Grading using artificial intelligence

[Rick-Rarecards]

Originally Posted by Case12

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?

[drcy]

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.

[Case12]

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.
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[Rick-Rarecards]

Originally Posted by Case12

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.
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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.

All major tech companies have their own AI platform.

Of course and my claim is they overstate their value and abilities.

[Case12]

Originally Posted by Rick-Rarecards

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).

[mrreality68]

Hi

I do not know enough about AI to know where the technology is at or not at. But I cannot believe we already do not have the technology with all the advances that are mentioned and the things AI already does things like

From Forbes Magazine

It can produce breathtaking original content: poetry, prose, images, music, human faces. It can diagnose some medical conditions more accurately than a human physician. Last year it produced a solution to the “protein folding problem,” a grand challenge in biology that has stumped researchers for half a century.

Not to mention that they already automating business processes, gaining insight through data analysis, and engaging with customers and employees.

So why would it not be able to do cards unless it is as someone mentioned all about the income stream of Cracking the cases to regrade?

[Peter_Spaeth]

Originally Posted by mrreality68

Hi

I do not know enough about AI to know where the technology is at or not at. But I cannot believe we already do not have the technology with all the advances that are mentioned and the things AI already does things like

From Forbes Magazine

It can produce breathtaking original content: poetry, prose, images, music, human faces. It can diagnose some medical conditions more accurately than a human physician. Last year it produced a solution to the “protein folding problem,” a grand challenge in biology that has stumped researchers for half a century.

Not to mention that they already automating business processes, gaining insight through data analysis, and engaging with customers and employees.

So why would it not be able to do cards unless it is as someone mentioned all about the income stream of Cracking the cases to regrade?

I think it's perhaps a false equivalency, sort of like asking if we can put a spaceship on Mars why can't we cure the common cold? It may be very well suited for the tasks you mention but that doesn't necessarily translate. PS no insult to anyone here but that it can diagnose better than a human doctor, based on my recent experiences, doesn't impress me.

[Snowman]

Part 1 of 3...

I apologize in advance for the ridiculously long string of posts that this is going to be, but I did warn you that this is a lengthy discussion, so I guess I'm at least partially delivering on that promise lol.

There is a lot of confusion here, and across the hobby, about what the term "AI" means and how it works. I'll try to clear up some of this confusion as this is my field of expertise. I write "AI" or "ML" code for work every day and have a strong understanding of how these algorithms work, why they work, where they are likely to go wrong and why.

First, I have to point out that while 'Artificial Intelligence' and 'Machine Learning' are perhaps kissing cousins, they're not quite the same thing. The problem of grading cards with computational algorithms is a 'Machine Learning' problem, not an 'Artificial Intelligence' problem. Artificial Intelligence is when you have recursive algorithms that make use of something like deep learning or ensemble models with deep neural networks where the algorithms can often learn from themselves. This would be something like Alpha Go or Alpha Zero (the best chess "player" in the world) or Deep Mind, or Tesla's self-driving cars that learn how to drive better through simulations of millions of miles. The people coding these algorithms set up the framework and outline the "rules" for it to be able to learn on its own and then sorta turns it loose on the problem. This is not applicable to the challenge of grading cards.

Grading cards is a 'Machine Learning' problem. Specifically, computer vision and classification. In order to understand why grading cards is not a problem well suited for machine learning, you have to first understand how these algorithms work and what their limitations are because there are some important limitations that I would argue render this technology borderline useless for the specific application of grading cards. I will go into this in more detail below, but for now, I'm just pointing out that I take issue with using the term 'AI' for grading. This is NOT an AI problem. It is a machine learning problem. However, "AI" sounds cooler, so "AI grading" it is, right? This is a marketing ploy. OK, I digress.


How computer vision works:

Imagine you have a photo of you and your family sitting down in the park having a picnic, surrounded by fields of green grass. Try to visualize the photo. You know how that image looks to you, but what does it "look like" to a computer? Everyone is familiar with the binary 1s and 0s at the operating system level of a computer, but let's pull back from that and try to interpret how a computer might see color and objects in a photo. Most of you are probably familiar with RGB colors, but if not, it's helpful to know that colors on your computer screen can be rendered using RGB color values, which range from 0 to 255 for Red, Green, and Blue. So every tiny little pixel in your family photo can actually be defined by it's RGB color values. Those pixels in your photo that are part of the green grass surrounding you all look something like [0, 255, 0] meaning 0 parts red, 255 parts green, and 0 parts blue. Now imagine how that entire photo could be represented in a giant map mathematically. Break it down into 3 different matrices or grids: one for red values, one for green, and one for blue. The matrix for red would have a ton of ~0s in it (pretty much everywhere that the green grass is located) but would have higher values in the center of the matrix which correspond to where the people are sitting since people have red color tones. The green matrix would have a ton of ~255 values in it since there is grass all around you, but it would have lower values in the center where the people are since people aren't green. Make sense?

OK, so now we have 3 different matrices, or think of them as maps if that helps, where each pixel from the photo has a corresponding color value. These matrices full of numeric values are what enable computers to "learn" from photos. What sorts of things can a computer learn from an image? Quite a lot actually. One of the primary ways a computer can tell that something is different about a particular section of a photograph is through something known as "edge detection". Edge detection makes use of some fancy math to identify where the edges of an object are located in the photo. So in our example here, one of the "edges" would be where the green grass meets up against the people in the center of the photo. The mathematical values are different on each side of this "edge", which helps the computer to detect that this is an important location in the photo, and it learns to pay attention to it. Make sense? Great. If not, well, I apologize for being a crappy teacher. But this is the gist of how computers see an image and how they use mathematics to identify key aspects of a photo (or a scan in the case of grading cards). If you're perceptive and you can visualize how these matrices of numbers might look to the ML algorithms, you can probably already see how this could be problematic for grading cards. I'll get into that below. It's a pretty lengthy discussion though.


How machine learning classification models work:

One of the most common machine learning problems that data scientists work on is building classification models which aim to classify (or "categorize", or "label") something as belonging to a particular class. A simple example of this might be to build a model to predict whether or not someone is Male or Female based on a set of attributes that the computer learns from. So we might train that algorithm by feeding it the height, weight, hair length, ring finger to middle finger length ratios, hip measurements, shoe size, eyelash length, how fast they can run, and how much time they spend each month shopping or talking on the phone (stereotypes be damned). Then we feed all of that data to the algorithm for each person and tell the computer whether that person is a male or a female. The computer would then learn what each profile looks like and would be able to provide probabilistic estimates of someone being a male or a female for any new data you threw at it. So a person who is 6'2", 195 lbs with a size 12 shoe that runs a 5.1-second 40-yard dash with medium length hair might get classified as having something like an 81% probability of being a male and a 19% probability of being a female according to the algorithm (note that these algorithms are almost never quite as confident as you might want them to be). In addition to binary classes like 'Male' and 'Female', or 'yes' and 'no', or 'true' and 'false' type problems, there are also what are known as multi-class classification problems. So this might be something like classifying whether an animal is a fish, bird, mammal, reptile, or amphibian. The output for a model like this might be something like 3% probability of being a fish, 12% bird, 7% mammal, 46% reptile, and 32% amphibian if you were feeding it with the data of a monitor lizard. Multi-class classification problems are much less performant than binary classification problems for obvious reasons. More options lead to more variance leads to more uncertainty, which equals more errors made by the computer when classifying.