Neil S. Gaikwad , the mit media lab
Neil S. Gaikwad
@neilthemathguy | | photography
the media lab, massachusetts institute of technology
ai and data science powered systems for sustainability
ॐ असतो मा सद्गमय । तमसो मा ज्योतिर्गमय । मृत्योर्मा अमृतं गमय ।।

Research Highlights

(12) 2017 - awarded the MIT Graduate Student Life Grant for the MIT SP Graduate Student Dinner Seminar Series initiative
(11) 2017 - paper got accepted at the AAAI-2018, New Orleans, USA.
(11) 2017 - Future of Human-Centered Markets in the Era of AI at TEDxBeaconStreet, Boston.
(10) 2017 - invited to the Industry Panel at the AAAI HCOMP conference, Canada--- decided to step down to increase the panel's gender diversity.
(09) 2017 - invited at the HPI-Stanford Design Thinking Research Program, Potsdam Germany.
(08) 2017 - awarded best paper honorable mention for the ACM UIST research paper, Quebec City, Canada.
(07) 2017 - presented the crowd-computing tutorial at the International Conference on Computational Social Science, Germany.
(06) 2017 - awarded MIT's Kaufman Teaching Certificate (KTCP), evidence of a strong commitment to the teaching enterprise.
(01) 2017 - guided inventors across India to solve pressing societal challenges in agriculture and healthcare using AI [MIT, Innovating for Billions].
(10) 2016 - presented the paper at the ACM UIST, Tokyo, Japan.
(09) 2016 - paper got accepted at the ACM CSCW, Portland, USA.
(06) 2016 - EteRNA featured in Werner Herzog's film ``Lo And Behold: Reveries of the Connected World’’ (Trailer)
(06) 2016 - Daemo, crowdsourcing marketplace helped build the Stanford Question Answering Dataset.
(01) 2016 - one of the finalists in the Knights News Challenge (top 20 of 1,000+). Led the efforts and co-authored the grant.
(09) 2015 - Daemo, crowdsourcing marketplace completed its first project with Microsoft Research.
(10) 2014 - EteRNA got listed as one of the 25 great ideas invented at the Carnegie Mellon SCS.
(02) 2014 - paper got accepted to PNAS.
(01) 2011 - co-launched EteRNA, crowd computing game that helps invent medicine.


I'm a graduate researcher in the Scalable Cooperation research group led by Iyad Rahwan at the MIT Media Lab. My research interests fall at the interface of Interactive Machine Learning (Human centered AI), Engineering of Economics, HCI, and Nonparametric Bayesian Inference. I design, engineer, and empirically analyze self-organizing socio-technical systems that fuse human and artificial intelligence to solve problems that neither can solve alone. At the MIT Media Lab, I'm a part of the Innovating for Billions in Emerging Worlds Leadership Council and a member of the Space Exploration initiative.

My research harnesses Artificial Intelligence, Human-Centered Design, and Data Science to address pressing societal challenges with application to sustainable market design, human-machine team design, future of creative work, and crisis response. I'm one of the principal creators of Daemo, a Self Governed Open Sourced Crowdsourcing Marketplace. I led its technical architecture and invented Boomerang, an incentive-compatible reputation system. My contributions draw on fundamentals from algorithmic game theory (incentive design), structured finance (guilds organization as tranches), and human-centered design (prototype tasks), with aspiration to augment trust in sharing economies. I was also one of the principal creators, inventors and founding members of EteRNA, crowdcomputation game that harnesses human creativity with machine learning to help invent medicine. EteRNA has reached over 100,000 citizen scientists across the world and was listed as one of 25 great ideas emerged from the CMU School of Computer Science. It was also featured in Werner Herzog's film ``Lo And Behold: Reveries of the Connected World’’ (Trailer).

In the past I've have worked with Michael Bernstein from the Computer Science department at Stanford University. I earned M.S. from the School of Computer Science at Carnegie Mellon where I worked with Adrien Treuille at the Robotics Institute Graphics Lab and Anita W. Woolley at the OBT Group.

I come from a small town near the Western Ghats (Sahyadri in Sanskrit) of India. One of the major missions of my life is to open up the STEM (Science, Technology, Engineering, and Mathematics) research and educational opportunities for students, women, and minorities across the globe. In this pursuit, I moved back to academia from the mathematical finance industry (Wall Street).

Neil S. Gaikwad
नील गायकवाड

ॐ असतो मा सद्गमय ।
तमसो मा ज्योतिर्गमय ।
मृत्योर्मा अमृतं गमय ।।

MIT Media Lab
75 Amherst St,
Cambridge MA, 02139
Email: gaikwad[at]mit[dot]edu
Arts: photography
Instagram: neilgaikwadphotography



A Voting-Based System for Ethical Decision Making
Noothigattu, Ritesh., Gaikwad, S., Awad, Edmond., D'Souza, Sohan., Rahwan, Iyad., Ravikumar, Pradeep., Procaccia, Ariel D.
AAAI 2018: Association for the Advancement of Artificial Intelligence.
{keywords} | Algorithmic Ethics and AI | Computational Social Choice | Voting |

We present a general approach to automating ethical decisions, drawing on machine learning and computational social choice. In a nutshell, we propose to learn a model of societal preferences, and, when faced with a specific ethical dilemma at runtime, efficiently aggregate those preferences to identify a desirable choice. We provide a concrete algorithm that instantiates our approach; some of its crucial steps are informed by a new theory of swap-dominance efficient voting rules. Finally, we implement and evaluate a system for ethical decision making in the autonomous vehicle domain, using preference data collected from 1.3 million people through the Moral Machine website.

PRESS: Social Media Has Failed Its Self-Driving Test (Bloomberg) | Researchers go after the biggest problem with self-driving cars (Axios)

Crowd Research: Open and Scalable University Laboratories
Vaish, Rajan., Gaikwad, S., Kovacs, Gezza., Veit, Andreas., Krishna, Ranjay., Ibarra, Imanol., Simoiu, Camelia., Wilber, Michael., Belongie, Serge.,
Goel, Sharad., Davis, James., and Bernstein, Michael.
UIST 2017: ACM Symposium on User Interface Software and Technology
{keywords} | Research at Scale | Massive Online Open Research | Open Innovation |

Research experiences today are limited to a privileged few at select universities. Providing open access to research experiences would enable global upward mobility and increased diversity in the scientific workforce. But, how do we coordinate a crowd of diverse volunteers on open-ended research? How could a PI have enough visibility into each person's contributions to recommend them for further study? We present Crowd Research, a crowdsourcing technique that coordinates open-ended research through an iterative cycle of open contribution, synchronous collaboration, and peer assessment. To aid upward mobility and recognize contributions in publications, we introduce a decentralized credit system: participants allocate credits to each other, which a graph centrality algorithm translates into a collectively-created author order. Over 1,500 people from 62 countries have participated, 74% from institutions with low access to research. Over two years and three projects, this crowd has produced articles at top-tier Computer Science venues, and participants have gone on to leading graduate programs.

PRESS: A Stanford-led Platform for Crowdsourced Research Gives Experience to Global Participants (Stanford News)

PDF Web [ACM UIST Best Paper Honorable Mention]
Boomerang: Rebounding the Consequences of Reputation Feedback on Crowdsourcing Platforms
Gaikwad, S., Crowd Research Members, Vaish, Rajan., and Bernstein, Michael.
UIST 2016: ACM Symposium on User Interface Software and Technology
{keywords} | Human-centered Market Design | Incentive Engineering | Reputation & Trust Systems | Open Innovation |

Paid crowdsourcing platforms suffer from low-quality work and unfair rejections, but paradoxically, most workers and requesters have high reputation scores. These inflated scores, which make high-quality work and workers difficult to find, stem from social pressure to avoid giving negative feedback. We introduce Boomerang, a reputation system for crowdsourcing that elicits more accurate feedback by rebounding the consequences of feedback directly back onto the person who gave it. With Boomerang, requesters find that their highly-rated workers gain earliest access to their future tasks, and workers find tasks from their highly-rated requesters at the top of their task feed. Field experiments verify that Boomerang causes both workers and requesters to provide feedback that is more closely aligned with their private opinions. Inspired by a game-theoretic notion of incentive-compatibility, Boomerang opens opportunities for interaction design to incentivize honest reporting over strategic dishonesty.

* In the readings of advanced Human Computation classes at KAIST, Cornell University, University of Washington

PDF System
Crowd Guilds: Worker-led Reputation and Feedback on Crowdsourcing Platforms
Whiting, Mark., Gamage, Dilrukshi., Gaikwad, S., Crowd Research Members, Vaish, Rajan., and Bernstein, Michael.
CSCW 2017: ACM Conference on Computer-Supported Cooperative Work
{keywords} | Human-centered Market Design | Organizational Engineering | Self Governing Systems | Open Innovation |

Crowd workers are distributed and decentralized. While decentralization is designed to utilize independent judgment to promote high-quality results, it paradoxically undercuts behaviors and institutions that are critical to high-quality work. Reputation is one central example: crowdsourcing systems depend on reputation scores from decentralized workers and requesters, but these scores are notoriously inflated and uninformative. In this paper, we draw inspiration from historical worker guilds (e.g., in the silk trade) to design and implement crowd guilds: centralized groups of crowd workers who collectively certify each other’s quality through double-blind peer assessment. A two week field experiment compared crowd guilds to a traditional decentralized crowd work model. Crowd guilds produced reputation signals more strongly correlated with ground-truth worker quality than signals available on current platforms, and more accurate than in the traditional model.


RNA Design Rules From a Massive Open Laboratory
Lee, Jeehyung., Kladwang, Wipapat., Lee, Minjae., Cantu, Daniel., Azizyan, Martin., Kim, Hanjoo., Limpaecher, Alex.,
Gaikwad, S., Yoon, Sungroh., Treuille, Adrien., Das, Rhiju., and EteRNA Participants
PNAS 2014: Proceedings of the National Academy of Sciences of the United States of America
{keywords} | Human Machine Symbiosis at Scale | Collective Intelligence | Wisdom of Citizen Scientists |

Self-assembling RNA molecules present compelling substrates for the rational interrogation and control of living systems. However, imperfect in silico models—even at the secondary structure level—hinder the design of new RNAs that function properly when synthesized. Here, we present a unique and potentially general approach to such empirical problems: the Massive Open Laboratory. The EteRNA project connects 37,000 enthusiasts to RNA design puzzles through an online interface. Uniquely, EteRNA participants not only manipulate simulated molecules but also control a remote experimental pipeline for high-throughput RNA synthesis and structure mapping. We show herein that the EteRNA community leveraged dozens of cycles of continuous wet laboratory feedback to learn strategies for solving in vitro RNA design problems on which automated methods fail. The top strategies—including several previously unrecognized negative design rules-were distilled by machine learning into an algorithm, EteRNABot. Over a rigorous 1-y testing phase, both the EteRNA community and EteRNABot significantly outperformed prior algorithms in a dozen RNA secondary structure design tests, including the creation of dendrimer-like structures and scaffolds for small molecule sensors. These results show that an online community can carry out large-scale experiments, hypothesis generation, and algorithm design to create practical advances in empirical science.

PRESS: Videogamers are recruited to fight Tuberculosis and other ills (Wall Street Journal) | RNA game lets players help find a biological prize (New York Times) | How turning science into a game rouses more public interest (WIRED) | Game lets citizen scientists participate in creating large-scale library of synthetic RNA designs (Scientific American) | Rebooting science outreach (ASBMB Today) | Why video games are key to modern science (CNN) | Online game helps predict how RNA folds (New Scientist)

PDF System

Crowdcomputing and Citizen Science for Large-Scale Experiments
Gaikwad, S., Dsouza, Sohan., Vuculescu, Oana., Mao, Andrew., and Rahwan, Iyad.
IC2S2 2017: International Conference on Computational Social Science
{keywords} | Human Machine Symbiosis at Scale | Crowd Computing | Citizen Science |

Historically, scientific experiments have been conducted at a small scale either with artificial environments or with the expertise of limited number of scientists. While social science literature investigates very deep questions to understand human behavior, many experiments are usually limited by the number of participants and duration of a study. On the contrary, computer science literature exploits advanced computational techniques to crunch voluminous datasets, but research designs are generally not experimental, which limits the opportunity to generate causal inferences. In this tutorial we demonstrate how crowdcomputing can enable computational social scientists to engage with millions of users on the Internet and study human behavior at scale for a longer time. We showcase pitfalls and lessons learned from various crowdcomputing and citizen science projects. Furthermore, we provide insights about how to build a sustainable citizen science community to scale science beyond the traditional laboratories. We envisage this tutorial will help computational social scientists effectively use crowdcomputing to investigate deep research questions and longitudinally validate their hypotheses in large scale experiments.


Prototype Tasks: Improving Crowdsourcing Results through Rapid, Iterative Task Design
Gaikwad, S., Crowd Research Members, Vaish, Rajan., and Bernstein, Michael.
AAAI HCOMP 2017: AAAI Conference on Human Computation
{keywords} | Human-centered Market Design | Sharing Economies |

Low-quality results have been a long-standing problem on microtask crowdsourcing platforms, driving away requesters and justifying low wages for workers. To date, workers have been blamed for low-quality results: they are said to make as little effort as possible, do not pay attention to detail, and lack expertise. In this paper, we hypothesize that requesters may also be responsible for low-quality work: they launch unclear task designs that confuse even earnest workers, under-specify edge cases, and neglect to include examples. We introduce prototype tasks, a crowdsourcing strategy requiring all new task designs to launch a small number of sample tasks. Workers attempt these tasks and leave feedback, enabling the requester to iterate on the design before publishing it. We report a field experiment in which tasks that underwent prototype task iteration produced higher-quality work results than the original task designs. With this research, we suggest that a simple and rapid iteration cycle can improve crowd work, and we provide empirical evidence that requester “quality” directly impacts result quality.

PDF System
The Daemo Crowdsourcing Marketplace
Gaikwad, S., Crowd Research Members, Vaish, Rajan., and Bernstein, Michael.
CSCW 2017: ACM Conference on Computer-Supported Cooperative Work
{keywords} | Human-centered Market Design | Sharing Economies | Self Governing Systems | Open Innovation |

The success of crowdsourcing markets is dependent on a strong foundation of trust between workers and requesters. In current marketplaces, workers and requesters are often unable to trust each other’s quality, and their mental models of tasks are misaligned due to ambiguous instructions or confusing edge cases. This breakdown of trust typically arises from (1) flawed reputation systems which do not accurately reflect worker and requester quality, and from (2) poorly designed tasks. In this demo, we present how Boomerang and Prototype Tasks, the fundamental building blocks of the Daemo crowdsourcing marketplace, help restore trust between workers and requesters. Daemo’s Boomerang reputation system incentivizes alignment between opinion and ratings by determining the likelihood that workers and requesters will work together in the future based on how they rate each other. Daemo’s Prototype tasks require that new tasks go through a feedback iteration phase with a small number of workers so that requesters can revise their instructions and task designs before launch.

PDF System
Designing a Constitution for a Self-Governing Crowdsourcing Marketplace
Crowd Research Members, Gaikwad, S., Vaish, Rajan., and Bernstein, Michael.
CI 2017: Collective Intelligence Conference
{keywords} | Human-centered Market Design | Sharing Economies | Self Governing Systems | Open Innovation |

A key principle of Daemo is to provide crowd workers and requesters with a means of governing the development and evolution of the platform into the future. To facilitate this, we introduced the Daemo constitution, outlining the goals of the platform, relationship between members of the community and its standards, methods for seeking ideas, amending the constitution and resolving conflicts. The Daemo constitution has been a collaborative effort by the Stanford Crowd Research Collective. However, we have also endeavored to solicit feedback from the MTurk worker and requester communities via TurkerNation, Reddit and other channels.

Daemo: a Self-Governed Crowdsourcing Marketplace
Gaikwad, S., Crowd Research Members, Vaish, Rajan., and Bernstein, Michael.
UIST 2015: ACM Symposium on User Interface Software and Technology
{keywords} | Human-centered Market Design | Sharing Economies | Self Governing Systems | Open Innovation |

Crowdsourcing marketplaces provide opportunities for autonomous and collaborative professional work as well as social engagement. However, in these marketplaces, workers feel disrespected due to unreasonable rejections and low payments, whereas requesters do not trust the results they receive. The lack of trust and uneven distribution of power among workers and requesters have raised serious concerns about sustainability of these marketplaces. To address the challenges of trust and power, this paper introduces Daemo, a self-governed crowdsourcing marketplace. We propose a prototype task to improve the work quality and open-governance model to achieve equitable representation. We envisage Daemo will enable workers to build sustainable careers and provide requesters with timely, quality labor for their businesses.

PRESS: The future of work the peoples uber (Pacific Standard)

PDF Poster System
Crowd Research: Research At Scale
Vaish, Rajan., Gaikwad, S., Ginzberg, Adam., Ibarra, Imanol., Kovacs, Geza., Krishna, Ranjay., Morina, Durim., Mullings, Catherine.,
Simoiu, Camelia., Veit, Andreas., Wilber, Michael., Zhou, Sharon., Belongie, Serge., Goel, Sharad., Davis, James., Bernstein, Michael.
HCI+Design Open House 2016: Stanford University
{keywords} | Democratization of Science & Education | Open Innovation Laboratories |

Scientific research is becoming increasingly collaborative, yet primarily limited to professional researchers in labs and universities. Can we scale traditional research approach and invite anyone from around the world to participate, and crowdsource large-scale, open-ended research problems? We're scaling the research process to solve open-ended novel problems, by providing access and connecting hundreds of people with top researchers.

Click to see my detailed analysis of PageRank & Credit Distribution

Can Gaming Help Biology? Preliminary Results From the EteRNA Project
Lee, Jeehyung., Cantu Daniel., Gaikwad, S., Kladwang, Wipapat., Treuille Adrien., Das Rhiju., and EteRNA Players.
BioX 2011: Interdisciplinary Initiatives Symposium Poster Session, Stanford University
{keywords} | Human Machine Symbiosis at Scale | Games With Purpose | Collective Intelligence |


The Effects of Team Strategic Orientation On Team Process and Information Search
Woolley, Anita., Bear, Julia., Chang Jin., and DeCostanza, Arwen.
Organizational Behavior and Human Decision Processes, 2013
{keywords} | Team Dynamics | Strategic Behavior |

We tested the effects of team strategic orientation on team member perceptions, work strategy and information search. In Experiment 1, 80 teams worked on a hidden profile decision-making task. A defensive team strategic orientation increased members’ perceptions of the problem’s scope, leading to a more process-focused work strategy and broader information search compared to an offensive team strategic orientation. When teams needed critical information from the environment, defensive teams outperformed offensive teams; offensive teams performed better when critical information resided within the team. In Experiment 2, these findings were replicated with 92 teams performing a different decision task. When making a second decision, half of the teams were led to change their strategic orientation; teams shifting from offense to defense altered their information search behavior more readily than did teams shifting in the opposite direction, suggesting an asymmetric adaptation effect.

Evidence For a Collective Intelligence Factor in the Performance of Human Groups
Woolley, Anita., Chabris, Christopher., Pentland, Alex., Hashmi, Nada., and Malone, Thomas.
Science 2010
{keywords} | Collective Intelligence | Team Dynamics |

Psychologists have repeatedly shown that a single statistical factor—often called “general intelligence”—emerges from the correlations among people’s performance on a wide variety of cognitive tasks. But no one has systematically examined whether a similar kind of “collective intelligence” exists for groups of people. In two studies with 699 people, working in groups of two to five, we find converging evidence of a general collective intelligence factor that explains a group’s performance on a wide variety of tasks. This “c factor” is not strongly correlated with the average or maximum individual intelligence of group members but is correlated with the average social sensitivity of group members, the equality in distribution of conversational turn-taking, and the proportion of females in the group.

PRESS: What Google Learned From Its Quest to Build the Perfect Team (New York Times) | Forget IQ: The Emerging Science of Collective Intelligence (Time) | Why some teams are smarter than others (New York Times) | Social savvy boosts the collective intelligence of groups (Science) | A better way to pay workers (The New Yorker)


Students Supervised & Mentored

Some of the students with positions/scholarships they acquired after the projects:

Aditi Mithal (Awarded Google Venkat Panchapakesan Memorial Scholarship, CS grad program at the University of California, Los Angeles)
Aditi Nath (CS grad program at Arizona State University)
Ankita Sastry (Info Security grad program Carnegie Mellon)
Karan Rajpal (CS grad program at Cornell University)
Nalin Chhibber (Mathematics grad program at the University of Waterloo)
Prastut Kumar (The Google Summer of Code Berkman Klein Center Harvard University)
Prithvi Raj (Awarded Dr. Shanker Dayal Sharma Medal at the IIT Kanpur)
Rahul Sheth (Undergraduate program at the University of California, Los Angeles)
Radhika Bhanu K (CS grad program at Cornell University)
William Dai (Undergraduate program at the University of California, Berkeley)

I'm a recipient of MIT's Kaufman Teaching Certificate Program (KTCP), evidence of a strong commitment to the teaching enterprise. Teaching and mentoring have been an integral part of my quest--- to help grow and inspire a new generation of creative thinkers, scientists, designers, inventors, humanists, and engineers who will collectively engage in resolving some of the biggest societal-challenges of our time. I get a great satisfaction when my students succeed, and their accomplishments give me an immense sense of gratification that cannot be measured. I continue to supervise, mentor, and empower students to do impactful research. Similar to Dr. A.P.J. Abdul Kalam, I would like to be remembered as good teacher and mentor (listen to my interview at MIT's WMBR 88.1 FM Post-It Wall).

I believe entrepreneurship can be taught and students can be trained to innovate technology ventures with a passion for making the world a better place. As the teaching assistant for S. Thomas Emerson, a charismatic teacher at Carnegie Mellon, I had an opportunity to guide various student projects. These projects focused on market research, product design, pricing, financial forecasting, and other factors needed to develop a strong business plan for technology-based ventures.

Feel free to get in touch if I can help you in any ways.

Outreach & Service

Reviewer 26th International World Wide Web Conference WWW 2017.

Reviewer iConference 2010, University of Illinois Urbana-Champaign.

Chair of the Graduate Student Seminar Series, Committee on Scholarly Interactions (CoSI), a graduate organization dedicated to bringing top thinkers to the MIT campus and the Sidney Pacific graduate residence, MIT's largest graduate community.

Started an initiative to address educational and environmental challenges in a village in Ahmednagar, Maharashtra state. As a first step, participated in the government's 20 million tree plantation program that aims to enhance the state forest density from 20% to 33%. Due to the worst drought of 2015, many farmers committed suicides and around 330 million people were affected in India. I'm looking forward to redefine the ways in which agriculture is being done.

Volunteered to teach kids in the Big Brothers Big Sisters program to build robots. This initiative was a part of 100 Robots for 100 Kids, a grassroots project by graduate students at the CMU Robotics Institute.

Co-organized an EteRNA session with J. Lee and P. Kinney for Leap@CMU, summer enrichment program for high school students.


I'm also an artist and a progressive rock guitarist. My aerial photograph `Glacial Place' has featured on National Geographic. Through the lens of my camera, I spread awareness about cultures and burining societal issues across the globe (e.g., climate change, farmer's suicide, education, etc). You can follow my portfolio at `Explore the Planet Earth'

The Game of Cricket

My research draws a lot of inspiration from the sport and team dynamics. I was a former cricket player— opening batter and leg spinner. In the US leagues I also kept wickets (similar to being a catcher in Baseball) for Jermaine Lawson, former international cricketer and Harshal Patel, Royal Challengers Bangalore's fast-bowler in the IPL.

Some of the archived man of the match scorecards: [146 not out], [71 not out]

Sports (Life) Quote: "and so you touch this limit, something happens and you suddenly can go a little bit further. With your mind power, your determination, your instinct, and the experience as well, you can fly very high" -- Ayrton Senna, one of the greatest Formula One drivers of all time.

Networks Analysis of MOOR (Massive Open Online Research)

Play with the RNA

Requirement: Adobe Flash
Where The Mind is Without Fear
Where the mind is without fear and the head is held high

Where knowledge is free

Where the world has not been broken up into fragments

By narrow domestic walls

Where words come out from the depth of truth

Where tireless striving stretches its arms towards perfection

Where the clear stream of reason has not lost its way

Into the dreary desert sand of dead habit

Where the mind is led forward by thee

Into ever-widening thought and action

Into that heaven of freedom, my Father, let my country awake.

Nobel Laureate Rabindranath Tagor
The Impossible Dream (The Quest)
To dream the impossible dream
To fight the unbeatable foe
To bear with unbearable sorrow
To run where the brave dare not go

To right the unrightable wrong | To love pure and chaste from afar
To try when your arms are too weary | To reach the unreachable star

This is my quest | To follow that star
No matter how hopeless | No matter how far

To fight for the right | Without question or pause
To be willing to march into Hell | For a heavenly cause

And I know if I'll only be true | To this glorious quest
That my heart will lie peaceful and calm | When I'm laid to my rest

And the world will be better for this
That one man, scorned and covered with scars
Still strove with his last ounce of courage
To reach the unreachable star!

Joe Darion (From The Man of La Mancha)