Creating something is a form of expression. You put a piece of yourself into it. Effort, time, but also a piece of your craftmanship and thinking about how something should ideally be. For many artists, the act of creating their art is so important that giving it up would give up an essential part of their being.
These days, software engineers are rediscovering just how important crafting software is to their identity. It has been true before that for many professional software engineers, their weekend hobby project was … creating software. For fun. It gives purpose to them to create something useful. Many times, the software is useful to exactly one person, and that’s okay.
The advent of large text interpolators has created a scare among those that deal with text-based work. It is now quite possible to recreate software “from scratch”, if one has a technical specification, and a test harness. There are a few misunderstandings, however.
1): Firstly, such a reproduction is a derivative work, because the LLM has been trained on all of github, often including the specific code and thus interpolating copyrighted work rather than extrapolating.
2) Secondly, reproducing someone else’s work with LLMs does not build the skill to adapt the work to new specifications. Yes, you can adjust the prompt, but as anyone who works with LLMs knows while it feels cool and fast, it is neither a stable nor efficient workflow, you have to edit code yourself as well. Earlier this year I “lost control” over a software repository by letting an LLM make too many edits, to the point where I was unfamilar with the code and could not debug the mess any longer. I had to backtrack, delete large parts, and with discipline, built by hand the complex code that is not interpolatable from LLM training data.
3) Thirdly, I have serious doubts many AI-created software packages will still be maintained after a year. Interest of the “creators” is largely not long-term in the domain, but rather with the AI toys.
I still have hope that there ultimately is a feedback loop of “Has this person delivered value?” Not, “Has this person appeared to have delivered value?”, not “Has this person done something trendy?” but looking back at the created change in the bottom line.
For me, there are three lessons:
1) Creation gives purpose. From AI prompting pride in the creation cannot be generated, even if you give yourself titles like “Prompt engineer”. Effort is a key differentiating factor.
2) Creation builds domain expertise. You can confidently argue about fine points. If you build with LLMs, your knowledge is shallow. Critical reading about a topic, including the background not directly relevant to a task at hand, is key here for retaining a big picture view of where a project should go.
3) Computing and Society: Only by working and listening to people can you build something that matches a real need. One of the reasons why I departed from classical IT is that I felt many tools were built by software engineers for improving the lives of software engineers, but lacking a bigger goal. With AI, even if coding were to fall away as a skill, the skill to identify requirements through dialogs and embedding a solution with people remains an essential software engineering skill.
I find research software engineering quite fulfilling. I build tools that are used by myself and my colleagues to investigate the Universe. I call them software telescopes. Could I find similar purpose in another domain? Probably. Could I find purpose in life without building something and putting my heart into it? Maybe not.
Today, we walked 100 meters from home to the park, set up a inflatable couch, leaned back and watched the Perseid meteor shower 🌠 . It was beautiful. Garching is one of the very few places on Earth where you can see the night sky clearly, and work as an astronomer.
Garching is a small city, but it is big enough that it has one of everything you need.
The saying goes that those who keep their office door open have a better career. I do not have hard evidence supporting this, but I know that some people shut themselves off in their offices and have then not had a lot of exposure or opportunities.
Chance encounters are quite powerful. “Just a quick question …” through an open door opens the door for something else.
When talking with junior scientists, especially when genders are mixed, keeping the door open is good practice. It avoids the hint of any suspicion that something improper could happen in a meeting. It opens yourself to being audited by a passer-by. This can provide safety to the junior scientist as well. When it is exceptionally noisy, I ask for permission from the junior scientist whether it is okay if I close my office door.
Eye contact is important when giving a talk. If you find it unpleasant to look people in the eye, you can look just above the audience and will achieve the same effect of the audience feeling seen, and you not just bumbling to the screen.
Eye contact can be unpleasant to give but also to receive. Personally, I cannot hold eye contact for a long time, but several close colleagues struggle with this even stronger. It is perhaps related to being on the spectrum, perhaps personal preference, perhaps how socially safe you feel in the situation. I curious to what degree inability to hold eye contact limits people’s social status.
In any case, it is important to realize how important eye contact is for the other person and calibrate accordingly. If my opposite needs it to feel trust and a human connection, I give a bit more than feels comfortable to me. If I feel they cannot stand it, I avoid long eye contacts and make by gaze wonder across the room while I go on a tangent in the conversation.
When you introduce yourself, don’t through your name out a mumbled string of syllables. It is difficult for those whose culture is not the same as yours to catch your first and last name (or others).
Say: My name is (short pause) <firstname> (one second pause) <lastname>.
It will be more memorable for everyone.
I am quite bad at remembering people’s names. A trick to help yourself is to use the name immediately in the first sentence you speak to them after they introduce themselves. “So Francis, what do you think about X?”.
I usually need to get to know a person first and build up some mental image of a person. That helps the person stick in my memory. That will be quite a bit later than when the time of exchanging names. However, I find usually people are not so offended if you say an hour or a day later: “Remind me, what was your name again?” It seems more important that you try.
Resources
Here is the video where I got the “say your name” from:
And two more videos on speaking clearly and slowly:
You can also undermine your communication by having a poor or uncalibrated microphone in telecon (Zoom) sessions. Test it occasionally by connecting both your phone and your laptop to the same session. Use headphones to listen from one device how the microphone picks up your audio. A laggy video, a oversteered or rough audio is similar to poor choice of dressing at the work place – it makes you look like a person who does not care how they appear to others. A crisp sound makes you appear more professional.
Co-authorship and acknowledgement are forms of recognition for direct contributions to a work. I want to elaborate on the policy I follow, influenced by the groups were I have worked.
There are multiple aspects of how to contribute to a paper: “conceiving the idea, designing the study/experiment, collecting the data, analysing the data, writing the manuscript”.
Acknowledging experiment design in astronomy
It is sort of obvious that you should offer co-authorship to someone who gave you the idea for the study you are executing. Unfortunately, there are people in astrophysics who take data and ideas from others without credit. One second-hand horror story from decades ago is that a young postdoc was giving a presentation in CalTech about an idea to observe M-dwarf spectra for the first time, and laying out a strategy to achieve it. During question time, a senior researcher said: “That’s a great idea! I’m gonna do that.” Another second-hand horror story is that a member of the NICER instrument team used his privileged position to monitored ad-hoc transient observing proposals (where the data go public immediately) and scooped people’s work. This became so bad that in transient conferences, people would hide their source coordinates; yet the person once went up to a speaker saying “Don’t think that just because you hide the coordinates I can’t find your source.”. We must not let these behaviours proliferate. Do not hire these people.
It is less obvious how to credit someone who enabled the data sets that your study builds upon.
At the start of my career I have underestimated the effort people put in writing observing proposals. This experiment design work is crucial and enables scientific studies. So if the data is public, it is professional to ask the PI of the observing proposal, if they have not published a paper on the observations yet, whether they would like to be co-authors. This would probably not apply if it has been more than 2-3 years since the data were taken, but funding issues, or a PhD student dropping out can delay data exploitation significantly. If you know the PI, you should always ask.
I do a lot of work on relatively old, archival observations, and that there is freely available, well-calibrated data is one of the best things of astronomy. I have acknowledged these effort with extensive citations. Here are two examples: (1) In a huge archival study of 900 gamma-ray bursts discovered by Swift, I cited over 165 Astronomer Telegrams, because they contain follow-up observations and key quantities like redshifts. (2) In a recent SED fitting paper analysing data from ultraviolet to infrared based on spectroscopically identified quasars, I spent 1.5 pages acknowledging the server infrastructure and all the surveys:
Acknowledging project-specific contributions
There are nice guidelines by some journals, that I agree with. Unfortunately, there does not seem to be a astronomy journal giving co-authorship guidelines, or at least I have not seen it.
My favorite guideline write-up is by the medical journal ICMJE, but the recommendations from Springer Nature are also similar):
Co-authorship requires:
Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND
Drafting the work or reviewing it critically for important intellectual content; AND
Final approval of the version to be published; AND
Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
The forth point means, that a co-author should be able to defend the paper in a conference discussion, when someone says the study is garbage. If they don’t want to have their name on it, don’t have your name on it.
The third point implies you need to get the okay of the person to submit it with their name on it, and indeed you absolutely cannot submit a paper to a journal with people’s names without them agreeing to be co-authors. So if you are not careful, you can end up in a difficult limbo, where you have put a person’s name on the co-author list, shared drafts, they never reply, and now you cannot submit. People in permanent positions can keep you, without a permanent position, in this state forever. That’s why, in my opinion, you should never put people’s name into the co-authors list in drafts before they have sent you comments, and thereby implicitly or explicitly saying they would like to be coauthors. That is, I believe in a opt-in co-authorship policy, rather than a opt-out policy, because it avoids the stalemate. It creates an incentive to say yes and send comments.
For the second point, drafting the paper, you can help me a lot. Pointing out relevant papers to cite is a crucial contributions – literature review is a lot of work if you have the ambition to be thorough. Going beyond criticising text and providing improved text blocks is a crucial contribution for someone like me who has a hard time writing. I’m gonna love having you as a co-author if you do these, even if you did not touch the data or were in any discussion meetings.
Regarding the first point: Indirect contributions, such as holding workshops, making software and tutorials publicly available, are not part of acknowledgements or co-authorship. Maintaining the IT infrastructure of the institute is important to enable this and other project, but part of the affiliation as an implicit acknowledgement. Project-specific scientific software support that enables smooth project completion should be acknowledged. You can be as generous as you want in the acknowledgements! There is no cost and it only helps generate good will. Acknowledge reviewers, hallway discussions and encouragements. Custom code development enabling the project should count towards co-authorship.
Managing expectations: be upfront about your policy
You do not have to agree with my co-author ship policy, and I do not expect you to have my policy. But: be up-front about what your policy is. What threshold do you apply for co-authorship?
This can avoid frustration and conflicts by being upfront. I had two collaborations where I spent time in many meetings discussing the paper data analysis and interpretation, providing references from a literature review, and thought that I would be a co-author but the first author did not. Both cases were first authors from (astro)particle physics – I learned that they have a much higher threshold for co-authorship.
Personally, I think that is dumb: There is essentially no cost to including someone as a co-author. It is encouraging to continue collaborating. Can you imagine my excitement when I was included into the list of contributors to the VLC project? I became and remain a hard-core fan of the project. So err on the side of generosity. I realise, however, that telling people to be generous is futile; you can only grow the feeling of generosity by experiencing wealth, kindness and generosity from others setting an example. The counterargument that <=3 authors allows all to be named in the citation is, to be frank, pathetic.
In my most recent paper, I copied the ICMJE guidelines into the latex/overleaf as comments just above the co-author list, to be upfront. I kept a list of co-author names and their substantial contributions, but commented out until they gave comments.
Confidence in interview settings. I try too much to please people and say what I think they want to hear. I should instead be clear what I stand for and what I don’t, and argue clearly. It should not be that easy to rattle me.
Expressing my value clearly. Here there is something I don’t understand. Obviously my work has immense value, as seen by citations, invitation to give talks, and in almost every conference I meet someone who thanks me for my work. On the other hand, it does not seem to translate to the bottom line of a physics department?
Expressing my long-term vision clearly. I need to more clearly articulate how my approach differentiates from other’s use of AI and data science, and the long-term perspectives. I have to express this with high level language and not be too excited about explaining technical details.
Accepted funding proposals for a track record of self-sufficiency. However, two interview panels gave me feedback that they are not worried, and that I would be able to acquire sustained funding once hired.
Prizes and awards. I am not a fan of accolades and do not even want to think about being on a stage receiving an award. That said, it is a way for the community to recognize a person. The approach of encouraging others to suggest me for an award is really against my nature.
Handling rejections. Part of writing funding proposals or applying for a position is getting excited about the prospect, seeing opportunities, which vanish with a rejection letter, no matter how politely it is formulated. I should take it better, as a positive that I tried and put myself out there, and grow a thicker skin. I think I am improving on this already.
Exponential growth is unintuitive. The default heuristics of our brains is that things will continue as they are, or keep growing linearly.
In the most intense phases of COVID19, exponential growth of case numbers, and thus hospitalizations, and the trigger-happy reaction needed to stop this exponential growth curve early was understood by most people.
In the case of climate change, there is a similar phenomenon. We consider the warming curve of 0.x degrees per year, and it seems linear. However, the number of extreme heat days, and the number of extreme weather phenomena, are the tail end of a distribution that is widening. Their numbers is growing exponentially, and with it the cost, both monetary, of human lives, and life quality – the fraction of the year where it is safe to go outside into nature may shrink.
It is easy to become blackpilled and say it is all pointless because there is too little political will and action. Exponential climate change damage is extremely bad. Have a look at the details and compare the +2° +3°, +4°, +5°, +6° scenarios in the IPCC report (2023, 2014).
But there is a uplifting, positive side for exponentially bad impact.
Small actions, including and especially in the later stages past +2° warming, have a huge benefit in avoiding an even worse outcome.
Conflicts are common place when working in scientific collaborations, and with people in general, whether in private or professional settings. Be it, that others take undue credit, screw someone’s plans over, belittle contributions, are confrontational and rude, or mock someone.
Conflicts are stressful. Disappointment, anger, frustration, fear, insecurity, are intense emotions that can arise.
My gut reaction when conflicts arise — affecting me or someone else — is to avoid engaging with the conflict and hope it resolves itself. The trouble is, behaviours do not magically resolve themselves but perpetuate when left alone. The issue becomes bigger, frustration becomes bitterness, and, handling the situation is more difficult, if not impossible.
My learning over the years has been that I should do the exact opposite of what my gut tells me: When I feel there is a social uncertainty or something off, immediately communicate about it. Say “This feels off to me, what do you think?”. I find people are happy to adjust and see the point if the issue is still small!
My challenge is usually that I might not have the right words to express issues clearly. But it is more important to have the right intentions than the right word.
A senior colleague of mine once said (paraphrasing):
“We can all manage to fill our week with things to do. It is not difficult to be busy. The challenge is to actively and consciously steer what you decide to spend our time on, and prioritize things that will have an impact.”
When you look back on the last week or two, have you spent the week on something that you are proud of? Something that positions you better for your future?
Busy work
The possibilities what to work on are endless (write observing proposals, write funding proposals, attend or organise conferences, read papers, advise students, collaboration policies, help prepare data for someone, give comments on papers, create press releases, give outreach talks, etc etc etc). Busy work creeps up over time. What you stepped up for once, you feel responsible to keep doing.
You will even feel productive and helpful when filling your work week with such items.
Tasks seem urgent. You may discover things needed immediately by other work. But you will not discover what no one has thought of. If you do not schedule time to stop and think, being creative about scientific ideas and thinking critically is difficult.
You start to feel like you are missing out on progress. You feel like you are getting dumber while young colleagues get science done.
But it is not true. Firstly, research shows that people do not become dumber quickly, with work performance peaking at about 55 years. But juggling more mental items can feel like walking through jam.
Advice and techniques (for myself)
One technique that I could use more is to create four quadrants for tasks:
urgent & important
urgent & not important
not urgent & important
not urgent & not important
Then prioritize important tasks.
When you are in charge of administrative tasks, there is a power imbalance. The other person needs some approval from you, whether this is a travel request or a pull request of code. But they are probably highly motivated and for them it is both urgent and important. One technique, that I have found is satisfying for both sides, is to offload much of your administrative work to the requester. To do this effectively and without being a nuisance, it is best to make the requirements for successful approval transparent upfront. But you can require them to make one iteration of improvements, ideally only one.
As I become more senior, I have become more aware of other scientific sub-areas and people working on them. Tracking this larger and larger scope is mentally taxing, regardless of whether out of interest or out of a feeling of competition.
More knowledge and a wider field of view helps anticipate hurdles down the road earlier, and thus spend more time planning projects in your head. This may feel like hesitation and slow progress. But it is actually active work on experiment and project design and increases the likelihood to deliver on time and the scientific impact.
