I did marine biology field work almost 5 decades ago as a lowly junior lab tech. Work always has downsides, for me it was not really the Scots winter, cold feed, chapped hands, the land-rover having to reverse up steep icy roads to get back from the harbourside: it was washing the glassware and dealing with sodium hydroxide weighing (it absorbs moisture from the air so its a fools game). But, field work also brought amazing experiences, I visited the seaside 70+ times over a year, and got an insight into what a time series really means when you cover the tidal and weather and seasonal cycles.
It's also always error-prone. Nothing in the field is perfect. Reality is a bad approximation for your model at times, if you take a model centric view.
I would be immensely skeptical that field work is ever going away. There may be aspects of truth in this around cost of travel, risk, seniority.
I've always enjoyed field work, much of the code I've written has been well outside of any office.
Exploration geophysics paid for me to travel to and across more than half he countries on the planet, calibrating old maps, datums, projections against the 'new' WGS84, scaling peaks to stage base stations, getting familiar with the ins and outs of tides, magnetic fields, gravity, radiometric backgrounds, finding a good band in Mali ...
Douglas Mawson ("home of the blizzard") had a rich life after Antartica as a field geologist, exploring the flinders ranges. He found a radium mine and was shipping ore to Europe for a while. He led students on field trips, one of whom, Reg Sprigg caught the bug, explored as much as he could, persuaded the Australian petro and uranium sector to fund pushing tracks into his favourite spots, and then converted the landscape into the Arkaroola Wilderness Sanctuary. I got to spend a night there last year on a flight safari to Lake Eyre, it's an amazing place, dark sky with a big telescope, wildlife, well worth a visit.
Mawson had the field trip of a lifetime (for his two mates, it was the end of their lifetime!) and it didn't end his bug for the outside. I don't think he was made to sit in a lab.
I'd say your Mali trip was the same: it hasn't made you want to stop being outside from the sound of it.
I've "retired" to argriculture tech and labour support for W.Australian family grain production. We've almost finished harvest and I've been doing a lot of scrolling and posting here while hanging about near idle "on call" fire tenders (we had a hundred fires, mostly from lightening strikes, in a single week just recently)
The Mali trip was notable for random types firing weapons at our aircraft while we were running lines with 80m ground clearance - we had to armour the cockpit bellies and stuff the fuel tanks with mesh.
> The Mali trip was notable for random types firing weapons at our aircraft while we were running lines with 80m ground clearance - we had to armour the cockpit bellies and stuff the fuel tanks with mesh.
Datums can get dull fast but there's adventure inherent in surveying. You should write a book, or at least a chapter or two. "Nadir Point" has a nice ring to it...
There was always something happening, whether it was shipboard fires in the disputed parts of South China Sea or India / Pakistan engaging in cross border nuclear tests in our survey zone.
That last one followed several of us about for years, anytime we crossed a US controlled border they got interested in how we knew what they didn't ...
.. look, we just happened to be there with a 42 litre doped Sodium Iodide crystal pack and 256 channel gamma ray spectrometer just as the tests kicked off ...
I also have spent quite awhile as an exploration geophysicist. I miss it! I work purely with satellite data now, which is decidedly less tangible.
I've done a fair bit in the field, but a huge part of my career has been mining old datasets and reinterpreting things in light of new data/etc.
What the article is describing isn't new in any way. But it also doesn't remove the need for fieldwork or the need for the experience of having done fieldwork to use existing datasets. Observational sciences (e.g. geology, biology, etc) where you can't easily replicate the environment you are studying in the lab are always going to hinge on some sort of fieldwork.
Finding creative ways to use existing data doesn't change that.
I worked in a research lab like 30 years ago and it was all on computers. We had loads of generic data collected by someone somewhere and we just looked for patterns to infer sequences. I wrote Java and C++ and got my name on a paper. There were maybe a dozen scientists in the lab and they were all just coders with expertise in one or another field of biology. It was called a "dry lab".
I don't do research that requires fieldwork, but even in office and industrial settings, I notice that there's less need and interest in visits.
Of course, in-person exchanges still happen, but there's something of a default to do most things remotely because it's more efficient (and honestly, easier for all parties involved). The result is that you don't get to see cool or unusual machines/setups that often, and some flair of doing research is lost.
I can imagine that that's especially painful for new ecologists, because fieldwork is also a way to experience things that you otherwise wouldn't. Hopefully, we can bring some of it back with edge devices and models.
> But, field work also brought amazing experiences, I visited the seaside 70+ times over a year, and got an insight into what a time series really means when you cover the tidal and weather and seasonal cycles.
I’m not exactly sure if we share a similar experience, but living on a trail in the Santa Cruz mountains affords me the opportunity to hike the same trails every weekend, year round (or even daily).
I’m not taking measurements, but it’s incredible to witness the effect of seasons on familiar territory just a few miles outside town. The weather changes, the wildlife changes and the air changes (moist to dry and back).
It’s an incredibly special experience to revisit the same place time and time again and witness the impact of … time. I hope you found something else to replace your familiar seaside.
It’s been sad seeing journalism in the online era, where so much (not all!) content is produced without really visiting or researching things. Often it’s based only on statements / tweets, sometimes more seeping based on phone calls, sometimes reading a book on the topic, but rarely do journalists seem to show up anywhere.
When reading something like Didion’s piece on the LA highway central command, it shows how irreplaceable lived experience is.
I think this is a classic old-vs-new tale. I started my PhD in biochemical research where analyzing data by hand was definitely a "craft" in some aspects. Later I forewent going to the lab entirely and instead spent all my time on developing machine learning for automated data analysis. But just like field work, you still need people in labs who can continue the craft.
The article should perhaps introspect a bit more instead of setting up a false dichotomy between "rainforest field work or computers".
Working in geology, I find the opposite problem. Field work is so highly valued that we're at a place where we have so much data and not enough people really working and analyzing it. My general impression is that in some subfields work that's done exclusively using preexisting data is kind of looked down on. In my opinion tons and tons of money is essentially wasted collecting new data - and then it's poorly catalogued and hard to access. You typically have to email some author and hope they send you the data. People are fiercely protective of their data b/c it took a lot of effort to collect and they want credit and to be in on any derivative work (and not just a reference at the bottom of a paper)
I would say the main workflow is collect some new data nobody has collect before, look at it and see if it shows anything interesting, make up some interesting publishable interpretation.
It feels like it'd be smarter to start with working with existing data and publish that way. If you hit on some specific missing piece, go collect that data, and work from there. But the incentive structures aren't aligned with this
The AI angle is really shoehorned in, but irrelevant to the larger problem. Sure, it allows you to annotate more data. Obviously it's more fun to go do field work than count pollen grains under a microscope. If anything AI make it easier to do more fieldwork and collect even more data b/c now you can in-theory crunch it faster
The current situation with the way big tech plays fast and loose with other people's data, I don't suppose the siloed nature of geological data is going to get better any time soon.
Perhaps creating secure private clouds for scientists, away from AI scrapers etc that scientists can access, with associated counter-surveillance, is the way forward.
I'm a GIS guy working on cloud native tech, but with a focus on privacy. I have a local-first Mac native product nearing beta. I'm thinking a lot about what data sharing options can be at the moment.
i dont see what the problem is. AI is mostly irrelevant. okay they scrape your data.. but then what? If the data isn't offically published and doesnt have a DOI, anything built on that wont be accepted
Some people scrape charts in publications to extract data. This has been done for a while. Maybe AI could automate this step. Thatd be useful
I understand that publications are the currency of academics but they're largely irrelevant in business. Geological data are valuable and if an oil exploration company finds a nice dataset they can scrape, they're not going to publish it.
From a pure business perspective, AI is largely about copyright circumvention. The laws are lagging and people are making serious money from data theft.
Am I understanding it correctly. So internally if a company is using a competitor's stolen data directly, then if anyone finds out they're in legal trouble. But if they train a model and then use the model, then they're in the clear?
Yes I think there's evidence for that. Looking at recent precedents, even if the data are illegally downloaded, big tech has been getting away with using copyrighted data, for example:
Sounds like a business opportunity for someone to create a web portal for making available such data with licensing terms, indexing and cataloging it with a nice search engine, etc.
what you need is people uploading data in consistent well documented formats. There are all sorts or projects that do this, but there is a strong incentive to not upload things, or sort of half upload it.. but in a way where anyone using it is going to have to reach out to you. Not suggesting bad intentions, Maybe youre still working with the data and expect to publish more and dont want someone swooping in and beating you to the punch. Typically journals require data availability, but its kind of informal and adhoc
I am involved in both botanical field work and ML, unfortunately most of the data that I have gathered and analyzed in the last 12 years indicates how quickly many ecosystems are degrading. Often I wonder why I do the analysis, simply taking a photograph in the same spot ~7 years apart allows any average person to see that things are not on a positive trajectory.
Attempting to convince people to change course and focus on restoration has mostly been a losing battle, with much larger forces behind the main detriments that make local changes feel inadequate.
As a kid I had problems with Foundation (Asimov) premise that loss of scientific knowledge can be the trigger not just the result of civilizational collapse - not anymore.
More pernicious than loss of knowledge imo is loss of trust into the scientific process. „Research“ on social media is seen as superior to expert consensus by a frightening number of people.
People that trust something without understanding at least the principles can cause quite some issues.
One of the issues that I think affects these people is that the scientific process can not guarantee that something is "correct" or "incorrect". Something that at a point was known to be correct can be later disproved by a later experiment or more specific conditions.
Some people want/need simple certainties, and as soon as they stumble upon something different, they will shift their trust to something "simple and clear". And they can do that again and again, as long as they don't need to accept some things are complex or unknown.
I do not know any school system (not that I am an expert or searched for it, just an impression) that emphasizes this dynamic nature of understanding, or that tries to make people accept the unknown.
Random thought: language seems to have evolved as part of social interactions to facilitate group dynamics in some way. So people will privilege linguistic 'transactions' with other people over some kind of formal development of an argument from evidence.
(Been reading some of Wojcieck Zurek's semi-pop articles on decohrerence, and he alway puts a paragraph about the evolution of language in and points out that our intuitions won't work well in the quantum domain).
My post above was downvoted. Thankfully some kind souls have redeemed it. I'm surprised it wasn't flagged and disappeared to Gitmo, like 90% of my completely innocuous posts are.
There is still field work that happens. AI can not replace that. You'd need to literally simulate the whole world before AI can even get close to gather all data obtainable here. For instance, on a given ant hill: which plant species will be more prevalent there? (For those not knowing a lot about ants: some ant species carry specific plants or defend plants against invaders. The most usual example is for leaf-cutting ants, but there are many additional examples, and for various reasons you will also find different plant species to be more prevalent close to an ant hill in a forest area, than other plant species.) AI can steal existing data, but there is no way it can gather real data UNLESS you are able to monitor this. This is possible via machines, e. g. drones, but AI does not understand what it is doing and even with instructions you still may be able to just hallucinate data. So perhaps one day this may all be fully automated (sensor systems can do all humans can do too, of course), but right now this is simply not the case. And this is just one example for many more.
Machine learning and data science are not new things in science. It's great that we have the ability to share and work with existing data sets, collect data remotely with sensors, and build software to create models, but we'll always need people to go out and collect updated data, place censors and verify that what models predict is actually happening.
> Scientists who run long-term ecological studies, in particular, report that they struggle to find funding.
It's cheaper and easier to do stuff sitting at a desk. In theory that's a good thing if it means more work gets done, but field work has to happen too. For many people it's the best part of the job, for others it's a pain that has to be suffered through to get the data they need. Hopefully there's room (and funding) for both kinds of people to do the work they want.
I'm a scientist in industry. It's remarkable how many smart people think that science can be done without data. I've heard managers ask: "Why do we need to gather data? Can't we just model it? The customer doesn't want to see data. They just want an answer."
There's also a strong belief in "statistical magic." Faced with a bad or insufficient data set, someone will say: "Let's give the data to <statistician> and have them work their magic on it."
That the results actually have to be influenced by the data in some way is something that has to be explained to people. In all of my years as a scientist, I've learned that there's still no substitute for good measurements. Good data can be cheaper than analysis of bad data.
Data science is not fundamentally about data or science. It's about either justifying decisions that have already been made or delegating decisions to an unbiased casting of bones to let the gods decide.
> It's remarkable how many smart people think that science can be done without data.
It’s so important that we write these down, so when these people have forgotten why they’re not making any progress and they’re searching for answers, they’ll find what we wrote down and say “ohhh, we had too much hubris thought we were smarter than everyone else and didn’t listen to how important actually going outside is.”
People that I'm currently working with are using AI to try to extract data from the text of published papers, getting access to raw data sets doesn't seem to be a priority.
It should be easily accessed without even having to ask (links should be automatically provided and maintained by whatever entity published the paper).
I have a PhD in Ecology and a BS in CS. I find the bifurcation portrayed here exaggerated. The best modern ecologists merge rigorous fieldwork with advanced modeling; we need to harness vast, underutilized datasets, not just generate new ones.
The 'computer scientist' quote illustrates a frustrating trend: tech-centric 'drive-bys' that lack the ecological context required for good science. On the flip side, the 'old guard' who ignore modern data assimilation are leaving massive potential on the table. The field is rightfully shifting from site-specific anecdotes to foundational, broad-scale work, but we need both skillsets to do it justice.
Seems to me there are potentially opportunities for greater returns to data gathering work as quality data can inform many more papers in the future. How that will work still needs to be brokered…
Absolutely. There are a few excellent projects in this vein, as well - where deeper investment in data gathering, done in ways to optimize its broad use in research, is occurring.
An example is the National Science Foundation NEON project, which is a long-term ecological monitoring initiative with common field methodologies across 81 North American sites. https://www.neonscience.org/
Huh. I weirdly enough have worked with a lot of those sites from the remote sensing side, but never really know what the overall project was. Just "use the NEON sites for examples". I should have looked it up more at the time. Thanks for sharing!
"Others have expressed concern about ‘AI colonialism’, a practice in which data, collected remotely in poorer countries, are siphoned off for analysis in well-equipped labs elsewhere."
That’s not what the article says at all, but go on with your doomer takes.
Instead of counting bears in the forest by hand, you set up a hundred trail cameras and then use computers to count bears 24/7 across an entire area. This is field research, on a scale that was previously impossible.
All of your peaceful science drones (just the same as attack drones) will have to be equipped with countermeasures such as flares and chaff, to defend against the primitive jury rigged SAM systems and anti-aircraft batteries the Forest People of 2056 will use to shoot down anything that intrudes upon their domain, years after the Great War (No, For Real This Time ZOMG) destroys half the planet and now it's the Hunger Games future they have planned for us.
It's also always error-prone. Nothing in the field is perfect. Reality is a bad approximation for your model at times, if you take a model centric view.
I would be immensely skeptical that field work is ever going away. There may be aspects of truth in this around cost of travel, risk, seniority.
Exploration geophysics paid for me to travel to and across more than half he countries on the planet, calibrating old maps, datums, projections against the 'new' WGS84, scaling peaks to stage base stations, getting familiar with the ins and outs of tides, magnetic fields, gravity, radiometric backgrounds, finding a good band in Mali ...
Loved it.
Mawson had the field trip of a lifetime (for his two mates, it was the end of their lifetime!) and it didn't end his bug for the outside. I don't think he was made to sit in a lab.
I'd say your Mali trip was the same: it hasn't made you want to stop being outside from the sound of it.
I've "retired" to argriculture tech and labour support for W.Australian family grain production. We've almost finished harvest and I've been doing a lot of scrolling and posting here while hanging about near idle "on call" fire tenders (we had a hundred fires, mostly from lightening strikes, in a single week just recently)
* https://www.watoday.com.au/national/western-australia/wa-bus...
* https://www.youtube.com/watch?v=yulvSvtFVqc
^ Further south than I'm based, and a header fire, not a strike. Okay when caught early - life and town threatening if not.
Oh, yeah: Songhoy Blues: https://www.youtube.com/watch?v=BOValSt7YOY
The Mali trip was notable for random types firing weapons at our aircraft while we were running lines with 80m ground clearance - we had to armour the cockpit bellies and stuff the fuel tanks with mesh.
Datums can get dull fast but there's adventure inherent in surveying. You should write a book, or at least a chapter or two. "Nadir Point" has a nice ring to it...
Mine Camps .. with a Long S ( https://en.wikipedia.org/wiki/Long_s ) once appealed, but I fear getting cancelled.
There was always something happening, whether it was shipboard fires in the disputed parts of South China Sea or India / Pakistan engaging in cross border nuclear tests in our survey zone.
That last one followed several of us about for years, anytime we crossed a US controlled border they got interested in how we knew what they didn't ...
* https://www.nytimes.com/1998/05/13/world/nuclear-anxiety-the...
.. look, we just happened to be there with a 42 litre doped Sodium Iodide crystal pack and 256 channel gamma ray spectrometer just as the tests kicked off ...
I've done a fair bit in the field, but a huge part of my career has been mining old datasets and reinterpreting things in light of new data/etc.
What the article is describing isn't new in any way. But it also doesn't remove the need for fieldwork or the need for the experience of having done fieldwork to use existing datasets. Observational sciences (e.g. geology, biology, etc) where you can't easily replicate the environment you are studying in the lab are always going to hinge on some sort of fieldwork.
Finding creative ways to use existing data doesn't change that.
Of course, in-person exchanges still happen, but there's something of a default to do most things remotely because it's more efficient (and honestly, easier for all parties involved). The result is that you don't get to see cool or unusual machines/setups that often, and some flair of doing research is lost.
I can imagine that that's especially painful for new ecologists, because fieldwork is also a way to experience things that you otherwise wouldn't. Hopefully, we can bring some of it back with edge devices and models.
I’m not exactly sure if we share a similar experience, but living on a trail in the Santa Cruz mountains affords me the opportunity to hike the same trails every weekend, year round (or even daily).
I’m not taking measurements, but it’s incredible to witness the effect of seasons on familiar territory just a few miles outside town. The weather changes, the wildlife changes and the air changes (moist to dry and back).
It’s an incredibly special experience to revisit the same place time and time again and witness the impact of … time. I hope you found something else to replace your familiar seaside.
It’s been sad seeing journalism in the online era, where so much (not all!) content is produced without really visiting or researching things. Often it’s based only on statements / tweets, sometimes more seeping based on phone calls, sometimes reading a book on the topic, but rarely do journalists seem to show up anywhere.
When reading something like Didion’s piece on the LA highway central command, it shows how irreplaceable lived experience is.
The article should perhaps introspect a bit more instead of setting up a false dichotomy between "rainforest field work or computers".
Why be sceptical? The model will answer consistently, inconvenient truths won't get in the way.
I would say the main workflow is collect some new data nobody has collect before, look at it and see if it shows anything interesting, make up some interesting publishable interpretation.
It feels like it'd be smarter to start with working with existing data and publish that way. If you hit on some specific missing piece, go collect that data, and work from there. But the incentive structures aren't aligned with this
The AI angle is really shoehorned in, but irrelevant to the larger problem. Sure, it allows you to annotate more data. Obviously it's more fun to go do field work than count pollen grains under a microscope. If anything AI make it easier to do more fieldwork and collect even more data b/c now you can in-theory crunch it faster
Perhaps creating secure private clouds for scientists, away from AI scrapers etc that scientists can access, with associated counter-surveillance, is the way forward.
I'm a GIS guy working on cloud native tech, but with a focus on privacy. I have a local-first Mac native product nearing beta. I'm thinking a lot about what data sharing options can be at the moment.
Some people scrape charts in publications to extract data. This has been done for a while. Maybe AI could automate this step. Thatd be useful
From a pure business perspective, AI is largely about copyright circumvention. The laws are lagging and people are making serious money from data theft.
https://news.bloomberglaw.com/us-law-week/big-tech-wins-in-c...
what you need is people uploading data in consistent well documented formats. There are all sorts or projects that do this, but there is a strong incentive to not upload things, or sort of half upload it.. but in a way where anyone using it is going to have to reach out to you. Not suggesting bad intentions, Maybe youre still working with the data and expect to publish more and dont want someone swooping in and beating you to the punch. Typically journals require data availability, but its kind of informal and adhoc
Attempting to convince people to change course and focus on restoration has mostly been a losing battle, with much larger forces behind the main detriments that make local changes feel inadequate.
One of the issues that I think affects these people is that the scientific process can not guarantee that something is "correct" or "incorrect". Something that at a point was known to be correct can be later disproved by a later experiment or more specific conditions.
Some people want/need simple certainties, and as soon as they stumble upon something different, they will shift their trust to something "simple and clear". And they can do that again and again, as long as they don't need to accept some things are complex or unknown.
I do not know any school system (not that I am an expert or searched for it, just an impression) that emphasizes this dynamic nature of understanding, or that tries to make people accept the unknown.
(Been reading some of Wojcieck Zurek's semi-pop articles on decohrerence, and he alway puts a paragraph about the evolution of language in and points out that our intuitions won't work well in the quantum domain).
There is still field work that happens. AI can not replace that. You'd need to literally simulate the whole world before AI can even get close to gather all data obtainable here. For instance, on a given ant hill: which plant species will be more prevalent there? (For those not knowing a lot about ants: some ant species carry specific plants or defend plants against invaders. The most usual example is for leaf-cutting ants, but there are many additional examples, and for various reasons you will also find different plant species to be more prevalent close to an ant hill in a forest area, than other plant species.) AI can steal existing data, but there is no way it can gather real data UNLESS you are able to monitor this. This is possible via machines, e. g. drones, but AI does not understand what it is doing and even with instructions you still may be able to just hallucinate data. So perhaps one day this may all be fully automated (sensor systems can do all humans can do too, of course), but right now this is simply not the case. And this is just one example for many more.
> Scientists who run long-term ecological studies, in particular, report that they struggle to find funding.
It's cheaper and easier to do stuff sitting at a desk. In theory that's a good thing if it means more work gets done, but field work has to happen too. For many people it's the best part of the job, for others it's a pain that has to be suffered through to get the data they need. Hopefully there's room (and funding) for both kinds of people to do the work they want.
There's also a strong belief in "statistical magic." Faced with a bad or insufficient data set, someone will say: "Let's give the data to <statistician> and have them work their magic on it."
That the results actually have to be influenced by the data in some way is something that has to be explained to people. In all of my years as a scientist, I've learned that there's still no substitute for good measurements. Good data can be cheaper than analysis of bad data.
It’s so important that we write these down, so when these people have forgotten why they’re not making any progress and they’re searching for answers, they’ll find what we wrote down and say “ohhh, we had too much hubris thought we were smarter than everyone else and didn’t listen to how important actually going outside is.”
The 'computer scientist' quote illustrates a frustrating trend: tech-centric 'drive-bys' that lack the ecological context required for good science. On the flip side, the 'old guard' who ignore modern data assimilation are leaving massive potential on the table. The field is rightfully shifting from site-specific anecdotes to foundational, broad-scale work, but we need both skillsets to do it justice.
An example is the National Science Foundation NEON project, which is a long-term ecological monitoring initiative with common field methodologies across 81 North American sites. https://www.neonscience.org/
You should be able to publish data as a paper and get academic credit for doing that. Then others can publish analyses of that data, crediting you.
Someone is jumping the shark.
90% of the time it is spend analyzing data or writing up proposals/grants/papers. i don't think AI was the turning point.
that's the original title before editorializarion
I always felt like one of the primary motivations to pursue science was being able to bail out of the office for the entire summer for "field work"...
Instead of counting bears in the forest by hand, you set up a hundred trail cameras and then use computers to count bears 24/7 across an entire area. This is field research, on a scale that was previously impossible.
All of your peaceful science drones (just the same as attack drones) will have to be equipped with countermeasures such as flares and chaff, to defend against the primitive jury rigged SAM systems and anti-aircraft batteries the Forest People of 2056 will use to shoot down anything that intrudes upon their domain, years after the Great War (No, For Real This Time ZOMG) destroys half the planet and now it's the Hunger Games future they have planned for us.