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In the general case, I think that we would not be able to tell. Unless the programmers explicitly program into the simulation the tools for us to interact with the external world, we would not be able to collect evidence of something external to the simulation. We are limited.
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I am agnostic to whether we live in a simulation or not, but I don't think that this hypothesis brings a lot in terms of answering existential questions. We could live in a simulation inside of a simulation inside of a simulation inside of a simulation..... meaning that there is an infinite depth of simulations when we choose to consider this possibility. In my view, being the first rung of existence or being a million simulations deep is the same. Discovering that we are in a simulation just shifts the existential question one universe higher.
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I have been reading some texts about theories of how the brain thinks (predictive coding), and it seems like what we experience as "consciousness" might be the result of our brain simulating what our next sensory experience will be. So, in that sense, we are all experiencing our brain's predictive simulation.
Sal
Ooh, cool! 😁 That detector seems to be working only in "Geiger mode", which means that it can count the number of X-rays/Gamma particles but it does not estimate their energy. So, the dedicated devices are still better in that they allow you to identify the source of the radiation by measuring the counts and the energy distribution simultaneously.
It probably would not be too difficult to build the open gamma detector into something like a pinephone. I don't think that has been done yet.
My experience with phone zoom has been underwhelming so far, but I would like to check out the Samsung S2x's 10x zoom when I have the chance!
Still, I really like using binoculars because they transport me next to what I am looking at and do so in very high definition. I do have >100€ binoculars though, colors look very nice through them. I think it will be difficult to replicate via a screen.
Yes. The camera pixels generate a current in response to light. You can add some filters to block certain wavelengths of light (like UV) from getting to the camera sensor, and tune the pixels so that they respond more to to specific colors. But X-rays and gamma rays can just pass through the filter. Often they will pass through sensor as well, but, in the cases that they do get absorbed by the sensor, they can also produce a current that to the camera's readout electronics looks like other light would.
The gamma detectors I mentioned are very very sensitive. They respond to single X-ray/Gamma ray particles. These detectors can count how many individual particles collide with a small crystal cube every second. These crystals are special in that they produce a very tiny flash of light when an X-ray or gamma particle collides with them. As an added bonus, these sensors can directly measure the energy of the particles by measuring the strength of the flash, and from this information they can construct not only the total counts but also a spectrum. With this extreme sensitivity these detectors can measure small quantities of radiation that come from space, from rocks, and from other materials.
I looked for a video of a phone going through an X-ray machine, and found these:
https://www.youtube.com/watch?v=E8iSoPhtY3s
https://www.youtube.com/watch?v=V1YaroH6lHA
The white specks that you can see near second 25 (first video) and second 34 (second video) could be a result of the X-rays. I am not sure, but it seems reasonable to me. On contrast, when I put my radiacode through the X-ray machine in the airport the radiacode reacts very strongly and becomes saturated.
Radiation detectors. Such as the Radiacode or the Open Gamma Detector.
Binoculars are quite portable, very useful, and phones don't do a good job at zooming in like that.
Smart watches integrate with phones but the phones by themselves are not so good at measuring the heart rate and other parameters directly.
Mini projectors. UV flashlights. Tools in general... There is so much actually. What type of gadgets are you looking for?
Only those who complete life without believing in a God pass into the next stage of the simulation.
I have used XMPP for some time now and I tried Matrix for a bit, but have stuck with XMPP until now.
I found it practically very easy to set up a prosody XMPP server in a raspberry pi. In XMPP you have the core standard that is kept quite minimal and then you can extended your implementation using XMPP extension protocols (XEPs) in a highly modular fashion. This approach of building on top of a light core using well-documented extensions I like very much.
With Matrix, JSON is used instead of XML. I think that JSON is a nice format when trying to look under the hood at how the message data is structured. XML is a bit of a pain to look at in my opinion. And I think JSON might be more efficient in how it moves the data around. So, that is a big positive for me. But I Matrix appears to be more focused on being feature rich than on having a flexible modular structure. While it does have extensions, successful extensions do have a chance of being eventually integrated into the core protocol. This makes the core feel bloated to me, because I have very minimal requirements.
In terms of security, in XMPP you start with the core and then you select the type of encryption that you like (OpenPGP, OMEMO, etc). OMEMO encryption has plausible deniability built into its design, and for me, plausible deniability is a property that I consider important for messaging. The modular approach to XMPP also means that these are choices that one gets to make in an active manner, and the protocols are open protocols that come from outside of XMPP. With Matrix you get their encryption protocol as part of the core - it is a protocol that they designed and that you need to accept to use their tool with encryption. It is probably a good protocol, but I don't think it has plausible deniability built in, and that's a choice you did not get to make.
As for moderation, I don't know. Do they mean moderation tools, or the actual absence of moderators and unmoderated communities? Because the latter is more a property of the people using the tool that the tool itself. You can have your own private communities.
If someone asks me, I could recommend Matrix but would rather recommend XMPP, depending on what they are looking for specifically.
Snowden's Permanent Record book was a strong motivator for me.
Ah, bidding technology has advanced since I sniped Tony Hawk Pro-Skater for the Nintendo 64 on ebay. Why start bidding over a week early, though?
I find it strange that they are out-bidding each other like this when there are over 8 days left. I am not too experienced with bidding, but I thought that the normal strategy was to maybe place a placeholder bid if there is no activity, but more generally one waits until the last second to set a reasonably high bid. Going on a 1v1 with fast out-bidding over a week early seems bad for everyone except the seller. Perhaps someone can explain.
Someone needs to explain to Musk how to debug with the JSON so that the ipv6 GUI does not overflow into the git API front-end
These past few days I have been learning about bootloaders, kernels, assembly, and general OS stuff. In that process I stumbled upon your project of Redox OS.
I like the concept of the more modular micro-kernel architecture. Using Rust seems like the right choice if one were to start an OS from scratch today.
Very cool stuff. I'll use your project as my reference as I continue to learn. Happy to find you in Lemmy! Just wanted to stop by to give you a thumbs up 👍
I kept a Lion's mane petri dish stored in the fridge for well over a year.
I decided to make an attempt at refreshing it by transferring into fresh petri dishes. After a week I noticed some strong mycelium growth.
After inoculating a grain jar with one of the cultures, I decided to have a look under the microscope to double check, just in case.. And that's when I noticed a morphology that I had never seen before. It looked nothing like Lion's Mane. The full length of the mycelium is covered with these pegs with a sphere at the end.
After some searching, I am almost convinced that this is a Verticillium sp. - a new contaminant for me!
I then checked all of the petri dishes and they are all this same fungus. So, time to get a new fresh culture 😅
Cross-posting to the OpenSource community as I think this topic will also be of interest here.
This is an analysis of how "open" different open source AI systems are. I am also posting the two figures from the paper that summarize this information below.
ABSTRACT
The past year has seen a steep rise in generative AI systems that claim to be open. But how open are they really? The question of what counts as open source in generative AI is poised to take on particular importance in light of the upcoming EU AI Act that regulates open source systems differently, creating an urgent need for practical openness assessment. Here we use an evidence-based framework that distinguishes 14 dimensions of openness, from training datasets to scientific and technical documentation and from licensing to access methods. Surveying over 45 generative AI systems (both text and text-to-image), we find that while the term open source is widely used, many models are ‘open weight’ at best and many providers seek to evade scientific, legal and regulatory scrutiny by withholding information on training and fine-tuning data. We argue that openness in generative AI is necessarily composite (consisting of multiple elements) and gradient (coming in degrees), and point out the risk of relying on single features like access or licensing to declare models open or not. Evidence-based openness assessment can help foster a generative AI landscape in which models can be effectively regulated, model providers can be held accountable, scientists can scrutinise generative AI, and end users can make informed decisions.
Figure 2 (click to enlarge): Openness of 40 text generators described as open, with OpenAI’s ChatGPT (bottom) as closed reference point. Every cell records a three-level openness judgement (✓ open, ∼ partial or ✗ closed). The table is sorted by cumulative openness, where ✓ is 1, ∼ is 0.5 and ✗ is 0 points. RL may refer to RLHF or other forms of fine-tuning aimed at fostering instruction-following behaviour. For the latest updates see: https://opening-up-chatgpt.github.io
Figure 3 (click to enlarge): Overview of 6 text-to-image systems described as open, with OpenAI's DALL-E as a reference point. Every cell records a three-level openness judgement (✓ open, ∼ partial or ✗ closed). The table is sorted by cumulative openness, where ✓ is 1, ∼ is 0.5 and ✗ is 0 points.
There is also a related Nature news article: Not all ‘open source’ AI models are actually open: here’s a ranking
PDF Link: https://dl.acm.org/doi/pdf/10.1145/3630106.3659005
ABSTRACT
The past year has seen a steep rise in generative AI systems that claim to be open. But how open are they really? The question of what counts as open source in generative AI is poised to take on particular importance in light of the upcoming EU AI Act that regulates open source systems differently, creating an urgent need for practical openness assessment. Here we use an evidence-based framework that distinguishes 14 dimensions of openness, from training datasets to scientific and technical documentation and from licensing to access methods. Surveying over 45 generative AI systems (both text and text-to-image), we find that while the term open source is widely used, many models are ‘open weight’ at best and many providers seek to evade scientific, legal and regulatory scrutiny by withholding information on training and fine-tuning data. We argue that openness in generative AI is necessarily composite (consisting of multiple elements) and gradient (coming in degrees), and point out the risk of relying on single features like access or licensing to declare models open or not. Evidence-based openness assessment can help foster a generative AI landscape in which models can be effectively regulated, model providers can be held accountable, scientists can scrutinise generative AI, and end users can make informed decisions.
Figure 2 (click to enlarge): Openness of 40 text generators described as open, with OpenAI’s ChatGPT (bottom) as closed reference point. Every cell records a three-level openness judgement (✓ open, ∼ partial or ✗ closed). The table is sorted by cumulative openness, where ✓ is 1, ∼ is 0.5 and ✗ is 0 points. RL may refer to RLHF or other forms of fine-tuning aimed at fostering instruction-following behaviour. For the latest updates see: https://opening-up-chatgpt.github.io/
Figure 3 (click to enlarge): Overview of 6 text-to-image systems described as open, with OpenAI's DALL-E as a reference point. Every cell records a three-level openness judgement (✓ open, ∼ partial or ✗ closed). The table is sorted by cumulative openness, where ✓ is 1, ∼ is 0.5 and ✗ is 0 points.
There is also a related Nature news article: Not all ‘open source’ AI models are actually open: here’s a ranking
PDF Link: https://dl.acm.org/doi/pdf/10.1145/3630106.3659005
We are having a pumpking growing competition at work and I live in an apartment, so I'm working with what I have 😆
The plant already produced many male flowers. From what I have read, the male flowers usually come out 10 - 14 days before the female flowers. They open up for a single day and then they close and fall off.
I found out that tey are edible, so I stuffed a few of them with some left overs as a culinary experiment.
And the first female flower has arrived!
Hello!
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