dudeami0

From my understanding, you are pretty safe as long as you don’t provoke them (walking through the middle of them might be considered provoking) or near their calves. This article from the UK states “Where recorded, 91% of HSE reported fatalities on the public were caused by cows with calves”. Basically, mothers with a child are going to be very protective.

Cows are a domesticated creature, so they are generally docile, but I would exercise caution because if need be they will use their mass and strength against you. I’ve heard of stories of farmers running from cows and narrowly escaping under a fence. Most of these did involve a farmer trying to separate a calve from it’s mother. I’ve also heard stories of cows jumping fences.

And as far as memes go:

Sadly it wasn’t a bid to open source the AI, rather than a bid for payment.

This would only affect the 12V rail though no? It’s not like they are beefing up the 5V rail that supplies your USB ports in excessive amounts. Picking a random PSU from pcpartpicker, the CORSAIR RM650e vs RM1200e (650W vs 1200W) both have a +5V@20A rail. There would be no need to have a larger 5V rail to support gaming cards.

Also correct me if I am wrong, most PSU’s are more efficient at 20-50% utilization, not 100%. I’m basing this off the higher ratings for 80 Plus.

Your computer doesn’t “waste” electricity, power usage is on-demand. A PSU generally has 3 “rails”; a 12V (this powers most of the devices), a 5V (for peripherals/USB) and 3.3V (iirc memory modules use this). Modern PSUs are called Switched-mode power supplies that use a Switching voltage regulator which is more efficient than traditional linear regulators.

The efficiency of the PSU/transformer would be what determines if one or the other is more wasteful. Most PSUs (I would argue any PSU of quality) will have a 80 Plus rating that defines how efficiently it can convert power. I am not familiar enough with modern wall chargers to know what their testing at… I could see the low-end wall chargers using more wasteful designs, but a high quality rapid wall charger is probably close to if not on par with a PC PSU. Hopefully someone with more knowledge of these can weigh in on this.

Thermometers, like most measurement devices, are always accurate until you get two of them. Each device has a specific tolerance (or should, otherwise it’s probably a horrible tolerance), for a grill thermometer this will look like -/+5C/10F. Additionally, everything used to read a measurement needs to be calibrated regularly to ensure proper function, otherwise readings cannot be trusted. For a thermometer, the easily accessible way to calibrate are to use ice water (does it read 0C/32F) and boiling water (does it read 100C/212F). Using these constants will allow you to adjust your thermometer and get a (more) accurate reading.

In my humble opinion, we too are simply prediction machines. The main difference is how efficient our brains are at the large number of tasks given for it to accomplish for it’s size and energy requirements. No matter how complex the network is it is still a mapped outcome, just the number of factors weighed is extremely large and therefore gives a more intelligent response. You can see this with each increment in GPT models that use larger and larger parameter sets giving more and more intelligent answers. The fact we call these “hallucinations” shows how effective the predictive math is, and mimics humans abilities to just make things up on the fly when we don’t have a solid knowledge base to back it up.

I do like this quote from the linked paper:

As we will discuss, we find interesting evidence that simple sequence prediction can lead to the formation of a world model.

That is to say, you don’t need complex solutions to map complex problems, you just need to have learned how you got there. It’s never purely random attempts at the problem, it’s always predictive attempts that try to map the expected outcomes and learn by getting it right and wrong.

At this point, it seems fair to conclude the crow is relying on more than surface statistics. It evidently has formed a model of the game it has been hearing about, one that humans can understand and even use to steer the crow’s behavior.

Which is to say that it has a predictive model based on previous games. This does not mean it must rigidly follow previous games, but that by playing many games it can see how each move affects the next. This is a simpler example because most board games are simpler than language with less possible outcomes. This isn’t to say that the crow is now a grand master at the game, but it has the reasoning to understand possible next moves, knows illegal moves, and knows to take the most advantageous move based on it’s current model. This is all predictive in nature, with “illegal” moves being assigned very low probability based on the learned behavior the moves never happen. This also allows possible unknown moves that a different model wouldn’t consider, but overall provides what is statistically the best move based on it’s model. This allows the crow to be placed into unknown situations, and give an intelligent response instead of just going “I don’t know this state, I’ll do something random”. This does not always mean this prediction is correct, but it will most likely be a valid and more than not statistically valid move.

Overall, we aren’t totally sure what “intelligence” is, we are just an organism that has developed more and more capabilities to process information based on a need to survive. But getting down to it, we know neurons take inputs and give outputs based on what it perceives is the best response for the given input, and when enough of these are added together we get “intelligence”. In my opinion it’s still all predictive, its how the networks are trained and gain meaning from the data that isn’t always obvious. It’s only when you blindly accept any answer as correct that you run into these issues we’ve seen with ChatGPT.

Thank you for sharing the article, it was an interesting article and helped clarify my understanding of the topic.

Disclaimer: I am not an AI researcher and just have an interest in AI. Everything I say is probably jibberish, and just my amateur understanding of the AI models used today.

It seems these LLM’s use a clever trick in probability to give words meaning via statistic probabilities on their usage. So any result is just a statistical chance that those words will work well with each other. The number of indexes used to index “tokens” (in this case words), along with the number of layers in the AI model used to correlate usage of these tokens, seems to drastically increase the “intelligence” of these responses. This doesn’t seem able to overcome unknown circumstances, but does what AI does and relies on probability to answer the question. So in those cases, the next closest thing from the training data is substituted and considered “good enough”. I would think some confidence variable is what is truly needed for the current LLMs, as they seem capable of giving meaningful responses but give a “hallucinated” response when not enough data is available to answer the question.

Overall, I would guess this is a limitation in the LLMs ability to map words to meaning. Imagine reading everything ever written, you’d probably be able to make intelligent responses to most questions. Now imagine you were asked something that you never read, but were expected to respond with an answer. This is what I personally feel these “hallucinations” are, or imo best approximations of the LLMs are. You can only answer what you know reliably, otherwise you are just guessing.

As for the article, I think this is generally PR and corporate speak. Whatever their reasons were, they apparently didn’t shut down the initial XMPP servers until 2022 so it was a reliable technology. There “simplification” was bringing users into their ecosystem to more easily monetize their behaviour. This goes along with your last paragraph, at the end of the day the corporation is a for-profit organization. We can’t trust a for-profit organization to have the best of intentions, some manager is aiming to meet a metric that gets them their bonus. Is this what we really want dictating the services we use day to day?

Google tried to add support for it in their product

Is like saying that google tried to add support for HTTP to their products. Google Talk was initially a XMPP chat server hosted at talk.google.com, source here.

Anyone that used Google Talk (me included) used XMPP, if they knew it or not.

Besides this, it’s only a story of how an eager corporation adopting a protocol and selling how they support that protocol, only to abandon it because corporate interests got in the way (as they always do). It doesn’t have to be malicious to be effective in fragmenting a community, because the immense power those corporations wield to steer users in a direction they want once they abandon the product exists.

That being said, if Google Talk wasn’t popular why did they try to axe the product based on XMPP and replace it with something proprietary (aka Hangouts)? If chat wasn’t popular among their users, this wouldn’t of been needed. This could of been for internal reasons, it could of been to fragment the user base knowing they had the most users and would force convergence, we really can’t be sure. The only thing we can be sure of is we shouldn’t trust corporations to have the best interest of their users, they only have the best interest of their shareholders in the end.

Q1: Correct

Q2: Not at present, but it’s a highly requested feature. I would imagine it will be around soon^tm in one form or another.