Machine learning is fundamentally conservative - <AUTHOR> http://blog.lareviewofbooks.org/provocations/neophobic-conservative-ai-overlords-want-everything-stay/ <comment><USER2> Teach AI using our behaviour, AI learns our behaviour. A bit like our children. I'm genuinely confused as to the alternative.

The objection seems to be based on the falasy that technological progress equals social or political "progress". Why on earth would we expect AI descision making to display a lack of prejudice when human decision making is suffused with it.

The only people who expect technology to act like a benevolent god are the ones who have replaced their god with it. All technological progress does is to increase the power and influence of human beings. The progress the writer seems to want is socio- political, not technological.

<comment><USER3> > Why on earth would we expect AI descision making to display a lack of > prejudice when human decision making is suffused with it.

Particularly when we consider what we mean by prejudice, which is presumably something like _Making a decision on grounds which we deem it important to ignore_. This is a very complex concept. It's a function of society, and changes with society. It's not something with a rigorous definition.

Obvious example: reasonable modern people know it's indefensible to make an engineering hiring decision on the grounds of ethnicity, regardless of whether there are any correlations associated with ethnicity. This is even enshrined in law in many countries.

To make a decision on the grounds of someone's qualifications and job experience, however, does not count as prejudice.

We should expect a machine learning system to act as a correlation-seeker (that is after all what it is designed to do), without a nuanced understanding of what prejudice means.

We've seen this issue crop up in the context of an AI having a say in parole decisions. [0] also relevant discussion at [1]

[0] [https://www.forbes.com/sites/bernardmarr/2019/01/29/3-steps-...](https://www.forbes.com/sites/bernardmarr/2019/01/29/3-steps- to-tackle-the-problem-of-bias-in-artificial-intelligence/)

[1] [https://news.ycombinator.com/item?id=13156153](https://news.ycombinator.com/item?id=13156153)

<comment><USER4> > We should expect a machine learning system to act as a correlation-seeker

We should expect machine learning systems to dismiss accidental indirect correlation for the benefit of the variables that are directly correlated. There are plenty of algorithms that achieve that, it's only that gradient descent doesn't.

The fact that our AIs are becoming biased is a bug. It should be fixed.

<comment><USER5> > The fact that our AIs are becoming biased is a bug. It should be fixed.

In many cases, it's the data that is biased. In that case it's impossible to differentiate between bias that the AI should learn and bias that the AI shouldn't learn.

Let's assume we have a database of incidents where a person was found to have cannabis. This database has the following data items: a timestamp, the persons name, the persons ethnicity and the amount of cannabis that was found. Now assume further that black and white people have the same base rate of cannabis use (which according to the studies I found seems to be the case). The last thing we have to assume in this case is that this database was created by racist policemen who arrest more black people for cannabis consumption.

An AI trained using this data would assume a higher base rate of cannabis consumption by black people. It's impossible for this AI to differentiate between correlations it should learn (for example that people who used cannabis multiple times and were found to have much of it are worth looking at) and (untrue) correlations that it shouldn't learn (that black people have a higher base rate).

The correct solution here is to use a dataset that is not biased, but it's hard to tell whether a dataset is biased.

<comment><USER6> The data can't tell you how the groups differ, since you can't tell the difference between criminal behavior and policing behavior. So you have to add some priors. The most progressive approach is to assume that there are no intrinsic differences between protected groups, and any difference in the data is the legacy of past discrimination.

You can add such a prior by adding a term to the loss function that penalizes any difference between the way the groups are treated. The math isn't hard, only the political decision of what is protected and what isn't.

<comment><USER3> > assume that there are no intrinsic differences between protected groups, and > any difference in the data is the legacy of past discrimination.

I don't see why we should assume that this would reflect reality.

If a law has racist roots, i.e. if it is written to target a particular ethnicity, then we should expect that certain ethnicities really do break that law more than others.

</comment></comment></comment></comment></comment></comment><comment><USER7> Conservative in this sense means something like 'resistant to deviation from established norms'. I think a lot of the headline-only readers take conservative to mean 'of the character of a specific political movement' which ironically seems more activistic than change resistant.

<comment><USER8> Perhaps you could call it "integral controller" (like the I in "PID controller")? Because systems that have a memory behave like ones, and we are definitely in a feedback loop with those systems.

And, from what I remember from my control theory classes, the integral part of a controller introduces lag, inertia, generally making the output more resistant to input changes.

(Also note that the "non-conservative" Proportional and Derivative components in a PID by definition don't learn - they react to input and its change.)

<comment><USER7> That's a pretty interesting corollary! I suppose I could extrapolate that in such a system, too much change over too little time would invite a strong conservative response / lag to future input.

Our 20 year old millennium has seen a tremendous rate of change in both technology and social norms. Perhaps politics has some similar inertial dynamics.

</comment></comment></comment><comment><USER9> The article calls machine learning "conservative" because it only tells us what is, and not what should be. I don't think that's a useful framing. It's more accurate to say that, like all statistical techniques, machine learning is _descriptive_ , not prescriptive. Not everything in the world has to be prescriptive.

<comment><USER10> We generally want ML to imitate us (or human generated data), thus we teach it to do so. But there are also generative ML (like GANs) and simulation based ML (like AlphaGo) which can be more creative. There is nothing stoping us from letting agents evolve in a complex environment and be creative. It's just not commercially useful to do that yet. Doctorow writes like he doesn't understand much math behind ML, yet has strong opinions on it.

Every time a random number is involved in the training process (stochastic selection of mini batches, noise injection, epsilon-greedy action selection), a form of exploration is taking place, finding novel paths of solving the problem at hand. The mixing of noise with data is paradoxically useful, even necessary in learning.

<comment><USER9> Sure, but no matter what, you can't derive an "ought" from an "is". At the end of the day we merely tell these algorithms what is the case. No matter what goes on inside, they cannot output moral prescriptions.

<comment><USER11> >Sure, but no matter what, you can't derive an "ought" from an "is".

The same applies to humans, no?

<comment><USER12> No it doesn't. When a humam gets wronged by what "is" they can likely feel or imagine a better "ought".

<comment><USER13> They can feel or imagine an "ought", but the point of Hume is that they can't argue for it.

<comment><USER14> And yet humans do, all the time. It's similar to Hume's attack on causality. You can't show that A caused B, but yet we all act like it's the case, when B always follows A. Kant's critique comes next.

</comment></comment></comment></comment></comment></comment></comment><comment><USER15> Examples are good and on point, the conclusion is not. When he tries to frame it all in some grand political/quasi-philosophical manner, it becomes outright wrong and stupid, but I won't argue with that part, because it won't be useful to anyone.

What I want to point out is that nothing he says should be attributed specifically to "machine learning". Machine learning is a set of techniques to make inferences from data automatically, but there is no implicit restriction on what the inferences should be. So machine learning is not "conservative" — almost all popular applications of it are. There is no inherent reason why an ML-algorithm should suggest the most similar videos to the ones you watched recently. The same way you can use (say) association learning to find most common item sets, you can use it to find the least common item sets with the given item, and recommend them instead. Or anything in between. But application designers usually choose the less creative option to recommend (understandably so) stuff similar to what you already got.

Sometimes it's ok: if the most popular thing to ask somebody to sit on nowadays is "my face" it's only logical to advice that, I see nothing wrong with this application. But many internet shops indeed could benefit from considering what a user has already bought (from this very shop), because it isn't likely he will want to buy a second similar, but different TV anytime soon. Or, when recommending a movie, you could try to optimize for something different than "the most popular stuff watched by people that watched a lot of stuff you did watch" — which is a "safe" thing to recommend, but at the same time not really interesting. Of course, finding another sensible approach is a lot of work, but it doesn't mean there isn't one: maybe it could be "a movie with unusually high score given by somebody, who also highly rated a number of movies you rated higher than the average".

<comment><USER16> The point is that ML today is based on pattern recognition and memorizing a stationary distribution.

This stationary distribution is the source of the conservativeness and central to algorithms that we call "machine learning". ML always tries to replicate the distribution when it makes infererences, so it is fundamentally biaed against changes that deviate from that distribution

The Future and the Past are structurally the same thing in these models! They are "missing" but re-creatable links.

AI is the broader term, but in pop culture AI and ML are very much synonymous.

<comment><USER15> Well... no, not really. It is kinda hard to discuss in general, because it depends so much on the details: the application and the algorithms in question. But there is nothing inherently conservative about ML algorithms.

I see why you assume that "stationary distribution is the source of the conservativeness", so maybe I should clarify this moment. It is kind of true in the most general sense: sure, when querying the stationary distribution we can only ever ask how things _are_ in a timeless universe. How anything _new_ can be obtained this way? The problem is, that if we are this general, then the word "conservativeness" loses any meaning, since _everything_ in the [deterministic] Universe can be framed as a question of "how things are", _everything_ is conservative, _nothing new_ can be obtained anywhere, ever.

And we don't even need to get this general for the word "conservativeness" to lose practical sense. When you ask another human for an advice, all he ever does is, in essence, pattern recognition and querying his internal database of "how things generally are to the best of his knowledge". Yet you don't call every human advice ever "conservative": only the kind of advice that recommends the safest, most popular thing, thing that everybody likes, pretty much ignoring the nuance of your personal taste. In fact, even then, you call it "conservative" only if you can notice that, which means that the recommendation isn't _new_ for you personally (and by that criteria alone most humans would lose to a very simple, absolutely currently implementable music recommendation algorithm, since they probably know much lesser number of "artists similar to what you like" than Spotify knows: the only thing Spotify has to do to win is not to recommend the most popular choice almost every time).

One more thing. I could probably convey to you that assuming "ML = conservativeness" is wrong much faster by invoking the example of reinforcement learning, since it is sort of intuitive: there is the obvious existence of "time" in that, you can imagine a "dialogue" where it adapts to what user wants using his feedback, etc. It is easy to see how it could behave "not conservatively". I intentionally avoided doing that, since it can lead to the false impression that RL is somehow different and less conservative than other algorithms. On the contrary, the point I'm trying to make is that _every_ algorithm, even the purest form of memorizing the stationary distribution (like Naïve Bayes) is not inherently conservative. It all depends on what exactly you ask (i.e. how you represent inputs and outputs) and how you query the distribution you have (e.g. how much variability you allow, but not only that).

So, when you see the application that uses ML algorithm and behaves "conservatively", it isn't because of the ML algorithm, it is because of the application: it asks the ML algorithm wrong questions.

</comment></comment></comment><comment><USER17> While Cory is talking about a slightly different facet of conservatism, I found it quite ironic that he made a rather conservative statement himself:

>Nor is machine learning likely to produce a reliable method of inferring intention: it's a bedrock of anthropology that intention is unknowable without dialogue. As Cliff Geertz points out in his seminal 1973 essay, "Thick Description," you cannot distinguish a "wink" (which means something) from a "twitch" (a meaningless reflex) without asking the person you're observing which one it was.

Was this mathematically proven? It's definitely an interesting statement, since a lot of "AI" systems try to predict intention and do a piss poor job of it, but to quote that the anthropological ancestors have proclaimed for eternity that a computer can never know even the slightest fraction of intention from just observation seems hypocritical.

<comment><USER13> This was one of the stupidest parts of a fairly weak essay. Of course you can distinguish a wink from a twitch without asking, otherwise we wouldn't use winks for surreptitious communication.

<comment><USER18> As an anthropologist you are in theory studying humans from out of a shared cultural context and it is easy to argue that a machine which hasn't even the slightest semblance of a shared cultural context would not be capable of distinguishing a twitch from a wink with 100% reliability. Not all human gestures are universal either so even if you trained your AI to read faces it would likely be trained on a small subset of human cultures and would produce varying results. In regards to the greater question of knowing intention I'd agree that algorithms do a much poorer job of guessing my intention than me directly communicating it and I am annoyed that so much of our on-line communication is filtered by algorithmic social engagement that an encourages shallow interactions.

<comment><USER19> This sounds the the classical "intelligence being something people have".

It is futile to talk about 100% correctness in this field

> ... machine which hasn't even the slightest semblance of a shared cultural > context ...

Why is this universally true?

> ... it would likely be trained on a small subset of human cultures and would > produce varying results.

As it has when you travel to foreign countries.

There is not such hing as understanding artificial intelligence. There is a task of understanding _intelligence_ and attempting to implement it without giving birth.

</comment></comment></comment></comment><comment><USER20> He specifically used the example of predictive policing. I have a question: is it racist to send police patrolling areas of high crime, if those areas have a majority ethnic demographic? Should that information be discarded, and all places patrolled equally? Should they be patrolled less? It seems like there is no winning. You're either wasting resources, ignoring a problem, or being perceived as racist.

<comment><USER21> The problems of appropriate policing in so-called "high crime" neighborhoods are well understood. Many academic studies, as well as popular non-fiction like Jill Leovy's Ghettoside and Chris Hayes' Colony in a Nation, have discussed the issue. To sum up the literature in a few sentences, the problem is that minority neighborhoods are both overpoliced and underpoliced. There are a lot of useless arrests for minor crimes like jaywalking which makes the residents of these neighborhoods hostile to the police. (These arrests are driven by the debunked theory of broken window policing.) Simultaneously, there's not enough effort put into solving serious crimes like murder. In this context, the actual effect of predictive policing is that it ends up doing more of the useless over-policing, which unfortunately makes these neighborhoods even worse.

So, how does this relate to your question? The point is that predictive policing is solving the wrong problem. What's needed are not more accurate neural nets predicting crime, but techniques for addressing the underly sociological factors that cause crime.

Taking a step back and speaking broadly, Cory's point is that the focus on data and quantitative analysis is causing problems in two ways: (i) people are using quantitative methods to solve the wrong problems, and (ii) they seem to be oblivious to (and in some cases actively hostile to acknowledging) the harms being perpetrated by their methods. Both of these problems seem to be driven by a lack of understanding of well understood (but non-quantitative) social science literature.

<comment><USER22> Broken windows policing is not "debunked". A lot of people would prefer for ideological reasons that it not work, but it does. Anyone who lived in NYC can tell you that.

<comment><USER21> Speaking of NYC specifically, a lot of cities across the US, as well as worldwide, experienced a major decrease in crime at the same time that NYC did. Most of the cities were not practicing broken window policing. That's one reason to be skeptical.

From an academic perspective, it is true that there is some debate about the efficacy of broken windows policing, but even the most supportive academic studies find only a small correlation between violations of "order" (like jaywalking and graffiti) more serious crimes. There's just isn't any evidence at all that the way to reduce serious crimes is by going after jaywalkers.

</comment></comment></comment></comment><comment><USER23> Sure, many of the examples ring true.

However, I would refine the claim "Machine learning is fundamentally conservative" to say "Reducing a distribution of predictions to only the most likely prediction is conservative."

<comment><USER24> Also similarity is conservative. There are alternatives.

For a product/content recommendation you could include the product that's maximally different, the second most similar, you could exclude products within the same category, you could choose randomly and weight by conversion rate. We need more creativity and experimentation here I believe.

<comment><USER25> * you could include the product that's maximally different, the second most similar, you could exclude products within the same category, you could choose randomly and weight by conversion rate*

This is interesting. Has anyone tried that or knows of a website that uses this?

<comment><USER24> I have personally implemented discarding the top-n (small n, like 3-5) most similar results in a recommendation system as well as adding a totally random item in the results but it was a small scale system.

The top-n removal was a small but noticeable amount better. That was in part due to the specific content in the database as well as the audience, but it's a thing to try I believe.

</comment></comment></comment></comment><comment><USER26> It isn't /fundamentally/ conservative, it is just typically programmed to choose the most conservative (highest probability) predictions. You could integrate a liberal aspect by fuzzing the decision process to choose from lower probability predictions.

More creativity, and ability to escape local minima, but at some cost when dealing with 'typical' cases and when making particularly damaging mispredictions.

<comment><USER27> I think the point is rather that you _can 't_ get a more useful prediction by choosing a lower probability description _unless_ you have AGI. Only an AGI could tell that you're not in the mood for "Hey" to be followed by "darling", and only a superhuman AGI could realistically compensate for human bias in data sets.

<comment><USER26> Without AGI there are still cases when the lower probability prediction will be better, and will lead to escaping a local minima. I'd argue that the potential benefits of calibrating that axis dynamically exist with or without AGI.

<comment><USER28> Are you describing the explore/exploit tradeoff or simulated annealing in this case?

</comment></comment></comment></comment><comment><USER29> _Mom: If all your friends jumped off a bridge, would you, too?

ML Algorithm: yes!!_

<comment><USER30> If all my friends are jumping off a bridge, it's probably bungee jumping.

I mean they could have all simultaneously gone insane, or be infested by alien mind parasites, but going bungee jumping is the much more likely reason.

</comment></comment><comment><USER31> This is a problem in almost every academic field right now. Peoples' lack of sophistication with understanding the mathematical/philosophical constraints of their tools is incredibly scary.

For example, people throw around the low labor productivity stat all the time to prove that no automation is happening, not realizing that GDP is the numerator of that stat. Well, GDP is a pretty terrible gauge of who is benefitting from technological innovation, as it is distributed incredibly unevenly, probably more so than income even. The problem with automation isn't that it's not happening, it's that only a very small number of people are capturing the wealth that it is generating. Also, it is generating a small number of professional jobs, but mostly the jobs it is generating suck (Uber driver, AMZN warehouse worker, etc).

<comment><USER32> Likewise, looking only at nominal wage increases is a "pretty terrible" way of gauging who benefits from increased wealth. If improvement in productivity (automation) results in lower prices (or better or more goods for the same price), that increases the purchasing power of a worker's earnings, even if their wage has not risen.

In other words, a worker is richer if they can buy more and better stuff even without any rise in their wage.

The fact that everyone can afford a smartphone, including those with jobs that "suck", does not reconcile with "only a very small number of people are capturing the wealth that it is generating."

<comment><USER33> This argument forgets that where people spend most of their money, housing, education, healthcare, costs are rapidly raising in real terms and have not followed Moore's law in usefulness.

Yes technology has made cool gadgets for consumption, but that isn't what wealth is about. From Wikipedia: "Wealth is the abundance of valuable financial assets or physical possessions which can be converted into a form that can be used for transactions."

The average worker hasn't seen a real pay ride in 40 years, and their ability to build wealth through savings has been greatly diminished.

<comment><USER34> So basically, improvements in productivity are only benefitting people in areas where productivity has improved? That seems more like a tautology to me than something which can be fixed by gnashing teeth about the distribution of wealth

<comment><USER35> How did you get from

> _The average worker hasn 't seen a real pay [rise] in 40 years_

to

> _So basically, improvements in productivity are only benefitting people in > areas where productivity has improved?_

?

Worker productivity has increased enormously in the last decades.

<comment><USER34> I didn't? Obviously, I got that from the "housing, education, healthcare, costs are rapidly raising in real terms and have not followed Moore's law in usefulness" part...

</comment></comment></comment></comment></comment></comment><comment><USER36> I am open-minded to the idea that "Machine Learning" is conservative in the manner Doctorow describes.

However, I do wish we would not use the word "conservative" as an epithet. I think it's quite likely that "conservative" is exactly what we should be looking for in algorithms and prediction engines.

The fact that their properties are misused and infused with _reactionary_ (not necessarily conservative) biases by humans should not make us attach morally negative properties to being conservative.

FWIW, my conservatism leads me to be suspicious of employing these tools in the first place ...

</comment><comment><USER37> When you have a dumb machine that simply solves for maximum likelihood, using past data to predict the future is what you're going to get. Why is this surprising?

I don't understand what he's arguing for, exactly. That we should have AGI all of the sudden to understand intent, and detangle causation vs correlation? I don't think anyone in the machine learning community would argue against that, but the question is how.

What's new here?

<comment><USER38> I don't think Cory is writing for the machine learning community.

Cory is trying to inform people that AI and ML _aren 't_ a magical solution to all our wasteful systems because in some cases we need to throw away the system and start again, and AI can't tell us that. It can only tell us the best way to run the current system. That's what he means when he says AI is conservative.

It's not surprising at all if you understand AI but most non-tech people don't.

<comment><USER37> It has the same flavor generally of people who criticize something that's hard but offer no solutions — a style that has become increasingly popular IMHO. This also isn't a new angle for the non-informed audiences. It bears repeating for those audiences, but I'd wish he'd at least point to others who have already echoed such concerns.

<comment><USER39> Paragraph two cites the inspiration for this piece, a 2017 article by Molly Sauter: [https://reallifemag.com/instant- recall/](https://reallifemag.com/instant-recall/)

And he points to three other sources right in the first paragraph.

</comment></comment></comment></comment><comment><USER40> Alpha zero is not conservative at chess or go. It doesn't have to have seen a position before to evaluate it.

You can always train a model to reject a class just as easily as you train it to accept a class. So train it to reject a common class and accept a mutant and it will function more like an evolutionary algorithm that protects against bad luck like bacteria with antibiotics.

I am way more optimistic for AI I guess.

</comment><comment><USER41> There are systems of algorithmic inference that are not conservative. Constraint satisfaction and logical deduction can create novel insights.

<comment><USER42> ...also, as soon as you bring a network element or social element into it, it may no longer be this conservative/self-reinforcing thing. For example, if the algo behind spotify were to identify a "music taste clone" of yours somewhere in the world, they could present you with music you've never heard about that your clone has liked, and vice-versa. So you actually start discovering new stuff that you end up liking.

Furthermore, there is a psychological element at play around mirroring back your own intelligence at you (see Eliza / Rogerian Psychotherapy) in a way that will lead you to new thought.

</comment></comment><comment><USER43> _Nor is machine learning likely to produce a reliable method of inferring intention: it's a bedrock of anthropology that intention is unknowable without dialogue. As Cliff Geertz points out in his seminal 1973 essay, "Thick Description," you cannot distinguish a "wink" (which means something) from a "twitch" (a meaningless reflex) without asking the person you're observing which one it was._

I see this and other examples of "explainability" from time to time as proof why humans are not a "Black Box."

However it rests on two faulty assumptions.

1\. The explanation will be truthful

2\. The explainer can always reliably describe the actual cause of their own actions

For the purposes of theory, you could explain away #1. However a minute of introspection will make you realize that you would fail at #2 the vast majority of the time - using story telling and retroactive explanations to explain your behavior.

</comment><comment><USER44> > Search for a refrigerator or a pair of shoes and they will follow you around > the web as machine learning systems "re-target" you while you move from > place to place, even after you've bought the fridge or the shoes

> ...

> This is what makes machine learning so toxic.

I'm saddened to learn about our toxic contributions to society and can't wait to hear about alternative mind-reading approaches for fridge recommendation in the next article.

Words like 'conservative' and 'toxic' are misleading because they imply that there are better alternatives that are not being chosen. Far better are the terms by commenters here, 'descriptive' and so on. That the article is not written for machine learning practitioners makes it even more misleading.

</comment><comment><USER45> I am not a fan of this article. I have seen many critiques in this vein, this is just another car in the train. None of them have quite reached the point of confronting what is bothering many: What will we do when machine learning (or science, or anything, really) comes to a conclusion we find unpalatable, for whatever reason?

It could be any conclusion, not just those conservatives dislike. Using myself as a target, what if we eventually have enough sampling data to show that people of Irish extraction _are_ more prone to alcoholism, and people of Scottish extraction _tend_ to be statistically more thrifty? (This suggests that I would be a cheap drunk). How will we cope with that?

It is true, ML is prone to some black-boxiness, but it could be any statistical extraction. We might very well use other methods besides ML to show the correlates once suggested.

Will we simply put in a hard override to get the answers we want to get, the answers we find comfortable? History shows we have seen whole governments subscribe to this idea before. Ignore the results, publish what pleases.

I've no easy answers here, but my guess is that history will repeat itself.

<comment><USER46> > What will we do when machine learning (or science, or anything, really) > comes to a conclusion we find unpalatable, for whatever reason?

What makes you think it hasn't already happened? Numerous times?

Biological differences between sexes? IQ differences between differentiable subsets of people? The gender equality paradox?

We already know what will happen when findings are unpalatable. We'll think of ways to explain why they don't matter and can't possibly be right, or that we weren't measuring the right thing in the first place. And these are just the more obvious cases!

<comment><USER47> >or that we weren't measuring the right thing in the first place.

Which has been true, actually. For example, human facial recognition systems that can't properly distinguish dark-skinned faces. What is your conclusion in those cases? That dark-skinned people don't have faces? All look the same? Aren't human? Or is it more likely that the system is flawed in some way? Is that flaw in line with existing biases? What then is the chance that the flaw is BECAUSE of our existing biases, in some way or fashion?

From what I've read on HN, many people involved in developing ML technology seem to be overly concerned with their systems spitting out "uncomfortable truths", and less concerned with flaws in their data capture, training, or system design processes. We're putting the cart before the horse, and the giddiness with which it's being done is troubling in and of itself.

<comment><USER46> Sure--nobody's arguing that we're always measuring the right thing.

It may be more relevant in developing ML technology because readers here are the people who have to (at some point) decide whether they've found an uncomfortable truth or falsehood. Or that it's potentially biased, but may or may not be relevant. If I'm doing this at work, I may need to be able to explain to my boss whether or not what we're doing is going to be called racist or sexist, and that's regardless of whether what's being done actually is racist or sexist.

It's easy if you're using ML to detect whether an image contains a bird or train. And it's easy when you point to an obvious example with bad data (e.g. inability to differentiate dark faces). But that's not all cases. Sometimes it's good data and the findings are unpalatable. That was the root of the question.

<comment><USER47> We say "unpalatable," but what we really mean is "politically incorrect." There are segments of our society which would be overjoyed to have such "concrete" evidence of their terrible beliefs. Therefore, it is important to observe OP's thesis: that ML "findings" are conservative; that they tend to reproduce preexisting biases; and that we should be interrogating the input data, systems, and output of ML-applied tasks that return "controversial" findings.

I agree that it's important to be aware of the potential for these sorts of findings, but I continue to disagree that our takeaway should be an impetus to gird ourselves for the backlash when The Machine spits out, "Black people are dumb," rather than an impetus to observe the bedrock notion of scientific inquiry (you know, skepticism).

>It's easy if you're using ML to detect whether an image contains a bird or train. And it's easy when you point to an obvious example with bad data (e.g. inability to differentiate dark faces). But that's not all cases. Sometimes it's good data and the findings are unpalatable. That was the root of the question.

I think the "Newspaper Error Rule" (or, I guess, the "Cockroach Rule") applies here: if you recognize one when you're looking, how many are you missing when you're not, particularly when you're in less familiar territory? The _less_ obvious examples are the ones we should be _more_ wary of. And if your boss is expecting a quick, cut-and-dry answer to questions that have vexed society for centuries, just because we've applied a sophisticated (but still limited) statistical model to it... I'm just saying, maybe you don't want to go down in history as the 21st century's version of a phrenologist.

<comment><USER46> I think we mostly agree on premise but are disagreeing on emphasis.

> I agree that it's important to be aware of the potential for these sorts of > findings, but I continue to disagree that our takeaway should be an impetus > to gird ourselves for the backlash when The Machine spits out, "Black people > are dumb," rather than an impetus to observe the bedrock notion of > scientific inquiry (you know, skepticism).

There is no shortage of pressure to avoid ML conclusions like "black people are dumb." We are already girded there. This isn't the first HN article about recognizing ML biases, and it's not like we're wading through headlines about how racial stereotypes are justified due to some all-knowing ML algorithm. We know we should be interrogating biases.

That said, it will always be possible to dismiss any evidence-based science by claiming there are unfalsifiable flaws with data or methods. That was the point of the comment I responded to. Outside the context of ML, this already happens, so we can already predict how it will happen with ML. And it is.

Nobody's saying, "just trust the machine if it says black people are dumb." I'm saying, "if the machine says sickle-cell anemia is most common among those with sub-Saharan African ancestry, it's still worth checking your methods and data, but it shouldn't be ignored just because unfalsifiable reasons why the analysis could be wrong can be postulated."

<comment><USER47> The tendency to say, "Don't worry, we've got it covered," is precisely where my worry stems from. It is better than not having any awareness of the potential problems, to be sure, but only so much so.

Your last analogy is an example of this. The link between sickle cell anemia and ancestry is a causal link that could be shown conclusively with a genetic test for a SNP. Finding that gene may have been an involved effort undertaken with a great many resources, but that link was established with near-perfect data and a clear conclusion.

The most promising are applications for ML are situations on the exact opposite end of the spectrum: problems where data is imperfect, noisy, or incomplete, where conclusions are not certain, but simply likely, to some measure of confidence.

We want to use ML to come to answers approaching conclusive, when a solid conclusion is impossible or unlikely, or would too long to obtain by conventional means. And we won't know how that conclusion is reached. So, in terms of conclusions that could spark wars, genocide, untold suffering, maybe we need to be more serious about our vigilance in regards to what we can control.

To put it bluntly, these concerns people have seem akin to NASA being more worried about handling post-catastrophe PR and internal messaging than keeping the shuttle from exploding in the first place.

<comment><USER46> Your perspective doesn't lead to the discovery of the true causal link for sickle cell anemia, though.

It's what leads to us pretending there can't be any unpalatable differences between ethnic groups, clutching to presumptions about maybe the health care system being too racist, and avoiding looking deeper into it because we think we've already decided what the answer is (racism). Meanwhile real people are suffering because they aren't getting treated as effectively as they could.

We don't know what we don't know, so it's impossible to tell how many people are suffering or for what reasons.

<comment><USER47> We know what racism begets. We've seen the way that it has been used to warp inquiry, ethical guidelines, and clinical outcomes, often unintentionally and through processes that diffuse blame to below the level of individual bigotry. It would be nice if we lived in a world without a track record of pursuing the ramifications of assumed "unpalatable differences," such that what you're suggesting would be sound, but we don't. We, at this moment, are trying to climb out of a hole of ignorance - in medicine, in social science, in economics, in much of the quantifiable and model-able world - dug by presumptions of "unpalatable differences."

So, I say again, the the priorities, as you've described them, are out of wack. We are much more at risk of jumping to damaging conclusions than we are of missing helpful breakthroughs. Our vigilance should be tuned in regard to this.

</comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER48> >> Empiricism-washing is the top ideological dirty trick of technocrats everywhere: they assert that the data "doesn't lie," and thus all policy prescriptions based on data can be divorced from "politics" and relegated to the realm of "evidence."

Well data _doesn 't_ lie. Because data doesn't _say_ anything. People interpret data and they do so according to their own inherent biases. And if the data is already biased (i.e. gathered according to peoples' biases) its interpretations end up far, far away from any objective truth.

</comment><comment><USER49> The author writes:

>Machine learning systems are good at identifying cars that are similar to the cars they already know about. They're also good at identifying faces that are similar to the faces they know about, which is why faces that are white and male are more reliably recognized by these systems — the systems are trained by the people who made them and the people in their circles.

But this seems like an absurd claim. Surely it's more likely that darker skin reflects less light than paler skin and so features are harder to detect on darker skin people. This explains why facial recognition works better on white people than on black people. Likewise women are more likely to wear makeup than men are and therefore are more likely to have a visually different face which could cause problems for facial recognition.

To assume that dark skinned people aren't involved in creating or testing facial recognition, or that women aren't, seems bizarre and vaguely racist/sexist. It's also a strange and I think poor assumption to think that the different accuracy rates for facial recognition on different demographics are due to a bunch of white dudes being too dumb to train their models on the faces of anyone but themselves.

The author also writes about how recommendation systems are "conservative" but to me it actually seems the opposite. Recommendation systems get you to stuff that you haven't tried before. They take what they know about you and progress you to new content or products.

For example, I used to listen to listen to a lot of country music on YouTube. Over time my recommendations evolved to include Irish folk music - which I quite like but would've never discovered without YouTube gradually testing and expanding my musical tastes.

<comment><USER38> _To assume that dark skinned people aren 't involved in creating or testing facial recognition, or that women aren't, seems bizarre and vaguely racist/sexist._

It's unlikely anyone making a facial recognition system would choose to release a version that didn't work, _at the very least_ , for everyone on the team.

If the team is only white men then you can see why they might accidently release something that only works on white men's faces. If there was a woman or a PoC on the team they'd have someone saying "Hang on, it doesn't work on _me_!"

The important thing to learn here is that any system is only ever as good as the data used to test it. If your test data sucks then the system you build is also going to suck.

<comment><USER49> This might be a valid theory if facial recognition was a single niche product made by unsophisticated or under resourced developers. That's incredibly far from reality though. There are many well funded groups of serious professionals and academics working on facial recognition with data sets of millions of faces.

It's just objectively not correct and not reasonable to think that the reason facial recognition works better on some demographics than others is because nobody thought of training models on those demographics. If you believe that is the case, then do you think people working on facial recognition reading this blog post are slapping themselves on the forehead saying "Of course. Train the models on someone besides the dev team!"?

<comment><USER38> I was talking about testing the application rather than training the model. You can train a ML model on a diverse range of faces, but if, for example, an application that uses the model doesn't calibrate the camera properly it still won't work for darker faces.

<comment><USER49> I don't understand.

The different accuracy of facial recognition by demographic group is common across multiple facial recognition systems. It's highly unlikely that all of the affected systems owe their inaccuracy to a post-training configuration issue.

To me it seems more like you are arguing for the sake of arguing rather than that you actually believe in your point. You are just trying to conceive of imaginative possibilities that preserve the "dumb facial recognition developers forgot non-white people exist" interpretation. The problem is that your imagined possible alternatives are not plausible explanations.

<comment><USER38> I gave a single example of why an app might fail at facial recognition in some cases. _Obviously_ I wasn't suggesting that would be the case for every app that doesn't work. Suggesting I was is a strawman of ridiculous proportions.

I also didn't say anything about ML training data in the post you replied to. My point was about facial recognition app developers not testing with a diverse range of inputs, which is why we see apps failing in public when people try to use them with inputs the app hasn't been tested against. If the apps were well tested those cases would have been caught before the public ever saw them.

My original point stands - if your testing sucks then it's very likely that your app will suck too. There will be problems with it that you don't know about, and the users will find them very quickly.

<comment><USER50> Do you have any data showing facial recognition doesn't work well on black people? My understanding is that this is an urban legend - it's not true, the algorithms work fine assuming a decent quality camera and video (same for everyone).

</comment></comment></comment></comment></comment></comment></comment><comment><AUTHOR> Here's the Molly Sauter essay that this article takes core inspiration from: [https://reallifemag.com/instant-recall/](https://reallifemag.com/instant- recall/)

</comment><comment><USER51> the opposite would be also described as terrifying: machine learning making up the future of humanity. I m not sure if the author's point is particularly important. ML learns from the past, it doesn't have enough personality or 'intent' to be a 'conservative'. On average it will keep doing what people did. Also, not all ML systems do, a RL system may be trained with another objective, e.g. radicalism.

</comment><comment><USER52> Conservative or is not yet full context aware?

</comment><comment><USER53> Can we stop trying to place complex topics and people and positions onto a one-dimensional scale?

</comment><comment><USER54> Why is the HN title so different from the article's title?

<comment><USER55> (a) The submitter changed the title when submitting.

(b) A mod changed the title after it was submitted.

(c) The site changed the article title after it was submitted.

I suspect (b), given the baity-ness of the article title: "Our Neophobic, Conservative AI Overlords Want Everything to Stay the Same", in accordance with the guidelines:

> _" please use the original title, unless it is misleading or linkbait; don't > editorialize."_

[https://news.ycombinator.com/newsguidelines.html](https://news.ycombinator.com/newsguidelines.html)

If you think a title has been changed inappropriately (or should be changed), you can let the mods know directly using the Contact link in the footer.

<comment><USER56> Let the mods know publically, by posting comments. Don't trust the mods to be genuine in private conversations.

<comment><USER55> The mods don't see every comment, so if the goal is to let the mods know, email is the most reliable way. Of course it's up to them how to act on it. Just commenting isn't going to improve anything, as they legitimately might not see it. It just increases the likelihood of inaction.

</comment></comment></comment></comment><comment><USER40> i was hoping for a roast of AI similar to when he beat the semantic web into a pulp last time... now I wonder if the semantic web messed him up too much in revenge.

</comment><comment><USER57> > Data analysis is as old as censuses of the tax collectors of antiquity — > it's as old as the Book of Numbers! — and it is unquestionably useful. But > the idea that we should "treasure what we measure" and a reliance on > unaccountable black boxes to tell us what we want and how to get it, has > delivered to us automated systems of reaction and retreat in the guise of > rationalism and progress. The question of what the technology does is > important, but far more important is who it is doing it for and who it is > doing it to.

See "The Cult of Information" by Theodore Roszak.

<comment><USER58> I think the idea of ML as "unaccountable black boxes" is a bit of a bait-and- switch from the problem being described. The problem is that ML has _no_ political biases, it just minimizes some loss function on the data you give it. So it can't correct for implicit biases in how that data was collected, or how the model's outputs are used.

If you fitted a decision tree to predict recidivism risk, it would be extremely easy to interpret. But if black men are rearrested more often in your dataset, then black men will likely have a higher predicted risk on average—no matter the causes of that feature of your dataset.

<comment><USER12> Your example demonstrates a political bias in a dataset that can lead to a biased ML model. This is what some people mean when they say ML can be biased.

<comment><USER59> If I slap my friend Tom once a day, train an ML model to detect which of my friends is going to be slapped next, and then find out it always predicts Tom, the model isn't biased against Tom: the model is correctly showing me my own anti-Tom bias.

I don't get to stand there and blame the model when I'm the one doing the slapping.

<comment><USER60> For us, technologists, yes, the distinction between "AI bias", and the bias in the data is clear. The point however, is when it comes to the general public, "AI" is the whole thing, and actually the public has absolutely no saying (perhaps even no knowledge) about the data; nevertheless, technocrats will argue that "data doesn't lie".

Edit: the auto correct had written "data doesn't like"

<comment><USER59> It's not just biased data, though, it's an objective function optimising a biased metric.

We've picked a metric, recidivism rate, that is believed to be inherently biased because cops arrest a lot of protected minorities. The model has correctly predicted that cops will arrest a lot of protected minorities. The general public has then turned around and shot the messenger rather than hold cops accountable for all that arresting they're doing.

</comment></comment></comment></comment></comment></comment><comment><USER61> This headline alone seems like a shark jumping moment.

</comment><comment><USER62> And I for one welcome our neophobic, conservative AI overlords! /s

</comment><comment><USER63> Doctorow is a smart guy, which is why the weasel words he uses seems disingenuous. For example "machine learning is fundamentally conservative, and it hates change".

Mate, it doesn't hate anything. It's math.

<comment><USER64> He wasn't being literal...

<comment><USER63> So? He is still attaching emotional value to a technology. You could say the same thing with "machine learning doesn't handle change robustly". Same meaning, different emotion.

<comment><USER65> Math isn't technology, and like you said yourself, AI is just math. Attaching emotional value to math is done _incredibly_ frequently for all sorts of reasons.

Attaching emotional value to technology is also fine. "Machine learning is fundamentally conservative" gets the point across better than "machine learning doesn't handle change robustly," which is vague and doesn't give a person looking for something to read any reason _why_ that would matter.

<comment><USER63> > Attaching emotional value to math is done incredibly frequently for all > sorts of reasons.

Writing alarmist articles for clicks being one of those reasons.

> Attaching emotional value to technology is also fine.

Why??? Doing this with other things would be ridiculous. For example, instead of "The poor are disproportionately affected by climate change" a journalist could write "The climate hates the poor". Which subtly plays down the human cause in all of this.

> "Machine learning is fundamentally conservative" gets the point across > better

It demonstrably doesn't. If you just read this thread, you'll find enough people discussing whether "conservative" has a political meaning or just a "deviation from the norm" meaning.</comment></comment></comment></comment></comment>


Late to the NoSQL Party with FoundationDB - <AUTHOR> https://medium.com/@doug316/late-to-the-nosql-party-with-foundationdb-a871bc546261

<comment><USER2> Ugh...

> Having to translate a read or write operation into a specialized, picky, > verbose, and often confusing language

Another "SQL is hard" post. Its better to use general purpose or non-data oriented languages to work with data?

> only to have it most certainly be translated once again by the database > server to do the work, seemed inefficient to say the least.

The author talks about programming in Ruby. An interpreted language. Of course all code is translated (interpreters, compilers, JIT).</comment>


What Steve Jobs Taught Me About Being a Son and a Father - <AUTHOR> http://www.nytimes.com/2015/08/07/fashion/mens-style/what-steve-jobs-taught-me-about-being-a-son-and-a-father.html

<comment><USER2> That story is really about pride in your craftsmanship, what a wonderful story.</comment>


Sparrow coming to iPhone - <AUTHOR> http://www.sprw.me/comingsoon/

<comment><USER2> 97%. It has been a really long wait.</comment>


Vectorflow: Netflix minimalist neural net lib for sparse data - <AUTHOR> https://medium.com/@NetflixTechBlog/introducing-vectorflow-fe10d7f126b8 <comment><USER2> I'm really excited that established companies like Netflix are using D. And I think this is true for most of us in the D community.

</comment><comment><USER3> Shallow neural network library that fits most practical use cases and works extremely well on sparse data.</comment>


Ask HN: Does anybody follow the Pomodoro Technique religiously? - <AUTHOR> I happened to watch the video on Productivity by Robert C Martin (author of Clean Code). He recommends following Pomodoro technique everyday to estimate our avareage number of tomatoes/day on the long run and also achieve the benefits of high productivity. Has anyone followed this? If yes, what are your estimates and have you reaped the benefits? <comment><USER3> I used the Pomodoro Technique to transition from the "Lazy-SOB" side to the "Deep Flow" side. It's a regulative tool, generalized for the average person, so depending where you are on the spectrum, you'll have different results.

For myself, the consistency was awesome. I applied it to work, leisure, studying, socializing - everything. Before this, I would binge. Go out for hours at a time, play games for hours at a time...I didn't track/measure anything.

I started having more time to accomplish my goals (because I was deliberately making time to do so) and I also got my hobbies/leisure activities under control.

But about a year ago, I would get frustrated when I would be getting into my state of flow just about when the timer went off. I decided I wanted more time in my flow state, so I decided to not follow the technique when I was doing work-related things.

Now, I only use it when I know I'm going to do something leisurely - <USER2> video games. It now serves as a way for me to avoid getting into the "flow" for things that I really should be cognizant of, while freeing up time to be in the flow of things I'm passionate about.

</comment><comment><USER4> I've used the Pomodoro technique religiously for the last year. Some interesting conclusions that I drew from my yearly review:

1) I have a maximum sustainable rate of about 8 pomodoros (4 hours) high focus work per day. This can be temporarily overridden, but I work much less the following days.

2) Bimodal days seem to work best for me. One big block in the morning, followed by a long-ish lunch, and then another big block in the afternoon. Similar to PG's essay on maker vs. manager schedule.

3) Having many small unrelated tasks is inversely correlated with number of pomodoros completed. Usually I get the most pomodoros in when I have 2 big tasks for the day.

</comment><comment><USER5> I dislike the rigidity of the original pomodoro technique, so I made a flexible one without the fixed time slots. You only have a minimum working duration, work for however long you like. I found that way it doesn't break my flow.

[https://www.niftytools.online/flexpomodoro/](https://www.niftytools.online/flexpomodoro/)

</comment><comment><USER6> I use the Pomodoro technique at work, except my pomodors are 50 minutes (I use the Focus app for Mac, and you can customize the pomodoro length and name your pomodoro).

On a productive day, I can get about five 50-min pomodoros.

The technique helps me A LOT psychologically. Once that timer starts, I do not do anything else from what I have named my pomodoro. It helps me focus my attention and keeps me from drifting off to HN or reddit or whatever.

That said, the standard pomodoro time of 20 minutes is WAY too short for programming tasks. The technique itself is solid though.

<comment><USER7> Similar to apps like Focus and Awareness, there's a piece of hardware called Saent [1] that serves the same purpose as well as indicating to others when you're working deeply and shouldn't be interrupted. I haven't bought one myself yet but have heard good things and considered it.

[1]: [https://www.saent.com/](https://www.saent.com/)

</comment></comment><comment><USER8> I've used "strict" Pomodoro technique at times. At its simplest, the structured rhythm of timeboxing and breaks is a useful tool for keeping focused on work that benefits from high focus, e.g. plowing through some "heavy implementation" work, coding, debugging, grinding away in CAD, etc. On the other hand, I've found that it can be a detriment to other kinds of work, more creative or high-level design oriented work. Some days, it's just better to be a bit more relaxed and free-form, even get out and take a walk and let ideas percolate.

I've always found the estimation bit to be tricky, because its viability depends hugely on 1) the kind of work you're doing and 2) whether you're running an ongoing planning deficit or not. To point 1, the less well-defined your work is, the harder pom-level estimation is. For example, consider a task that boils down to "learn how to apply, new, complex set of APIs to solve problem X", but might just be written as "implement wireflow 2a". At some point, despite planning effort, you end up with tasks that are indivisible atoms with high variability. I don't necessarily feel it's worth putting a huge amount of time learning to precisely estimate those, if it's even possible. (I'd love to hear counter-examples from folks, tho.) My personal approach is really to try to bubble up overall variability/uncertainty to a higher-level than counting-the-poms, then mostly use poms to maintain focus/velocity.

As to point 2, part of the Pomodoro Technique is supposed to be doing a planning pom at the start of the day, and that's really the minimum. Sometimes that's not sufficient (e.g. you have higher-level planning/workflow problems), or sometimes your planning skills just need work. If you're at least doing your planning pom, that gives you time to reflect upon and begin to address these higher level issues.

</comment><comment><USER9> I have tried, but its hard to keep with it.

I have lots of fires to put out at a moments notice. I think it would work well for people that are able to focus on one project at a time.

</comment><comment><USER10> Not religiously, but I have a pomodoro app which I keep installed for work I'm struggling with. If I'm lacking motivation to start or finish something I'll stick it on and get going.

It's incredibly easy to procrastinate when we have Facebook, HN, WhatsApp, email - whatever, but I know if I start a pomodoro it's only ever 25, 22, 17, 9, 3 minutes until I can take 'reward' myself with the aforementioned for a few minutes. It's easy to push through knowing I only _have_ to do (at most) 25 minutes more work. And once I'm rolling, it's a lot easier to continue.

I use Harvest throughout the day to check how much _actual_ work I'm getting done. This helps me make better estimates of times and costs (as well as see the days I'm most and least productive).

<comment><USER11> > I'm lacking motivation to start or finish something I'll stick it on and get > going.

This. I find that if I am not sure where to start, or not in the mood, using the technique helps me get started and keep focused. If I know what I'm going to be working and what do next, I can just jump into it.

</comment></comment><comment><USER12> I've had success with a slacker version of my own creation, somewhat like the "magic dots" technique. I have a liquid toy that takes about eight minutes for the blue syrup to run from top to bottom. I start a work period by turning it over, and just work. Occasionally I look at the toy, and if it looks done more or less, I connect two of my dots (of which there are four per group, so six connections fills the group). I do this on a post-it note, and paste the note into a notebook at the end of the day.

That's basically it. I'm counting the number of reasonably solid starts into "flow" I make each day. That seems to be enough to make me a lot more productive.

</comment><comment><USER7> In my opinion the principle of having dedicated focused work sessions and counting up how many you have each day is timeless.

Aside from that, I don't follow anything about Pomodoro specifically.

One issue with Pomodoro is taking 1 session, then 1 break. In my opinion, one should flex this "rule" in flow. It's more valuable to do, eg 3x uninterrupted sessions followed by a break of 1-3x your normal break length than 3 * (work, break).

I also prefer 15-minute blocks.

B.F. Skinner, the esteemed psychologist known for his work in behaviorism, reinforcement, and conditioning is known for using a similar approach.

<comment><USER13> Plenty of people use the Pomodoro technique without a session end timer. They can take their 2-3 minute rest when they're out of focus and it has been > 25 minutes.

</comment></comment><comment><USER14> Why so many people are talking about "apps" and other websites to help doing your pomororo ? (Pomodoro's creator encourages a low-tech approach, using a mechanical timer, paper and pencil.)

<comment><USER14> I love the snoozing when I do pomodoros in caffes. I always imagine people around as being puzzled : "Wtf is this guy having a cooking timer by his computer ??"

</comment></comment><comment><USER15> I have used it for years, not religiously but often enough. I guess that with time you start tweaking it to your particular needs. When I was studying 25 minutes was the perfect length of interval while research and write a paper. At work, doing creative work (development/design/UX/...) I found that 25 mins. was too short and it would actually break my focus, thus I changed it to 60/10 min.

Nowadays I use an app called Forest, it provides an element of encouragement of how many trees have I planted and how can I get other trees :-D

</comment><comment><USER16> I am always interested in reading about it because it has never made sense to me since it appears to be designed to interrupt you just when you've finally achieved 'flow'

<comment><USER17> If you can achieve flow that easily, then you likely don't need the Pomodoro technique. It's for those who have a hard time focusing to begin with.

<comment><USER16> Thank you, now I understand

</comment></comment></comment><comment><USER14> How do you deal with a pomodoro whom task isn't finish ? Do you just continue the task ? Or you just stop, and plan the end of the task for the next pomodoro ?

<comment><USER18> That is exactly what you do. While on first sight this seems like it'd impede flow, having everything in small chunks makes it much much easier to get back into the flow.

<comment><USER19> Yes, my experience is similar. A typical task will take at least 3 or 4 pomodoro.

There are few important aspects that contribute to being able to continue easily: \- you know the Pomodoro ends at a specific time, so you can properly close, make notes, etc. \- because you stop 'in the middle' you actually feel eager to get back to it. (contrary to normal breaks where you probably stopped because you didn't feel like working anymore)

In general I believe it works because you rely less on flow. Flow is great, but it also comes and goes. If you can easily get into your flow and stay there for hours, you probably don't need this, but for those weeks when it doesn't come naturally, this can really work.

</comment></comment></comment><comment><USER20> I've actually been actually working at this for the past few weeks. First, the caveat that I haven't implemented a good tracking system beyond casually jotting down Xs next to my task list to count the # of strict Pomodoros I allocated to each one. I'm also not strict about this and definitely don't compute statistics expect useful metrics. I'm much more interested in day-to- day rhythym improvements, interruption control, and focus training. For higher level scheduling, I've experimented following Cal Newport's recommendation to schedule every minute of my day ([http://calnewport.com/blog/2013/12/21/deep- habits-the-import...](http://calnewport.com/blog/2013/12/21/deep-habits-the- importance-of-planning-every-minute-of-your-work-day/)). On days it works, it goes great, but I'm still working to integrate this method with the need for reactive changes of plans. With that, a few observations and outstanding questions about the Pomodoro side of things:

\- Interruptions definitely affect my feeling of accomplishment and may affect my results. I find even the smallest external interruption or moment of weakness triggers my internal critic, resulting in an arguably more detrimental cascade of self-criticism. An avalanche of blog posts argue that these minor interruptions dramatically impact my productivity for other reasons. I totally buy this anecdotally but won't attempt to justify it since I suspect most people here agree anyway.

\- Sometimes 25 minutes just doesn't cut it. I especially chafe at the forced 5 minute break during my 1.5 hour period pre-standup where I haven't eaten at all and am caffeinated. I know the creator of the technique and blog authors like Martin claim that I should be able to slice all of my tasks such that I don't need longer than 25 minutes, but I disagree. While I enjoy holding the state of a program in my head and occasionally finding the zone, I'm willing to acknowledge Martin's overstated but partly true point that the zone can induce tunnel vision and the downsides that go along with that. However, I've also observed that 3 break-interleaved chunks of work can zap my energy more one large block of work would have.

\- How do people deal with waiting for things that take longer than a minute? I've recently been working with jobs that take multiple hours to run. It's difficult to both schedule my Pomodoros such that I have a free one to check the result of this job. Even worse, the validation of the job can take between a minute if it succeeded and hours if it failed. This makes budgeting hard.

\- Should I budget Pomodoros for checking email and Slack or include that in my breaks? Ideally, I'd use breaks to recharge and not context-switch between communication platforms. But, while I'm not an always on, 10-minute to respond to any email guy, a consistent multiple hour time-to-respond to any communication is a recipe for face-to-face interruptions in the age of the open office.

\- Should I include lower level planning in my break or 25-minute chunk? I often find going from high-level task statement to knowing exactly what I need to do requires a few minutes to orient myself. I'd be fine including this in my Pomodoros except this orienting can involve firing off a quick question to a colleague or searching through my emails / Slack messages. Maybe I just need to get better at gathering requirements beforehand...

To be clear, I'm not putting down the technique. I suspect any time management strategy would reveal the issues I described above and that we simply don't hear about the pains of actually implementing a system beyond a week of casual usage (see any blog post with a just-so title like "I Adopted <> and It Changed My Productivity Forever" as an example).

As I look back at my bullets, I've realized I'm mostly looking for wisdom from some seasoned Pomodoro veterans. I see one or two people on this thread who fit this description, but overall I'm disappointed with the ratio of people who want to sell the technique or have tried it to people who have used it consistently for months or years. This seems to be a common problem among productivity techniques.

Meta-comment: I recognize this comment could be condensed, and "if I had time I would have written a shorter letter" ([http://quoteinvestigator.com/2012/04/28/shorter- letter/](http://quoteinvestigator.com/2012/04/28/shorter-letter/)).</comment>


Ask HN: How do I contact memstash.co? - <AUTHOR>

Does anyone know how to contact those guys at http://memstash.co ?
I've been trying to suggest/complain about some stuff but I can't find any way to contact them through the site.
Thanks. <comment><USER2> <https://twitter.com/MemStash></comment>