Probably, but if you want to improve your dental health, maybe some people find it useful to collect data.
You can make the same case for a lot of personal data collection. A lot of it just seems silly excessive and pointless, but you’ll always find someone who wants to use the data somehow.
My case of silly excess is notifications. I want all sorts of appliances to be able to notify me of current status and completion, probably including a rice cooker. One of my shortcut meals is chicken strips/breasts over rice: I can fill the rice cooker and air fryer, then go do something else for 20 minutes or so. How do I know when they’re done? It would be nice if it could tell me, then I don’t have to think about it or pay attention to it
It’s useful for a lot of stuff but it’s been waaay over hyped, mostly by youtubers desperate for content. So I think a lot of people are having a counter-reaction to that.
Once everyone calms down and realise it’s not an automatic-do-everything-machine, they’ll appreciate the circumstances in which it actually is useful.
Bonus points: find the landing page for any tech startup from the last 12 months that doesn’t mention AI or LLMs
In customer service. If I see a bot I know there is a 95 % chance I won’t get the information I am looking for. If I could get the information online somewhere, I wouldn’t have contacted customer service in the first place! I just want to interact with another human being who’s able to understand my queries.
And judging from the prompts many customer service lines use, there are also a lot of people who call customer service for the simplest/dumbest reasons. Wouldn’t be the first dummies kept us from having nice things.
But I’ll 100% acknowledge that even with perfect customer service, 99% of companies will enshittify it with AI if it promises to save them money.
The porn hasn’t been very good. I can get around dudes and chicks with like fourteen fingers, no toes, and nipples that hover over their skin… but I draw the line at the dirty talk being “I’m glad I could help you with that.”
It depends. If there is a component that evaluates the sensor status through some form of runtime and then regulates the temperature based on that, you could call it coding (I don’t think this is ever done since it has no practical use). Else, it’s just system architecture.
Of course, there is some overlap within those areas because they both rely on logic, but the latter would not be considered coding.
If you study CS, you will most likely have a course that gives you a basic idea about system architecture and if you study engineering, you will probably have to code some small thing or at least have a course on the basics. So yeah, not entirely distinct.
Bluetooth with mobile devices I’d agree. But my work pc hates Bluetooth devices. Such as refusing to use the correct audio channel with headphones, so I still use wired headphones.
I’ve always felt Windows could be temperament with Bluetooth, especially pre Windows 7. Like XP seemed to be a shitshow for Bluetooth.
Bluetooth audio has always been absolutely awful in windows as far as I recall. Bluetooth in general is super temperamental, I recall fighting with data loggers my first job out of uni that only connected via Bluetooth. Older ones were serial and were actually reliable.
It’s partly a timing thing, and maybe that puts the issues in the apps. If I am using my headset, it also works in things like Teams and Slack. However audio doesn’t want to switch if I turn in the headset in the app. Even worse, if I turn the headset on right before launching a call. It’s better now that I turn in the headset, then do a slow count to ten before starting a call, but still not reliable
I hate everything wireless when it comes to PC peripherals. They just randomly stop working or have a weird, noticeable lag. I have a 3 bucks WLAN adapter on my RPi that surprisingly works OK, but that’s it.
Bluetooth is like the SpongeBob “repeating then saying something different” meme where you go through the whole annoying pairing process, then it plays through the PC speaker anyway
Quite possibly. I don’t think I’ve ever had any Bluetooth device work without hiccups. My old earbuds used to disconnect or lose pairing all the time. A couple of game controllers I have only worked intermittently for years. My phone is always losing connection in our car. I’ve ironed out some of the problems, but I’ve never had Bluetooth just work for me.
I use Bluetooth all the time for speakers and headsets, also the PlayStation 3 controller was Bluetooth, so would that not mean AI will be a top of the line tool in 2 years? I personally don’t use it for anything at the moment, but in 2003 Plantronics released Bluetooth headsets for corporate environments (IP phones usually still used to this day).
Seems like more of a we aren’t sure where this tool is most useful yet, but it will be used by many people around us.
That’s very fair. I don’t like how unpredictable Bluetooth is when you have multiple peripherals and multiple hosts paired to eachother and all within range of eachother.
Having two pairs of AirBudz, one AirBudz pro, and an Anker speaker attached via BT to my phone and all of them function exactly when I want perfectly… Bluetooth works amazingly.
Nope! The budz only produce audio when they’re in my ears or my partner’s. I can select to mirror audio to both of us at the same time when we’re exercising. The speaker only works when I turn it on, but it connects immediately every time. I suppose I forgot about my car, which works but has a delay cuz the infotainment system kinda sucks.
That scene in Better Call Saul with the investment guy permanently on his BT earpiece was such a wave of nostalgia for me, used to see those everywhere in the 2000s with a little blue light on them flashing.
It wasn’t so much that it was put in stuff that wasn’t useful it was more that it was put in stuff that needed something better than Bluetooth but they put in Bluetooth because it was new and shiny rather than old boring radio.
The problem with Bluetooth, especially back then, was that the range was terrible (about 1 ft in my experience), you couldn’t connect to more than one thing at a time, it consumed quite a lot more power than radio (we have ultra low power modes now), and the bandwidth wasn’t great either (still somewhat the case but the bandwidth has improved). So you had things like Bluetooth car keys which were like keyless entry systems we have today, but rather than using radio they used Bluetooth so half the time you’d go near your car and nothing would happen.
A lot of the cases where things used to have Bluetooth now use Wi-Fi today. Of course there were always things that had Bluetooth for a gimmick, but the vast majority of it was simply things that had Bluetooth when something else would have been the better option. Back then Bluetooth headphones were seen as a gimmick because they basically didn’t work, now they work, so they’re not a gimmick anymore. The perception of if something is or is not a gimmick is more about if it works rather than if it actually is a useful product.
Radio in this case meaning an analog signal. Just fire the appropriate good vibes energy at the car rather than trying to send some packets over a wireless network. It didn’t work mostly due to the lack of any kind of redundancy. If a packet got dropped then it just dropped, it was gone.
When we started adding some decent protocols to Bluetooth it became more reliable, but it’s still not great range because of the frequencies used. Of course these days you would use Wi-Fi not analog radio, to get all of the advantages of Bluetooth but a much greater range and reliability.
The general idea is that every Device can communicate with every other Device. Bluetooth was added to everything in hopes that we could better automate every aspect of our lives when a critical mass of devices can talk to each other. The Bluetooth receiver in your alarm clock tells your coffee machine to start remotely. But we quickly realized that the overhead isn’t worth the payoff. But up until that point we made Bluetooth glasses, beanies, dash buttons, replaced inafred in most devices, power tools and appliances. It wasn’t that bad, but there were moments when you would pick up a smart nose trimmer and wonder why they included it.
And in the mid 2010s it got worse where everyone and their mother put bluetooth in anything and everything. IoT became accessible, only to be used in the dumbest way to try and get rich from Kickstarter.
only to be used in the dumbest way to try and get rich from Kickstarter
Followed of course by an obligatory Shark Tank appearance asking $2 million for 4% of their product where all profits go to the Zuck (the cost per customer acquisition is the entire margin and its all spent through facebook and ig ads)
I was in an auto parts store yesterday and saw that you can buy a can of that stuff to fix your AC and the damn can has Bluetooth capabilities. So no, we’re still not done putting Bluetooth where it doesn’t need to be.
That sounds cool, I don’t have a smart home setup, but Bluetooth sounds kinda nice to me for changing the temperature on the thermostat in the house, car not so much. Now I do know many people who use Bluetooth to cast their phone calls to their hands free devices in cars, as well as to hook up those diagnostic tools and have the error codes go to your phone instead of buying a product that costs hundreds of dollars to have a screen you would only use for that one purpose.
Oh no, this was strictly for the can to refill your car’s air conditioner liquid…the name is drawing a blank for me what it’s called, exactly.
I’ve seen some of these cans have a digital display on them which I guess this Bluetooth is supposed to replace. But it’s still so weird to me especially because these cans are generally disposable.
AI traditionally meant now-mundane things like pathfinding algorithms. The only thing people seem to want Artificial Intelligence to mean is “something a computer can almost do but can’t yet”.
AI is, by definition these days, a future technology. We think of AI as science fiction so when it becomes reality we just kick the can in the definition.
This is such a half brained response. Yes “actual” AI in the form of simulated neurons is pretty far off, but it’s fairly obvious when people say they AI they mean LLMs and other advanced forms of computing. There’s other forms of AI besides LLMs anyways, like image analyzers
The only thing half-brained is the morons who advertise any contemporary software as “AI”. The “other forms” you mention are machine learning systems.
AI contains the word “intelligence”, which implies understanding. A bunch of electrons manipulating a bazillion switches following some trial-and-error set of rules until the desired output is found is NOT that. That you would think the term AI is even remotely applicable to any of those examples shows how bad the brain rot is that is caused by the overabundant misuse of the term.
What do you call the human brain then, if not billions of “switches” as you call them that translate inputs (senses) into an output (intelligence/consciousness/efferent neural actions)?
It’s the result of billions of years of evolutionary trial and error to create a working structure of what we would call a neural net, which is trained on data (sensory experience) as the human matures.
Even early nervous systems were basic classification systems. Food, not food. Predator, not predator. The inputs were basic olfactory sense (or a more primitive chemosense probably) and outputs were basic motor functions (turn towards or away from signal).
The complexity of these organic neural networks (nervous systems) increased over time and we eventually got what we have today: human intelligence. Although there are arguably different types of intelligence, as it evolved among many different phylogenetic lines. Dolphins, elephants, dogs, and octopuses have all been demonstrated to have some form of intelligence. But given the information in the previous paragraph, one can say that they are all just more and more advanced pattern recognition systems, trained by natural selection.
The question is: where do you draw the line? If an organism with a photosensitive patch of cells on top of its head darts in a random direction when it detects sudden darkness (perhaps indicating a predator flying/swimming overhead, though not necessarily with 100% certainty), would you call that intelligence? What about a rabbit, who is instinctively programmed by natural selection to run when something near it moves? What about when it differentiates between something smaller or bigger than itself?
What about you? How will you react when you see a bear in front of you? Or when you’re in your house alone and you hear something that you shouldn’t? Will your evolutionary pattern recognition activate only then and put you in fight-or-flight? Or is everything you think and do a form of pattern recognition, a bunch of electrons manipulating a hundred billion switches to convert some input into a favorable output for you, the organism? Are you intelligent? Or just the product of a 4-billion year old organic learning system?
Modern LLMs are somewhere in between those primitive classification systems and the intelligence of humans today. They can perform word associations in a semantic higher dimensional space, encoding individual words as vectors and enabling the model to attribute a sort of meaning between two words. Comparing the encoding vectors in different ways gets you another word vector, yielding what could be called an association, or a scalar (like Euclidean or angular distance) which might encode closeness in meaning.
Now if intelligence requires understanding as you say, what degree of understanding of its environment (ecosystem for organisms, text for LLM. Different types of intelligence, paragraph 4) does an entity need for you to designate it as intelligent? What associations need it make? Categorizations of danger, not danger and food, not food? What is the difference between that and the Pavlovian responses of a dog? And what makes humans different, aside from a more complex neural structure that allows us to integrate orders of magnitude more information more efficiently?
A consciousness is not an “output” of a human brain. I have to say, I wish large language models didn’t exist, because now for every comment I respond to, I have to consider whether or not a LLM could have written that :(
In effect, you compare learning on training data: “input -> desired output” with systematic teaching of humans, where we are teaching each other causal relations. The two are fundamentally different.
Also, you are questioning whether or not logical thinking (as opposed to throwing some “loaded” neuronal dice) is even possible. In that case, you may as well stop posting right now, because if you can’t think logically, there’s no point in you trying to make a logical point.
A consciousness is not an “output” of a human brain.
Fair enough. Obviously consciousness is more complex than that. I should have put “efferent neural actions” first in that case, consciousness just being a side effect, something different yet composed of the same parts, an emergent phenomenon. How would you describe consciousness, though? I wish you would offer that instead of just saying “nuh uh” and calling me chatGPT :(
Not sure how you interpreted what I wrote in the rest of your comment though. I never mentioned humans teaching each other causal relations? I only compared the training of neural networks to evolutionary principles, where at one point we had entities that interacted with their environment in fairly simple and predictable ways (a “deterministic algorithm” if you will, as you said in another comment), and at some later point we had entities that we would call intelligent.
What I am saying is that at some point the pattern recognition “trained” by evolution (where inputs are environmental distress/eustress, and outputs are actions that are favorable to the survival of the organism) became so advanced that it became self-aware (higher pattern recognition on itself?) among other things. There was a point, though, some characteristic, self-awareness or not, where we call something intelligence as opposed to unintelligent. When I asked where you draw the line, I wanted to know what characteristic(s) need to be present for you to elevate something from the status of “pattern recognition” to “intelligence”.
It’s tough to decide whether more primitive entities were able to form causal relationships. When they saw predators, did they know that they were going to die if they didn’t run? Did they at least know something bad would happen to them? Or was it just a pre-programmed neural response that caused them to run? Most likely the latter.
Based on all that we know and observe, a dog (any animal, really) understands concepts and causal relations to varying degrees. That’s true intelligence.
From another comment, I’m not sure what you mean by “understands”. It could mean having knowledge about the nature of a thing, or it could mean interpreting things in some (meaningful) way, or it could mean something completely different.
To your last point, logical thinking is possible, but of course humans can’t do it on our own. We had to develop a system for logical thinking (which we call “logic”, go figure) as a framework because we are so bad at doing it ourselves. We had to develop statistical methods to determine causal relations because we are so bad at doing it on our own. So what does it mean to “understand” a thing? When you say an animal “understands” causal relations, do they actually understand it or is it just another form of pattern recognition (why I mentioned pavlov in my last comment)? When humans “understand” a thing, do they actually understand, or do we just encode it with the frameworks built on pattern recognition to help guide us? A scientific model is only a model, built on trial and error. If you “understand” the model you do not “understand” the thing that it is encoding. I know you said “to varying degrees”, and this is the sticking point. Where do you draw the line?
When you want to have artificial intelligence, even the most basic software can have some kind of limited understanding that actually fits this attempt at a definition - it’s just that the functionality will be very limited and pretty much appear useless. […] You could program image recognition using math to find certain shapes, which in turn - together with colour ranges and/or contrasts - could be used to associate object types, for which causal relations can be defined, upon which other parts of an AI could then base decision processes. This process has potential for error, but in a similar way that humans can mischaracterize the things we see - we also sometimes do not recognize an object correctly.
I recognize that you understand the point I am trying to make. I am trying to make the same point, just with a different perspective. Your description of an “actually intelligent” artificial intelligence closely matches how sensory data is integrated in the layers of the visual cortex, perhaps on purpose. My question still stands, though. A more primitive species would integrate data in a similar, albeit slightly less complex, way: take in (visual) sensory information, integrate the data to extract easier-to-process information such as brightness, color, lines, movement, and send it to the rest of the nervous system for further processing to eventually yield some output in the form of an action (or thought, in our case). Although in the process of integrating, we necessarily lose information along the way for the sake of efficiency, so what we perceive does not always match what we see, as you say. Image recognition models do something similar, integrating individual pixel information using convolutions and such to see how it matches an easier-to-process shape, and integrating it further. Maybe it can’t reason about what it’s seeing, but it can definitely see shapes and colors.
You will notice that we are talking about intelligence, which is a remarkably complex and nuanced topic. It would do some good to sit and think deeply about it, even if you already think you understand it, instead of asserting that whoever sounds like they might disagree with you is wrong and calling them chatbots. I actually agree with you that calling modern LLMs “intelligent” is wrong. What I ask is what you think would make them intelligent. Everything else is just context so that you understand where I’m coming from.
I had a bunch of sections of your comment that I wanted to quote, let’s see how much I can answer without copy-pasting too much.
First off, my apologies, I misunderstood your analogy about machine learning not as a comparison towards evolution, but towards how we learn with our developed brains. I concur that the process of evolution is similar, except a bit less targeted (and hence so much slower) than deep learning. The result however, is “cogito ergo sum” - a creature that started self-reflecting and wondering about it’s own consciousness. And this brings me to humans thinking logically: As such a creature, we are able to form logical thoughts, which allow us to understand causality. To give an example of what I mean: Humans (and some animals) did not need the invention of logic or statistics in order to observe moving objects and realize that where something moves, something has moved it - and therefore when they see an inanimate object move, they will eventually suspect the most likely cause for the move in the direction that the object is coming from. Then, when we do not find the cause (someone throwing something) there, we will investigate further (if curious enough) and look for a cause. That’s how curiosity turns into science. But it’s very much targeted, nothing a deep learning system can do. And that’s kind of what I would also expect from something that calls itself “AI”: a systematic analysis / categorization of the input data for the purpose of processing that the system was built for. And for a general AI, also the ability to analyze phenomena to understand their root cause.
Of course, logic is often not the same as our intuitive thoughts, but we are still able to correct our intuitive assumptions based on outcome, but then understand the actual causal relation (unlike a deep learning system) based on our corrected “model” of whatever we observed. In the end, that’s also how science works: We describe reality with a model, and when we discover a discrepancy, we aim to update the model. But we always have a model.
With regards to some animals understanding objects / causal relations, I believe - beyond having a concept of an object - defining what I mean by “understanding” is not really helpful, considering that the spectrum of intelligence among animals overlaps with that of humans. Some of the more clever animals clearly have more complex thoughts and you can interact with them in a more meaningful way than some of the humans with less developed brains, be it due to infancy, or a disability or psychological condition.
How would you describe consciousness, though? I wish you would offer that instead of just saying “nuh uh” and calling me chatGPT :(
First off, I meant the LLM comment seriously - I am considering already to stop participating in internet debates because LLMs have become so sophisticated that I will no longer be able to know whether I am arguing with a human, or whether some LLM is wasting my precious life time.
As for how to describe consciousness, that’s a largely philosophical topic and strongly linked to whether or not free will exists (IMO), although theoretically it would be possible to be conscious but not have any actual free will. I can not define the “sense of self” better than philosophers are doing it, because our language does not have the words to even properly structure our thoughts on that. I can however, tell you how I define free will:
assuming you could measure every atom, sub-atomic particle, impulse & spin thereof, energy field and whatever else physical properties there are in a human being and it’s environment
when that individual moves a limb, you would be able to trace - based on what we know:
the movement of the limb back to the muscles contracting
movement of the muscles back to electrical signals in some nerves
the nerve signals back to some neurons firing in the brain
if you trace that chain of “causes” further and further, eventually, if free will exists, it would be impossible to find a measurable cause for some “lowest level trigger event”
And this lowest level trigger event - by some researchers attributed to quantum decay - might be / could be influenced by our free will, even if - because we have this “brain lag” - the actual decision happened quite some time earlier, and even if for some decisions, they are hardwired (like reflexes, which can also be trained).
My personal model how I would like consciousness to be: An as-of-yet undiscovered property of matter, that every atom has, but only combined with an organic computer that is complex enough to process and store information would such a property actually exhibit a consciousness.
In other words: If you find all the subatomic particles (or most of them) that made up a person in history at a given point in time, and reassemble them in the exact same pattern, you would, in effect, re-create that person, including their consciousness at that point in time.
If you duplicate them from other subatomic particles with the exact same properties (as far as we can measure) - who knows? Because we couldn’t measure nor observe the “consciousness property”, how would we know if that would be equal among all particles that are equal in the properties we can measure. That would be like assuming atoms of a certain element were all the same, because we do not see chemical differences for other isotopes.
systematic teaching of humans, where we are teaching each other causal relations. The two are fundamentally different.
So you mean that a key component to intelligence is learning from others? What about animals that don’t care for their children? Are they not intelligent?
What about animals that can’t learn at all, wheere their barains are completely hard wired from birth. Is that not intelligence?
You seem to be objecting that OPs questions are too philosophical. The question “what is intelligence” can only be solved by philosophical discussion, trying to break it down into other questions. Why is the question about the “brain as a calculator” objectionable? I think it may be uncomfortable for you to even speak of but that would only be an indicator that there is something to it.
It would indeed throw your world view upside down if you realised that you are also just a computer made of flesh and all your output is deterministic, given the same input.
So you mean that a key component to intelligence is learning from others? What about animals that don’t care for their children? Are they not intelligent?
You contradict yourself, the first part of your sentence getting my point correctly, and the second questioning an incorrect understanding of my point.
What about animals that can’t learn at all, wheere their barains are completely hard wired from birth. Is that not intelligence?
Such an animal does not exist.
It would indeed throw your world view upside down if you realised that you are also just a computer made of flesh and all your output is deterministic, given the same input.
That’s a long way of saying “if free will didn’t exist”, at which point your argument becomes moot, because I would have no influence over what it does to my world view.
My main point is that falsifying a hypothesis based on how it makes you feel is not very productive. You just repeated it again. You seem to get mad by just posing the question.
You are unfortunately wrong on this one. The term “AI” has been used to describe things other than AGI since basically the invention of computers that could solve problems. The people that complain about using “AI” to describe LLMs are actually the ones trying to change language.
I think the modern pushback comes from people who get their understanding of technology from science fiction. SF has always (mis)used AI to mean sapient computers.
LLMs are a way of developing an AI. There’s lots of conspiracy theories in this world that are real it’s better to focus on them rather than make stuff up.
There really is an amazing technological development going on and you’re dismissing it on irrelevant semantics
The acronym AI has been used in game dev for ages to describe things like pathing and simulation which are almost invariably algorithms (such as A* used for autonomous entities to find a path to a specific destination) or emergent behaviours (which are also algorithms were simple rules are applied to individual entities - for example each bird on a flock - to create a complex whole from many such simple agents, and example of this in gamedev being Steering Behaviours, outside gaming it would be the Game Of Life).
They didn’t so much “evolve” as AI scared the shit out of us at such a deep level we changed the definition of AI to remain in denial about the fact that it’s here.
Since time immemorial, passing a Turing test was the standard. As soon as machines started passing Turing tests, we decided Turing tests weren’t such a good measure of AI.
But I haven’t yet seen an alternative proposed. Instead of using criteria and tasks to define it, we’re just arbitrarily saying “It’s not AGI so it’s not real AI”.
In my opinion, it’s more about denial than it is about logic.
Op is an idiot though hope we can agree with that one.
Telling everyone else how they should use language is just an ultimately moronic move. After all we’re not French, we don’t have a central authority for how language works.
Telling everyone else how they should use language is just an ultimately moronic move. After all we’re not French, we don’t have a central authority for how language works.
There’s a difference between objecting to misuse of language and “telling everyone how they should use language” - you may not have intended it, but you used a straw man argument there.
What we all should be acutely aware of (but unfortunately many are not) is how language is used to harm humans, animals or our planet.
Fascists use language to create “outgroups” which they then proceed to dehumanize and eventually violate or murder. Capitalists speak about investor risks to justify return on invest, and proceed to lobby for de-regulation of markets that causes human and animal suffering through price gouging and factory farming livestock. Tech corporations speak about “Artificial Intelligence” and proceed to persuade regulators that - because there’s “intelligent” systems - this software may be used for autonomous systems that proceed to cause injury and death on malfunctions.
Yes, all such harm can be caused by individuals in daily life - individuals can be murderers or extort people on something they really need, or a drunk driver can cause an accident that kills people. However, the language that normalizes or facilitates such atrocities or dangers on a large scale, is dangerous and therefore I will proceed to continue calling out those who want to label the shitty penny market LLMs and other deep learning systems as “AI”.
I’ve given up trying to enforce the traditional definitions of “moot”, “to beg the question”, “nonplussed”, and “literally” it’s helped my mental health. A little. I suggest you do the same, it’s a losing battle and the only person who gets hurt is you.
Words might have meanings but AI has been used by researchers to refer to toy neutral networks longer than most people on Lemmy have been alive.
This insistence that AI must refer to human type intelligence is also such a weird distortion of language. Intelligence has never been a binary, human level indicator. When people say that a dog is intelligent, or an ant hive shows signs of intelligence, they don’t mean it can do what a human can. Why should AI be any different?
You honestly don’t seem to understand. This is not about the extent of intelligence. This is about actual understanding. Being able to classify a logical problem / a thought into concepts and processing it based on properties of such concepts and relations to other concepts. Deep learning, as impressive as the results may appear, is not that. You just throw a training data at a few billion “switches” and flip switches until you get close enough to a desired result, without being able to predict how the outcome will be if a tiny change happens in input data.
With regards to the dog & my description of intelligence, you are wrong: Based on all that we know and observe, a dog (any animal, really) understands concepts and causal relations to varying degrees. That’s true intelligence.
When you want to have artificial intelligence, even the most basic software can have some kind of limited understanding that actually fits this attempt at a definition - it’s just that the functionality will be very limited and pretty much appear useless.
Think of it this way: deterministic algorithm -> has concepts and causal relations (but no consciousness, obviously), results are predictable (deterministic) and can be explained deep learning / neural networks -> does not implicitly have concepts nor causal relations, results are statistical (based on previous result observations) and can not be explained -> there’s actually a whole sector of science looking into how to model such systems way to a solution Addition: the input / output filters of pattern recognition systems are typically fed through quasi-deterministic algorithms to “smoothen” the results (make output more grammatically correct, filter words, translate languages)
If you took enough deterministic algorithms, typically tailored to very specific problems & their solutions, and were able to use those as building blocks for a larger system that is able to understand a larger part of the environment, then you would get something resembling AI. Such a system could be tested (verified) on sample data, but it should not require training on data.
Example: You could program image recognition using math to find certain shapes, which in turn - together with colour ranges and/or contrasts - could be used to associate object types, for which causal relations can be defined, upon which other parts of an AI could then base decision processes. This process has potential for error, but in a similar way that humans can mischaracterize the things we see - we also sometimes do not recognize an object correctly.
Oops accidentally submitted. If someone disagrees with this as a fair challenge, let me know why.
I’ve been presenting this challenge repeatedly and in my experience it leads very quickly to the fact that nobody — especially not the experts — has a precise definition of AGI
While they are amazingly effective at many problems we throw at them, I’m not convinced that they’re generally intelligent. What I do know is that in their current form, they are not tractable systems for anything but relatively small problems since compute and memory costs increase quadratically with the number of steps.
Think about the ways that information tech has revolutionized our ability to do things. It’s allowed us to do math, produce and distribute news and entertainment, communicate with each other, make our voices heard, organize movements, and create and access pornography at rates and in ways that humanity could only have dreamed of only a few decades ago.
Now consider that AI is first and foremost a technology predicated on reappropriating and stealing credit for another person’s legitimate creative work.
Now imagine how much of humanity’s history has had that kind of exploitation at the forefront of its worst moments, and consider what might lie ahead with those kind of impulses being given the rocket fuel of advanced information technology.
Add comment