The dawn of post-theory science?
theguardian.com
theguardian.com
I'll take the other side.
Consider psychology, economics, ecology, climate, many health & biology related fields, etc. These fields have struggled to succeed within the Popperian/Newtonian framework. We haven't found an F=ma or E=mc^2 that is fundamental, predictive and generally useful. This has resolved to a lot of frustrating modeling and unfalsifiable theories. P-hacking, replication crisis & such.
Marginalist economics is a great example. If you squint, it kinda conforms to Popperian models. In practice though, it's not workable. In practice, it's riddled with post fact storytelling, subjectivity and opinion.
The "high science" mode of enquiry is amazing. It has arguably advanced us more as a species than any other idea. It is, however, not the only mode of enquiry. We walk around the world forming tentative theories for how the world works. They're generally not simple, expressible or general. They are, however, predictive. Jane knows how Joe will react to X. She doesn't know it with certainty or accuracy, but she's got pretty high chances of being right. Call that a theory, a model... whatever. It's a real way of having knowledge. Farmers' understanding of the farm.
There's no guarantee that the phenomenon we're interested in will be expressible in an F=ma manner. Complexity exists.
That's a fair point. But having a good theory doesn't necessarily mean that it's expressible as a succinct equation. The theory of how metabolism works is insanely complicated, but it's a good theory nonetheless, because every part of it plays a functional role, it explains a wide range of phenomena and it's logically compatible with other successful theories. None of that holds for deep learning models.
Besides being an overly provocative term, I don't think "post-theory science" is a belligerent to "theory-based" science. It's not logically compatible or incompatible with other theories, because it's not logically based.
It may be contradict theoretical predictions, and that's where empiricism comes in. Long term, if successful, deep learning models of metabolism may be useful or irrelevant to the development of a theoretical understanding.
It's entirely possible we haven't found such models because of the p-hacking, poor replication and publications with low signal to noise ratios, and not vice versa. Psychology's replication rate was in the mid 30% range. It would be hard to see any general trends with such unreliable data.
This is possibly just a semantic question. If so, I have no problem calling it something else.
Nothing is performing those steps in a NN.
To take a reasonably provocative example: if a new climate model predicted extremely low rates of change, would it be tuned so it showed change in the accepted range? If so, then how useful is this model to science?
The basic issue with going just empirical is that there is no way of knowing if the relationship actually exists or if what is observed is a symptom of something else. (Or, in the extreme case: random. Some bits in finance have that problem, for example, finding an edge in markets).
Most successful application of ML etc. are not relying in areas where there are no ideas about any relationships, but rather where there are a lot of confounders and the relationships are complex/difficult to formulate.
Sticking with on of the examples, we know that proteins fold in somewhat predictable ways, because we do have theories around the "mechanics" there. So good applications are not strictly in the realm of scientific theory but rather in being able to apply and use such theories in previously inaccessible ways.
But we were broadly unable to leverage those theories and knowledge into a predictive system. AlphaFold largely dropped all that (IIUC), and works much better without it.
(I was once a PhD candidate in protein folding, and left in part because I felt the field was unlikely to make any substantive progress. It didn't, for at least 30 years, until NN-based "modelling" started to replace all approaches up till then)
This is a good application of ML since there is an independent “check” on its work. And it’s hard to call it “the end of theory” when theory is still tightly coupled with the system, just at the back end instead of the front end.
That is not true of many applications of ML, like Facebook, which simply spit out solutions at scale with no way to independently check them. We should not think the same way about all ML systems; there is a lot more to it than just the neural network.
Theory is not involved in this. We know that a given protein will always (more or less) fold into the same configuration every time, generally on their own. We know this by doing experiments in which the protein is denatured (heated to break internal covalent and some ionic bonds, and then left to anneal (cool down), and then inspected at the atomic level for the resulting structure. They always fold back to the same shape. This is an experimental determination, not a theoretical one.
Nobody was ever successful at devising any comprehensive theory that explained how they did this, though they of course identified many different elements that are likely involved in some way (hydrophobicity, local charge, possible compactness and many more).
But there is (currently) no theory that connects protein amino acid sequence to structure, which is why AlphaFold was such a huge deal. It makes the connection (somehow), without any theory builtin, and without any theory to explain.
In fact, there is not even a theory that requires that they always fold into the same shape.
On the other hand, trying to use an AI to make predictions which atom of a radioactive element was to decay next would run afoul of current theories.
It is theoretically possible? Probably. Does it have anything to do with how AlphaFold works? no. Does it have anything to do with verifying AlphaFold's predictions? no.
It isn't theory that led us to notice that (almost all) proteins fold naturally into the same shape every time they are denatured and annealed. It was simple experimentation.
The attempts to create a theory of how that happens have been measured by the success of those attempted theories to make successful structure predictions. They have failed (pretty dismally in most cases).
So there is no theory that allows us to make such predictions. We simply have experimental evidence that proteins can adopt their "normal" structure by themselves, essentially every time. We have no functioning theory of how they do this, and no way to use this non-existent theory to make predictions.
My point is much more basic, if you will. So I meant "allow" as in there is no theory saying that we cannot do it. Not that we have a theory how to do it.
It works really well to eg predict and understand the impact of tariffs or taxes or deregulation.
Yes, economists can't predict recessions in advance. And economics also tells you exactly why: the efficient market hypothesis tells you that if people knew that stock prices were going down next year, prices would already react to that knowledge today.
Aren't they actually pretty good at predicting recessions? Thought the joke is that they predicted 12 of the last 7.
We see this with people all the time. Jane thinks she knows how Joe will react to X but Joe does something unexpected.
A system that makes accurate predictions an unpredictable percentage of the time is not that useful. But it might be great for fooling ourselves or others.
Sure it fails sometimes, but nobody would voluntarily give up that ability.
It is like saying "Somewhere between Ghandi and Wonder Woman, India's independence movement took a back seat"
It is clear that WW has no relevance just as Zuckerberg to science.
Also, comparing one of the most respected human minds of all time to a simply grotesquely powerful active living person this way is something that is very distasteful from a news publication.
> Also, comparing one of the most respected human minds of all time to a simply grotesquely powerful active living person this way is something that is very distasteful from a news publication.
A more generous interpretation could be that on social networks, emergent properties are hard to predict beforehand with theory, and require observation of the actual phenomenon. That's actually behind a large body of scientific work, the best known being agent-based models.
Also I think it's biased to simpy discard the scientific contribution of FANGs with emotionally loaded words like "grotesquely": from algorithms to new methods, there's much good to be found in what large companies (that you may like or dislike) have done.
Isaac Newton lived from 1642 – 1726 so technically:
* 1642-1649 : Charles I (until he got the chop)
* 1649-1660 : Interregnum, Cromwell and Son
* 1660-1685 : Charles II (Restored, partied a lot)
* 1685-1688 : James II (Too Catholic, overthrown)
* 1689-1702 : William and Mary ('Leveraged buyout' by the Dutch as Matt Christman puts it)
* 1702-1707 : Anne (Last English monarch... see Acts of Union)
* 1707-1714 : Anne (As monarch of GB)
* 1714-1727 : George I
So quite a few monarchs!
Butbas far as we know, not a lot of people were actually interested in working on a theory (because why spend the time if you can just slap a neural network on it?)
Even if some people are, they are likely working inside Facebook and we might not know about it.
The analogy there is more in comparing time periods than persons relevant to the development.
https://blog.google/technology/ads/update-our-political-ads-...
Why is Google bashed so much more than FB or Amazon (product disinformation)?
I would attribute that to personal feelings and confirmation bias.
Facebook has received a lot of negative press coverage in recent times, for their help to nationalist parties in India, their behaviour in Myanmar, Burma, for their mishandling of fake news in the US and Europe, etc. They've also been at the heart of multiple legislative investigations in the US and abroad.
Comparatively, Google has received little backlash and attention for their own misdeeds.
In former, hypothesis specifies which variables are likely independent, dependent, must be controlled, so on, then makes observations to confirm. In latter, data is first collected with as many variables as possible, then we attempt to identify which variables are independent, etc, then confirm.
To be cost efficient, many science experiments chose former. AI models use latter (get data, then find patterns, aka dependencies across variables). But even in latter, 1) human scientists can and do use the approach when appropriate, and 2) someone must have chosen the variables to observe which is a hypothesis (but lower precision is permitted in favor of higher recall).
Long story short: It is sensational and in the best interest of the media to make it seem like some drastic change is happening, but the way we the humankind is growing our collective knowledge is still fundamentally consistent, except with some added techniques thanks to low cost of studying large scale data, and it would serve us well to focus on the fundamentals and basics. The scientific approach.
Science is the process by which we reject verifiably incorrect ideas of the world. There are a lot of USEFUL tools on this planet which do not fall under that umbrella, and there have always been. AI is just the latest one we have.
Are we going to say that any sort of spiritual practice (which can be seen as form of unscientific therapy that can provide value to people) is "post-theory science"?
Are we going to say that a thousand year old medicine which did not understand placebo effects is "post-theory science"?
Are we going to say that when people meditate to understand how they feel, and then they can't accurately explain the biochemical processes is "post-theory science"?
Just because we can come up with very big functions which can approximate complex data (AI) means nothing about how we use them to understand the world.
The world has always had non-scientific tools to complement science. And always will have. Science always took longer to catch up. Over time some have become obsolete, because science figured out how to explain what they did. Some have not.
Gimmeh a break, there's nothing new here, science ends when we prove things are unknowable. Not when magicians, AIs and humans think other tools perform better.
Are we going to say that a thousand year old medicine which did not understand placebo effects is "post-theory science"?
Are we going to say that when people meditate to understand how they feel, and then they can't accurately explain the biochemical processes is "post-theory science"?
I mean, we kinda do - in the sense that, yes, there is a lot of knowledge about actually effective medicine in those, but a lot less why particular remedies work and why others don't work.
This can easily lead to "cargo cult" like behaviour, where all you can do is blindly repeat certain rituals and hope they work, but you cannot really refine your methods or develop new methods.
(Actually, that's not quite correct. A lot of pre-modern science absolutely tried to develop theories and models behind their observation - as even the original "cargo cult" did - but those were wrong and didn't allow predictions)
An important note. I an not in favor of those practices. All I claim is they can live alongside science, despite all their flaws. So can AI. Science is not becoming post-theory. It always lived alongside non-theory stuff.
No, this is absolutely the wrong takeaway from the article. NN's are not magic, they are completely understandable in terms of how they work and what they do.
However, they do not (easily, if at all) allow the construction of an explanation of why, given input X, they produce output Y. You cannot (easily, if at all) construct a theory of how they generate outputs. So you end up settling for (fairly, or even remarkably suprisingly) accurate predictions, but without the sense that you understand how they are generated, even though you are 100% clear on the mechanism.
That's not magic.
> There are a lot of USEFUL tools on this planet which do not fall under that umbrella, and there have always been. AI is just the latest one we have.
The point of the article, at its heart, is precisely to ask whether these NN-based approaches are in fact science or not. It's not insisting that they are, but pointing out that they are create a gray zone that is qualitatively different from the 3 examples you offer as other non-scientific approaches to understanding. Why? Because they operate in a domain still legitimately filled with much of the language of science, but not much of the technique. That's not true for spiritual practice, traditional medicine or meditation.
The article does make a few solid points, but the conclusion isn't supported. Even automated Deep Learning based science draws from theories. The data, the underlying statistics, the model, and how we interpret the results are all theory-driven.
We are not entering a new, objective era of science. We are merely chosing to ignore the theory and hiding behind a curtain of statistics.
If there's one thing that Covid has taught us, it's that The Modelling is a vengeful God who must be appeased with many sacrifices.
FWIW, a significant part of the "inconvenience" as a parent of two very young kids comes from my refusal to accept that putting kids in front of screens for extended and frequent sessions is an acceptable compromise when balancing the pressures of life under Covid restrictions. Apparently, most people have decided this is ok, along with other horrors like enforcing isolation requirements on young children.
Placing all of this at the feet of "The Modelling", and none of it at the feet of the virus itself from what I've read so far, is so ridiculous that it doesn't even warrant a discussion, sorry.
The OP, and indeed the article linked, is on the topic of modelling and statistics, so that is the subject of discussion.
Come now. Black Death? The Depression? Two World Wars? And your main complaint is that your kids are having too much screen time?
I mean, don't get me wrong: in-person interaction is critical, and having it replaced by screen time is certainly detrimental. But not nearly as detrimental as having to sleep in bomb shelters every night, or killing 50% of the population.
My kids are not having too much screen time. My complaint is that Covid restrictions have made the already-tough job of being an accountable, responsible parent almost impossible. Screen time is just the tip of the iceberg. You talk about sleeping in bomb shelters - how many kids have been told they now carry a disease that could kill if they get too close to someone, and what will that do to their mental and social development? How many parents now believe this justifies what would previously be called child abuse? Many of the ones I know, that's for sure.
At one time, it was considered brave to hug a HIV-positive person (and when the risks were unknown). Now, we selfishly impose social restrictions on those closest to us, to reduce an already-tiny level of personal risk.
I very specifically didn't. I compared the overall societal outcome, to those other singular events.
>Your comment reeks of entitlement and privilege.
Yours reeks of poor reading comprehension and sweeping assumptions, as well as an inability to comprehend the scale of the different experiences different people have had under Covid restrictions. In my previous comment, you'll note that most of my concerns are for others, and for the society I live in as a whole. I don't know why I bother, as once again I am branded "selfish", "entitled", etc etc. In fact, due in part to the choices I and my close family have made over the past two years, it looks like we are going to do just fine. I absolutely cannot say the same for some of those I know. A close friend who recently had their first child has essentially had a mental health breakdown (I suspect, due to a mix of pre-existing inclinations, and the stress of being a first time parent under Covid restrictions), and due to the state they are now in, are refusing any external help at all.
The sheer number of stories like this will become more widely known in due course, and defending lockdown-style policies will become less and less palatable.
>as if the virus doesn't even exist in your mind
I suppose you'd go on to say I've "done my own research on Facebook", I'm an anti-vaxxer, etc etc. The virus is real. Our response has been vastly overblown and will continue to have have disproportionally massive side-effects (in my opinion). Those side-effects will mostly hurt others I care about (as I have described in all of my comments). Strange that complaining at all about the effects of restrictions, even when it is mostly about others or wide-reaching societal problems, makes me "selfish" or "privileged".
1) negative impacts on individuals and societies of SARS-COV-2
2) negative impacts on individuals and societies of policies designed to tackle (1)
That's a hell of claim to make. It seems to hinge mostly on one or both of two theories about the world (and the disease):
a) deaths due to the virus were going to be about the same no matter what
b) the deaths somehow count for less than the afflictions of the livinga) will be forever unknowable, although we can make some broad observations across various countries' responses to Covid and the overall outcome. EG: Australia has had strict lockdowns since March 2020, yet their cases are now spiking horribly (as I've heard so many times, with deaths lagging a few weeks). Where I live (Wales, UK) has had a mask mandate since September 2020. We were not allowed other people in our houses from March 2020 till mid-2021. Most places of entertainment have been closed around half the time, etc etc. Yet our observed outcome is objectively terrible.
All I can say is that my subjective experience has been dominated by the side-effects of Covid restrictions. I personally know only one person who has had anything more than relatively minor symptoms from Covid. I accept that my experience is probably biased in one direction, but I doubt there are comparatively many people whose life has been dominated by Covid itself.
b) is a contentious subject, and our inability to discuss it shows that we have become too detached from the reality of life and death as a society. Tell me - is it better that a young child has been correctly "isolated" in their room for many days, and told that it's necessary so they don't kill anyone with Covid, and grows up into a disturbed adult, or that an elderly person with several chronic health issues dies a year or so earlier than "expected"?
Death is not the worst thing.
Same date, deaths in above 64 and older range: 621617
(EDIT: numbers are for the USA, but the general proportions seem to apply worldwide)
So
> it's necessary so they don't kill anyone with Covid, and grows up into a disturbed adult, or that an elderly person with several chronic health issues dies a year or so earlier than "expected"?
is pushing the bounds of reality a little when 1/3 of the deaths are in a demographic that is hardly described by what you've written above.
Consequently, I think a closer summary of your position would be that the psychic/emotional harm to people caused by our attempts to stop COVID from overwhelming the health care system outweighs the deaths of at least 200k people who were far from the end of life.
I don't consider that an indefensible position, but it needs to be stated clearly, with numbers so that people can see the numerical weight behind your moral take on this.
I'm not too familiar with data and methods related to Covid deaths in the US, but here's a breakdown of deaths with Covid from the UK for 2020:
https://www.ons.gov.uk/aboutus/transparencyandgovernance/fre...
The deaths with Covid for the above 65 age group outweigh those in the below 65 group by 10:1 (and continue to drop exponentially with age).
Even with the large elderly and frail populations in the Western countries we inhabit, and the (frankly) enthusiasm for counting deaths as Covid deaths [1], the absolute number of Covid deaths is also still relatively small. It's certainly nowhere near the scale of the great historical pandemics, nor does the disease itself present as much of a horror as diseases that ran rampant within living memory - for example, smallpox.
[1] Early in 2020, medical guidance in the UK was changed such that it became significantly easier to record any viral respiratory death as Covid, the requirement for a confirming test was removed, and the requirement for a cornonary investigation was also removed. I can link the documents if required.
Also, to repeat myself from so many past threads in so many places: the issue with COVID is a public health problem, not a personal health problem. The risk of death can be (and is) fairly low, but the risk of hospitalization is high enough that the primary public health care motivation is not preventing death from COVID but "flattening the curve" so that health care systems are not overrun. When that happens, normally non-fatal accidents and diseases become vastly more serious, and the overall risk and demographics of risk expand dramatically. This is why it makes little sense to focus on the mortality rate for COVID - that was never the central issue.
As for issues with healthcare - this is very pertinent for me here in the UK, as "protect the NHS" was the mantra for almost everything we've done since March 2020. It would seem that one of the side-effects of Covid restrictions (esp. as they apply to healthcare) is that, despite all of us sacrificing so much to "protect" our health service, and despite the NHS admitting record low numbers of patients since March 2020 (the data is publicly available), it has collapsed anyway.
The answer was obviously no, at some point we still need to run actual tests. Without that we just have a proposition of what might happen. And the longer we leave running actual tests to feed back into the models the more "out of sync" with reality we risk becoming.
I think thats key to where this article just misses the real world situation. We're heading towards an environment where we can rapidly make propositions of outcomes for a hypothesis based on what we already know to help remove low % avenues and focus in on opportunities. Hypothesise and test still need to happen, but hopefully the predict phase will mean researchers hit dead-ends less often and earlier and can move to other avenues quicker and with fewer resources wasted.
> We are not entering a new, objective era of science.
TFA does not contain the word "objective". That is not the claim at all.
This is no different from alphafold. It’s an acknowledgment that we know the the theory, we now just want a computational shortcut that allows us to move on to more important problems that have been blocked by not being able to predict protein structures from sequences quickly.
This type of non parametric (or in modern parlance ML) thinking is a great new adjunct method of thinking but this has not and probably won’t fully replace hypothesis based research. The author is clearly a science writer but doesn’t look like has actual science experience so they’ve just went and wrote whatever made sense to them. It’s an interesting problem anyway so anything that provokes discussion is a good thing.
Also if theory is dead why are the scientists they interview in this piece obsessed with generating new theory...
>Greenhalgh’s message is not that we need theory without evidence, or evidence without theory. Her message, informed by what seems to me is a very reasonable reading of the history and philosophy of science, is that theories (“mental models”) are in most cases necessary, and we should recognize them as such. We should use evidence where it is available, without acting as if our evidence, positive or negative, is stronger than it is.
https://statmodeling.stat.columbia.edu/2021/10/22/how-did-th...
Until we can come up with a way to automate large-scale robust causal inference, there's no end of theory. Because of course everything is correlated with everything in real life, the problem is isolating reasonable relations/networks/models of causal effects.
Err, what now? The neural net now substitutes for experiments as well? Replication crisis solved, I guess. Just don't deal with humans at all and social science finally makes working predictions!
I stopped reading after this sentence.
Chucking a bunch of variables into a linear regression, getting an r^2 of 0.3 and calling it day is so far removed from Newtonian science that I'm not sure it should even be called science.
In reality chucking a bunch of variables into a linear regression is the easy part (well, with exceptions), getting ,or creating, good, interesting and important data that were not already analysed to death is a problem. Imho the revolution is the realising value in observational data and not using statistics. It's all about the data.
When discussing machine learning, data-mining or statistics with somebody far removed from the machinery (so for example from linguistics to biology), they always focus on what's possible with the tools but in reality the available data is what's constraining everything. Most tools (especially fancy ones like neural networks etc.) only work under certain constraints the are hard to achieve, it's not a hammer. You can always use a hammer and get results.
Err, there is a theory: "What if we have so much computer power that we can try every combination and optimize for what fits best?". These algorithms aren't completely silent either, there's people behind them that write them, feed them data, guide the learning process, predict an outcome, etc - this breathless "We just don't know how it works???" is ignorant AI fearmongering, because the next line will be "What if it decides humans should be destroyed???" or whatever.
Some AI algorithms ended up doing racial profiling; that wasn't because "we just don't know how it works???", it was because it was fed racially profiled data. It's not magic, it's not unscientific randomness, and the outcomes can be predicted.
Whether or not we understand everything that the ML systems are doing, we can still observe if events corroborate their predictions, and will be forced to debug or reprogram them if they are not learning well.
Also, I am guessing that many if not most of these ML systems do not start learning from zero, but have some pre-ML naming, sorting, and classification scheme baked into their algorithms. If that is the case, increasing the accuracy of the scheme - refining the concepts of the theory - should only increase the accuracy of the systems' predictions.
For instance, kinetic energy can be given by: E_k = (gamma - 1) * mc^2 (technically, this is just one part of SR, and the fact that is consistent with the rest of SR is a benefit beyond simply the accuracy)
Another "theory" predicting cancer (or some other outcome) could be on the form: P=logistic(sum(beta_i * x_i)), where x_i are identifiable risk factors.
The latter would be the theory presumed in logistic regression, and is usually a gross oversimplification, but still very usable. Arguably, this is the same situation that we have with Newtonian kinetic energy (E_k=mv^2/2), which is also known to be a simplification.
I think the above direction extends naturally to Neural Networks, where the core assumption (linearity of logit(P) in X) is replaced by some given NN topology. In fact, logistic regression is just an example of (a very simple) such network.
In other words, the topology of the network (possibly even abstracted to a set of networks) becomes the new "Theory".
If we define "Theory" that way, I would argue that when comparing two such "Theories", and assuming equal explanatory power, the one that requires less computational power (or similarly, fewer parameters) is the better one.
As the technology matures, I expect Machine Learning / Neural Networks to be able to re-descover most of the theories where Science seems to be 100% accurate (like General Relativity), and to be able to "learn" the specific solution that minimizes the number of parameters. That solution may indeed by identical to Einstein's formulation.
In cases where our best theories are quite poor approximations (ie predicing cancer), machine learning models would be more complex/have many more parameters. I don't think that is a problem, as if we think we have a better understanding with current theories, we are simply fooling ourselves.
My main point is, AlphaFold isn’t displacing any general theory that predicts structure from sequence. It’s displacing other machine learning systems, which are mostly displacing just getting/making the protein and doing the X-ray crystallography (where you really do have a straightforward geometry optimization problem)
Again not my field so maybe I think about it a bit weird
When you're using ML models to reason inductively from data you can produce systems that do some work, they can aid with the practical and empirical aspects of science, but they don't replace science because they don't produce knowledge.
This is like the Skinner behaviourism debate from half a century ago but with a dog from Boston Dynamics instead of the organic version
Edit: Uh oh. HN may not be ready to accept this, so maybe it’s less progressive than I thought. The metaphysics of the Enlightenment went out the window a long time ago. Functionalism does not require Reason. Knowledge isn’t necessarily anthropocentric.
Not saying I like this either, but it’s certainly the transhumanist future we’re currently charting. Personally, I think we’re peering into a nihilistic abyss and need to fuse a metaphysic back into the loop to get back on the right track.
I wouldn't put a lot of money on that in the future given that even 3 years ago AI could prove about 58% of a set of theorems: https://mathscholar.org/2019/04/google-ai-system-proves-over...
From that to inferring then proving new theorems in the future or even inferring new theories in other fields (that can then be tested empirically if needed) I think we're not very far from what the person you replied to suggested
Call me again when AI develops a new theorem from scratch, in a concise manner
Humans may need theory to perform science, but only because we're slow and have bad memories is it necessary for us to rely on faint signals like intuition, eureka moments, and pulling all-nighters in the lab. Machines don't need narratives and metaphors to represent their knowledge of the world, nor do they need reason or explanations of how and why. They just need to function.
ML systems as they exist do not produce knowledge for humans, and they do not understand anything themselves. They're mechanisms that do work. A chess computer doesn't understand chess any more than a lever understands physics, even if they can beat humans or lift heavy objects. Your TI-83 is not a mathematician despite the fact that it can calculate much quicker than you can.
If you're suggesting that at some point we may built some sort of AGI-scientist that can actually reason about something, potentially at a level we can't understand, sure. But that is not what ML is, that is science fiction, and it is not how ML systems will be used in the scientific process, they will aid as tools as they're doing right now, and that is what everyone will tell you who worked on AlphaFold.
What I’m saying is that “reason” is an anthropocentric concept and is not a prerequisite for the production of knowledge, or even conducting science for that matter.
And the first point you make is very important. The fact that we have neural nets in our heads that we didn't and don't really understand hasn't changed anything about what science is. We reason about the world and we do science because having general models about the world we are in is more powerful than merely collecting or correlating data. And that will be the case for any kind of intelligence.
So I would reformulate the observation of the article as follows: we have become extremely good at building useful tools that embeds knowledge that we do not understand.
If I may attempt an analogy, it reminds of Ptolemaic tables: quite useful at predicting planetary motions with some accuracy. What they lacked in understanding, they made up with accumulated data and complexity. Maybe AlphaFold is just that: a Ptolemaic table built on top of a huge swath of data.
We will probably build a lot of these tools in the future, as we have very powerful tools to build them. But I don't think they will replace a more fundamental understanding (that is to say science), that will simply lag behind.
To be honest, before I can agree or disagree with that, I'd really like to know more about how AlphaGo actually plays Go. Like, how the network is structured, how an actual decision is produced, etc.
I feel, we're talking about increasingly lofty and abstract concepts while we don't have much of a clue what actually goes on on the ground.
That isn't knowledge, we as a species can never actually know anything, since that's saying that some things can never be proved wrong, which goes against the philosophy of science.
Fact is the enemy of truth, knowledge the enemy of science.
Science is about creating models and then studying them and making predictions off of them. The making of the model is an inductive and empiric ordeal, whereas working on said model is a matter of deduction.
But with counter-intuitive claims like:
> Facebook’s machine learning tools predict your preferences better than any psychologist.
breezily thrown in without anything to back them up, it doesn't feel like a very serious piece
"Post theory science" really probably means "explanations for complex behaviours are complex and in some cases beyond simply testable theories". We've seen this in quantum physics, where things like string theory are on the edge of testability.
In the first example in the article, they find limits of the behavioural-economic Prospect Theory. This isn't surprising - the tools used to develop that were pretty primitive compared to what we have now.
In this case this is a lot more like how science used to be done: collect data, make some observations, discover they don't align with the current theory and then people might be stuck for a while before they come up with a new one.
Learnt computer models are helpful because if they are good enough they simulate the real world better than many simple mathematical models do.
Indeed, I'm sad too when reading some of the comments here
> "Post theory science" really probably means "explanations for complex behaviours are complex and in some cases beyond simply testable theories"
Case in point: agent based modeling
https://www.publichealth.columbia.edu/research/population-he...
> "Agent-based models are computer simulations used to study the interactions between people, things, places, and time. They are stochastic models built from the bottom up meaning individual agents (often people in epidemiology) are assigned certain attributes. The agents are programmed to behave and interact with other agents and the environment in certain ways. These interactions produce emergent effects that may differ from effects of individual agents. Agent-based modeling differs from traditional, regression-based methods in that, like systems dynamics modeling, it allows for the exploration of complex systems that display non-independence of individuals and feedback loops in causal mechanisms."
https://www.nasa.gov/consortium/AgentBasedModeling/
> "ABM enables an examination that would take years, decades, or centuries, to be performed in a matter of minutes. An important topic that must be addressed in any ABM application is the identification of the bounds of the work and the relationship between the model and the related real-world situation."
Here I think is the core of the whole problem: In case of people (as quoted "often people in epidemiology") how can we verify if ABM is correctly modeling people if we still don't know a lot about ourselves? For example we don't yet have a theory of consciousness, or we still are not able to accurately explain the process of how a child learns (yes we have multiple theories that we switch when one is not working) and so many other things.
The same can be said when we model other things from planets to animals and particles: How can we know that the model is accurate?
I am not saying the new way of doing science using "AI" is not science, but I am saying we need to move pass bold headlines and make sure that we results we take out of this have value and hold out.
To just run some simulation and trust it's output in any even remotely quantitative sense is no more scientific than reading the future from tea leaves!
The Popperian view that everything is testable and falsifiable (given that in the real world there are limited resources and time) is counter-productive. Going by that view no medicine or treatment are scientific (since none of them work 100% of the time)
Give me models that work and have predictive power. Skip the pedantry.
Correct. It is, up to a range. If I replicate the experiment I'll have a different Y% which might be a slight difference or not.
But since it's not 100%, your theory about that experiment has been falsified.
It's easy to talk about falsifiability giving examples about black swans but that's a naive view in terms of results which are not clear yes/no answers and where multiple variables interact.
A theory-based hypothesis in this case would be something like, we think this disease is caused by this specific protein causes a disease, and this drug candidate will bind well to the protein and inhibit the disease causing reaction, so the drug will be useful to treat the disease. There are experiments to be done to check each element in that chain of theory, including the statistical association of disease outcomes
I mean... Quantum theory itself has actually given lots of solid predictions. They are statistical in nature, sure, but easily testable, and some can even be done at home (stuff involving polarized light filters).
String theory... Is just math that works out. I don't even know if it predicts anything. However it's certainly more complex than to allow us to say "we've seen this in quantum physics" without a PhD in the field.
Honestly, I find the article superficial. A cute but ultimately shallow idea looking for arguments to support it.