Grading using artificial intelligence

[T205 GB]

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.

Originally Posted by Snowman

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.

[Snowman]

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!

[Leon]

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.

Originally Posted by Snowman

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!

[Lorewalker]

Did not read it because, frankly IDGAF, but maybe someone here does.

https://twitter.com/Paul_Lesko/statu...1sNcjxshzTxcPw