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Ben201000's Achievements


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  1. Thanks for the paper! Thank you all of that post. There was so much in it. I'm new to everything to do with detecting so all of it was extremely helpful. What are your ideas on other methods of acquiring the signal? Is multi frequency analysis something that could be used? I was thinking an approach to this could be to use a similar approach to photogrammetry or biometrics. Only using image and no location data. The phone/drone/AR glasses would map key points of each image and an AI detects the location of the coil against the image. I'm doubtful if this could work right now, but I 100% think it will sometime in the future. In regards to you mentioning that induction is maxed out, are there any new alternatives? Maybe technologies that pick up so much that they're practically impossible to turn into a human interpretable audio signal, but could all be feed into some kind of neural network? Is multifrequency something in this direction?
  2. Hi Jasong, Thank you for your post, it's got a lot of really good insight. What are your thoughts on the longer post I made about training an AI at each specific site? I also love Alpha Zero! It's so exciting the things that are being made 🙂
  3. Hi Mike, Thanks! I potentially will end up doing so. It sounds like detectors can already tell you the density compared to a set standard, or is this not the case?
  4. Thanks for your reply 🙂 I had worded the sentence badly. It goes through the set amount of data millions of times. I'll check out the detector with the GPS that you mentioned in your last post.
  5. Thank you for the insight. Initially I can't think of any solution to this but I really like your thinking. I'll have a ponder and see if anything comes to mind. I imagine the surveying industry will be the first to solve some precise gps that can get down to consumer cost. They have a lot of really interesting things with lidar and AI but I haven't seen anything around gps. I'm sure other members on this forum have a lot more knowledge than me around it. Once the accurate gps is solved and smart glasses become common I'm sure combining them wouldn't be impossible.
  6. Hi Clay, Thank you for such an in-depth reply. I completely agree, AI is a buzzword and I didn't mean to imply any true intelligence. By 2015 I mean the rise of deep neural networks and things like cat breed classification from images, and Dall E, GTP 3 etc. Is automatic ground balance something that is already 'solved' or would this be a useful application? By this do you mean a physical system as in some kind of laser or radar etc rather than an algorithm? I've replied a longer post to another member. I'd love to hear what you think about it. Thank you 🙂
  7. Hi Chase, Thanks for the feedback. Do you see any useful scenarios in being able to train between two samples of anything while in the field?
  8. Hi Redz, Excuse me for the extremely dumb questions, but are hot rocks rocks that read similar to metal? Are there certain areas where there are lots which makes it harder to look for metals?
  9. Hi BrokeInBendigo, Thank you for both such in depth posts. I'll do my best to try and summarize what I understand from them and then add/ask anything more. First it isn't very useful (potentially impossible) to detect between metals. It is far more useful to balance the ground and reduce EMI. Is this because EMI produces a lot of noise and leads to digging holes without any metal in? Or is it mostly distracting and mentally tiring over the hours? Secondly the ground conditions can vary enormously, essentially making a single model that knows everything impossible. Do the ground conditions vary hugely within one detecting session or is it more in different geographical areas? Something I hadn't considered was the scale of the area. I.e would a very experienced detector walk through a 5 kilometre square area and be able to narrow down a few 100 meter square areas that are worth detecting? If so, could AI be useful attached to a drone that scans a 2km area with various sensors attached, and then tries to narrow a few 100m areas to look in. As I write this I realise getting training data for this would be extremely hard. I really like the ideas around having a screen showing the raw waveforms. It seems from everyone's response that conditions can be so variable that the AI would have to be trained on each separate detection session. This presents a few challenges but could be possible. YouTube/Spotify etc all train customised AI models for each person. Right now it's possible to take about 30 photos of a bird vs not-bird on a hike, and train an AI in about two minutes on a standard laptop, with about 90% accuracy (not a hugely relevant example, but this was unthinkable 7 years ago). Of course no one wants to carry a laptop around, but there are AI specific training chips which are tiny. One on the iPhone as mentioned, but also more specialised ones like the Google Coral and Nvidia Jetson Nano. There is also the possibility of sending the waveforms over to servers to quickly train, but I imagine there isn't any 4G where everyone is detecting. Latest thought: There is a small AI training chip (no larger than a smartphone), with a screen (probably use smartphone app for it). The raw waves from the coil somehow connect into the chip. There are 3 buttons on the screen (label 1, label 2, train AI). When someone reaches the specific site they begin by choosing the labels. Maybe metal vs not-metal, but it could be anything depending what the detector thinks could work. As you dig holes you click which label it turned out to be. After say 30 holes you click 'train' and wait about 2 minutes. It then starts giving you a real-time prediction in a percentage of which label it thinks the coil is detecting. In no way does this replace the detector, you'd still use your usual method, there would just be something extra to test against. The approach of this method would be to have little AIs that are personalised to each persons local knowledge, and these AIs are each persons property unless they decide to share them. They may find that it works well distinguishing between two local features and they use it for that. Or that training after 10 samples is handy but they only trust the results about 40% of the time. Or that 500 samples is required, but that's okay because they regularly detect on similar ground. Or they upload and combine data with others in similar ground conditions. My last thought was that to collect data wouldn't it be possible to bury a few known items and scan over them in the specific soil? Or on a larger scale bury lots of samples in an area and scan over at different heights, add more soil above and scan again etc? To clarify how 30 samples could possibly work. We could use transfer learning on an AI trained on millions of images (called ImageNet), and then it is taught the new samples. Some people have had a lot of success turning waveforms into images and then training them with this AI. Thank you so much for the Dall E prompt booklet! I've got access to it so will definitely have a read through. Also are you in Bendigo, Victoria, Australia? I'm in Melbourne 🙂
  10. Welcome, I was wondering if anyone if using any form of artificial intelligence in their gold detectors? I'm an artificial intelligence (AI) programmer. Last night I was watching Aduk gold on Netflix and suddenly wondered if anyone was using AI? A quick google didn't return much. I'm not sure how familiar everyone is with AI, but essentially since around 2015 it's blowing away peoples expectations year upon year. Simply put, traditional computer programs require humans to code in rules that then lead to a result. AI does that backward, it takes the results and creates its own rules to get to that. To do this with gold would require the creation of training data to feed into the AI. I don't know anything about gold detecting but I imagine you'd bury some pieces of gold and go over it with the detector, then save the waveforms (or equivalent) onto a computer. These become the gold samples. Then also bury things that most often give the most false positives compared to gold, and save those waveforms. The exciting thing compared to a few years ago is that not a huge amount of training data is needed. It's possible to take huge AIs trained by Google and then teach them the new gold samples far quicker with a lot higher accuracy. The process of training is basically the AI guessing 'gold vs not gold', and over millions of iterations it starts to learn. It then can be saved and used in the field to give a percentage estimation of how likely it thinks something is gold. I don't know anything about gold detecting but this is how I would see it used practically. Am I right in thinking that a fair amount of time is spent digging up false leads? If it's not and most of the time is spent surveying the area then the AI isn't very useful. But if there is a lot of time digging up false leads, then if the AI could save someone digging up 90% of the false leads would this create a lot of value? I'm really interested in any thoughts that any of you have regarding this. Cheers Ben
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