Dyson Unveils New 'Wet and Dry' AirBlade Faucet - <AUTHOR> http://www.tomsguide.com/us/AirBlade-Tap-Faucet-Cost-Release-Date,news-16715.html <comment><USER2> Except for one issue: it actually slows down the efficiency of the bathroom.

See, by splitting up washing and drying into two steps, you allow two people to be in the process of cleaning their hands at a time.

By bundling the two steps together, you reduce the amount of stuff in the bathroom, but you also reduce the efficiency of the bathroom.

This AirBlade Faucet is a great idea for low-usage bathrooms: less paper towels, less equipment to maintain, etc. For high-volume bathrooms, (airports, etc) this would actually be a step backwards to put these faucets in, if a goal of the bathroom is to be able to quickly serve a large group of people.

<comment><USER3> That's only if you assume unlimited paper towel stations. In reality hand drying is the bottle neck. So if every sink has its own dedicated dryer it will speed things up.

<comment><USER4> But you wouldn't get a queue for paper towels. People would grab 2 or 3 towels and move aside.

<comment><USER3> Maybe but there's a bit of a shuffle getting around people who have grabbed towels. And there are always the people who stand by towels to dry because that's where the trash can is.

Actually, I guess separating the towel dispensers and trash cans in existing bathrooms would be a smart efficiency move since it would encourage people to grab a towel and keep moving.

</comment></comment></comment></comment><comment><USER5> Nothing grinds my gears more than Dyson products. Oh sure I'll spend £400 on an insanely complicated vacuum with hundreds of springs and hinges and clips that breaks 6 months later and does no better job than a simple, virtually indestructable Henry that cost just 1/4 the price.

Or £300 on a FAN.

And just what the FUCK is a digital motor? <http://www.dyson.co.uk/vacuumcleaners/ddm.aspx>

Over-priced, over-engineered, completely unnecessary rip-off bullshit.

<comment><USER6> My wife and I have 3 Dyson vacuums. We purchased one and then acquired a couple more over the years. Two are standard room/floor vacuums and another is more like a carpet/floor 'broom'.

They are expensive but all have worked flawlessly. One we've owned for more than 8 years and it's in perfect condition. No bag and it sucks up pet hair like you wouldn't believe. Better than any other vacuum I've ever owned.

As for the Henry vacuums, I'm not sure you'd get the same power for 1/4 the price. There appear to be some models that are that price differential but once you get up to the higher power models it's still cheaper but about 3/4 the price.

Dyson is a lot like Apple. They make dependable, designed products that simply work. You can get by with something else but it will most likely be inferior in form and function.

<comment><USER7> _Dyson is a lot like Apple._ ... people tend to have strong opinions about them even if they've never had much to do with them :)

another data point - we have a single dyson we bought over 7 years ago and it's still going strong. Thing's built like a tank and works better than any other vacuum I've ever used (not that many, to be honest)

</comment></comment></comment><comment><USER8> The airblade hand driers are the only hand driers I've used that thoroughly dry your hands in a reasonable time frame. Even high air flow traditional driers don't come close to airblade driers.

They do depend on blowing from both sides of your hand at once though. This seems like it will double the drying time.

<comment><USER9> Excel's Xlerator does better job. And there is no walls to touch (accidentally) while drying your hands. And no moldy water pool at the bottom.

<comment><USER10> True on the walls and the water pool. Not true on the drying faster. The sheet of air is much more efficient than the xlcerator.

<comment><USER11> I've had to do 2 or 3 passes with AirBlades to get my hands dry. I've never had that happen with an xcelerator. In my recollection, the xcelerators are faster and more pleasant to use.

</comment></comment></comment></comment><comment><USER12> As other posters have mentioned, one big drawback of the Dyson Airblade (stand alone unit) is that it's _loud_. 90 dBA loud [1]. I've never seen more than two Airblades in one restroom, with this combo unit, there could be double that number. That could lead to an unpleasant environment for guests of the establishment and potential OSHA violations for employees.

From using it, it appears to me that most of the noise is caused by the turbulence of the high-speed airflow interacting with hands and the enclosure (Fullerton's paper comes to the same conclusion). In that case, all you can really do to reduce the noise is reduce the flow speed, which reduces the effectiveness of the dyer action.

[1] <http://www.acoustics.org/press/159th/fullerton.htm>

<comment><USER13> They have one of these (non-combo) Dyson airblades in a breastfeeding area in a mall I live near.

It's horrendous - my wife couldn't feed whenever anyone used the blade. So now whenever we go, we put a "do not use - baby is feeding" note on the hand- dryer.

It's not a drop-in replacement for towels or normal air-dryers by any means.

<comment><USER14> Not to mention the number of three year olds that refuse to enter a room with a jet engine affixed to the walls.

Maybe there's a market there? Just as The Wiggles showed that there is money in adapting adult music for kids, maybe there is money to be made by taking children into account in the design of public facilities? Venues that are frequented by families (shopping centres, child care centres, schools, ...) might be prepared to pay for a line of appliances that don't make life unpleasant for their customers?

</comment></comment></comment><comment><USER15> It looks like the air blowing in the sink like that could cause germs to fly all over the place.

<comment><USER10> You're worried about the germs? I'm worried if there is water in the sink and the fan kicks in at 400mph, what happens to that displaced water?

The AirBlade when it's not properly calibrated makes a big enough mess as it is...

</comment></comment><comment><USER11> The old AirBlade had some huge design flaws. I've been in busy restrooms with AirBlade dryers which were wet and gross. Not pigsty level, but that's not really what you'd want to be touching after you've just washed your hands. Well, a problem with AirBlades is that it's very easy to accidentally touch the dryer cavity when you're plunging your hands in and drawing them out. Sometimes the strong blast of air even encourages this to happen. I have encountered more conventional air dryers which have touch less activation, and hyper fast airflow that are just about as quick as the Dyson, and which give better results, because you can rub your hands beneath the blower and adjust which parts get the most airflow according to dampness. AirBlades allow nothing of the sort. I think the design is gimmicky and is more about differentiating Dyson's product than real utility. Unfortunately, other hand dryer companies don't have Dyson's marketing budget.

<comment><USER16> Rubbing your hands together has actually been linked to increasing bacteria counts[1].

I personally quite like the Dyson driers and the new tap seems interesting.

[1] [http://www.telegraph.co.uk/science/science- news/7991492/Rubb...](http://www.telegraph.co.uk/science/science- news/7991492/Rubbing-hands-together-after-washing-them-increases-the-danger- of-contamination-scientists-warn.html)

<comment><USER11> That one study was paid for by Dyson. Also, the result only really supports airblades being better in medical and food preparation contexts. In the context of most public restrooms, transferring commensal bacteria doesn't seem that big a deal. As stated in the article, we all have them and can never get rid of them with ordinary hand washing. I think it suffices in that context for one to wash and dry hands quickly and comfortably. If someone found an epidemiological difference between hand dryer types, I'd pay attention. Otherwise, going by bacteria counts, one should rather lick a toilet seat than eat a tortilla chip with salsa.

</comment></comment></comment><comment><USER17> This model doesn't allow you to control the temperature of the faucet which might be important in a home setting. It seems like a great idea for public restrooms however.

Here is a video of James Dyson describing the faucet: <http://youtu.be/3y4-fnTXzio>

<comment><USER18> Interesting comment from YouTube.

 _dysonteam 5 hours ago Hi there, it's Jack at Dyson. In total, our engineers created over 3300 prototypes of the new Dyson Airblade hand dryers, and every component was subjected to hundreds of tests. Our unforgiving test team was tasked with finding fault and weakness and paying attention to detail – they simulated hand washing 213 million times. They even doused the machines in chemical mixes and tipped beer over the Airblade hand dryer range to make sure they are not damaged by harmful substances._

</comment></comment><comment><USER19> Two slightly on/off-topic rants about public washrooms:

First, the one thing I don't like with the normal AirBlade is the tendency for the water to drip off your hands, onto it, and then to pool below it on the floor. Not much water, mind you, but enough to make both the floor and the plastic onto which the water first drips a bit nasty (in some places). This might just be a cleaning issue in said places, though.

Second, the one mistake I hate seeing washrooms make is to have a tiny faucet where, in order to get your hands properly wet, your hands are almost forced to run into / touch the back of the sink.

</comment><comment><USER20> Interesting timing on Dyson's announcement this week - yesterday NPR ran the first of a 2-part piece on the Excel Dryer Co [1], a US-based Dyson rival.

Though it's not mentioned in the transcript, late in the nine-minute piece, the Excel VP takes the reporter into Excel's R&D where he's shown one of their latest inventions, a faucet that dispenses soap, water, and then drying air...

[1] [http://www.npr.org/2013/02/04/171090394/how-one-company- rein...](http://www.npr.org/2013/02/04/171090394/how-one-company-reinvented- the-hand-dryer)

</comment><comment><USER21> Am I the only one who hates the AirBlade dryers because they splash me in the face (presumably partially with the water that's been pooling in crevice along the bottom if it's being heavily used)?

<comment><USER22> How is that even possible? Are you putting your head through the loop?

</comment></comment><comment><USER23> But what are we gonna use then to open the germy bathroom doors, for the 95-ish% of us who use the paper towel we dry out hands with to also open the door and then drop in a bin next to it?

</comment><comment><USER24> I will stick with separate sinks combined with automatic paper towel dispensers. Why? Its more hygienic. After my work converted what did we notice?

Two trashcans, one by each door of the bathroom and now people dry their hands and grab handles with the paper towel ditching it in the can at the end.

With all the concern over the flu and other contagious diseases, let alone witnessing some of my coworkers habits, I am all for a simple solution.

Besides paper towels always work.

<comment><USER25> Are you assuming that paper towels are more hygienic, or are you aware of studies? Personally, I doubt that the door handle out of the bathroom is any dirtier than other doorhandles in the building.

<comment><USER26> I always assume the interior door handle of the bathroom is the cleanest one in the building. Many people touch it after washing their hands.

<comment><USER27> The pessimist in me says, many people also touch it without washing their hands.

</comment></comment></comment></comment><comment><USER28> Just heard a report on the radio about these kinds of dryers. Excel Dryer, Inc. in Massachusetts was actually the first to sell a high-speed dryer like this (the Xlerator). The Dyson AirBlade came later. Interesting stuff:

[http://www.npr.org/2013/02/04/171090394/how-one-company- rein...](http://www.npr.org/2013/02/04/171090394/how-one-company-reinvented- the-hand-dryer)

<comment><USER29> The Xlerator works far better than the Dyson, too, it just doesn't look as strange and doesn't seem to have attracted as much press attention.

Every time I've attempted to use a Dyson dryer, it's turned off as I've been drying my fingers, leaving them still wet. It seems like it'd be trivial to put a few seconds worth of delay into the automatic cutoff (or position the sensor higher), but no, they all seem to be configured this way. I've watched probably dozens of people run into the same problem while using them, so I don't think it's just me.

Hopefully the Excel dryers will remain after the Dyson dryers get pulled out; I can't imagine they'll age gracefully or attractively with the water dripping down into/onto them, in low-maintenance situations.

<comment><USER22> Isn't the Xlerator just a more powerful (and louder) blower? The Dyson is a different concept, uses way less energy and dries much more efficiently than standard driers, sometimes in one 5-second pass (I haven't seen an xlerator).

<comment><USER11> Xlerator can get my hands dry in an acceptably short time. One pass with an AirBlade leaves me with damp hands more often than not. By the time I have my hands feeling comparably dry with an AirBlade, there's often no time savings left at all. (There is, but only over the low-velocity blowers, which I think are generally horrible.)

</comment></comment></comment></comment><comment><USER30> This is one of those practical inventions that I could see being in everyone's home (and bathroom) in twenty years, with future generations asking: "Did people really use unhygienic towels to dry their hands after washing, grandpa?"

<comment><USER31> What I really want is something like that for the shower. No more getting out wet when it's cold.

<comment><USER32> Use one of the Dyson dryers first. Your hands get cold - they use hot air, but evaporation makes it feel pretty cold until they're actually dry.

<comment><USER33> Skin temp drops to dew point and not a degree lower. Wind speed can mess with total heat energy flux thru a surface via windchill, but thats more a long term / higher delta t effect. Hence the Floridians who are looking confused at your claim and the Arizonians who shiver along with you.

</comment></comment></comment></comment><comment><USER34> How does the faucet know when you're done washing and want to start drying?

<comment><USER35> Separate sensors for faucet, left dryer and right dryer would be my guess.

</comment></comment><comment><USER36> This kind of thing has been around for ages in Japan <http://www.flickr.com/photos/kalleboo/4611597467/lightbox/>

</comment><comment><USER37> A 1400W fan? Maybe it was the Dyson AirBlades that caused the power to go out at the Super Bowl! A typical box fan (dorm room style) is 100W and puts out 2,000 cubic feet per minute, whereas the airblade puts out 56 cfm @ 14x the power draw.

That is a lot of power, and it's interesting to see from the article "the $40-$43 per year cost of a regular Dyson AirBlade dryer." Although I'm sure their salesmen always compare it to the labor & materials cost of refilling paper towel dispensers.

<comment><USER38> But in this case it's not really the CFM that matters, it's also the static pressure. Especially since they're trying to make a nice sheet flow at good speed.

</comment></comment><comment><USER39> The description makes it seem prone to blow whatever remnants of water out of the wash bowl and onto your shirt/pants. Here's to hoping they figured that part out!

<comment><USER40> Someone on Youtube asked that, here was the reply:

 _Hi there, it's Jack at Dyson. The Dyson Airblade Tap hand dryer is designed for commercial environments – these commercial washrooms do not have plugs due to health and safety issues, and the risks related to flooding. So we wouldn't expect this to be an issue._

</comment></comment><comment><USER41> But when will they release a Dyson wet/dry Bidet?

<comment><USER35> I think the Japanese toilet seat makers like ToTo have a huge head start in that area.

<comment><USER42> Kind of like when Mitsubishi first came out with the jet towel years ahead of Dyson's "airblade"?

</comment></comment></comment><comment><USER43> Seems like a great idea, until you put one in a high traffic area. Pretty much doubles the time each person spends at the sink.

</comment><comment><USER44> I finally ran across one of their (or is it someone else's) high speed hand dryers. It was about as miserable an experience as I'd expected. Also, I felt that I should be wearing hearing projection -- LOUD!

At a glance, I expect people to end up wearing at least a few drops of waste water.

</comment><comment><USER45> I think the whole idea of having air blasting into a sink is asking for sanitation problems. Sinks are very dirty and if you are blasting air into them it would then create aerosols of the grime and spit in the sinks!

</comment><comment><USER46> Hopefully this device operates at under 100 decibels.</comment>


Email Attack on Vendor Set Up Breach at Target - <AUTHOR> http://krebsonsecurity.com/2014/02/email-attack-on-vendor-set-up-breach-at-target/

<comment><USER2> It's pretty genius. Go after vendors, don't go through the front door.

The mindset to defend effectively has to treat everything entering or leaving managed network as hostile DMZ. Further, internal networks should be reasonably viewed as hostile as well (NSA fail). It make securing APIs, data transfers, enterprise-IoT and everything inside or outside a real PITA but it's necessary because of the potential losses. Finally, least privilege balanced with usability should be reinforced on a continual basis.</comment>


SoundBrake 2.0 device makes headphone users less oblivious - <AUTHOR> https://www.kickstarter.com/projects/914595512/soundbrake-20-the-awareness-device-for-headphones <comment><USER2> Cool concept, but why isn't it an app? Couldn't you use the mic on my phone to listen for volume spikes, or even fine-tuned sound patterns?

Surely a modern phone CPU can do audio processing a great deal faster than a common microcontroller. Plus it would be far easier to keep the phone charged than to worry about a second device running out of battery.

<comment><USER3> I agree that an app has better ergonomics, but I believe it would murder the phone's battery life. Better to run out of battery on a third-party device than your phone.

Plus, a separate device allows you to use it with your laptop or your phone.

<comment><USER2> > murder the phone's battery life

I don't think that kind of audio processing would be an issue for battery life. They're able to do everything on a coin-sized battery in their system. From their prototype pics, they're using this: [https://www.adafruit.com/product/1572](https://www.adafruit.com/product/1572)

That's a 120mAh coin cell that they say lasts for 50 hours. They might be using something different in their final product, but we can safely assume that their algorithm isn't super power-hungry and running a small mic isn't going to do anything to your 3000-something mAh phone battery.

Obviously implementing this is a higher-level language like Java is going to incur some overhead, when compared to their microcontroller implementation... But the point is, they're not doing some advanced pattern matching on a database of sounds or anything that's going to use crazy amounts of CPU-time.

> Better to run out of battery on a third-party device than your phone.

I disagree on that - it's really easy to remember to charge my phone. I do it every single night. Other devices that I don't use for days at a time are easy to forget about.

> a separate device allows you to use it with your laptop or your phone.

True - it would need to be ported to work on those platforms.

</comment></comment></comment><comment><USER3> This is a cool idea. When posting your own projects on HN, it's better if you prefix with "Show HN:" so that it's clear to us that you're advertising something, rather than a random person endorsing a product you like.

<comment><AUTHOR> Thanks smt88!</comment></comment>


A User Interface Definition Language in Common Lisp - <AUTHOR> http://www.defmacro.org/ramblings/ui-dsl.html <comment><USER2> I was skeptical of the title. How would this be different than using sexp's to define xml? I read it anyway because defmacro.org never disappoints and it didn't today either.

The concepts that he presents have been around for a very long time, but I've never seen them implemented in a way I really liked. I've already bought into this way.

</comment><comment><USER3> 1\. Good use of objects. 2\. The use of CSS provides a good way for customizing look and feel without diving into source code so much.

</comment><comment><USER4> Ok, I'm seriously interested in this. Nice work!

</comment><comment><USER5> Nice work, once again! Keep it up, Slava.</comment>


Sometimes, requiring explanability is counterproductive - <AUTHOR> https://www.facebook.com/story.php?story_fbid=10156577899252143&id=722677142 <comment><USER2> Okay, I feel like a crazy person arguing against a luminary of the field, but this is so misleading as to be disingenuous and make me trust the guy less. He's throwing causality and explainability into the same bucket and arguing against the need for the latter.

>We often hear that AI systems must provide explanations and establish causal relationships, particularly for life-critical applications.

>Yes, that can be useful. Or at least reassuring.

>But sometimes people have accurate models of a phenomenon without any intuitive explanation or causation that provides an accurate picture of the situation.

It goes on to argue mostly against the need for intuitive explanations, not the establishing of causal relationships.

>Now, if there ever was a life-critical physical phenomenon, it is lift production by an airliner wing.

>But we don't actually have a "causal" explanation for it, though we do have an accurate mathematical model and decades of experimental evidence.

The physical models we have are causal ones. The intuitive abstractions like bernoulli's principle may not work, but analysis based on navier stokes sure does. You plug your shape (cause) into the equations and see what forces (effect) occur. That's causation.

>You know what other life-critical phenomena we don't have good causal explanations for?

> The mechanism of action of many drugs (if not most of them).

Using an industry that's nearly synonymous with a randomized controlled trial as a refutation for the need of a causal relationship is crazy talk. The _mechanism_ may be missing, but the causal explanation is that via a series of RCTs it's established that the drug _causes_ the effects.

I get that half of this is trying to go against a percieved need for intuitive explanations, but it weirdly lumps causation in there.

<comment><USER3> Explainability -> gradients

"How much does this input seem to confuse this output? What is the pattern across inputs for how this model is systematically confused?"

Causality -> counterfactuals

"How would the outcome be different if _x_ was different? If I acted differently, would I get a more favorable outcome?"

You're right to say these are two different things. They are.

And they're different _still_ from _interpretability_ , i.e., "What are the explicit patterns that this model is seeking in the data?"

DL practitioners routinely mix up explainability and interpretability but I would never in a million years have seen LeCun be so intellectually dishonest as to lump causality in there with them.

<comment><USER4> The thing is, I would claim that causality, explainability and interpretability are all mixed together in human informal discussions of various phenomena. As others on the thread have pointed out, Pearl's causality isn't everyone's causality. A tension structure can disrupt our "common sense" idea of what's holding up what but tensions structure doesn't seem at all like a black-box, _unexplainable_ item. The way the article mixes the range of these issues seems definitely wrong but I don't think that means the line between all things is normally crystal clear.

</comment></comment></comment><comment><USER5> > Now, if there ever was a life-critical physical phenomenon, it is lift > production by an airliner wing. But we don't actually have a "causal" > explanation for it, though we do have an accurate mathematical model and > decades of experimental evidence

This is argument is complete nonsense. Navier Stokes has a rigorous derivation based on extremely high-fidelity assumptions, such as conservation of mass, momentum, and energy. We understand these assumptions, and we understand regimes in which using N-S would result in catastrophe (such as rarefied gases, relativistic velocities, etc.)

Neural networks require data. The Navier stokes does not need to be 'trained'. Deep networks have very little a-priori knowledge baked-in (from a baysian perspective there are priors such as translation invariance that are intrinsic). They are admittedly extremely useful, because they are high dimensional (and so are universal approximators) and can be trained efficiently.

Furthermore, you can develop an intuitive approach to many fluid flows. I can provide a much better estimate of the drag profile for a given wing geometry than an untrained person. No such analog is possible with deep nets, which as significantly more opaque in terms of dynamics and non-linear response.

The only way his comments make any sense is if you assume he isn't talking about physical models, like Navier stokes, and instead consider turbulence models, such as RANS or LES. These are parameterized models, and are used for turbulence modeling. They also have little physical intuition. However, this is not the same as saying we do not have high confidence in physical-based systems such as Navier stokes.

Source: I have a PhD in CFD and several ML publications.

<comment><USER6> Yann was not making a statement about deep nets' ability (or lack thereof) to fly a plane in a way that matches expert design.

He's making the point that the ML field's obsession with causal inference (and causal discovery) is overrated, _precisely_ because our gold standards of interpretable, safety-critical systems (airplane flight) are based on Navier Stokes/CFD. Planes were made to fly based on empirical validation of these models, long before we gained a more detailed understanding of how causality (the equations and models themselves are time reversible, implying that they contain imperfect knowledge of causality)

<comment><USER7> > Planes were made to fly based on empirical validation of these models, long > before we gained a more detailed understanding of how causality

And their success is so repeatable that if they fail once we make a Really Big Deal out of it. I don't think any ML model is close to that level of engineering rigor, let alone deep learning.

Moving on to aerodynamics, we have a pretty good causal model and can simulate the system given a pattern of boundary conditions. Further, some people (who have studied aero/CFD) can even intuitively predict approximately what happens (otherwise we would have a hard time designing planes!). It just so happens that it's not as simple as high school physics, and cannot be compressed in to perfect+simple rules (trade off between those two).

Speaking in the context of time reversibility, you are being fast and loose, and using the word "causality" in a sense that is irrelevant to the rest of your comment.

</comment></comment></comment><comment><USER8> _> Now, if there ever was a life-critical physical phenomenon, it is lift production by an airliner wing._

I nominate this as the worst analogy of the year.

Airplanes are rigorously tested under the same conditions they will operate in. AI _by definition_ is tested under conditions that are different from the environment it will operate in, _because that 's the whole point of AI_ \- we want algorithms that adapt themselves to novel information.

</comment><comment><USER9> I agree with Yann here ... I think the demand for explainability is like a person asking for a "faster horse" when what they really need is a car.

When people ask for explainable models, what they really want (in my opinion) is calibrated and robust _uncertainty estimates_ .

Good uncertainty estimates would let them know when to trust a model's prediction and when to ignore it.

For example, a model trained to predict dog breeds should know nothing about cat breeds, and there should be some way to quantify when it doesn't know!

I've been doing a review of techniques that are becoming more popular in this area:

[https://blog.everyhue.me/posts/why-uncertainty- matters/](https://blog.everyhue.me/posts/why-uncertainty-matters/)

<comment><USER7> While uncertainties are useful, they are certainly not enough. Eg: I can construct adversarial examples which are predicted incorrectly with high confidence.

The underlying reason why high confidence is not enough is that even strong/confident correlations could be misleading when seen in causal light — a black box model trained to predict credit performance might be very confident in rejecting loans for applicants from "poorer" zip codes and approving those from "richer" zip codes — even though those are not actual causes... therefore somebody could exploit the system by renting an address in a rich neighborhood for a couple of months when taking out a big loan (the analogue of adversarial examples).

<comment><USER9> I stated that rather than explainability, for most use cases, people just want _calibrated and robust_ uncertainty estimates NOT _low quality and uncalibrated_ uncertainties estimates.

Your example points to models that provide low quality uncertainty estimates, but that's not true for all deep learning models.

I believe it's these low quality uncertainty estimates that lead people to look toward "explainability" as a solution, but for the majority of use cases, I think people just want better uncertainty estimates so that they can "know when they're model doesn't know".

There are techniques now to get higher quality, calibrated, uncertainty estimates that don't suffer from the problems you mentioned and I've outlined these solutions in my posted link above.

Additionally if you're interested, there is some nice recent research from google on the subject:

[https://ai.googleblog.com/2020/01/can-you-trust-your- models-...](https://ai.googleblog.com/2020/01/can-you-trust-your-models- uncertainty.html)

and from oxford:

[https://arxiv.org/abs/1912.10481v1](https://arxiv.org/abs/1912.10481v1)

<comment><USER7> Thanks for the links here and above; will take a look.

> calibrated and robust uncertainty estimates NOT low quality and uncalibrated > uncertainties estimates.

Could you explain what you mean by "calibrated" and briefly summarize the essential idea behind what allows the learning of robust uncertainty estimates, if not a causal understanding?

If you haven't already, look up work by Scholkopf, Janzing , Peters and co (over the last decade) for a justification of why causal reasoning is exactly what you want if you want to generalize across covariant earth/dataset shift (which is basically what the Google blog post is about).

</comment></comment></comment></comment><comment><USER10> I hate the comparison to how airfoils work. It's a single question that can be answered at a variety of levels, but the basics of how they work is simple and cannot be gainsaid: that they deflect air down.

All the complications come in the exact details of _how_ they deflect air down. How much is lower than ambient pressure above the wing redirecting slipstreams vs higher than ambient pressure below the wing doesn't fundamentally change the answer, though those details certainly matter (especially when designing a wing).

In contrast, even for a single "AI", how it responds differently to different input is unlikely to be even remotely explainable by the same high level principles, and it's not clear that the details don't matter.

<comment><USER11> Hmm... I'm certainly rusty on physics principles, but as far as I remember the (flawed) classic bernoulli effect explanation of airfoil (assume equal travel time ... pressure differential causes lift) does not really directly talk about moving air down at all, and would hypothetically still work if all downward air movement from the top of the wing were magically canceled out, (and airfoil had an attitude of 0 degrees, so no downward air movement from the bottom).

Now granted even with that magic, there would be downward air movement relative to the airfoil, due to the airfoil rising, but that would be an effect of the lift, not a cause.

Now I could well be overlooking something here, and having invoked magic to violate conservation of momentum, this thought experiment is not rigorous, and is discounting the fact that the equal time hypothesis is unfounded, and even wrong. (But in reality the air above moves faster, so we would see a larger effect than predicted, even before accounting for redirected airflow.)

Of course there is no magic canceling out any downward momentum of the air from the top of the wing, and planes normally have a nose up attitude, so the bottom of the wing also directly reflects air downward, so downward directed airflow is definately also a cause of lift.

</comment></comment><comment><USER12> I challenged LeCun about explain-ability of neural nets a few years ago, he seemed to dismissed the need for it. As I recall his explanation was that the weights & configuration of the neural net is enough to give you an equation which is explanation enough. I'd link the post but it seems to have disappeared.

LeCun is also heavily biased against Bayes.

<comment><USER13> He's not wrong though. We know precisely how any given neural network works, it's just not understandable to us.

The point is though, the functions we're trying to learn, like image classifiers, have no responsibility to us to be understandable. In fact, the brightest minds of several generations have worked hard on trying to write down rules to do image classification, and they never came up with anything that works.

There is a huge space of functions that are beyond what a human can understand, where we can't write down rules to express the function. This is precisely where we need to use machine learning to find the functions. Lack of explainability is not a bug, the entire point of a neural nets is to find functions that are beyond what we can understand.

<comment><USER12> Lack of explainability is not a bug, it's a wholly missing feature.

There are a whole set of mathematical functions that are beyond the range of a typical human mind to understand. But there are proofs that exist that explain how they are correct.

I don't think it's a big ask for a new model to be able to justify its own determinations.

Of course, that makes things slightly more difficult for all those in research and those selling snake oil and everyone in between. But that's what the difference between science and alchemy is isn't it?

<comment><USER13> If I show you a picture and ask you if its a dog or a cat, you can answer the question, but you can't tell me why. You can make up a story to justify why it's a dog or a cat, but if someone tries to implement the procedure you give, it won't work. If it did, we could do computer vision by implementing your justification.

You are a black box AI. I can nevertheless trust you to classify dogs vs cats.

<comment><USER12> No. I'm natural general intelligence with no safety features. One that has a hallucinationary view of the world an a neural architecture tracing back a couple of hundred of millions of years that continuously learns and has redundant features, and has the ability of self reflection.

We're talking about algorithms that are based on a simplified neural architecture, no redundancy, no self reflection and are still quite immature.

Nevertheless we're being asked to trust a black box AI, that you cannot interrogate?

Yes, of course, what's the worst that can happen?

<comment><USER13> It may well be that the human visual system is better for most tasks, but then that is what matters, which system is better for the task. The presence of an explanation doesn't matter. Neither system comes with an explanation.

On the other hand, if you place Magnus Carlsen against AlphaZero in a game of chess, I will bet on AlphaZero. If however you reduce the complexity of AlphaZero down to a level that it can produce an explanation I can understand, I would instead bet on Magnus Carlsen.

Of course we should care about the quality of AI systems, but chasing a human understandable explanation is just the wrong way to go about it, since it in many cases necessarily limits quality of the decisions.

<comment><USER14> Again the non-sequitur argument of an explainable model must be worse than a deep learning system and there has to be a tradeoff.

You don't need to reduce complexity to induce explainability. You just need to decompose the function into smaller parts which you can understand.

Contrastive LRP for example is a Function decomposition technique for explaining deep neural networks with high fidelity.

</comment></comment></comment></comment></comment></comment></comment><comment><USER15> I think the type of explainability Yann LeCun is describing in this post is not the same as what people really want when they talk about explainable AI. To use his example, I think most people would be fine with an AI that said "we're doing X because that's what heuristic Y prescribes for the situation", and getting from where we are now to something like that is the challenge. FWIW i see a similar dialogue in biomedical contexts quite a bit, where there's some cool but hard-to-trust work being done in data-driven image restoration ([https://www.biorxiv.org/content/10.1101/236463v5.abstract](https://www.biorxiv.org/content/10.1101/236463v5.abstract)). There's still a lot that could be done short of programs providing a priori mathematical proof for all models.

<comment><USER16> > because that's what heuristic Y prescribes for the situation

people will keep asking "why" and keep escalating, but that's a flawed approach to evaluating a model. every answer is literally constructed and thus can't be trusted on its own. The best that an evaluator can do is to evaluate the training dataset

</comment></comment><comment><USER17> Interesting exchange in the comments:

Gaurav Mukherjee:

Well argued! Yes, it is true that we don't have causal relationships for a number of phenomena. But the absence of evidence is not the evidence of absence. So corner cases of failure may exist in all these phenomena which can cost lives. Does this mean that we halt the fast paced progress of AI research or any other scientific pursuit? No! But leveraging empirical evidence that can't be fully explained in situations where lives are at stake, should require a very high bar of regulation. Responsible scientists and engineers agree that causality is important to understand and they do all they can to understand how systems work. However there are likely many among us who do not employ similar standards to the application of ill understood techniques. When it comes to regulation, we must pay heed to the worst in us.

Yann LeCun:

Actually, there is no clear definition of causality in classical and quantum physics, simply because the elementary equations of motions are time reversible (if you also reverse charge and parity). For every phenomenon that can occur, the same phenomenon can occur backwards in time (with corresponding anti-particles).

Take a Feynman diagram where an electron and a positron annihilate to produce a gamma ray photon. It can be interpreted as a gamma ray photon spontaneously creating an electron-positron pair. It's the same diagram where time goes right to left instead of left to right.

How can one possibly say that A causes B, when B could very have caused A in a time-reversed view point?

Even worse, most physical phenomena have loopy causal graphs. Motion "causes" friction. But friction limits the velocity of motion. Most differential equations have coupled terms with loopy interactions in which quantity x affects quantity y and vice versa. You rarely have y(t+dt)=f(x(t)) in physics. More often than not, you have coupled equations y(t+dt)=f(x(t)) and x(t+dt)=g(y(t)).

In these (frequent) cases, x cause y, but y also causes x. There is something like that going on in fluid dynamics, which is why it's difficult to come up with "simple" causal explanations.

Only when collective phenomena are considered does the "arrow of time" appear to have a definite direction (with the 2nd law of thermodynamics).

Edit: para spacing

<comment><USER2> >Actually, there is no clear definition of causality in classical and quantum physics, simply because the elementary equations of motions are time reversible (if you also reverse charge and parity). For every phenomenon that can occur, the same phenomenon can occur backwards in time (with corresponding anti-particles).

This is super misguided. Causation doesn't need physical grounding. The usual definitions are grounded in counterfactuals and interventions.

It's like defining the causal effect as the difference in potential lines of code I write today, depending on whether I do or don't eat breakfast this morning, but then arguing that under classical mechanics the universe is deterministic so I couldn't possibly take an intervention to not eat breakfast. Really, it's just another argument that counterfactuals are counter factual, and therefore don't have a clear definition. But yeah, that's in the name already.

Regardless, physical equations are pretty much all causal, because they are stable under intervention. If I intervene to do whatever, they still hold. That lets me _cause_ a change in friction be _intervening_ and swapping out a material.

<comment><USER18> I thought causality was a function of time by definition?

<comment><USER2> Not quite, especially since time is such a weird concept when you dig far enough into the math. There are a bunch of competing definitions of a causal change, but arguably the most conventional one is the difference between an outcome if you do one thing versus another.

For example, looking at whether I write this comment now instead of getting back to work after lunch, you could imagine that I'm more inclined to slack off after a heavy meal. We have two hypotheticals. Scenario 1) I eat curry for lunch. Scenario 2) I eat salad for lunch. In scenario 1, I am a bit sleepy and log on to HN and write this. In scenario 2 (hypothetically) I just get right back to work and this comment never exists. The causal effect (hypothetically) is that having curry instead of salad for lunch made this comment exist.

Compare that to the effect of the comment I wrote before lunch. We can still talk about the causal effect of lunch on what I wrote before lunch. Assuming that physics works the way I think it does, the causal effect is that nothing changes, but it's still a thing you can formalize. You could imagine an episode of star trek involving time travel, where my actions today do affect things yesterday. Starfleet statisticians could still run time-reversed randomized trials, they'd just have to be really careful about their experimental designs.

<comment><USER7> I like your original comment further up, but disagree with the following bit:

> We can still talk about the causal effect of lunch on what I wrote before > lunch.

To the extent that the situation occurs in the physical universe, that is meaningless. The future can never be a causal parent of the past. That says there is much more to the typical causality question than just the direction of time.

The difference between the "time reversibility" in physics and the typical ML example is that once you've decided on something as the effect, the other thing must be the cause. So the problem is quite simple if you're not trying to figure out the nature/direction of time, and especially not with microscopic physics.

Anyone curious about the relation between time reversibility in physics and causal reasoning could look at Scholkopf+Janzing 2016 for an interesting line of thought.

Somebody should tell Yann Lecun that a little knowledge is dangerous.

<comment><USER2> >The future can never be a causal parent of the past.

That's a testable statement. The reason it's testable is because we can define a hypothetical causal effect of the present on the past, and then do experiments and show that it's always zero. I only mean that we can talk about it, not that we can show it to be anything but a non-effect, so I'm not sure I see where we're in disagreement.

<comment><USER7> Ah, guess I misinterpreted "can still talk about" in the context of response to its parent comment.

</comment></comment></comment></comment></comment></comment></comment><comment><USER4> From article:

" _How does lithium treat bipolar disorder?_ "

The thing is, that this isn't a question that's _insignificant_. There has been quite a bit of debate whether various drugs reverse the basic process happening with a given psychiatric disorder or whether they provide a different change which allows a person function. The phrase "chemical imbalance" has been based on the supposition that various drugs that change brain chemical distributions "cure" various conditions - but the question these drug are directly reversing a condition or whether they are adding something more seems important, even if we assume the drugs are broadly useful for helping people function in society.

</comment><comment><USER19> Yann is undoubtedly a pillar of the deep learning era of AI. But, I strongly disagree with what he is saying.

Firstly, the Navier-stokes equations existed before mechanized flight was discovered. It was not explicitly invented to reason about flight, in that way that some of the deep learning "theory" is being projected. It also worked perfectly (in sub-sonic speeds) for almost every situation involving flight.

ML has time and again proven to not be internally consistent, with contradictions on nearly every corner.

Batch norm was "theoretically" thought to be an essential part of neural networks, until suddenly it wasn't. Drop out was considered essential to avoid overfitting, until it wasn't. We still do not know if an information bottle neck is good or bad. ADAM's whole formula was incorrect and no one realized that for 6 years.

> The mechanism of action of many drugs (if not most of them). > An example? > How does lithium treat bipolar disorder?

This example does more to disapprove his point that approve of it. Medical and nutritional sciences are among the least understood fields out there, with some of their "fundamentals" flipping on their head over the last 50 years. The only reason these "sciences" continue being used is because medicine is essential. A reasonably effective solution with side effects is still better than dead people. It is a begrudging compromise, not a example to be emulated.

AI is not essential. AI imposes on your life without consent. AI will soon be ingrained into every single aspect of your life.

Yann seems to be conflating explainability with causality. Explanaibility can also mean fully observed correlation. Explainbility can mean predictable and reproducible behavior of ML models given a hypothesis. Explanability can mean the ability to ascertain if the change in model performance was because of the hypothesis or a way to exploit an unintended aspect of the data/model architecture.

Explainability fundamentally allows ML researchers to make strides in the field in a meaningful way, and not blindly throw different computational structures at thousands of GPUs and let luck of the draw decide what works and when.

Looking back at ideas such as Transformers and ResNets, there was literally no way for the authors to guess that this new computational structure would revolutionize the field. It could easily have been an idea someone else tried or rotted in someone's TO-TRY list. Explainbility and some theoretical logic around NN development would allow for a systematic way to go about research in the ML community as whole. That's unlikely to happen, but I would rather see people strive for it than not.

</comment><comment><USER14> When a person's claim to fame and research is dependent on ignoring explainability and causality in research, he will ignore it to the best of his means.

To him, all the precursors to clinical trials (selection of a molecule from restricted set of molecules which satisfy certain causal criteria) then followed by extensive multi million dollar experiments to safeguard the last 1-2% of uncertainty is equivalent to a barely preprocessed neural network.

I mean let's ignore the basic principles of pharmacology and medicine and just run every possible molecule through humans since that is a valid approach according to him.

</comment><comment><USER20> For those interested in a great read on why causality really does matter, give "The Book of Why" a read. I'm not convinced Judea Pearl's modeling approach is the most rigorous, but it does a clear and convincing job putting causality (esp. w/ data fusion!) at the heart of modern systems (including ML).

For those on a more mathematical bend, check out "Causality" by the same author or "Causal Inference for Statistics, Social, and Biomedical Sciences" by Imbens and Rubin

</comment><comment><USER21> Another more basic example is gravity. We have a statistical intuition of gravity, we depend on it being there and acting in a consistent way for almost everything in physics. But we don't have an accurate understanding of how gravity works or why it's there. We have theories, but nothing proven.

But it's so easy to prove gravity's properties (on Earth) by consistent results with experimentation, that it's literally a science project in every elementary school.

</comment><comment><USER22> It depends, while Yann Lecun has an interesting point of view.

The argument for explainability depends on the risk of harm from an AI model decision. Explaining why airplanes fly is moderately interesting but why did the 737-max crash is much more interesting and needed. While the former is probably only needed for people studying aerodynamics, the latter is meant for passengers, regulators, airlines, governments, etc.

Here is a tweet thread we posted in the past: [https://twitter.com/krishnagade/status/1182317508667133952](https://twitter.com/krishnagade/status/1182317508667133952)

</comment><comment><USER23> In re airplanes, deflecting air down is a perfectly intuitive explanation. Change in momentum of air means change in momentum of wing in opposite direction by the third law.

</comment><comment><USER16> he s probably correct. A lot of what we call "human explanation" is in reality a rationalized version of a hunch, but not necessarily the best representation of it. At most situations, some part of the brain crosses a threshold and causes a decision or action, but it's rather rare that humans can explain that action correctly. It's also rare that causation can be established from unambiguous temporal order (e.g. clouds/rain); in most cases our rationalizations are post-hoc.

We get a glimpse of it from recent language models: It's become rather easy to start blurting out language that is convincibly and comfortingly coherent, and it can be nudged to point to one direction or another. that doesnt mean it's true

</comment><comment><USER24> I'm surprised he made the drug analogy. It takes decades and hundreds of millions dollars to be able to test a drug on humans. I'm curious if he thinks we need a similar safeguard for all AI-human interactions.

<comment><USER25> It takes decades and hundreds of millions of dollars in order to build a statistical argument that explains likely effects, side-effects and risks of prescribing that drug to the population at large.

It takes that long and costs that much because there is no safe way to do this with human physiology other than to carefully, slowly and expensively try it and measure the consequences.

We can take any AI system and model its behaviour with statistical certainty much more cheaply and quickly, and be more confident in its future behaviour.

Remember, AI and ML are fancy words for some form of a regression to a mean more often than not. If we run hundreds of millions of tests of such a system in simulation for a very wide range of contexts/inputs (which is cheap to do), we can have a much higher degree of confidence in a short space of time around behaviour of that system than we will for any drug test, even if we still don't fully understand the causality.

<comment><USER14> If you were ever involved in the drug discovery process, then you would know that statistical evidence through clinical trials is only for the last ounce of drug testing. Out of 10,000+ drug molecule candidates only ~ 5 molecules get selected for clinical trials. A lot of molecules are removed as candidates for toxic components/ solubility issues/ chemical stability issues etc.

So even when we do not understand the precise impact of drugs on humans and there is no safer mechanism to test, we leave only 0.5% of candidate molecules to empirical/statistical evidence in the form of clinical trials.

On the other hand if drug discovery was treated as a pure AI problem, we would have thousands of unverified and unsafe molecules in clinical trials.

Causal principles get us to 99.5% of the way in drug discovery. Unfortunately not so in AI.

<comment><USER25> I'm not involved in the drug discovery process, but I can imagine that manufacturers would be interested in helping to use AI to deal with the funnel much more effectively to get to those ~5 molecules much more efficiently.

You're still left with double-blind trials and having to get large sample groups to try those molecules though.

And it's for that reason that drug discovery is always likely to be quite slow, complex and expensive - the efficiency gains will be pushed towards the top of the funnel to make new ideas reasonable to explore, I would imagine.

My point was that when you're not dealing with human physiology and instead dealing with problems that are more tractable through AI - i.e. using regression to tune algorithms through patterns in data - you are going to get quicker and more impactful returns without the same complexity.

And - critically - it's OK to often trust the AI solution you have without understanding causality. If you later find it's doing something odd that is undesirable, you can use that data to help tune the algorithm again without having to understand the causal relationship.

Put another way, you can teach an AI to get better without necessarily understanding the subject completely yourself.

<comment><USER14> Let's see. Robotics problems were claimed to be tractable through AI. However, a large majority of robotics solutions today are 90% derived through control systems (which follow some degree of causal analysis) followed by AI to optimize the last few percentages if possible.

Finding something odd for an algorithm (especially a deep neural network) is hard because they fail in just so many ways. For example, lenet for mnist almost always gives high confidence predictions for random tensors(torch.randn). Most imagenet models fail in the presence of just 20-30% salt and pepper noise. (Both of these are problems solvable through simple preprocessing techniques)

<comment><USER14> Not to mention the fact that most models are trained without a background class and tend to give overconfident predictions on out of distribution samples.

</comment></comment></comment></comment></comment></comment><comment><USER26> Yann LeCun and others debated this for an hour at NeurIPS 2017.

[https://www.youtube.com/watch?v=93Xv8vJ2acI](https://www.youtube.com/watch?v=93Xv8vJ2acI)

There's a lot of good points in there from both sides, but what really stuck with me is that given the choice between an explainable model and a black box model that works better (more accurate predictions), most people choose the black box model.

<comment><USER14> Which is strongly misleading tradeoff. For a ton of tasks, deep learning methods are no better than white box regression or tree ensemble methods.

And there is no reason to expect that a deep learning model has to be unexplainable. He's putting up a mystical interpretability vs accuracy tradeoff which does not exist.

There is a ton of research out there making deep neural networks (slightly to significantly interpretable)

</comment></comment><comment><USER27> Not all explanations are causal. The explanation literature in the philosophy of science goes pretty far back, but here are some of the highlights:

The Deductive Nomological Model (Hempel and Oppenheim, 1948) tries to explain a phenomenon using a deductive argument where the premises include particular facts and a general lawlike statement (like a law of nature) and the conclusion is the thing to be explained.[1]

The Statistical Relevance Model (Wesley Salmon) attempts to fix some shortcomings in the DN model that allowed explanations using particular facts and general laws that were not at all relevant to the phenomenon being explained. The idea is that you can explain why X hasn't become pregnant by saying that X has taken birth control, and people who take birth control do not become pregnant, and that would fit the DN model, but this explanation is not statistically relevant if X is male.[2]

Unificationist accounts (Philip Kitcher) seek to unify scientific explanations under a common umbrella as was done with, e.g. electromagnetism. If it is possible to have a unified theory of something, each element becomes more explainable based on its position within that unified theory [3]

pragmatic and psychological accounts tend to fit more closely with the kinds of rationalizations that we've seen as some explanations of AI. They can be fictional, but they don't have to be [4]

IMO we don't currently have an adequate account of explanation within the philosophy of science that works for deep neural networks. This is what my dissertation research focuses on.

[1] [https://en.wikipedia.org/wiki/Deductive- nomological_model](https://en.wikipedia.org/wiki/Deductive-nomological_model)

[2] [https://plato.stanford.edu/entries/scientific- explanation/#S...](https://plato.stanford.edu/entries/scientific- explanation/#SRMod)

[3] [https://plato.stanford.edu/entries/scientific- explanation/#U...](https://plato.stanford.edu/entries/scientific- explanation/#UniAccExp)

[4] [https://plato.stanford.edu/entries/scientific- explanation/#P...](https://plato.stanford.edu/entries/scientific- explanation/#PraTheExp)

</comment><comment><USER28> As a somewhat outsider to deep learning, intuitively it seems to be true that if you were able to demystify the black box, then it would be easier to improve your models (as you understand where it succeeds and fails and why). From this perspective, explainability would be incredibly productive.

<comment><USER29> I am not convinced the explanations we'd get from these models would be interpretable enough to drive their conceptual improvement. Deep nets just don't learn nice interpretable features, nothing in the training objective makes them do so. E.g. they make use of really dubious features [1], and might not actually learn "hierarchies" of features as we thought they were [2].

[1] [https://arxiv.org/abs/1905.02175](https://arxiv.org/abs/1905.02175)

[2] [https://arxiv.org/abs/1904.00760](https://arxiv.org/abs/1904.00760)

<comment><USER14> Or you can actually visualize and quantify impact of aggregates of meaningful features within networks using the methodology described in NetDissect or in TCAV. But of course, that casts doubt into a lot of claimed mechanisms and tons of ML research as I have found out.

</comment></comment></comment><comment><USER30> I wonder where/if incompleteness theorem fits in this picture.

[https://en.wikipedia.org/wiki/G%C3%B6del%27s_incompleteness_...](https://en.wikipedia.org/wiki/G%C3%B6del%27s_incompleteness_theorems)

</comment><comment><USER31> Yeah AI world have a loose definition of explanability and interpretablity.

I also see this very dogmatic mindset that Deep Learning will do prediction and interpretability.

What is stopping you from building two models? a regression statistic model to do interpretability/explanability and another deep learning for prediction?

Like each coefficient in a regression have a t-test for significant in correlation with response. You don't have something like that in deep learning. Also I've seen many MLer use logistic regression as a classifier and ignoring the probability aspect like the Titanic dataset highlight the different mindset between statistician and ML. ML often will see this as a classify problem dead or not dead. Statistician will phrase to "What's the probability of this person dying with these covariates?"

You know why this matter? It really matter in health/medical/social science. Often time inference is what they want and they need to know what affect your health not just shoving in tons of data and covariates/features. Not only that you many not even have enough data for these data hungry ML models.

Another example is biostatistician figure out threshold between the benefit of taking an invasive procedure versus not taking it. We figure it out but giving a percentage and the doctor and experts will tell you where the threshold is, 20%, 40%? It's certainly not 50% that many MLer do.

> We often hear that AI systems must provide explanations and establish causal > relationships, particularly for life-critical applications. Yes, that can be > useful. Or at least reassuring.

To me this just an excuse to not learn statistic. He should really look into Propensity modeling under Rubin-Neymer causality model. This is what statistic is going into after regression for observational data.

With all the criticism I have for ML. I think it's just the mind set. I think the ML algorithms have a place and they're very good in certain domain such as NLP and computer vision. But to act as if they're the end all be all when statistic models have been there and use extensive in biostatistic and econometric fields is just hubris and ignorance.

While ML is making excuses for causality. Econometric and statistician are working to build causality model. IIRC econometric is doing structure equation while statistician are going for Rubin-Neyman model. There is debate on which model is better but that's ongoing we'll wait and see from all the research papers.

</comment><comment><USER32> Would be interested to see what Judea Pearl says about this. Not on twitter any more but back when I was, I recall him regularly tweeting about causality and AI's shortcomings in that regard.

</comment><comment><USER33> How do you validate the model without understanding where you might find problematic results? That doesn't require a narrative, maybe, but it does require not using black-box-only testing.

</comment><comment><USER34> Hopefully a mod can change the link.

> Please submit the original source. If a post reports on something found on > another site, submit the latter.

<comment><USER35> This is less about the article linked by Yann, but more about what Yann writes. He basically uses the argument made in the article, and adopts it to make a similar argument about AI. And that this argument about AI is the interesting content here (although the linked article is also interesting).

</comment></comment><comment><USER36> This is true for the strict Pearl-style form of causation.

The concept of what counts as a causative explanation can be more expansive, and it varies between disciplines. See the work of Nancy Cartwright.

[https://www.researchgate.net/publication/30527010_What_Is_Wr...](https://www.researchgate.net/publication/30527010_What_Is_Wrong_With_Bayes_Nets)

TL;DR we've been explaining "causes" without Bayes Nets for awhile, Bayes Nets unsubtly disregards the common-sense logic scientists use for their practice, including the way that explanations tend to be qualified by context.

</comment><comment><USER37> Einstein had a great quote: "The most incomprehensible thing about the world is that it is comprehensible." Well, turns out, a lot of the world is not comprehensible. And that's what made Einstein irrelevant for the latter part of his life.

Our overestimation of the comprehensibility of the world may very well be some version of the Drunkard's search principle. We are much more likely to know about what's comprehensible than what's not.

<comment><USER38> As far as I know, a lot of the criticism Einstein leveled against the foundations of quantum mechanics later in his career are more recently being revisited... instead of being irrelevant, some of his later work may simply have been too far ahead of its time.

</comment></comment><comment><USER39> The word the author was looking for when they wrote "explainability" is actually "explicable," as in something that can be explained, and is converse to the more commonly used word "inexplicable."

<comment><USER40> Good point.

Compare with efficiency vs efficacy.

</comment></comment><comment><USER37> For those not on FB:

Link: [https://www.scientificamerican.com/article/no-one-can- explai...](https://www.scientificamerican.com/article/no-one-can-explain-why- planes-stay-in-the- air/?fbclid=IwAR0-S3nl5P0-vKUsr0oN5eeXmTVPTHMRZ9L_5bDqQStD-7uEHdukXC5JR6o)

"We often hear that AI systems must provide explanations and establish causal relationships, particularly for life-critical applications. Yes, that can be useful. Or at least reassuring.

But sometimes people have accurate models of a phenomenon without any intuitive explanation or causation that provides an accurate picture of the situation. In many cases of physical phenomena, "explanations" contain causal loops where A causes B and B causes A.

A good example is how a wing causes lift. The computational fluid dynamics model, based on Navier-Stokes equations, works just fine. But there is no completely-accurate intuitive "explanation" of why airplanes fly. Is it because of Bernoulli principle? Because a wing deflects the air downwards? Because the air above the wing want to keep going straight but by doing so creates a low-pressure region above the wing that forces the flow downwards sucks the wing upwards? All of the above, but none of the above by itself.

Now, if there ever was a life-critical physical phenomenon, it is lift production by an airliner wing. But we don't actually have a "causal" explanation for it, though we do have an accurate mathematical model and decades of experimental evidence.

You know what other life-critical phenomena we don't have good causal explanations for? The mechanism of action of many drugs (if not most of them). An example? How does lithium treat bipolar disorder? We do have considerable empirical evidence provided by extensive clinical studies.

This is not to say that causality is not an important area of research for AI. It is. But sometimes, requiring explanability is counterproductive."

</comment><comment><USER41> If Facebook lawyers understood the implications of LeCun's argument, they wouldn't be happy!

There are two types of explanations here: (1) why did the data come to be as it is, (2) why did my ML make the prediction it did.

Science looks for the answer to (1), and causal models are a great way to think about it. Science and engineering, when they go hand in hand, build a machine by saying "Here is data, let me do science to understand nature's underlying laws, and my machine shall be based on those laws". The machine is inherently explainable because it's based on scientific laws.

In the ML world, we can bypass the "learn scientific laws" part, and jump straight to "build a machine based on data". So the best answer to (2) has got to be "my ML made the prediction it did because of its training data". As Pearl said, ML is just curve fitting, so the only way to "explain" a ML prediction is to say "here are the points that the curve was fitted to". Prediction is just reading a value off the curve. Think the machine is biased? Look for bias in the training dataset! Think the machine is inaccurate? Look for sparsity or conflict in the training dataset!

So the consequence of LeCun's distinction is that, when the GDPR calls for explainability of ML decision making, it is really calling for sharing of the training data. Facebook, watch out!

</comment><comment><USER42> This guy is a total genius. Shame he doesn't care more about who he is working for and what his researches are used for.</comment>


After 19 years, http:// prefix is getting ditched (in Google Chrome) - <AUTHOR> http://code.google.com/p/chromium/issues/detail?id=40865 <comment><USER2> I agree with the sentiment on the thread that removing <http://> is a UX bug. Copy & Paste in this case is paramount over UI simplification. Keep it simple... do not make it a configurable option and do not remove <http://>.

<comment><USER3> I'm using the latest dev release that has this feature implemented. Let me tell you how it actually works. The location bar looks like this:

        news.ycombinator.com/item?id=1263512

When I select it and press Ctrl-C it copies this to the clipboard:

        http://news.ycombinator.com/item?id=1263512

Nothing is broken.

<comment><USER4> Useful to know, but I have to agree with portman. What about switching to an https: URL? Loads of sites default to http when they _should_ be secure.

<comment><USER5> Perhaps it would be better to add a white 'Not identified' badge in the place where the green Certificate Name (or red broken certificate name) goes?

Clicking it would reveal a message that the site is not identified, and information being sent to it is viewable to third parties.

Nobody in my immediate family know what <https://> means. They especially have no idea what its absence means.

<comment><USER4> _... They especially have no idea what its absence means._

Oh, I wholly agree, this makes sense for the average user. It's just wasted space to most people. They've already dimmed everything but the domain name (a _brilliant_ idea in every single way), making the prefix' "cognitive impact" rather minimal already. To _that_ end, I don't personally see that removing it is necessary, nor helpful. So at best it maintains the status-quo, and arguably lowers it slightly.

But, since these decisions are largely made for the majorities, it _does_ make sense that they're going that route; I'm not really annoyed, I'm just mildly disagreeing with the decision.

As to the white non-secure flag, I'd _tint_ it slightly yellow so it raises an eyebrow for people interested in their own privacy; white is too ignorable, and much harder to see at a glance. On the whole, it's an interesting idea, as it'd effectively turn the whole http/https battle into a slight-but-real push to move to https entirely.

<comment><USER5> > As to the white non-secure flag, I'd tint it slightly yellow so it raises an > eyebrow at people interested in their own privacy; white is too ignorable, > and much harder to see at a glance. On the whole, it's an interesting idea, > as it'd effectively turn the whole http/https battle into a slight-but-real > push to move to https entirely.

Exactly what I'm thinking. Just raise it as info, then change it to a warning progressively over time, with some advance warning to web developers.

</comment></comment></comment></comment></comment></comment><comment><USER6> I wonder how this will effect spdy://, Google's potential replacement for HTTP. Maybe it will be good; you can hide spdy:// too and make the difference transparent to the user.

</comment><comment><USER7> I commonly use file:// and other protocols. How will it handle those?

<comment><USER8> Every URI scheme except http: is still displayed.

This includes https: as you can see in the screenshot: [http://chromium.googlecode.com/issues/attachment?aid=-277348...](http://chromium.googlecode.com/issues/attachment?aid=-2773481718577223512&name=Screen+shot+2010-04-12+at+4.33.14+PM.png&inline=1)

</comment></comment><comment><USER9> Not only the <http://> prefix but the whole idea of URL is a notion that should not be user-facing in an ideal world. Even the pretty REST ones are the internet equivalent of the command line voodoo many non-technical users just don't get and type in or copy paste without knowing what it means. If the URL is too long only geeks understand its components, let alone edit it in place to go somewhere else.

It's debugging info for sure, but for most newbies is wasted vertical screen estate.

Bookmarks and web navigation via links are already hiding the fact that 'you go to URLs' now, entering a URL in the bar is the corner case because not everything is easily accessible from everywhere else in very few steps.

In the future the internet will be distributed and content addressable and not have a certain thing only available at a certain URL you have to remember, and then the URL will really go away. HTTP is client server, IP networks are peer to peer by nature.

<comment><USER10> In an ideal world, users would look at something they don't understand and figure it out. Not to a level of mastering all the underlying concepts, but to the level of becoming functional with it. In an ideal world, we don't feel the urge to hide something we think the user will not understand. Instead, in an ideal world, we _teach_ the user. Be it by presenting the subject in ways the underlying concepts become clear and obvious, be it in showing them progressive levels of functionality and abstraction. In an ideal world, users are intelligent, curious and do not feel the urge to run away from things, they feel, are too complex for them.

It's disturbing when someone advocates the idea of dumbing down what is a powerful idea in order not to upset those who think they would be upset, preventing them from learning something useful.

I use URLs for many things, from describing database connections to navigating hierarchical datasets in data-driven visualization tools. There is a lot to it beyond HTTP.

The Internet is not a blue "e". It's not a place of consumption, but one of sharing.

And when it becomes distributed, like you say, URLs could really serve us well, not by mapping to server:port/folder/document.html but to content and letting the underlying, non-http protocols, work out what's the best place to get the resource you need. You say "in the future" but p2p file-sharing networks use URLs in the form of network://hash-to-what-you-want.

If you assume your users are stupid, not only will you limit you product to those who know little, but you will rob them the possibility of learning something.

<comment><USER9> By ideal world I meant 'a better internet user experience within the next a decade or two' not 'all people are intelligent' as you meant. In the latter we would not be crouching over a browser anyway but do more evolved stuff :)

We do not teach users what an URL is when all she wants is to read her emails, just as you do not teach them how that browser is written or how the data travels to and from destination. I share your wish that people would be more inquisitive and learn but some people are just not like that for various reasons or do not have time for that and trying to teach them stuff they do not care about flies in the face of good user experience.

You use URL for many things including DB connections, this makes you a technical person. One of the reasons user experience is still shit in many applications is that many techies are convinced the user should be exposed to all the wonders of the underlying technology and consider this the noble quest of teaching them.

Yes - network://hash-of-what-you-want is the content addressability I mentioned. So the 'locator' in URL is not going to be meaningful. You do not locate a resource, you get some stuff wherever it may be located - assuming it is in one single place and not built from bits located in various parts. Anyway it was a tangent to the main topic - even if we denote the content someway it should not be exposed to your users.

Try asking around your non-tech relatives and see if they understand what the components of simple web url-s are. My mom sure does not, and I have no intention of teahcing her that .com is some silly legacy from when it was thought that if you are a company that is what your webpage address, will be named like or what is with the slashes or even worse the ampersands.

I do not assume users are stupid, they just don't give a shit about stuff that is cruft and that has little bearing on what they actually do on the web.

The fact that tiny URLs are such widespread also underlines the fact that URL is a necessary evil by which you send someone to a resource and not something to inspect by people. There are complaints about them being opaque but much less then you'd expect given their massive use.

So do not try to teach people who do not care about learning because they will not learn and you'll have wasted your time on them.

<comment><USER10> > trying to teach them stuff they do not care about flies in the face of good > user experience

The worry about "user experience" is the cholesterol-rich fast-food of design. It's terrible, dumbs users down, but, somehow, it's addictive. Imagine a world of immediate gratification and zero frustration. That's what many people are tying to build.

> they just don't give a shit about stuff that is cruft

Not everything you don't grasp, or that was decided before you were born, is cruft.

> The fact that tiny URLs are such widespread

Tiny URLs are a reaction against poorly-designed, overly complex URLs that convey little or no meaning. Nobody needs Bit.ly to go to <http://www.google.com>. I'm all in for not failing when a user fails to type <http://>, but I am completely against hiding it under a magical hood that will protect users from the dangerous unknown techniques under it.

Knowledge is power and, therefore, it belongs to the people. The better it's distributed, the better.

</comment></comment></comment></comment><comment><USER11> MobileSafari hasn't displayed <http://> since its inception.

<comment><USER12> Yes it has. I remember the hiding of it being one of the changes in an iPhone OS update.

<comment><USER11> Alas, then I remember wrong. Regardless Safari has done it for a while. If someone can find discussion of its removal I'll edit my original post.

<comment><USER13> [http://daringfireball.net/2008/11/treating_url_protocol_sche...](http://daringfireball.net/2008/11/treating_url_protocol_schemes_as_cruft)

<comment><USER11> I can't seem to edit my post, looks like mobile safari has done this since 2008 for what it is worth.

</comment></comment></comment></comment></comment><comment><USER14> Tim Berners-Lee's "one regret" was the addition of the // to web addresses: [http://bits.blogs.nytimes.com/2009/10/12/the-webs- inventor-r...](http://bits.blogs.nytimes.com/2009/10/12/the-webs-inventor- regrets-one-small-thing/)

Just think of the paper we'll save!

<comment><USER15> It seems to me that this comment was the driver for this Chrome "feature."

The problem as I see it is that the Chrome devs are fixing it at the wrong place. Changing one user app, when the standards state it's necessary to include just breaks things.

They need to change the standard, not the app. Until then, they've just got a broken implementation.

</comment></comment><comment><USER16> I'm surprised there's so much discussion about this topic. This seems a very straightforward and logical UI improvement.

I suspect commentary on this has become basically a bike shed color argument at this point.

</comment><comment> X-Istence I am still unsure as to whether or not I like this.

One of the first things I do when I get to a page that requires my credit card information or any kind of username/password login information that can lead to a compromise of accounts that hold personal information is look at the address bar for the <https://> in the URL.

<comment><USER17> Chrome still displays <https://>, and these days you should be looking for the company name anyway. See [http://chromium.googlecode.com/issues/attachment?aid=-277348...](http://chromium.googlecode.com/issues/attachment?aid=-2773481718577223512&name=Screen+shot+2010-04-12+at+4.33.14+PM.png&inline=1)

<comment> X-Istence EV certificates don't solve anything besides the fact that now the cert companies get to charge more for an SSL cert.

Checking for [https://<name](https://<name) of company> means I don't have to go hunting for where the EV cert sign is posted these days, the URL bar is still in a uniform location.

</comment></comment></comment><comment><USER18> Ugh, I run chromium nightlys and this change has caught me off guard, I really don't like it.

<comment><USER19> I have been running the 5.0 dev channel builds for a while and I didn't even notice this change until I read about it now. I think it's OK. It has been done in such a way that it never bothers the user.

<comment><USER18> Honestly, I thought it was a bug until I saw this thread.

</comment></comment></comment><comment><USER20> This makes absolutely no sense. Why try to fix something which is clearly not broken?

EDIT: Spelling

</comment><comment><USER21> the 7 of 8 chars in prefix is irrelevant, so, replacing them with an mini-icon in UI saves space and improves readability.

The better question is - where are all favicons? =)

<comment><USER22> Favicons only in the tab bar. If you have too many tabs for the window's width, they stop getting displayed first on the selected tab (making the progress indicator invisible too) and eventually none of the tabs will have favicons as they get narrower.

The stupid globe icon they added opens a useless focus-stealing "Page Info" popup window that's missing most of what should actually be shown in it (like cookies). They moved the bookmarks star to the right of the address bar to make room for it.

</comment></comment><comment><USER23> might as well axe out the 'www' while you're at it.

<comment><USER24> not while there are still stupid websites that don't work without it.

<comment><USER25> So let it be the browser's responsibility to try both with and without it. They'll either be the same, or one will be nonexistent or a not-found page; it's not like people put one site at www.x and another at x.

<comment><USER8> You would hope so, but unfortunately you'd be wrong - a tiny fraction of the time, but still...</comment></comment></comment></comment>


Show HN: Software is eating real estate commissions - <AUTHOR> http://www.insaana.com

<comment><AUTHOR> Hi, I'm a long time lurker and an infrequent contributor on HN. When I bought my first house, I was shocked to learn that the seller has to pay a 6% commission to agents, its 1-2% in other countries. This sounded outrageously high to me. I believe there is a huge opportunity in connecting sellers to buyers directly and that we don't really need agents. The app I built is an experiment to see if the idea is valid (I and my cofounder are convinced that it is) and if we can build a business around it. I know the kind of resistance I'll encounter, but I firmly believe this can be done.

Your comments are appreciated!

<comment><USER2> You're not the first person to have this idea. Be sure to look at the various FSBO marketplaces that have failed to disrupt the industry in the past, and try to understand why they failed so you can at least avoid making the same mistakes.

<comment><AUTHOR> Absolutely! We have seen many startups veering towards real estate agent model. Always helps to keep that in mind. No repeat businesses, less viral effects, urge to turn to brokers/agents and become a lead generation engine for them are all difficult issues to surmount. Thanks for the reminder.

</comment></comment></comment><comment><USER3> If you take care of legal stuff I am really interested in such service. I just don't want to sign the papers myself. I don't understand many of the legal matters with selling a house and afraid of being in trouble if I do it all myself.

<comment><AUTHOR> You hit the nail on the head. When I bought my house, I found the house, but I was scared of the paperwork and a realtor did that for me. And we plan to change that for both buyers and sellers, by using better, simpler software and real estate attorneys. We will soon be in a position to take care of end-to- end details of the transaction - inspections, tours, contingencies, mortgages, escrows. And, if someone does not want to use these services from us, they can go on their own too. Freedom of choice

</comment></comment><comment><USER4> You might want to reach out to the folks at Reesio. They were doing something very similar a couple years back but now seem to have pivoted

</comment><comment><USER5> How's it any different from zillions of fsbo sites our there?

<comment><AUTHOR> That's a question we asked ourselves several times. Here are some differences

# There is not one that gives the seller a list of pre-approved buyers

# Most FSBO's are still focused on listing - which in turn leads sellers to the Zillows of the world- back to square one.

# We are all about empowering sellers with a curated buyers list and tools/services to handle the transaction.

<comment><USER6> That sounds convincing from the seller's perspective. I bought a house a year back through a buyer agent. I don't see any difference as a buyer using this service.

<comment><AUTHOR> As a buyer you get an opportunity to connect with sellers before they have publicly list their house and sign on the dotted line to pay 6% commission. So not only you get access to off market listings but there is good chance you are going to get a good deal.</comment></comment></comment></comment>


How Facebook Live Streams to 800k Simultaneous Viewers - <AUTHOR> http://highscalability.com/blog/2016/6/27/how-facebook-live-streams-to-800000-simultaneous-viewers.html <comment><USER2> 150 engineers can simultaneously build 10 startups! Not impressed.

</comment><comment><USER3> This article says The Facebook is using MPEG-DASH. That's not what was reported by TFB in December. They suggest it's only RTMP. I've also seen reports of Flash being required on the desktop.

[http://www.usatoday.com/story/tech/columnist/2016/05/09/flas...](http://www.usatoday.com/story/tech/columnist/2016/05/09/flash- makes-one-its-last-stands-facebook-live/84130660/)

[https://code.facebook.com/posts/1653074404941839/under- the-h...](https://code.facebook.com/posts/1653074404941839/under-the-hood- broadcasting-live-video-to-millions/)

I suppose things change, but I would really like a discussion of the latency trade-off. RTMP is low latency. Every other streaming format (except for WebRTC) is high latency. If people expect a Skype or Facetime type interaction MPEG-Dash is not going to work.

<comment><USER2> Even sporting events are not broadcasted in real time, probably up to a minute delay, but most people still think they are watching it Live.

<comment><USER3> right, but if you are using Face-time or Skype the latency need to be < 500ms to not be annoying

<comment><USER2> Agree. But Facebook Live is a one to many broadcast, more similar to a Sporting event, rather than a one on one conversation.</comment></comment></comment></comment>