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Presenting Your “Whole Package” During Faculty Interviews

by Yu Gu, posted in Job, Students, Uncategorized

It looks easy. All you have to do is to pretend as someone who is better than yourself for half an hour (on the phone/Skype/Zoom), and then a day (on campus), to get that dream faulty job offer.

I tried that a few times as a candidate but was not very successful. I didn’t know what the problems were until years later after serving as the chair or a member on several faculty search committees.

Faculty interview is like speed dating. By the time you made to the interview, we (the search committee) have been impressed by your achievements (on paper), but we haven’t got to know you as a person. We are afraid of picking someone that we may regret and be stuck with for years… That scary thought motivates us to do a careful job.

So what do we care about? First and foremost, we like to know if you are a person we want to work with as a colleague. Are you an open and frank person? Would you see our institution as your future home? Are you the type that can make the people around you better? Do you hold a balanced view of different matters?

Second, we try to predict if you would become a star (not just meeting the tenure requirements) with our yet-to-be-proven fortune telling skills. Do you have a solid grasp of the fundamentals in your area? Are you passionate about something? Can you think critically and independently? Are you aware of ongoing trends in research and in the society? Are you an ambitious person with big dreams and a strong vision? Can you communicate well with different audience, make sound arguments, and be persuasive? Can you be an effective teacher? Can you handle pressure, stress, and setbacks?

Finally, we also want to know if your success would matter to other people. Are you bringing complementary skills (teaching, research) to the institution? Are you a team player? Are you more interested in yourself, the community, or the society? Can you lead a team to build something bigger than us individuals can do?

In a nutshell, we are looking for someone who is way closer to perfection than ourselves …

Of course, we don’t know how to evaluate all that… In robotics terms, we are facing a decision making under uncertainty problem, an active perception problem, and a bounded rationality problem (e.g., making decisions with incomplete information and limited time). Each of us on the committee tries to observe and to probe you with questions. We fall victims to our cognitive biases, jump into conclusions with insufficient data, while trying hard not to fill the missing pieces with stereotypes, imagination, and random thoughts/mood of the day.

So how to survive this complicated, stressful, inherently stochastic, and often biased process? There are a lot you can do before coming to an interview. Preparation and experience help. Iterate on the answers to common questions leads to better, more focused answers. Known what may come helps you to prepare and know when to relax.

However, a skillful search committee can/may see through some of the facade. There are things that can be couched by a good advisor in a matter of hours (e.g., which funding programs to target), but we are not hiring your advisor. Interview experience can be learned (someone had many interviews in the past is not necessarily preferred to someone on the first trip). Always done you homework is a good quality; but is only one of many that we are looking for. Worked on a cool project, like a NASA mission, only means you were on a large team. There are also things that any intelligent person can learn on the job later, without much risks.

What we really want to get to know better is you. We try to focus on things that would take real effort and experience to understand. For example, someone who has never taught a class before would likely not understand the true challenges in teaching and learning. Someone who only did what the advisor told him/her to do may not have deep insights on what is the next step, the step after that, and why. You are unlikely to be a good leader without appreciating the meaning of compromise and sacrifice. Your strong desire to help the community needs to be backed up with a purpose and a track record. You may pretend well for a minute, but if you didn’t have the real experience, this may not survive a few rounds of probing.

Faculty candidates often don’t know why they failed (or succeeded). Almost no search committee can provide frank and detailed feedback due to a variety of reasons. We won’t/can’t tell you that you’ve been acting like a teenage; your accent was not a problem; that name dropping/talking down other researchers did not serve your interest; your honesty and willingness to expose your vulnerability was appreciated; you didn’t seem to be prepared to write a proposal/teach a class/run a lab; etc. I have seen candidates apparently interviewed at many different places didn’t quite understand why they haven’t landed a job. I was in that boat for a few years as well.

So, pretending to be a better, more desirable version of yourself during the interview may not work out. I think a better strategy is to act like that version now, to identify and build up these experiences, and to collect honest feedback. It is never too late for doing that. After you have started on this path, you can follow what many people suggested to do during interviews: “just be yourself”. You no longer have to pretend to be someone better; you are a better faculty candidate.

An Idea of Ideas

by Yu Gu, posted in Future, Ideas, Uncategorized

There are 7.8 billion people living on this planet and everyone’s brain is running, fast or slow. Most of the time, people are thinking about more or less the same things: sports, weather, girls/guys/kids, shopping, promotion, money, politics, to name a few. Imagine how many times the same thought on “which phone to buy” goes around the globe? (sounds like a lot of redundancy and waste here, but that’s for a different topic…)

Occasionally, unique ideas pop up in the mind of a person, any person, often because she/he is in the right place at the right time (e.g., what if the blanket can fold itself after I pick up the baby?).  These ideas could be trivial or infeasible. We might feel good about our creativity for a few seconds, then it would just slip away out of the memory. Not known what to do with the ideas, we are throwing away an enormous number of intellectual products each day (hint: at least write them down like what I am doing with this blog…).

At the same time, our world is in a desperate shortage of creative ideas (just watch a few recent movies or see the design of all the new cell phones…). Once someone is in need of a solution (e.g., stop the pandemic), good ideas don’t come by on schedule. Beaming a lot of brainpower by a few smart people is not necessarily the answer.

So what can we do? How can we involve everyone in the creative process everyday on solving the world’s everything problems? I think we can benefit from something like a “Wikipedia for ideas”, where millions of diverse ideas are shared, debated on, grow and connect, and found by people who need them. How to discover incentives for everyday people to join this collaborative effort would be an important question to answer. Any ideas?

Engineering, Science, and Engineering Science

by Yu Gu, posted in Ideas, Robotics, Uncategorized

When I was a kid, my dream was to become a scientist. I was fascinated with reading early discoveries in chemistry, physics, and biology. Those scientists were my heroes.

Following a series of random and not so random events in life, I end up being an engineer, which I am equally happy about.

For a long time, I didn’t see much difference between science and engineering. We are all researchers. That was until I had my first proposal rejected by NSF, the National Science Foundation.

Scientists and engineers have different goals. Scientists discover and engineers create. Scientists observe something already exists (e.g., nature, universe, human society) and try to explain it. Engineers dream up something new (e.g., a bridge, a rocket, a material) and try to make it real.

For these reasons, scientists and engineers think and work in nearly opposite ways. Scientists observe, ask questions, form hypothesis, and then design experiments to test them. Engineers conceive designs, build prototypes, integrate parts into a system, and then perform evaluation.

If you ask an engineer to tackle a science problem, say why migrant birds often fly in formation; she/he may say let’s make airplanes fly in formation first, take measurements, and see what the data tell us. That was the kind of the mistake I made in writing my first NSF proposal.

Can someone function with both scientist’s and engineer’s minds? It’s very difficult. If you have sat in a meeting with both scientists and engineers, you would know that they don’t really speak the same language or live on the same planet (imaging adding a few artists into the mix!). Heck, they don’t even look the same. But the ability to handle difficulty is what sets one apart from the rest. I don’t have enough knowledge to comment on the importance of engineering to science, but in my opinion, it’s unlikely someone can be a great engineering researcher without sometimes thinking like a scientist.

Take robotics for example, we can always dream up robots that are more refined and algorithms that can squeeze out a few percentages of performance gain. In fact, we always want to do that because as engineers we feel itchy about flaws we can see, and improvements not made. Most of us live comfortably (or not so comfortably) in the cocoons we carefully engineered for ourselves. Every piece of silk we lay makes our world smaller. We are occupied and always so busy; while in the meantime, we ask, why innovation is so hard?

If only we could use some of our silk to explore, to take us to the next tree, and help us see a different world! What about taking a break from solving problems; spend some time to observe, ask why instead of how? We would be thinking like a scientist with the creativity and hands of engineers.

Of course, it would take a risky and painful transition to break the cocoon. But there is also no reason this cannot be done. Someone clearly had the wisdom at WVU a long time ago. After all, my office is in the Engineering Science Building (ESB).

Seventeen Mistakes that You Can Make in Writing Your First Paper

by Yu Gu, posted in Robotics, Students

Writing the first technical paper is hard. There are so many things that you want to write, and there are only a few pages that you are allowed to use. Once everything is put together, the manuscript does not read like any other paper that you have read. What went wrong?

Helping someone to write his/her first paper is also hard. I had my fair share of struggle with first time writers, whether English was their first language or not. The good news is that paper writing is not rocket science, everyone would eventually get it, sooner or later. To make this process a little less painful, mostly on my side :O), here is a checklist for you to use to avoid some commonly made mistakes:

  1. Not following the standard technical paper structure. There is no real need to innovate on the structure of the paper. Typically, we follow this order for a robotics paper: introduction/related work/contribution statement, problem statement, algorithm, experimental setup, results/discussion, conclusion and future work. The “related work” can be presented in the introduction section, as a separate section after the introduction, or near the end of the paper (seems to be a new fashion);
  2. Not paying attention to logic. Make sure to carefully design the paper before start writing. Every paper needs to have its overall purpose, theme, and flow. Every paragraph or sentence also needs to be logically connected to the ones before and after it;
  3. Not giving a clear and compelling argument at the very beginning of the paper.  Clearly identifying the objective in the first few sentences would reduce guesswork for the reviewers and channel their thinking to be aligned to what you want them to think. Pointing out the research gap and the potential impact would allow reviewers better appreciate the presented work;
  4. Not clearly framing the work in the literature. The main purpose behind the literature review section are: 1) to find the gaps/needs in the previous works to help identify the contribution of this paper; 2) to show that this work is built upon understanding of the history and the state-of-the-art (instead of coming out of thin air);
  5. Not clearly pointing out the innovation/contributions. Reviewers would be looking for this to make a decision, so try to help them out!
  6. Mixing problems with solutions in the problem statement section. This can have two negative effects: 1) it causes confusion and makes it harder to present the actual solution later; 2) it reduces the possible solutions to the problem and limits the imagination of the reviewer while reading the paper;
  7. Giving away the answer too early/easily. This is related to the previous point. Most of the time, if you present a solution right after stating the problem, people would trivialize it. This is like a movie spoiler. Present the problem, point out the challenges, let the reader fully appreciate the magnitude of the problem, then enlighten them. Writing a paper needs some serious storytelling skill!
  8. Not detailed enough so that the reader can reproduce the research or/and not concise enough so that the reader won’t get bored;
  9. The reasoning/argument is not bulletproof. Every statement needs to be backed up by facts and sound logic;
  10. Not providing proper evaluation of the results (e.g., metrics, statistics) and not providing confidence that this is a reproducible result. Spend some time to learn how to design experiments properly;
  11. Not providing insightful discussions of the results. Don’t just state the obvious!
  12. Not properly recognizing the limitations of the research. Every work has limitations. Typically this can be discussed in the assumptions (in the problem statement section) and in the future work section at the end;
  13. Not providing enough diagrams in the manuscript. A picture sometimes worth a thousand words. Try to have at least one figure, diagram, or table in each page. Attaching a cool video with the paper would also be important (especially for a robotics paper);
  14. Not fully describing every symbol used in equations;
  15. Not following the required format specified by the publisher;
  16. Having typos, grammatical errors, and formatting inconsistency;
  17. Forgetting to acknowledge sponsors, donors, other non-coauthor contributors.

Note that one of the most important parts of any form of communication is to understand the audience. For the papers, the reviewers are your peers but not necessarily very familiar with the specific problem that you are addressing. Their job is to make a binary classification (often) using the shortest amount of time (sort of like speed dating). Our job is to fight a series of cognitive biases of the reviewers. For example, it’s not uncommon for a reviewer to make a quick judgement after reading only the first few paragraphs (decisions based on incomplete information and heuristics). Once this judgement if formed, it’s difficult to change it. This is because they would be (subconsciously) looking for evidence to reinforce what they believed (confirmation bias). Now you see why the first sentence, the first paragraph, and the first page are so crucial to the success of a paper. Another human bias (availability heuristic) is that people value the most recent information more. This means you need to remind the reviewers all the good stuff about your paper at the end of it…

Well, so far we only talked about the organization aspect of paper writing. How to make sure what you published is not junk (instead, something that would make a technical impact to the community)? That’s another important topic, which we shall discuss at another time.

Looking for Talented and Motivated Students to Join IRL

by Yu Gu, posted in Job, Robotics, Students

My research lab is called the Interactive Robotics Laboratory (IRL). We are a group of creatively minded people that includes 10 graduate students, about a dozen undergraduates, and of course, me. Although we were only founded since 2012, IRL has made its name known in the robotics community. Our coming out party was the winning of NASA’s Sample Return Robot Centennial Challenge (total prize of $855,000). We were also the first group that developed precision autonomous pollination robot (just in case we won’t have enough bees in the future!). We are currently working closely with NASA’s Jet Propulsion Lab (JPL) in improving the autonomy of future Mars rovers (with seven students working for 10-weeks each at JPL).

We are also interested in the interactions among multiple robots. We are currently working on a cooperative UAV and UGV group in exploring underground tunnels. One of our bio-inspired ideas was recently selected by the highly prestigious NASA NIAC program. We are planning to send 100,000 micro ballooning spider probes into Mars’s global dust storm. With the funding form NSF, we are also working on human-swarm interaction: how can one human operator influence the global emergent behavior of a 50-robot swarm without directly controlling individual robots?

Our creative and futuristic work has drawn frequent media attentions. Our research was featured in over 65 news stories by media outlets such as the Discovery Channel, Wired, NASA 360, ABC News, Time Warner Cable, Associate Press, Aviation Week, and Air & Space Smithsonian Magazine.

Our success was building upon IRL member’s creativity, hard work, and ambitions. IRL provides a free thinking and collaborative environment that allow everyone to reach his/her full potential. We are also blessed for having the state-of-the-art facilities with more robots than human group members. At IRL, students are encouraged to develop their own research ideas, supported by resource provided internally and from external sponsors.

We are always looking for talented and motivated students to join us. Email me (yu.gu@mail.wvu.edu) if you are interested. As a diverse group, we are not looking for people of any particular background. For example, practical engineers and abstract thinkers are both appreciated in our group. Your GPA and GRE are also not as important as demonstrated ability to innovate and the obsessions towards creation (e.g.,  success in a related hobby). So make a case for yourself before emailing me.

Ph.D. Positions
We are always looking to fill 1-2 Ph.D. positions each year. These positions are fully funded (with tuition waived) by research projects or fellowships. To qualify, you should have excellent verbal and writing skills in English, should be motivated and capable of creatively working in a team environment. You can be enrolled in either the Department of Mechanical and Aerospace Engineering (for a ME or AE degree) or Lane Department of Computer Science and Electrical Engineering (for a CS, CpE, or EE degree). Please read the IRL guide for Ph.D. Study before applying.

M.S. Positions
IRL in general does not provide research assistantships to M.S. Students. If you are interested in pursuing a M.S. degree at IRL, you need to prepare your own funding or obtain an assistantship from the departments or WVU. IRL can assist perspective students with an outstanding credential to apply for Teaching Assistantships or Fellowships.

Undergraduate Research Positions
If you are currently a WVU undergraduate student who is interested in gaining research experience in robotics, you can participate in many different ways. For example, you can pursue a thesis or research credits at IRL; you can perform a senior deign project at IRL, or you can work as either a volunteer or hourly worker (limited positions available). Please contact me for details.

If you are from outside of WVU, please consider applying to our NSF REU program on human-swarm interaction.

Exchange/Visiting Positions

Please contact me if you are interested in visiting IRL. Generally, you would need to prepare your own funding support for these activities.

Why Future Warehouses May Look Like Monkey Houses

by Yu Gu, posted in Future, Ideas, Robotics, Uncategorized

The current-generation Amazon robot assisted warehouses are painfully boring to watch, once you realized how time, energy, and space inefficient they are. Each piece of merchandise has to travel on racks maybe thousands times heavier than itself, at a slow speed, through heavy traffic, while the majority of the warehouse volume (> 80%) is left unused.

Vision: I think most goods can be simply tossed up in the air by robots and be caught by other robots at distances. The future warehouses, which I would like to call them Monkey Houses, should be highly dynamic and densely filled with flying objects. It will improve the throughput of a same size warehouse by more than an order of magnitude and drastically reduce the energy consumption compared to the current systems.

Justification: while humans occasionally use throwing and catching for object handoff (e.g., sports), it is not generally considered as a reliable method, especially when there are multiple objects flying simultaneously. Robots, on the other hand (no pun intended), can be particularly good at this. This includes estimating object motion, performing fast and precise control actions for object catching, simultaneously tracking the trajectory of multiple flying objects, as well as communicating and coordinating with thousands other robots in making plans. With these super-human abilities (i.e., speed, precision, reliability, memory, and communication) of future robots, the engineering trades of future systems design often shift toward counter-(human)-intuitive directions.

More Detailed Vision: each rack in the warehouse will be a stationary robot that can throw and catch objects. Each type of object will have a g-loading rating, dictating how far it can fly in one hop (the packaging of some future goods may have to be redesigned to be better suitable for flying). The goods may go through multiple hops (i.e., catch and throw by robots in between) before reaching final destinations. All object information is shared and an air traffic management system will ensure objects flying pass each other with safe clearances. Like goods, small robots can also be tossed up in the air. They can intersect other flying objects to improve the flexibility of stationary rack robots…

Thinking beyond the warehouse settings, it is conceivable that the main mode of object handoff for robots in the future would be throwing and catching, once the reliability of such systems exceeds human’s capabilities. Compared to the continuous-contact object handoff between two robots, throwing and catching involves much less complex robot-robot interactions and thus is far simpler and robust for robots to perform. This would have many implications to the design of other future systems. For example drone delivery can be performed by throwing packages to balconies equipped with catching robots (or just baskets with nets). Battery changes for drones could be done by simply tossing batteries up and down. Exchange of cargos (and passengers) between two self-driving vehicles on the highway could be accomplished through the air. What other cool applications of robot throwing and catching can you think of?

A Survival Guide for Ph.D. Study at IRL

by Yu Gu, posted in Students

I am writing this post to help perspective/new Ph.D. students to understand the culture and dynamics at the Interactive Robotics Laboratory (IRL), and to help existing Ph.D. students to evaluate their progress toward the completion of their degrees.

A. You May be Boarding the Wrong Bus

Getting a Ph.D. degree is an important decision for a student. I really don’t think everyone is suitable for a Ph.D. The first thing I tend to do when a student walked in my office talking about getting a Ph.D. is to try to talk them out of it. It’s not because I think they are intellectually incapable, rather I feel there are many other options in life then getting a Ph.D.  In particular, I would strongly discourage you from pursing a Ph.D. degree if the main motivations are:

  • to get a higher paid job – you could find a good job easier without a Ph.D. (getting a M.S.is probably the best bang for the buck) and would most likely be financially more sound to start working several years ahead;
  • to stay in school because you are not sure what else to do – go get a real job! Quite often, spending 16-18 years in school creates the momentum to spend 5 more years in school. This is just not good reasoning!
  • to have a title of ‘Dr.’ associated with your name – nothing is cool anymore after you own it.

For the rest of us, who are curious about exploring the unknowns, Ph.D. study can be a rewarding experience. Think about this: you get paid (and tuition waived) to be educated, to work on cool projects, to play with fancy toys, and to talk with other intelligent people on a daily basis! Is there a catch? Of course! but we will talk about it later…

B. Where You Sit on the Bus also Matters

So what should you expect by the time of graduation? That depends on what you want to do afterwards. There are just not that many places hiring Ph.Ds. and your choices are pretty much limited to academia, government, and industry, where each requires a slightly different skill set. Clearly you are unlikely to be able to teach well if you never taught, or run a large project if you never managed a team before. So make up your mind early and talk to me about what you want so you can receive a customized training experience.

Overall, before you graduate, you should have:

  1. the ability to do independent research. You should have accumulated enough knowledge of your field of interest to be able to identify new research directions on your own;
  2. the ability to effectively convey your ideas and findings both verbally and in writing;
  3. a bag of relevant skills that are tailored for your intended career;
  4. a Curriculum Vitae (CV) that is strong enough for getting your dream job.

If you are short on any one of these, you may not want to graduate yet…

C. Action Items

To achieve these objectives, this is what I expect you to be doing during the next a few years:

  • Read. Reading is the most important way for you to catch up with the fast evolving field. I will provide the initial papers, but you need to find a lot more on your own! Google Scholar is a great place to start. Exploring the reference section of a paper and who cited it often brings you more papers to read. The more your read, the more you will feel the need to read more (a rare case that positive feedback is actually a good thing);
  • Think. Independent thinking is what makes you a scholar. Standing on the shoulders of giants (after reading their papers), we should be able to think just a little bit further (or different);
  • Build. As engineers we create things with our hands. Working with physical systems is very challenging, but is also rewarding to see the stuff you built works. It also inspires the creation of new ideas. You will learn the problem solving skills through solving real world problems;
  • Talk. This includes ‘asking’ if you have a question; ‘discussion’ if you want other people’s opinions or to bounce ideas around; ‘presenting’ of your problems, solutions, results, and conclusions; and ‘teaching’ other team members with what you know;
  • Write. We should not keep the best only to ourselves. I expect each Ph.D. student to present at least one conference paper per year and have a minimum of two journal papers accepted for publication before dissertation defense. Write your first paper early, even if you are not completely ready and the paper may likely to be rejected. It takes some time to get into the game;
  • Lead. I want every one of you to be a leader at IRL because you will be the future leaders wherever you will be. Leading to me means taking a step forward when confronted with challenges, taking on responsibilities when others are hesitating, and be a source of inspiration to others.
D. Three Stages

In the next a few years, you would likely to experience three stages:

During the first year, you will be taking most of the required courses. You will also be assigned with specific tasks. Some tasks will be for training purposes, some will be related to research projects, and others will be related to housekeeping. You are expected to be integrated into the research group quickly (just shadow someone to get started). It is always a good idea to ask around and learn something from everyone.

From the second year on, you are expected to grow your own research independence. The tasks that you will be assigned will be at a higher level, without obvious answers. You are expected to read, think, come up with, and test your own ideas. You will also start to play leadership roles in different projects.

From the third year on, I expect you to have a good understanding of the research field, be able to identify gaps in the state-of-the-art, and be able to provide your own contributions. After banging your head against walls in different directions for a few years you will find a part of the wall that might be weakest. This would be the time to write a research proposal, so that you can continue to bang your head against that part of the wall until it goes through. At this point, you will know a lot more in your specific research area than I do. You are also expected to help mentor and manage the activities of your junior colleagues.

In general, you should expect to graduate in 3-5 years if you already have a Master’s degree. Direct-track students can expect one additional year (4-6 years).

E. The Responsibilities

We are working as a group. Our long-term survival and reputation depends on many factors. During your Ph.D. study, I expect you to:

  1. maintain a high level of motivation and academic integrity;
  2. efficiently manage your time and resources;
  3. keep a positive, open, and curious mind;
  4. be systematic and meticulous in doing research;
  5. be responsible and take ownership of your work;
  6. be persistent and not discouraged by failures;
  7. be professional and respectful;
  8. be a good citizen, team player, and be willing to help others;
  9. keep a clean and safe lab environment.

As your research advisor, you can count on me to:

  1. provide inspirations and general research directions;
  2. identify and respect your interest, strength, and limitations;
  3. work with you to identify interesting, feasible, and clearly-defined research topics;
  4. locate resources for conducting the research;
  5. monitor progresses, perform quality control, and provide feedback in a timely manner;
  6. learn, self-improve, and keep an open mind;
  7. provide support for scholarship, fellowship, and job applications;
  8. provide career advice and other support;
  9. host a yearly picnic.
F. Things to Avoid

You should not treat graduate school as a 9 to 5 job. You will need to spend as much time and effort needed to train yourself and to get the research going.

You should not be bothered by seeing other students getting away with an easy graduation. If they got a degree without received proper training, they will simply not be able to compete with hard working students graduating around the world each year.

G. Other Random Advices
  1. Failure. You may actually learn more from a failed attempt than successful ones, as long as you ask the right questions. Some people see failures as defeats, others see them as challenges. It is simply a matter of perspective. I often felt having a productive year after received my 10th rejection letter of the year.
  2. Pressure. The ability to handle pressure and stress will be an important part of your life, especially after your Ph.D. This includes two parts: be effective and positive when the pressure is high (i.e., don’t collapse under pressure!); be productive and self-motivated when the pressure is low (i.e., don’t collapse under no-pressure!);
  3. Ashamed. Don’t feel ashamed if you think that you are a Ph.D. student but have no idea about (serial port, ROS, Kalman filter, replace with any technical term). Feel free to ask. People will not laugh at you; Ok, maybe they will, but not for long.
  4. Confidence. Self-coubting is a human nature. You have no idea how many times I have doubted about myself (and am still doing it). Feel free to question yourself, but don’t let it bother you;
  5. Science. If you are not interested in following general science developments, you are unlikely to be very creative with your own research;
  6. Travel. The period of graduate study is the best time to see the world. Although you probably don’t have too much money, you also probably don’t have too many other things to worry about, such as children. I will give you extra vocation time if you present me with a good travel plan. Tip: presenting papers in conferences is another way of getting free trips!
  7. Health. Go out and play whenever you have free time! Last time I checked, Morgantown is still in the mountains!
H. Conclusion

Ph.D. study at IRL is demanding (yes), fun (should be), and rewarding (absolutely!)

 

 

Why Future Robots May Also be the Kind Ones

by Yu Gu, posted in Future, Ideas, Robotics

We all know that greedy gets us nowhere. We were all probably told by our moms to be kind (e.g., friendly, generous, and helpful), who were probably told by their moms, and so on. This crazy idea may be traced all the way back to some named or unnamed philosophers, but how can it make sense? Why should we hand out our precious resources (e.g., time, things, or even opportunities) to others in this hyper-competitive world? Why shouldn’t we calculate the costs and benefits of all our potential options and pick a move that maximize some sort of utility functions (e.g., money or advancement)? This is precisely what we do when playing chess or tennis, when there is no friends and only one opponent in the game. Nobody expects us to be generous there.

The reason is probably that we are not capable of making many meaningful calculations in life. Beyond a few artificially constrained games, we are severely under-actuated and underpowered creatures that are trying to navigate in the vast ocean of human society. Each decision that we made may not change that much how we move up or down in a long term. Instead, the movement of waves below us makes far greater a difference. With a very limited horizon, we have no way to know for sure what’s around us and what’s coming up next. How can we make a decision then? Moms told us to use heuristics that have been proven to make long-term stochastic sense (ok, not in these exact phrases), which are to be kind, friendly, generous, and helpful, among others.

Now, let’s take a minute to think about robots. Our robots today are greedy. They are self-interested, having a tunnel vision (not literately) of the world around them, and trying to maximize some sort of utility functions. They work well in structured environments that can be fully modeled, and are getting better by days in more complex settings. If we use a linear interpolation to predict the future, the robots will get smarter, more capable, and more selfish. This is probably why the internet is full of worries about our future with robots.

I think this is like saying all chess and tennis players are greedy, and we should be careful with them. The robots today are self-centered only because the way their working environment are set up to be. As moms of robots, we, roboticists have to teach our robots how to survive in the real complex world, a world that greedy gets them nowhere.

If the robots are going to be as intelligent as humans in the future, we should not expect them to be that different from us: there will be good robots, there will be bad ones, and there will be many in between ones.  And all these robots have to deal with good, bad, and in-between humans. It’s this enormous diversity that invalidates short-term and self-centric thinking, and makes it more important to be kind to others. Call me wishful thinking, and I don’t know if we should feel happy or sad about this, but the kind robots are probably the ones that eventually will replace us.

My job

by Yu Gu, posted in Job

I only applied for one job, but they gave me many: instructor, recruiter, secretary, CXO, marketing coordinator, fortune teller, website designer, engineer, safety inspector, photographer, video editor, “fire” fighter, manager, accountant…

What a great deal! It turns out that I also have some time left to be a dreamer, thus the starting of this blog.

Gu