Author Topic: Machine Learning Algorithms  (Read 25187 times)

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Online tggzzz

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Re: Machine Learning Algorithms
« Reply #100 on: November 22, 2021, 09:22:19 pm »
Absolutely.
In the AF447 case, the PIC was asleep when things started to get problematic, but there was a copilot in his seat. Funnily enough, in this particular case, would the copilot have been asleep instead, the crash would probably never have happened. But that's just one particular case!

IMHO, #1 root cause in that one as well is still lack of basic skills and training of those basic skills. It's again the classic "oh, we are falling from the sky, I have no idea what to do, maybe pull the nose up so we go higher?!?"

As a pilot myself I agree completely. I find it absolutely incredible that an international airline pilot can be so lacking in basic flying skills. Have they turned completely into button pushers?

There is that tendency, allegedly.

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I think training in a standard small aircraft may be inadequate. In general they do very little stall training and these days often no spin training at all. When they actually do stall training, they are trained to initiate recovery in response to the stall warning horn sounding -- which generally happens around 5 knots faster than the actual stall, when the aircraft is actually still flying normally.

Before I went solo in a glider, I had to do a complete flight with the instruments covered up. Why? Because they all lie to you, and you have to recognise it happening.

Deliberately entering a spin at 1000ft or to lose altitude fast is entertaining - and anathema to powered pilots

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Training in gliders (sailplanes in the US) is much more stall intensive. There is no warning horn -- you have to learn to recognise the aerodynamic symptoms of stall yourself. And while Cessna pilots spend almost all their time boring holes in the sky and twice or more the stall speed, glider pilots spend a lot of time flying in maximum performance circles in thermals at just above the stall speed (or accelerated stall speed due to the steep bank angle and higher G load often used). As thermals are gusty, you often get actual stalls and it becomes absolutely ingrained what that feels like and you automatically make the required recovery action (easing the stick forward until it stops).

Full opposite rudder and after rotation stops centralise rudder and stick forward ( not for too long :) l.
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Online SiliconWizard

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Re: Machine Learning Algorithms
« Reply #101 on: November 22, 2021, 10:29:57 pm »
Any airliner pilot has flown small aircrafts AFAIK. That's not the problem. They are all supposed to have more than basic flying skills.

But an airliner is a very different beast. Sure the same skills would apply, but they have a lot more inertia, and of course as we said, a lot of automation, making them all but as direct as a small aircraft. Besides, the pilots are taught NOT TO fly them as they would  a small aircraft. They are taught exactly that. They are taught to use all the automation they can without questioning it.

There's also of course some "exceptional situation" training, but it's unfortunately less and less (as we have seen in the AF447 case), for time and cost reasons. But also maybe because neither the companies making airliners not the airline companies themselves want pilots to ever question automation. It's kinda linked to the very discussion we're having here: they want to convey the idea to pilots that it's statistically much safer to just follow automation than to try and correct it manually.

And we need bad crashes to temporarily change that direction. Then it ends up back to square one and runs in circles.
 

Offline brucehoult

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Re: Machine Learning Algorithms
« Reply #102 on: November 22, 2021, 11:07:18 pm »
Before I went solo in a glider, I had to do a complete flight with the instruments covered up. Why? Because they all lie to you, and you have to recognise it happening.

Yes, that's standard practise. You can judge your airspeed pretty well by the control responsiveness, level of wind noise, and (in stablised flight) the nose attitude relative to the horizon. It's not actually hard to fly like that, at least if you have an actual airfield to land on and don't need to do a precision short landing. Trying to land in a random 100 to 200 meter long paddock without a working airspeed indicator would be dodgy.

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Deliberately entering a spin at 1000ft or to lose altitude fast is entertaining - and anathema to powered pilots

You can get away with spin entry at 1000 ft in older slow lightweight docile metal or wood and fabric gliders such as Ka7 / ASK13 / Blanik. I've done it. I don't advise it in a modern fiberglass high performance trainer such as the DG1000 my club has been using since 2007 (which the above video was made in).

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As thermals are gusty, you often get actual stalls and it becomes absolutely ingrained what that feels like and you automatically make the required recovery action (easing the stick forward until it stops).

Full opposite rudder and after rotation stops centralise rudder and stick forward ( not for too long :) l.

That's only for a fully-developed spin. The point of stall avoidance training is to recognise the start of a stall and to not allow it to develop into a spin. Before autorotation has started, all that is necessary is a momentary relaxation of the back-pressure on the stick.

In many gliders when turning tight in a thermal you can actually hear the airflow start to break away near the wing roots when a gust hits you or if you're pulling a little too hard. It's a bit like the sound those modified turbo cars with noisy blow-off valves make, but less dramatic.
 

Offline rstofer

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Re: Machine Learning Algorithms
« Reply #103 on: November 22, 2021, 11:22:41 pm »
The problem with aircraft is that they don't have enough automation.  Consider the number of accidents caused by plugged pitot tubes.  The pitot tube heater is almost always a manual operation.  If the tube freezes, likely at altitude, the aircraft doesn't know airspeed and assumes a nose down attitude to increase speed.  Sometimes, the dirt gets in the way.

Why isn't the pitot tube heat automatic?  Either measure outdoor temperature and determine when to apply heat or just turn the thing on and leave it alone.

https://www.avweb.com/flight-safety/risk-management/pitot-static-system-failure/

You would think the pilots would 'know' that they are nose down.  The attitude indicator should show that.  Now you have conflicting information:  The attitude indicator says you are nose down and the airspeed indicator says you should be nose down but, in fact, you have plenty of air speed for level flight.  Just like you did a few minutes before the pitot tube froze up.  This should be pretty easy to simulate for training.

 

Online SiliconWizard

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Re: Machine Learning Algorithms
« Reply #104 on: November 22, 2021, 11:58:33 pm »
Pitot tube heating is already automatic on most modern aircrafts, actually. The auto mode is on by default and pilots can manually switch that to off for whatever reason would be required (following usually strict procedures.) The problem is that many aircrafts still in circulation are not modern.

As far as I've read - the older Boeing 737 models may not have an auto mode, but I believe the 737 MAX does, and most recent Boeing models, such as the 787. Most Airbus in circulation do have an auto mode too.

As far as I've understood, the AF447 issue was not that the heaters were not ON, but that the pitots got clogged anyway - which apparently can happen in very severe conditions, due to the quantity of small ice balls that can enter the tubes, so that it can get clogged temporarily. IIRC, in the AF447 case, the pitot eventually got rid of the ice, but that was too late. Airbus did have their pitot modified after this on this model, to limit the possibility of this happening, but I don't believe it was at all due to the pilots having forgotten to switch heaters on.

As to pilots knowing what's happening or not: the problem is that at some point, they do NOT know what is happening, because they realize they can't trust automation/or instruments, but also they are in a situation where they can't trust their own perception of things and their basic flying skills - precisely because a big airliner is pretty different from a small aircraft in terms of sensations, and because, as I said earlier, not being able to trust the plane's automated systems and instruments is in itself a big cognitive dissonnance for modern pilots.

Proper training should help of course, but as I said, training tends to be insufficient these days, and mostly on simulators, which only partially reproduces the physical movements/accelerations, and without the stress factor.
« Last Edit: November 23, 2021, 12:01:04 am by SiliconWizard »
 

Online tggzzz

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Re: Machine Learning Algorithms
« Reply #105 on: November 23, 2021, 12:07:48 am »
The problem with aircraft is that they don't have enough automation.  Consider the number of accidents caused by plugged pitot tubes.  The pitot tube heater is almost always a manual operation. 

What is this heater of which you speak? :) (Yes, gliders can fly higher than commercial airliners)

I've had a clogged pitot tube. It manifested itself during winch launch (i.e. at a critical time) and lasted until the aircraft came to rest. I noted the apparently low launch speed to the instructor, noted that we were climbing acceptably and controllably, and kept the nose lower than normal.  While flying I kept the nose lower than normal and ensured the aircraft was fully responsive to the controls.

It turned out not to be the traditional insect, but a flap of rubber.
There are lies, damned lies, statistics - and ADC/DAC specs.
Glider pilot's aphorism: "there is no substitute for span". Retort: "There is a substitute: skill+imagination. But you can buy span".
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Offline brucehoult

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Re: Machine Learning Algorithms
« Reply #106 on: November 23, 2021, 12:11:53 am »
The problem with aircraft is that they don't have enough automation.  Consider the number of accidents caused by plugged pitot tubes.  The pitot tube heater is almost always a manual operation.  If the tube freezes, likely at altitude, the aircraft doesn't know airspeed and assumes a nose down attitude to increase speed.

The *aircraft* won't do that. The aircraft will continue to fly at its trimmed angle of attack, and therefore EAS, and constant rate of climb or descent (possibly zero) depending on thrust setting.

Only a confused pilot or confused autopilot will manipulate the controls to produce the dive you describe.

Also, autopilots don't adjust speed by adjusting attitude. They are designed to operate in a relatively high speed environment, far from the stall, where adjusting rate of climb by using the elevator and adjusting speed by using the throttle and/or drag devices works. They work under the assumption that any commanded climb or descent rate will be within the capabilities of the engine or drag devices to maintain the speed. Autopilots in older or smaller aircraft don't have control of the throttle at all and work purely via the aerodynamic controls (elevator and ailerons, primarily). At the most they will automatically turn the autopilot off if the speed drops below some fairly high number e.g. 99 knots for the Garmin G1000 in the Quest Kodiak seen in the popular "Missionary Bush Pilot" youtube channel.

Autopilots are not designed or intended to replace a pilot, but only to reduce the pilot's workload. The pilot is still in charge and responsible for monitoring the progress of the flight and making adjustments or taking over control as required.

Which, incidentally, is exactly the same as Tesla's "autopilot" feature, or indeed the dynamic cruise control and automatic lane centering available (at least as an extra cost option, but often standard) on virtually every new car for sale today.
 

Offline brucehoult

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Re: Machine Learning Algorithms
« Reply #107 on: November 23, 2021, 12:27:28 am »
The problem with aircraft is that they don't have enough automation.  Consider the number of accidents caused by plugged pitot tubes.  The pitot tube heater is almost always a manual operation. 

What is this heater of which you speak? :) (Yes, gliders can fly higher than commercial airliners)

Only occasionally.

The glider flight below was higher than Concorde or U2, but not quite as high as the SR71 -- they almost certainly could have done it, as they were climbing strongly when they broke off the flight, but they have a protocol that they only allow each flight to go 10,000 feet higher than the previous highest flight and then analyse the data on the ground before the next flight.

Some of their flights:

52,172 feet, 3 September 2017
60,669 feet, 26 August 2018
63,776 feet, 28 August 2018
74,298 feet, 2 September 2018

Weather conditions were not suitable to further increase this on the 2019 expedition to southern Argentina, and COVID has prevented expeditions in 2020 and 2021.


 

Offline rstofer

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Re: Machine Learning Algorithms
« Reply #108 on: November 23, 2021, 01:33:51 am »
The problem with aircraft is that they don't have enough automation.  Consider the number of accidents caused by plugged pitot tubes.  The pitot tube heater is almost always a manual operation. 

What is this heater of which you speak? :)

The pitot tube on most aircraft (other than pleasure craft?) have the tube electrically heated.

In the F-106 Flight Manual on page 35 (of the pdf) on the right side control panel #17 points to the Pitot Heat switch.  The photo is not very good but the switch is really there.

It's a manual operation...

https://www.usaf-sig.org/index.php/references/downloads/category/101-f-106-delta-dart-convair?download=335:t-o-1f-106a-1-flight-manual-f-106a-f-106b-01-12-1972

On pdf page 133 item #29, there is a preflight test of the pitot tube heat system but it is left off until page 138 Takeoff item #10.  Note that the pitot tube heat is turned on and left that way until landing.  After landing, all switches are turned off on page 176 #3

FWIW, the F-106 interceptor was intended to launch a Genie nuclear tipped rocket into a crowd of incoming bombers.  The escape maneuver is around page 160.  Just in case you were wondering...

https://en.wikipedia.org/wiki/AIR-2_Genie

AFAIK, the F-106 was the only jet to be designed strictly as an interceptor.  Although highly capable, it was not intended to be a fighter.

I apologize in advance for the size of the download but the F-106 is the ONLY aircraft that has ever caught my interest.  When I was a kid, they had family day at Convair and I got to see the aircraft under construction.  My father did the final electrical tests before the planes flew up to Edwards AFB for final outfitting.

The climb-out must have been interesting because there was no desire to crash into Marine Corp Recruit Depot San Diego (MCRD).  It is right at the end of the runway at Lindbergh Field.  I saw one takeoff many years later and that plane could definitely go vertical and turn.

Designed with slide rules!

« Last Edit: November 23, 2021, 02:53:55 am by rstofer »
 

Offline brucehoult

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Re: Machine Learning Algorithms
« Reply #109 on: November 23, 2021, 05:00:44 am »
AFAIK, the F-106 was the only jet to be designed strictly as an interceptor.  Although highly capable, it was not intended to be a fighter.

I'll see your F-106 and raise you the English Electric Lightning.

First flight was two years before the F-106, introduction to front line service was a few months after, and the lightning served with the RAF until 1988, while the F-106 was replaced by the F15 in the USAF starting in 1981, and the last units retired from the National Guard in 1988.

Sticking to Convair, I reckon the Hustler was a more impressive aircraft. Not quite the same rate of climb or top speed, but it had pretty good range at supersonic speeds, setting coast to coast and New York to Paris speed records (the first supersonic transatlantic crossing), and also a flight from Tokyo to London in 8 1/2 hours averaging 1510 km/h (it had to slow to subsonic for the five refuellings) which is still today a record for a flight of that distance.

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I apologize in advance for the size of the download but the F-106 is the ONLY aircraft that has ever caught my interest.  When I was a kid, they had family day at Convair and I got to see the aircraft under construction.

Certainly it's understandable that the things you see as a kid are favourites.

Stuck on an island in the South Pacific Ocean, the most impressive thing around was the A4 Skyhawk, and occasional visits from allies' Tomcats, Harriers, and F-111s.
 

Online tggzzz

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Re: Machine Learning Algorithms
« Reply #110 on: November 23, 2021, 10:30:34 am »
The problem with aircraft is that they don't have enough automation.  Consider the number of accidents caused by plugged pitot tubes.  The pitot tube heater is almost always a manual operation. 

What is this heater of which you speak? :) (Yes, gliders can fly higher than commercial airliners)

Only occasionally.

The glider flight below was higher than Concorde or U2, but not quite as high as the SR71 -- they almost certainly could have done it, as they were climbing strongly when they broke off the flight, but they have a protocol that they only allow each flight to go 10,000 feet higher than the previous highest flight and then analyse the data on the ground before the next flight.

Indeed, but even the UK record is ~37kft, >10kft is common and >20kft isn't remarkable.

Bloody cold up there :)
There are lies, damned lies, statistics - and ADC/DAC specs.
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Offline brucehoult

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Re: Machine Learning Algorithms
« Reply #111 on: November 23, 2021, 11:08:13 am »
Indeed, but even the UK record is ~37kft, >10kft is common and >20kft isn't remarkable.

Bloody cold up there :)

Indeed it it. I've been to ~20k feet myself, in a Club Libelle. Don't aspire to higher. And I was half my current age then.

The powerful airbrakes got a good workout on the way back down.

https://upload.wikimedia.org/wikipedia/commons/thumb/f/fd/Glasflugel_H-205_Club_Libelle_Glider.JPG/1920px-Glasflugel_H-205_Club_Libelle_Glider.JPG
 

Offline rstofer

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Re: Machine Learning Algorithms
« Reply #112 on: November 23, 2021, 03:46:43 pm »
Stuck on an island in the South Pacific Ocean, the most impressive thing around was the A4 Skyhawk, and occasional visits from allies' Tomcats, Harriers, and F-111s.
Castle Air Museum (Atwater, California) has one of the British Vulcan fighter/bombers.  That thing is ginormous!

The Convair B-36 is an interesting bomber for the time.  The museum has a decommissioned Mk 17 thermonuclear bomb sitting alongside.  It is said that the bomb was found in the desert outside of Edwards AFB - not decommissioned.  I don't know about that but I have a great photo of my grandson kicking it.  I was teaching him all I know about EOD.  It's the brown bomb looking thing on the ground near the nose of the B-36.

Apparently, we're going to get a B-58 at some point.

The inventory:

https://www.castleairmuseum.org/collection

There's a lot of history in that museum.  They have a B-52 cockpit in the museum itself and a complete B-52 on the line.  It seems to be the first or second most popular aircraft in the running with an SR-71.  "Open Cockpit" days occur twice a year and some of the surviving pilots show up to tell the tales.  Our first stop is ALWAYS the F-106.  Then we look at the lesser craft.

No mention of the SR-71 can go by without the "LA Speed Check" video:

https://youtu.be/Lg73GKm7GgI

« Last Edit: November 23, 2021, 03:55:08 pm by rstofer »
 

Offline AaronD

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Re: Machine Learning Algorithms
« Reply #113 on: November 23, 2021, 06:47:22 pm »
No mention of the SR-71 can go by without the "LA Speed Check" video:

https://youtu.be/Lg73GKm7GgI

YES!!!  :-DD

Here's another good one:
 

Online tggzzz

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Re: Machine Learning Algorithms
« Reply #114 on: November 23, 2021, 08:12:51 pm »
Stuck on an island in the South Pacific Ocean, the most impressive thing around was the A4 Skyhawk, and occasional visits from allies' Tomcats, Harriers, and F-111s.
Castle Air Museum (Atwater, California) has one of the British Vulcan fighter/bombers.  That thing is ginormous!

..and they did things that a heavy bomber really shouldn't be able to do.

Hearing it while driving a car, and seeing the full delta shape even though it was 10miles away and had just taken off.

Even on its last valedictory flight, watching it stand on a wing.

Barrel rolling a heavy bomber, FFS? If there was an ML system there, predict what would it do in such ridiculous circumstances?

https://www.bbc.co.uk/news/av/uk-england-lincolnshire-34712344 or
« Last Edit: November 23, 2021, 08:15:28 pm by tggzzz »
There are lies, damned lies, statistics - and ADC/DAC specs.
Glider pilot's aphorism: "there is no substitute for span". Retort: "There is a substitute: skill+imagination. But you can buy span".
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Offline brucehoult

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Re: Machine Learning Algorithms
« Reply #115 on: November 23, 2021, 09:38:04 pm »
The Convair B-36 is an interesting bomber for the time.https://youtu.be/Lg73GKm7GgI

I've seen a B-36J with both piston and jet engines, at the SAC museum near Omaha.  And the "goblin" parasitic fighter intended to be carried by it.

Crazy stuff.
 

Offline brucehoult

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Re: Machine Learning Algorithms
« Reply #116 on: November 23, 2021, 09:45:33 pm »
Barrel rolling a heavy bomber, FFS? If there was an ML system there, predict what would it do in such ridiculous circumstances?

It's not a roll. It's just a wing-over, or "chandelle" as we glider pilots call them (not the same as what power pilots call a chandelle)

Doesn't require an aerobatics rating, we let students do them as there's nothing really bad can happen if you screw it up. We don't adhere to the "aerobatics is more than 60 degrees of bank or 30 degrees nose up or down" power flying definition -- 60 degrees bank is a standard thermalling turn in a glider.
 

Online tggzzz

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Re: Machine Learning Algorithms
« Reply #117 on: November 23, 2021, 10:09:54 pm »
Barrel rolling a heavy bomber, FFS? If there was an ML system there, predict what would it do in such ridiculous circumstances?

It's not a roll. It's just a wing-over, or "chandelle" as we glider pilots call them (not the same as what power pilots call a chandelle)

Irritatingly I have to agree with you.

Try this 1955 poor footage:


Quote
Doesn't require an aerobatics rating, we let students do them as there's nothing really bad can happen if you screw it up. We don't adhere to the "aerobatics is more than 60 degrees of bank or 30 degrees nose up or down" power flying definition -- 60 degrees bank is a standard thermalling turn in a glider.

Yes, it is quite fun to be pulling a few G with another glider at the same altitude as you on the opposite side of the thermal. To check relative position, you look upwards at the top of their head :)
There are lies, damned lies, statistics - and ADC/DAC specs.
Glider pilot's aphorism: "there is no substitute for span". Retort: "There is a substitute: skill+imagination. But you can buy span".
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Online SiliconWizard

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Re: Machine Learning Algorithms
« Reply #118 on: December 28, 2021, 02:49:18 am »
So, there we go: https://www.scmp.com/news/china/science/article/3160997/chinese-scientists-develop-ai-prosecutor-can-press-its-own

And, still the same pressing question, which remains stubbornly unanswered so far:
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“The accuracy of 97 per cent may be high from a technological point of view, but there will always be a chance of a mistake,” said the prosecutor, who requested not to be named because of the sensitivity of the issue. “Who will take responsibility when it happens? The prosecutor, the machine or the designer of the algorithm?”
 

Offline AaronD

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Re: Machine Learning Algorithms
« Reply #119 on: December 28, 2021, 03:47:18 am »
So, there we go: https://www.scmp.com/news/china/science/article/3160997/chinese-scientists-develop-ai-prosecutor-can-press-its-own

And, still the same pressing question, which remains stubbornly unanswered so far:
Quote
“The accuracy of 97 per cent may be high from a technological point of view, but there will always be a chance of a mistake,” said the prosecutor, who requested not to be named because of the sensitivity of the issue. “Who will take responsibility when it happens? The prosecutor, the machine or the designer of the algorithm?”

If the machine gives better-quality results than a human, does the question NEED to be answered?  Maybe the more pressing question should be, "Why are we not punishing the humans today who make worse mistakes than that?"  For example, if a judge sentences someone to life in prison for a $5 robbery with no history, why is that judge still serving?
 

Online ataradov

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Re: Machine Learning Algorithms
« Reply #120 on: December 28, 2021, 04:13:12 am »
I personally don't mind computers helping us makes decisions. They do the same in many other areas.

The issue here is that people feel somewhat in control when it comes to human judges. They are elected (or assigned by elected people) and can be removed.

Computer algorithms are not in people's control to that extent. And this open up more possibility for the people in control to rig things. Even if changes in algorithm are somehow voted on, it would still make it too easy to hide the nasty stuff. Unless "algorithms"  are designed to be easily applied by the human. But this would make it an expert system, not AI. And this is not what people in power are going for in cases like this.
Alex
 

Offline Smokey

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Re: Machine Learning Algorithms
« Reply #121 on: December 28, 2021, 04:19:30 am »
https://cs.stanford.edu/~jure/pubs/bail-qje17.pdf

I think that's the paper Malcolm Gladwell cited in his book "Talking to Strangers", that says a piece of software that only used facts about a criminal's history did a better job than face to face court judges at deciding who should get bail.
 

Online ataradov

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Re: Machine Learning Algorithms
« Reply #122 on: December 28, 2021, 04:34:33 am »
I have no issues believing that. But again, this is not AI, all you need is an expert system that takes information on the subject and previous cases and their outcomes. This is something that can be automated even without computers. Just make judges follow a very specific algorithm with no personal input.

Or if personal input is allowed to certain extent, then the same should be applied to other similar cases.
« Last Edit: December 28, 2021, 04:37:59 am by ataradov »
Alex
 

Online NorthGuy

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Re: Machine Learning Algorithms
« Reply #123 on: December 28, 2021, 04:53:12 am »
Just make judges follow a very specific algorithm with no personal input.

This is actually what real judges try to do. The problem is that the real world cases may be difficult to reconcile against formal definitions.
 

Online ataradov

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Re: Machine Learning Algorithms
« Reply #124 on: December 28, 2021, 05:01:45 am »
This is actually what real judges try to do.
They can try all they want, but rich white people get lighter sentences all the time. You don't even have to cherry pick cases, they are all over the place.

So, I would rather see some circumstance not be taken into account in a strict algorithm, than let a random judge decision have a significant weight.

And then have a reasonably simple way of extending the system to take that circumstance into account in the following cases. We already sort of do this, just not very efficiently. And even when we do, we still fail to apply those new rules.
Alex
 


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