use her AI and machine learning knowledge to develop a UX platform that evaluates XR technologies

As a gateway to the exploration of AI in many different fields of study, NYBG x AI has been integral to the professional development of 900 passionate undergraduate students this year, and they will only uplift more pioneers of the field in years to come!

ALL ABOUT ALL ABOUT ALL ABOUT MENTORSHIP MENTORSHIP MENTORSHIP

How to Find a Mentor & Maintaining a Relationship with Your Mentor

How to Find a Mentor

Having a mentor who takes an interest in your growth is a privilege. Mentors can be guides, helping us find our passions and teach us how to best pursue them. They can offer support and direction for those of us who are questioning our path. Relationships with mentors can begin and develop naturally, but they can also be sought out strategically. If you’re interested in finding a mentor, walk through the questions below.

What do I need to be mentored in?

Before searching for a mentor, it’s important to clearly establish what you ’ re looking to learn The person who mentors you in your research doesn’t have to be the same person who helps you develop your leadership qualities Focusing on a specific area can lend clarity to your relationship with a mentor

How can I grow?

While mentors can be excellent and necessary guides, your growth ultimately depends on you It’s a good idea to identify desired areas for growth before finding a mentor so you can work with someone strong in the areas you need help in. If you find yourself struggling with communication, for example, it may be helpful to seek a guide known for her public speaking skills. By evaluating your shortcomings honestly, you can actively work towards growth.

What kind of goals do I have?

Ideally, your mentor should, to some degree, hold a position or possess a skill you aspire to have. This could include someone working in a specific leadership role, successfully maintaining work-life balance, or impacting the community in a notable way. While there’s a lot to be said for finding your own path in life, using a mentor’s journey for guidance and inspiration can help you avoid some of the pitfalls you may encounter when chasing your dreams. Finding a mentor who excels at what you want to do can help you cultivate focus and direction. Your goals don’t need to be as specific as identifying a certain job you may want to simply explore your mentor’s field.

Where can I find potential mentors?

If there’s someone you ’ re close to in an organization or someone who’s already expressed an interest in helping you grow, that’s great! If not, mentors can be found through websites like LinkedIn, as well as school and company pages. Word of mouth should never be the only way you find a mentor platforms like LinkedIn are powerful tools that can connect you to people who do interesting, impactful work Along with sites like these, mentors can be found through a part time job, a recreational sports team, or even through a friendly neighbor

You’ll need to get comfortable cold emailing and reaching out first It can be scary to face rejection from someone you admire, but remember that if you don’t get a response, it’s likely more about their busy schedule than about you

How

can

I build the

relationship?

After getting an initial response from your mentor, your job is to do everything in your power to help your mentor guide you Asking thoughtful questions is essential most people love to share their experiences Show gratitude frequently and earnestly because whoever is showing you the ropes is likely doing it out of kindness and a desire to help you along Being a mentor and being mentored can be great experiences, but imbalances in the relationship can create a lot of stress for both parties involved

If there’s someone in your network who you look up to, reach out and start a conversation today. Offer to buy them a coffee and discuss the possibilities of establishing a mentorship. You may just find a valuable guide.

Maintaining a Relationship with Your Mentor

Finding a mentor is only the first stepin order to get the most out of your mentorship, it’s important to understand how to nurture and build upon your relationship.

Frequently revisit expectations

It’s important to establish clear objectives for what you hope to gain through your mentorship and communicate those expectations with your mentor upfront. However, goals can evolve, along with the amount of time and energy you ’ re able to invest in your mentorship. Regularly check in to revisit the goals you ’ ve set and make sure your meeting schedule is still working for both of you. Make adjustments as necessary. If you plan to maintain your relationship with your mentor long-term, expect that the nature of the relationship will change over time.

Consistency is key in maintaining a relationship with your mentor If you ’ ve set up regularly scheduled meetings and check-ins, make sure to always show up! Always be courteous: your mentor is probably busy, so if you need to reschedule a meeting, let them know as far in advance as possible Be present and engaged during meetings - preparing questions beforehand is a great way to show that you ’ re serious and that you value your mentor’s time For example, you can ask your mentor how she stays creative and encourages innovative thinking, what strategies she uses to balance her career and personal life, and what kinds of things she may have done differently in her own career if given the opportunity. Always bring a notebook or laptop to write down important pieces of advice from your mentor. Remember that preparation is half the battle: if you know exactly what you want to get out of a meeting and come prepared with the right questions, things will run much more smoothly. Be prepared

Give back to your mentor

Since mentors are generally more experienced, skilled, and better connected in a respective field, it’s easy to think, “I can’t possibly offer my mentor anything of value!” Don’t fall into that trap - every successful, enduring relationship requires give-and-take, and mentorships are no different. Your mentor will appreciate small acts of reciprocity: if, for example, she’s mentioned an interest in a certain niche of her field, do some research about it on your own time, and send her some links to interesting readings on the topic. Maybe your mentor has already read every publication on the subject (unlikely), but either way, she’ll be touched by your thoughtfulness.

Remember that your mentor is human

Mentors are people first - they have hopes, dreams, and challenges of their own It’s okay to get (appropriately) personal If your mentor just had a baby, check in and see how she’s coping with the stress of parenthood. Maybe your mentor is in the midst of a challenging, timeconsuming project and doesn’t have as much time to spare for you at the moment. Don’t just drop communication: send the occasional email to check in and reschedule meetings as needed. If your mentor’s birthday is coming up, write her a thoughtful note and thank her for the impact she’s made on you. Basic acts of kindness go a long way. Never be afraid to be considerate!

Establishing a mentorship can be one of the most beneficial things you do for yourself, both career-wise and personally. Treat your relationship with your mentor with respect, and remember that relationships flourish when both parties give 100 percent.

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What is

ChatGPT?

BY JOCEE HOLLADAY

Note: All Images in this article are generated by ChatGPT.

For hundreds of years, humans have been communicating with machines. Pressing down a foot pedal informs a sewing machine how fast to sew, pressing up and down arrows on a straightening iron determines how hot the tool is, and double-tapping on a phone “likes” the hundredth photo of your best friend’s cat. Computer programmers and engineers have been having even more complicated conversations with computers by writing computer code since the mid-1900s. While we have always been communicating with computers, it has always been in a simplified manner – until now.

Natural Language Processing (NLP) is the next step in communicating with computers. Instead of using physical inputs or code, NLP allows humans to talk to a computer as if speaking to another human. NLP is not a novel concept it first gained notable attention when Siri began listening to iPhone users in 2012, sparking a wave of screenless, listening devices designed to sit on countertops. While these devices were impressive (and very helpful at asking the weather and setting timers), these NPL tools have been limited in their functionality and usefulness. They were limited to answering basic questions and unable to understand nuance, sarcasm, or context until ChatGPT-3 was released a decade later in 2022. Utilizing NLP, ChatGPT immediately changed the way humans communicate with computers and sparked the current Artificial Intelligence (AI) phenomenon.

What is ChatGPT?

ChatGPT, believe it or not, is named exactly what it is – a chatbot that uses a Generative Pre-trained Transformer (GPT). Let me break it all down. The term "Generative" means that it can generate new content like images, music, and texts. For example, ChatGPT can generate recipes (though it hasn't quite mastered cooking yet)! “Pre-trained” means that the tool was trained on a large amount of data and can be further trained on more specific data to perform distinct tasks. The "Transformer" part? That refers to a type of neural network architecture tailor-made for decoding and generating conversational language

Let’s take a deeper look at each part of the acronym GPT to see how this new tool works and why you can’t go a single day without hearing "just ask ChatGPT" at least a dozen times before breakfast

Generative

The primary goal of ChatGPT is to predict the most appropriate next word based on the context provided That’s it! ChatGPT is simply playing a game of “Guess the next word” with itself While this process often results in answering questions, composing eloquent love letters (or breakup texts, your choice), or doing your homework, all ChatGPT is doing is accurately predicting the next word.

When given an input like “Once upon a time, there was a princess holding a powertool,” ChatGPT analyzes the entire input phrase and uses this context to determine what word makes the most sense to follow “powertool.” It generates a list of potential next words, each accompanied by a probability score indicating its likelihood of being the correct choice. The word with the highest probability is then selected and tacked onto the end of the sentence

For example, in our input, ChatGPT might predict the next word to be “that,” resulting in “Once upon a time there was a princess holding a powertool that” as the new statement This revamped statement (including the newly generated word “that”) becomes the new input for ChatGPT, kicking off another round of “Guess the next word,” where it picks what word comes after “that.” ChatGPT recalculates, chooses the next word, and repeats again and again until voilà! a fully fleshed-out, contextually spot-on response emerges.

In a nutshell, ChatGPT's knack for generating text isn't just about spewing random words it's about predicting the perfect words to continue the conversation. But how does it know which next word fits? That’s where the pre-processor comes in

| What is ChatGPT?

Chat + GPT = ChatGPT

Pre-processor

The bedrock of anything AI is data AI uses data to “train models ” Machines learn to recognize, memorize, and regurgitate data. Traditionally, AI models have been trained using supervised learning, where humans meticulously labeled, cleaned, and organized data for optimal machine understanding.

One of the innovations of ChatGPT was its use of unsupervised learning. The data used to train ChatGPT was unlabeled, uncleaned, and uncurated, which made getting data much easier. Now, instead of being limited to specific available datasets, any data could be used to train ChatGPT – which is how ChatGPT-3 was able to use a massive data bank – basically the entire internet With this immense data source, ChatGPT had access to social media, news, articles, and communication transcriptions, where it learned to speak like a human, text like a teenager, and write code like a software engineer

Transformer

The “Transformer” in GPT is the innovation that has started the current interest (read: obsession) in AI A Transformer is a specific type of neural network In the 1940s, neural networks were first developed as a way to study, understand, and replicate how human brains generate thought. The idea is that the human brain is made of neurons. These neurons interact with each other between different layers of the brain to create thoughts.

The transformer architecture in GPT introduced a breakthrough concept that departed from traditional neural network designs. Unlike earlier models that were a bit more structured, transformers utilize self-attention mechanisms. Basically, this mechanism allows each word in a sequence to interact with every other word and change the meaning Using this method, ChatGPT can tell what the word “model” refers to when used in context with computers versus fashion It considers the entire input instead of just individual words

There is no question that the advancement of ChatGPT and other AI tools is changing the world. But ChatGPT has some significant downsides. It is trained on data from the internet, which can be biased, incorrect, and harmful. ChatGPT can sometimes regurgitate problematic responses using this data.

Additionally, ChatGPT tends to “hallucinate ” Despite the massive data set, ChatGPT doesn’t always have enough data on a subject to answer a prompt correctly Since ChatGPT is a chatbot, and its goal is to play “guess the next word“ to make a comprehensible sentence and not to provide the most accurate information, it doesn’t realize that it doesn’t know what it’s talking about. It’s all just math, after all. So instead, ChatGPT continues to predict what word comes next, sometimes making up information in the process.

To showcase how great ChatGPT is at understanding context and playing “Guess the next word,” I pasted this entire article into ChatGPT-4o and asked it to provide a conclusion to this article that was fun, witty, and matched the writing style Here is what it generated (though I can’t help but wonder if ChatGPT was a little excited to brag about itself when writing this final paragraph):

“ChatGPT has revolutionized human-computer interaction, propelling us into an era where conversations with machines are as fluid as those with fellow humans. From sewing machines to sophisticated AI like ChatGPT-4o, our ability to communicate instructions and extract information has evolved drastically. NLP, powered by technologies like GPT, marks a significant leap forward, enabling tasks from essay writing to composing love letters with ease. Yet, amid its prowess, challenges loom—bias in data and occasional 'hallucinations' underline the need for cautious integration and ongoing ethical scrutiny. As we navigate this AI-driven future, ChatGPT stands as both a marvel and a beacon, reshaping industries and discourse alike.”

| What is ChatGPT?

DIY: How to Spot AI Generated Media

BY SOPHIA TANG

More than ever, it seems like AI is encroaching on every area of our life. From Alexa to Siri to ChatGPT, AI is literally at our fingertips. AI also brings numerous issues: AI-generated media is so realistic and human-like that misinformation can spread quickly. Luckily, we can take a few steps to determine when the content we consume has been generated by AI.

How to Spot

AI-Generated Text

Large Language Model-powered chatbots, such as ChatGPT, have the capacity to produce fully-fledged responses to long, nuanced questions in just a matter of seconds, and this has been raising integrity-related concerns Many educational institutions are banning the use of AI for academic writing Although the answers that LLMs produce may seem very thorough, certain key features may help you spot AI-generated text.

Context and content: AI-generated text often fails to fit into a larger context and thus sometimes misses the intended message and purpose of the text because it cannot understand reality As a result, AI-generated text can feel standard and undistinguished, while humans usually write with unique words and varying patterns

Accuracy: Because not all AI models are continuously trained and up-to-date on current events, AI-generated text sometimes contains inaccuracies, especially about more recent happenings. Also, AI tends to confidently present incorrect numerical reasoning and information that may not have a credible source.

Lack of Personal Feel: There is often less personal emotion or opinion in AI-generated text, and it seems more distant. Its monotonous and almost analytical method of approaching many situations indicates that the text might be AI-generated There is often minimal personal connection, empathy, reflection, insight, and humor However, if the prompt instructed the AI model to include humor or a specific emotion, the text might appear to have emotions, but it may seem superficial and surface-level

Repetition: Humans tend to vary their sentence structures, vocabulary, and tone, but AI rarely varies phrases or sentence structures Some AI models tend to be redundant, so the text may sometimes feel like it’s reiterating the same ideas but with different phrasing Because AI works by predicting words, they generally use common words, such as “the” and “it,” more often than humans do.

Unusual Sentence Construction: Although AI-generated text is typically grammatically correct, the sentences are often very unusual or overly complex Be cautious, however, because a good, specific prompt could also avoid this.

Flowery language: AI heavily relies on common phrases and often uses them excessively. Some keywords that AI tends to overuse include “delve,” “dive,” “craft,” “elevate,” and “harness.” These don’t directly signal that a paragraph is AI-generated, but the possibility is high if there are other signs.

Although AI-generated text may have some distinguishable characteristics, it is still extremely difficult to determine if a statement is AI-generated depending on the prompt and context. According to an MIT Technology Review article, “Another study found that untrained humans were able to correctly spot text generated by GPT-3 only at a level consistent with random chance.” As such, it is important to take this into account before making important or highimpact decisions.

Online tools like GPTZero assist in detecting AI-generated text; however, they do not always yield accurate results. While these websites can be helpful tools and indicators, be wary when making high-stakes decisions based on their conclusions

Let’s see if you can identify which of these are written by people and which are AI-generated!

This is my ideal Sunday: I start by sleeping in, waking up whenever the natural light floods into my room. I take a quick morning jog, soaking in the morning sunlight, to my local cafe and order a black coffee with one sugar. I meet up with my best friend, and we sit down to catch up for one hour, but it feels like only ten minutes. We drive down to the beach, put our toes in the sand, laugh as we run into the cold waves, and eat chocolate strawberries as we tan. I sleep well at the end of the day.

As I ponder the intricacies of quantum mechanics, I can't help but marvel at the delicate dance of particles and waves that define our universe. The realm of the very small, governed by the strange and often counterintuitive principles of quantum theory, reveals a world where particles can exist in multiple states simultaneously and where their properties are fundamentally intertwined with the act of measurement itself This delicate interplay, where electrons can be both particles and waves, and where entanglement links distant particles in ways that defy classical intuition, underscores the profound mystery and beauty of the natural world.

The economic implications of blockchain technology extend far beyond cryptocurrencies, potentially revolutionizing industries with enhanced security and transparency At its core, blockchain is a decentralized ledger that records transactions across multiple computers, ensuring that the data is immutable and accessible to all participants in real-time. This foundational characteristic can transform various sectors by providing a robust framework for trust and verification without the need for intermediaries.

As I trekked through the Amazon rainforest, I was captivated by the towering trees and the lively chirping of birds A small blue butterfly gently landed on my finger, but I walked forward and it quickly fluttered away The rustling of the bushes mystified me, and I feared that a large animal would appear I had to calm my heart rate down to remember what my job was here: collect insect samples.

Going on a walk can be a simple yet profoundly enriching experience. As you step outside, the fresh air greets you, filling your lungs with a sense of renewal. The rhythmic sound of your footsteps creates a soothing cadence, harmonizing with the subtle symphony of nature birds chirping, leaves rustling, and the occasional rustle of a small animal in the underbrush Each step brings new sights and sounds, whether you ' re wandering through a bustling cityscape, with its vibrant energy and diverse architecture, or meandering along a tranquil path in a forest, where the canopy above filters sunlight into a dappled mosaic on the ground

Answers

Human: This paragraph explains many sensory experiences and personal details The descriptions of waking up to natural light and going to a local cafe for coffee are very common activities Moreover, the emotional nuance of having the time pass quickly when with one ’ s best friend is relatable to many people It has a natural, conversational flow without overly flowery language and too many keywords The sentence structure is varied, with some compound and complex sentences while others are simple. There are natural transitions, so the paragraph doesn’t feel choppy.

AI: The paragraph repeats some ideas, such as the “delicate dance” and the interplay of “particles and waves, ” which AI sometimes does to emphasize key points. The text is highly abstract when it discusses quantum mechanics in a theoretical and generalized method rather than explaining it practically or giving relatable examples. Additionally, phrases like “fundamentally intertwined with the act of measurement” show the flowery language that AI often uses.

AI: Similar to the previous example, this paragraph also focuses on fundamental concepts without any nuanced or real-world examples. It lacks human experience or industry-specific examples. Also, the language may seem overly complex for this topic, with phrases like “decentralized ledger” and “foundational characteristic.”

Human: This paragraph includes emotional responses, like “captivated by the towering trees,” and specific details, like “small blue butterfly.” These details help the reader feel what the author is experiencing and visualize their environment. The beauty of the rainforest contrasts with the narrator’s anxiety and isn’t over exaggerated The paragraph has an authentic flow and natural phrasing without overemphasizing or overly describing certain details If AI were to generate a similar paragraph, it might sound like, “I noted the sound of rustling vegetation, leading to a concern about the potential presence of a large animal ”

AI: Although this paragraph is in the first-person perspective, the language is formal and descriptive, with phrases like “profoundly enriching experience” that don’t sound natural The paragraph is very general It discusses the feeling of walking in nature but later paints a picture of many different environments that one can walk in, which might be confusing

How many did you get right?

How to Spot

AI-Generated Images

Focus on the details: Images generated from AI often look right at first glance, but if you hone in on the details, you may spot some unnatural features, such as an extra finger or row of teeth. Moreover, text in AI-generated images is often absurd, inaccurate, or illegible. Textures and patterns in AI-generated images are often simplistic rather than detailed, which causes the picture to have an overly smooth appearance with an “airbrushed” feel.

Inconsistent lighting: Image-generating models seem to have trouble with light and shadow If a light source (such as the sun) points in a certain direction, but an object in the image is lit up on the side facing away from the light source, the image may be AI-generated.

Background: AI often puts most of its efforts into perfecting the foreground or the main subject and leaves the background messy or with mistakes For instance, buildings in the background might be crooked or structurally impossible If the background has strange features that don’t seem to match the rest of the image, it may be AI-generated.

Facial Features: AI still has trouble generating human faces They can appear strange, with odd eye features (like unnatural reflections or iris shapes) The person ’ s hair may scrunch together and flow unnaturally Also, the facial expressions might not match the emotion of the image The faces may also be too symmetrical or too asymmetrical.

Let’s practice! For each pair of images, one is AI-generated, and one is taken by a human.

Right: The image on the right has very saturated and bright colors, but the aspect that gives the image away is the “kiwis” in the photo They are far too green, and they look like a peel instead of a fruit However, the blueberries, strawberries, and kiwis look natural in the image on the left The hand has no mistakes, and the background is blurred, but we can tell there are two humans

Left: In the image on the left, it looks as if there are many chopsticks on the right, but then on the left, only the black chopstick is there What happened to all the brown chopsticks? And it’s strange that there’s only one black chopstick in the first place because they come in pairs The image on the right has many details, as you can clearly see the sushi topping and each grain of rice.

Left: The image on the left has an “airbrushed” feel that doesn’t happen in real life. However, the image on the right has many details, as you can see the different textures on the hilltop and the white building. The water also reflects the sun accurately.

Right: The clouds look a little flat in the image on the right, while the image on the left has clouds with dimension and texture. Also, there is snow on the ground in the image on the left, but no sign of snow on the trees.

AI Q&A

with Casey Fiesler with Casey Fiesler

QWhat ethical dilemmas do you foresee as AI becomes more integrated into daily life?

There are a lot of critical ethical issues related to AI that we ’ re already hearing a lot about: bias and discrimination, job loss, training AI with people’s content without their consent, energy consumption, deep fakes and other disinformation, and more. But one important thing to remember is that AI itself is not driving this train–these ethical dilemmas come from human decisions about how to build and how to use AI. For example, AI isn’t taking people’s jobs; humans are making decisions to fire people, often in order to cut costs. So many of these ethical dilemmas come down to: What kinds of humans do we want to be in the age of AI?

QHow do you feel about AI in education? Where do we draw the line between using it as a resource and abusing it?

For a lot of different contexts, including education, an important (and easy!) line to draw is not using AI deceptively. This means that you should be disclosing how

and when you use AI for things like schoolwork. And if disclosing that you used AI would get you into trouble that probably means it’s a time when you shouldn’t have used it. At the same time, educators should also be considering how to surface the appropriate ways that students can use AI.

QModels like ChatGPT and Gemini have shaped what we see as AI today. What are some sides of AI other than generative AI?

The term “artificial intelligence” goes all the way back to the 1950s! AI generally refers to machines performing tasks that would usually be associated with human intelligence, and there are a lot of examples you ’ ve been using since long before ChatGPT. Facial recognition that unlocks your phone, autocorrect when you ’ re sending a text, the algorithm that chooses the next video you see on TikTok And of course, remember that even generative AI isn’t magic–it’s all just code!

What role does AI play in enhancing innovation across different sectors? Is there a particular field you foresee leveraging AI more?

I think that some of the most exciting areas for AI innovation involve tackling problems that humans have had difficulty solving. For example, we are seeing AI used for early cancer detection and for predictive models that could help combat climate change

How do you continue to learn and grow in such a rapidly evolving field?

It is hard for everyone to know everything! I at least try to pay attention to both new research and news stories, but also it’s my job to keep up with technology so that I can teach about it. I get that it could seem really intimidating to even try to absorb everything that’s changing so quickly–so just remember that you don’t have to know everything! Even knowing a little bit about how AI works and what its limitations are will give you a leg up.

QWhat would you suggest for college undergraduates or recent graduates with a technical background who would like to break into AI?

Learn as much as you can! Take relevant classes, read books, try things, pay attention. The AI workforce needs people with technical skills who are also thoughtful about its impact on the world.

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