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  #1  
Old 08-09-2021, 07:50 AM
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Default Grading using artificial intelligence

I think the discussion we were having yesterday is likely to be buried in a thread nominally about something else entirely, so I am starting a new one. Hopefully the posters who weighed in on its deficiencies yesterday will do so here or reproduce their posts, I wasn't comfortable doing that. And hopefully any advocates will weigh in as well. My partially formed opinion based on what I've read here and elsewhere, and heard, is that this technology is a long way from being ready for serious use. And that it isn't likely to help with the problems we all know about, at least any time soon.
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Last edited by Peter_Spaeth; 08-09-2021 at 07:51 AM.
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  #2  
Old 08-09-2021, 08:20 AM
parkplace33 parkplace33 is offline
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Great topic. Here is my take:

I don’t think we are close nor do I think the grading companies want to seriously use this technology. I liken it to the car tire industry. Car tires typically last 60000 miles. Could a car tire company make a tire that lasts over that amount? Of course they could. But they want you to have to buy new tires often and spend money.

The same applies to grading companies. How many times do we hear “I didn’t get the grade I wanted” so I cracked it out and resubmitted it. If the technology was used properly, cracking and resubmitting cards wouldn’t be possible and thus, grading companies would lose money in the long run.
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  #3  
Old 08-09-2021, 08:29 AM
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Originally Posted by parkplace33 View Post
Great topic. Here is my take:

I don’t think we are close nor do I think the grading companies want to seriously use this technology. I liken it to the car tire industry. Car tires typically last 60000 miles. Could a car tire company make a tire that lasts over that amount? Of course they could. But they want you to have to buy new tires often and spend money.

The same applies to grading companies. How many times do we hear “I didn’t get the grade I wanted” so I cracked it out and resubmitted it. If the technology was used properly, cracking and resubmitting cards wouldn’t be possible and thus, grading companies would lose money in the long run.
Maybe in the future, but the only way to guarantee a card gets the exact same grade would be to store every card and their associated grades in some sort of huge database. The level of detail needed for each card to be able to match up the cracked card with the stored image in the database would be ridiculous. Think of it as using facial recognition software, except you have 50 Mantles who all look alike and the only differences are small changes in some of the "important" characteristics. Systems today are constantly learning, so the score you got last week might change a bit because the system updated its algorithm to account for something new it learned.

Last edited by Rick-Rarecards; 08-09-2021 at 08:31 AM.
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  #4  
Old 08-09-2021, 09:50 AM
Aquarian Sports Cards Aquarian Sports Cards is offline
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If computers can be good enough for facial recognition in casinos, a living, moving target, I don't understand how they can't be used to, at a bare minimum, recognize a card it has seen before. Also that is what I believe PSA is using Kevin's software for right now. Nat Turner is on record as hating the crack and resubmit game so this makes sense. Of course when things get back to normal it'll be interesting to see if the lost revenue from said game causes a change of heart on the matter.
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  #5  
Old 08-09-2021, 10:06 AM
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It is always good to sound like you are really for the hobby when you make so much off of it. I can think of few people that fit that description.

Resubmissions equal millions of dollars (probably). Yeah, as a business owner, lets just get rid of that stream of revenue.

So as AI in general goes, I agree it could take away some revenue. I always heard there have been pills? to make gas out of water but the oil companies always bought out the patent. (probably a tale but sounds reasonable)

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If computers can be good enough for facial recognition in casinos, a living, moving target, I don't understand how they can't be used to, at a bare minimum, recognize a card it has seen before. Also that is what I believe PSA is using Kevin's software for right now. Nat Turner is on record as hating the crack and resubmit game so this makes sense. Of course when things get back to normal it'll be interesting to see if the lost revenue from said game causes a change of heart on the matter.
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  #6  
Old 08-11-2021, 06:29 AM
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I always heard there have been pills? to make gas out of water but the oil companies always bought out the patent. (probably a tale but sounds reasonable)
You're thinking of an episode of the Munsters...

https://www.youtube.com/watch?v=Pnsw9Q2RV-E

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  #7  
Old 08-09-2021, 10:08 AM
Rick-Rarecards Rick-Rarecards is offline
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Originally Posted by Aquarian Sports Cards View Post
If computers can be good enough for facial recognition in casinos, a living, moving target, I don't understand how they can't be used to, at a bare minimum, recognize a card it has seen before. Also that is what I believe PSA is using Kevin's software for right now. Nat Turner is on record as hating the crack and resubmit game so this makes sense. Of course when things get back to normal it'll be interesting to see if the lost revenue from said game causes a change of heart on the matter.
I would bet they use gait, speech, height, and other characteristics along with the image.

This is a few years old but its not easy to tell twins apart, https://cacm.acm.org/news/226789-dis...twins/fulltext. You can think of all copies of a card as a twin.
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Old 08-09-2021, 10:11 AM
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Originally Posted by Rick-Rarecards View Post
I would bet they use gait, speech, height, and other characteristics along with the image.

This is a few years old but its not easy to tell twins apart, https://cacm.acm.org/news/226789-dis...twins/fulltext. You can think of all copies of a card as a twin.
Imagine trying to differentiate among the million or whatever Topps Update Trouts.
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  #9  
Old 08-09-2021, 08:22 AM
Rick-Rarecards Rick-Rarecards is offline
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Originally Posted by Peter_Spaeth View Post
I think the discussion we were having yesterday is likely to be buried in a thread nominally about something else entirely, so I am starting a new one. Hopefully the posters who weighed in on its deficiencies yesterday will do so here or reproduce their posts, I wasn't comfortable doing that. And hopefully any advocates will weigh in as well. My partially formed opinion based on what I've read here and elsewhere, and heard, is that this technology is a long way from being ready for serious use. And that it isn't likely to help with the problems we all know about, at least any time soon.
Pasting the information from yesterday:

I think of three questions AI/ML could help with
1) Detect if a card is real or fake
2) Classify the card (type, year, etc)
3) Classify the grade

In all of these cases, I can assure you people want to know why the algorithm gave the grade/class/ etc, e.g. explain how the algorithm got the result. This requires explainable AI, which is beyond what algorithms can do today. Furthermore, all of this requires a large training set (you need a lot of examples) including fake examples! Who has that many training examples sitting around? Not to mention the level of fidelity needed.

It is a very long discussion but I will try to give you a 30,000 ft view. You can create 1-3, but they would be very limited. There are technological limitations as well as practical limitations.

The easiest to understand are the practical limitations. So yes, if you can't explain the results the tools are useless. How crazy would the industry be if you received the following letter: "Dear Sir/Madam, our software has determined that your card has a 51% chance of likely being fake. Therefore, we are unable to certify it thank you for using our services."


The reason we can't explain the results are a technical limitation. Current AI/ML is a "blackbox" approach. You have an algorithm and train it on examples. Let's say I was creating an AI/ML tool to do 1) detect if a card is real or not. You basically show the tool a bunch of labeled examples so fake and real cards. It creates its own internal method to determine if a card is fake or real. You then test it on a bunch of cards that it has never seen before and compare its results to graders. If it does a good job you are good to go!

So where do the issues come from? Well if the algorithm has never seen a certain color, or a certain name before, never seen a type of error, there is a weird fleck of dust etc. Characteristics of cards that never existed in the training set (have you seen those cards that had a piece of fabric on them). So, you say well if it encounters something its never seen before it should tell someone to inspect the card! Well, that is an even more complicated problem (anomaly detection). Plus, it can't tell anyone what it didn't understand about the card that broke it (explainable AI). You might even say, well let's jus show it everything that has ever been graded before. This might cause something called overfitting, your algorithm is so fine tuned and specific that it will throw out anything not in its training.

It gets complicated the more you think about it. So this is essentially one of many problems just for the arguably easiest of the 3 problems.


So what could todays AI/ML do for detecting a fake card?

I will give you a possible system for 1) detecting a fake card. Assume that the industry agreed on a set of descriptors for how one would fake a card, categories as you will. I'm not familiar with all of the ways to create a fake card so apologies for the limited list: So we could say, 1) Reprint (passing off as original), 2) Washed old card and reprinted, 3) New print, 4) etc.

One could run an algorithm to first tell you if the card is fake or not. Then you could either do the following: have another tool tell you which of the categories is most likely (so pick the single top reason), or it could just give you the likelihood that it thinks the card falls in each of the categories, or you could have individual algorithms for each of the categories and have the system give a probability individually.

Again, these will all be blackbox answers. It won't give you the reason why it picked a certain category over another. It won't tell you which card was washed, AI/ML is not magic! The more detail you want, the more fine tuning hand crafted algorithms you need. Let's not forget, there are always new methods for faking cards so you would have to keep adjusting your algorithms and this means some fakes will always make it through.
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Old 08-09-2021, 08:38 AM
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Some AI SW developers and a HW engineer, if focused on this application, should be able to do this now. It may take them a year or two. Maybe longer to build the database and have experts confirm sampling/interpretation for a while. AI would take over from there (with some bias built in). Probably $2 -3M for pros and outsourcing. Maybe more because AI developers are in very high demand. Probably use Google Tensor Flow and Raspberry PI to start with. Just don't know if the ROI would be worth it.

Just finished consulting with start-up doing image 3D recognition, including movement and sound, then interpretation to animation translation. All wireless between sensors. Stand alone system and/or cloud. That's pretty hard, but they are doing it. I helped them get 10 patents granted on their app so far.

I left them start of pandemic. Yep, it affected me like others. So, I don't know what image probability levels they were going to get. They swore it would be above 95% as AI matured. Their app had to be very close to 100%.

This was a non-profit so ROI was not an issue. Large donors.

Last edited by Case12; 08-09-2021 at 08:47 AM.
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  #11  
Old 08-09-2021, 08:43 AM
Rick-Rarecards Rick-Rarecards is offline
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Originally Posted by Case12 View Post
Some AI SW developers and a HW engineer, if focused on this application, should be able to do this now. It may take them a year or two. Maybe longer to build the database and have experts confirm sampling/interpretation for a while. AI would take over from there (with some bias built in). Probably $2M for pros and outsourcing. Probably use Google Tensor Flow and Raspberry PI to start with. Just don't know if the ROI would be worth it.

Just finished consulting with start-up doing image 3D recognition, including movement and sound, then interpretation to animation translation. All wireless between sensors. Stand alone system and/or cloud. That's pretty hard, but they are doing it. I helped them get 10 patents granted on their app so far.

I left them start of pandemic. Yep, it affected me like others. So, I don't know what image probability levels they were going to get. They swore it would be above 95% as AI matured. Their app had to be very close to 100%.

This was a non-profit so ROI was not an issue. Large donors.
This in the industry is what we call a magic solution response. It doesn't directly address what the problem is. It speaks about a specific application, uses some indication of high probability, and lets you as the reader generalize this to everything. It doesn't indicate what the app is, what image 3D recognition they are doing. Movement and sound, that wont happen for cards why is it applicable? Google Tensor Flow and Raspberry PI ? So googles general platform and a cheap computer?

Last edited by Rick-Rarecards; 08-09-2021 at 08:46 AM.
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  #12  
Old 08-09-2021, 08:48 AM
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If graders are taking like two seconds to grade most cards (whether or not true, something said in another post), I would think AI would slow that down.
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  #13  
Old 08-09-2021, 08:57 AM
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Btw, huge database is not what AI is about. It will develop and decide on its own....thus, AI. Large confirmed samples are needed to start. The expert confirmation at beginning is what counts. I don't know what the probabilities are today. 2 years ago they were around 70%. Probably higher now. At minimum AI could be used as a filter to improve efficiency for this app.

Btw, this start-up had the SW and HW up and running. The developers were DOD contractors. They were in the expert confirmation process, which takes time and experts. Also, expert bias is a big concern because AI will grow on its own with that bias. Biggest concern we were concerned with.

All major tech companies have their own AI platform. We were using Google. Raspberry was our starting point, not ending. Just commenting on how to get up and running with new app in short time. Not debating what is best.
.

Last edited by Case12; 08-09-2021 at 09:02 AM.
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Old 08-09-2021, 09:03 AM
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Btw, huge database is not what AI is about. It will develop and decide on its own....thus, AI. Large confirmed samples are needed to start. The expert confirmation at beginning is what counts. I don't know what the probabilities are today. 2 years ago they were around 70%. Probably higher now. At minimum AI could be used as a filter to improve efficiency for this app.

Btw, this start-up had the SW and HW up and running. The developers were DOD contractors. They were in the expert confirmation process, which takes time and experts. Also, expert bias is a big concern because AI will grow on its own with that bias. Biggest concern we were concerned with.
.
Develop and decide what? What is the task that was being solved? 70% what? Look at GPT-3 how many parameters does it have and how training examples did it need? AI is not magic, you need to be more specific with the claims.

Quote:
All major tech companies have their own AI platform.
Of course and my claim is they overstate their value and abilities.

Last edited by Rick-Rarecards; 08-09-2021 at 09:04 AM.
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Old 08-09-2021, 10:27 AM
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Originally Posted by Rick-Rarecards View Post
Develop and decide what? What is the task that was being solved? 70% what? Look at GPT-3 how many parameters does it have and how training examples did it need? AI is not magic, you need to be more specific with the claims.

Of course and my claim is they overstate their value and abilities.
I agree with everything you say. And AI is not vudu
magic.

In this app we were translating unknown deaf sign language movements to text and voice to animated avatar with text and sound (and visa versa). Some of this is already available without AI. Our goal was the unknown body motions part.

It isn't just talk though They were in the process of developing it. Admitting only hardware prototype (but close enough for design patents and working sound/motion/display hardware) and software prototype (working to the point of needing expert image and motion feedback on known movement). These folk started from scratch and built to that point in two years. I don't know their progress since my leaving in March 2020.

I apologize for overstating. And you are absolutely correct that it depends on the app. The one they were doing was pretty tough though. (Unknown motion, image, sound, text, context).
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Old 08-24-2021, 12:34 PM
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Its not even close to working properly because this is what happens when your a Piece of Shit company and steal peoples ideas and work. I hope it all implodes leaving them with millions in damage control and a tainted reputation!
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Old 08-24-2021, 01:10 PM
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Its not even close to working properly because this is what happens when your a Piece of Shit company and steal peoples ideas and work. I hope it all implodes leaving them with millions in damage control and a tainted reputation!
I feel like I'm missing something here lol. What's the story behind this post?
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Old 08-24-2021, 05:04 PM
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Lets just say that it was to expensive and naïve of an endeavor for any grading company to pursue much less a commoner. Kinda like the Edison and Tesla story.

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I feel like I'm missing something here lol. What's the story behind this post?


Great post above and welcome to the board. I hope you know I posted in response to the thread question and not your post. Had not read them till much later in the thread.
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Last edited by T205 GB; 08-24-2021 at 05:21 PM.
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Old 09-22-2022, 07:47 PM
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I just wanted to post an update since I have recently looked into TAG grading and how they are attempting to automate the grading process with machine learning. They appear to have addressed what was probably my biggest concern of all, which is that high-resolution scans are insufficient for detecting surface flaws of cards even when those scans are being analyzed by "AI" (or machine learning).

Here is a link to their video about how their grading process/tech works:

https://youtu.be/5SPDCpYvDhQ

The key to what TAG is doing differently is not their "AI", but rather the imaging they are using to create scans that can show surface flaws. They are using something called photometric stereoscopic imaging which takes multiple images of a card using different light source angles with each image and then convolving them into one image that is then used by their machine learning algorithms to grade the cards. From the videos they've shared of how this works, I must say that I am impressed. You could definitely build some very useful grading algorithms with these images. Particularly with modern cards.

That said, many of the challenges I raised earlier still apply, and I think it will likely be a long time before they use this to grade vintage cards (if ever). The paper stock of vintage cards just has so much "noise" from a computer vision perspective that it would be extremely difficult (and VERY labor intensive) to build out these models even with photometric stereo imaging. They would still need to build a training data set for every disparate card type, and that is MUCH easier to do with ultra-modern cards where hundreds of thousands of copies are easily available. Building out a training data set for vintage cards would be much more difficult.

But this imaging technology does have the potential to be a game changer. As far as I'm aware, Genamint/PSA is not using this type of imaging. However, it is also perhaps worth pointing out that there is nothing TAG can do to prevent Genamint/PSA (or anyone else for that matter) from doing the same thing. Despite the numerous patents that they highlight on their website, one thing I've learned from working in this industry over the past decade-plus is that they absolutely cannot patent the use of machine learning algorithms for something like grading cards. You can't patent the application of machine learning for anything. It's like trying to patent a mathematical formula, and even if they somehow did receive a patent for something like this it would absolutely be thrown out if challenged. I was involved in a medical device startup several years back with a Harvard professor/heart surgeon and a team of patent attorneys who tried every trick they could to patent the use of AI/ML for all sorts of different purposes. It's simply not possible. Perhaps one of the patent attorneys around here could explain it better, but I promise you, they can't prevent another entity from doing the same thing, and they did not invent photometric stereo imaging.

That said, it looks like a pretty cool setup they have going. I'm hoping they do well and become a disrupter in the industry. There are a ton of problems I still foresee them encountering, and I could come up with a million questions I'd love to ask their team, like how they handle the fact that the grading scale we are all familiar with is very much non-linear with respect to differences in condition, but that's for another discussion.

If I have time, I may start a separate thread for TAG grading and revisit some of my points from earlier in this thread, but most of my concerns still apply. However, they do appear to have solved the riddle of getting surface flaws to show up in one single image, and that's a HUGE win!
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Old 09-25-2022, 07:11 PM
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I watched a few minutes of the video. It's interesting. They won't be doing vintage anytime soon though, so for me, it's a non-starter.

Quote:
Originally Posted by Snowman View Post
I just wanted to post an update since I have recently looked into TAG grading and how they are attempting to automate the grading process with machine learning. They appear to have addressed what was probably my biggest concern of all, which is that high-resolution scans are insufficient for detecting surface flaws of cards even when those scans are being analyzed by "AI" (or machine learning).

Here is a link to their video about how their grading process/tech works:

https://youtu.be/5SPDCpYvDhQ

The key to what TAG is doing differently is not their "AI", but rather the imaging they are using to create scans that can show surface flaws. They are using something called photometric stereoscopic imaging which takes multiple images of a card using different light source angles with each image and then convolving them into one image that is then used by their machine learning algorithms to grade the cards. From the videos they've shared of how this works, I must say that I am impressed. You could definitely build some very useful grading algorithms with these images. Particularly with modern cards.

That said, many of the challenges I raised earlier still apply, and I think it will likely be a long time before they use this to grade vintage cards (if ever). The paper stock of vintage cards just has so much "noise" from a computer vision perspective that it would be extremely difficult (and VERY labor intensive) to build out these models even with photometric stereo imaging. They would still need to build a training data set for every disparate card type, and that is MUCH easier to do with ultra-modern cards where hundreds of thousands of copies are easily available. Building out a training data set for vintage cards would be much more difficult.

But this imaging technology does have the potential to be a game changer. As far as I'm aware, Genamint/PSA is not using this type of imaging. However, it is also perhaps worth pointing out that there is nothing TAG can do to prevent Genamint/PSA (or anyone else for that matter) from doing the same thing. Despite the numerous patents that they highlight on their website, one thing I've learned from working in this industry over the past decade-plus is that they absolutely cannot patent the use of machine learning algorithms for something like grading cards. You can't patent the application of machine learning for anything. It's like trying to patent a mathematical formula, and even if they somehow did receive a patent for something like this it would absolutely be thrown out if challenged. I was involved in a medical device startup several years back with a Harvard professor/heart surgeon and a team of patent attorneys who tried every trick they could to patent the use of AI/ML for all sorts of different purposes. It's simply not possible. Perhaps one of the patent attorneys around here could explain it better, but I promise you, they can't prevent another entity from doing the same thing, and they did not invent photometric stereo imaging.

That said, it looks like a pretty cool setup they have going. I'm hoping they do well and become a disrupter in the industry. There are a ton of problems I still foresee them encountering, and I could come up with a million questions I'd love to ask their team, like how they handle the fact that the grading scale we are all familiar with is very much non-linear with respect to differences in condition, but that's for another discussion.

If I have time, I may start a separate thread for TAG grading and revisit some of my points from earlier in this thread, but most of my concerns still apply. However, they do appear to have solved the riddle of getting surface flaws to show up in one single image, and that's a HUGE win!
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