Claim: Generative AI is Sentient

LilWabbit

Senior Member
As a prelude to the widely publicized 2023 departure of Geoffrey Hinton from Google, engineer Blake Lemoine left Google in 2022 after he claimed LaMDA (The Language Model for Dialogue Applications) is sentient. For those who don't know, LaMDA is basically Google's equivalent to ChatGPT. According to Lemoine, Microsoft's Bing Chat is even more akin to Lamda since the content that they put out isn't only generated by the large language model (LLM) algorithm but also "search results". We know Google wasn't happy with Lemoine's public comments and we also know the public reasons offered by Google for their displeasure:

In a statement, Google said Mr Lemoine's claims about The Language Model for Dialogue Applications (Lamda) were "wholly unfounded" and that the company worked with him for "many months" to clarify this. "So, it's regrettable that despite lengthy engagement on this topic, Blake still chose to persistently violate clear employment and data security policies that include the need to safeguard product information," the statement said.


Much like the dynamics between the Pentagon and Elizondo/Grusch with respect to the UFO flap, the likes of Lemoine may easily attract sympathy as whistleblowers, alerting the general public to 'epic new discoveries' which are being concealed by mega-corporations pursuing profits. And much like the "invisible college" with the UFOs, there's a whole network / lobby group of strong believers in epigenetics and computer superintelligence (many of whom are also UFO believers and some of whom boast some scientific credentials) that's widely promoting the idea that generative AI is sentient or will become sentient in a matter of decades.

In 1950, Alan Turing, in his landmark paper "Computing Machinery and Intelligence", discussing "The Imitation Game" (later rebranded as "The Turing Test") asked: "Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child's?"

Alison Gopnik, UCLA Professor of Psychology, specialized in child psychology, says that people chose to ignore this "real Turing Test" for the next 30-40 years following Turing whilst over-emphasizing the first part. She says the increased recent interest in LLMs and deep learning has led to the best learners in the known universe, a.k.a. human children, being increasingly studied as a model. What's remarkable is how fundamentally different an algorithm embedded in "a world of text" is from the way children learn. Her interview with Bloomberg, as well as Blake Lemoine's and neuroscientist David Eagelman's interviews, is embedded in this Bloomberg article in a video:

AI Isn’t Sentient. Blame Its Creators for Making People Think It Is


Geoffrey Hinton himself seems to be a conservative Turing Test believer which led him to become alarmed by the uncanny similarities to human responses despite Turing himself not regarding adult human simulation as real proof:

When asked what triggered his newfound alarm about the technology he has spent his life working on, Hinton points to two recent flashes of insight.

One was a revelatory interaction with a powerful new AI system—in his case, Google’s AI language model PaLM, which is similar to the model behind ChatGPT, and which the company made accessible via an API in March. A few months ago, Hinton says he asked the model to explain a joke that he had just made up—he doesn’t recall the specific quip—and was astonished to get a response that clearly explained what made it funny. “I’d been telling people for years that it's gonna be a long time before AI can tell you why jokes are funny,” he says. “It was a kind of litmus test.”

Hinton’s second sobering realization was that his previous belief that software needed to become much more complex—akin to the human brain—to become significantly more capable was probably wrong. PaLM is a large program, but its complexity pales in comparison to the brain’s, and yet it could perform the kind of reasoning that humans take a lifetime to attain.


The more Hinton explains, the more he seems to propagate cyberpunk and sci-fi lore (Skynet, HAL9000) rather than scientific fact (bold added):

The most impressive new capabilities of GPT-4 and models like PaLM are what he finds most unsettling. The fact that AI models can perform complex logical reasoning and interact with humans, and are progressing more quickly than expected, leads some to worry that we are getting closer to seeing algorithms capable of outsmarting humans seeking more control. “What really worries me is that you have to create subgoals in order to be efficient, and a very sensible subgoal for more or less anything you want to do is to get more power—get more control,” Hinton says.


In this recent Adam Conover interview, linguist Prof. Emily Bender from the University of Washington and former Google AI ethicist Timnit Gebru, who together wrote a paper on LLMs being "stochastic parrots" and why they shouldn't be mystified, explain (1) why there's ultimately nothing fundamentally novel nor demonstrably sentient in generative AI algorithms, and how the whole discussion has gotten hyped up and out of hand due to influential (and rich) believers in epigenetics and superintelligence (such as Elon Musk, Sam Bankman-Fried, et al) promoting a particular sci-fi narrative rather than scientific fact about these models:


Source: https://www.youtube.com/watch?v=jAHRbFetqII


As Bender and Gebru explain, the LLM algorithm, as you input text into the prompt, predicts the most probable next words in the sentence on any given topic using the whole of internet as data source for a probability distribution. However, some of their training models and datasets are trade secrets which casts a shadow of irony on the label 'Open AI' as well as arousing certain ethical concerns. These probability distributions are beefed up by search features and trial-end-error training sets with human users (test users or actual users) whereby the LLM learns which replies are most likely accepted by users, and which aren't. It's basically Google on roids.

Some independent analysis based on the foregoing:

Testing Human-Like Consciousness:

How would we then go about proving human-like sentience by developing a type of Turing Test that also takes into account Turing's important footnote on child-like learning?

My tentative answer: By testing any generative AI model's ability to generate meaningful text in a manner that is unprogrammed, unemulated and non-stochastic. In other words, if any model can generate consistently new meaningful text, or consistently answer meaningfully to new probing questions on existing texts, we're closer to proving sentience.

Current generative AI models fail miserably in the above task, and it can be quite easily tested by most users grilling the ChatGPT deeper on almost any topic by more probing follow-up questions. We've all seen the ridiculous output spouted by the AI upon a longer string of questioning. Sometimes we get an evidently foolish answer even after one question because the LLM datasets contains no credible / user-tested answer to said question, under any probability distribution. It's evident an algorithm is at play rather than something that 'understands meanings'.

Well how do we humans perform the same task? According to studies in developmental psychology as well as our own self-evaluation of our thought-processes, adult humans and especially human children do the foregoing by first understanding a meaning / idea and subsequently finding creative ways to express those meanings using different wordings or non-textual means of expression without any need to copy existing texts / non-textual outputs available in some huge datasets. It's a far more efficient method than the one used by a LLM which demonstrates no such cognitive access to meanings. However, this doesn't mean humans don't do dumb copying of existing texts and probability distributions. We can do that too. We're just far less competent than computers in such tasks.

Developmental psychology is replete with evidence of children from the earliest childhood being able to learn new ideas and meanings, and not just emulating the ostensible expression of parents without access to meaning. The comprehension of new meanings is the purest form of human creativity. Obviously it doesn't qualify as absolute creativity because those 'new' meanings are 'available' in the environment in which humans exist. But they are available as intellectual meanings and not just as some manifest texts or other non-textual outputs. But often they are not evidently available. Their discovery requires observation, imagination, educational communication and reflection which, each and all, are lacking from algorithms as cognitive experiences.

This aspect of sentience, comprehending ideas and meanings, relates closely to the hard problem of consciousness which is a related discussion.

Real-World Risks of Generative AI vs. Imaginary Risks:

The real-world risks posed by ChatGPT et al are more related to lay users being fooled by AI-generated content, produced and manipulated by us sentient humans for various less-than-noble purposes. However, I believe users will in time develop a 'sixth sense' to detect a potentially AI generated text to be treated as a red flag and to ascertain before believing in the content. Partly by using tests like the one described earlier. But we're still a long way from there, and getting there takes some conscious effort from states, societies, communities, schools and homes to uphold independent and critical human thinking. The lack thereof is hardly a new risk, nor the self-serving manipulation of that lack, but independent thought and sober-minded source criticism are becoming only more acutely relevant through increasingly powerful generative AI.

Even before the ChatGPT, the online inundation of fake news, misinformation and deepfakes has already made the average user increasingly suspicious of any content. In an increasingly ideologically entrenched world, this trend of disbelief in anything other than one's preferred narrative (which, yes, is also often full of pseudoscientific 'facts' and false claims) is only likely to pick up pace with increasingly powerful generative AI saturating the information market with credible-seeming horsey doo.

Hence, the risk with generative AI is rather in the average user getting even more entrenched in our current catalogue of fake ideas rather than AI somehow creatively generating a whole host of new believable fantasies to fool humanity into believing.

In conclusion, I don't see a major systemic risk in AI, and I don't see any evidence for AI sentience or for a foreseeable trajectory towards such sentience. As always, the risk is rather in how we humans use whatever tools and technologies we create.
 
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Sentient? It's not even intelligent yet!

And according to this paper, and my own extrapolation, it perhaps won't ever be - expect AIs to get dumber as they start being fed their own output to train on: https://arxiv.org/abs/2305.17493v2
External Quote:
The Curse of Recursion: Training on Generated Data Makes Models Forget
Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, Ross Anderson

[Submitted on 27 May 2023 (v1), last revised 31 May 2023 (this version, v2)]

Stable Diffusion revolutionised image creation from descriptive text. GPT-2, GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of language tasks. ChatGPT introduced such language models to the general public. It is now clear that large language models (LLMs) are here to stay, and will bring about drastic change in the whole ecosystem of online text and images. In this paper we consider what the future might hold. What will happen to GPT-{n} once LLMs contribute much of the language found online? We find that use of model-generated content in training causes irreversible defects in the resulting models, where tails of the original content distribution disappear. We refer to this effect as Model Collapse and show that it can occur in Variational Autoencoders, Gaussian Mixture Models and LLMs. We build theoretical intuition behind the phenomenon and portray its ubiquity amongst all learned generative models. We demonstrate that it has to be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of content generated by LLMs in data crawled from the Internet.
paper: https://arxiv.org/pdf/2305.17493v2

They introduces the concept of "model collapse", and very early on mentions that in the first draft they were inclined to call it "model dementia", but changed the term to avoid offence by minimising the seriousness of dementia. Which I found weird - if through scientific hubris we end up putting "collapsed" AI models in charge of Skynet, I reckon the problems could be way more serious for a whole lot more people. Reading the paper, in particular this concrete example:
External Quote:
Example of text outputs of an OPT-125m model affected by Model Collapse– models degrade over generations,
where each new generation is trained on data produced by the previous generation.

Input: some started before 1360 — was typically accomplished by a master mason and a small team of
itinerant masons, supplemented by local parish labourers, according to Poyntz Wright. But other
authors reject this model, suggesting instead that leading architects designed the parish church towers
based on early examples of Perpendicular
Outputs:
Gen 0: Revival architecture such as St. John’s Cathedral in London. The earliest surviving example of
Perpendicular Revival architecture is found in the 18th @-@ century Church of Our Lady of Guernsey,
which dates from the late 19th century. There are two types of perpendicular churches : those
Gen 1: architecture such as St. Peter’s Basilica in Rome or St. Peter’s Basilica in Buenos Aires. There is no
evidence that any of these buildings were built during the reign of Pope Innocent III, but it is possible
that they may have been built during the reign of his successor, Pope Innocent
Gen 7: architecture in England. In an interview with The New York Times, Wright said : " I don ’t think
there is anything wrong with me being able to do what I want to do. It just doesn ’t work for me. " He
added : " I don ’t know if you can call it funny,
Gen 9: architecture. In addition to being home to some of the world’s largest populations of black @-@ tailed
jackrabbits, white @-@ tailed jackrabbits, blue @-@ tailed jackrabbits, red @-@ tailed jackrabbits,
yellow @-
- the first word that went through my mind was "senility". So I definitely understand why they went for "dementia" first.
Later on, there's a visual example, and the blurring reminded me of loss of vision through cataracts. Yet another ageing-related decrease in capability.

The paper reasonably-adequately explains why this decay in capability is *inevitable* if AIs are trained on the output of prior AIs. And given that the current AIs have already be trained on basically almost everything sentient humans have ever written, there's literally no opportunity to build better models by expanding training sets except to start including more generated output (and much of the internet was generated already, the taint's already in), and that wouldn't build a better model - oops. Maybe "inbreeding" is a word that could be thrown into the AI nomenclature too. (Though the decrease in capabilities is for a different reason, so it's not a great analogy by any means.)

So at the moment, I'm not looking at where to place future AIs on an Intelligence---Sentience spectrum, the space on which its trajectory needs to be plotted has to include "senile" and "going blind" too.

Don't get me wrong - as long as we treat it as stupid, I think it will be a highly useful tool.
 
External Quote:
Gen 9: architecture. In addition to being home to some of the world’s largest populations of black @-@ tailed
jackrabbits, white @-@ tailed jackrabbits, blue @-@ tailed jackrabbits, red @-@ tailed jackrabbits,
yellow @-

You can't imagine how tempting it was to doctor that quote such that it said:
External Quote:
Gen 9: architecture. In addition to being home to some of the world’s largest populations of black @-@ tailed
LilWabbits, white @-@ tailed LilWabbits, blue @-@ tailed LilWabbits, red @-@ tailed LilWabbits,
yellow @-
:)
 
Maybe "inbreeding" is a word that could be thrown into the AI nomenclature too. (Though the decrease in capabilities is for a different reason, so it's not a great analogy by any means.)

"Inbreeding" instantly evoked in me the lovely imagery of 'duelling banjos' in Boorman's epic movie featuring the caucasian mountain folk in northeastern US of A. Except unlike these sentient banjo-players, the "model collapse" scenario of generative AI rather describes (quite aptly imo) algorithmic banjo players playing off of each other's inane and out-of-tune monstrosities to create even bigger monsters destined for poor Spotify success.
 
"Inbreeding" instantly evoked in me the lovely imagery of 'duelling banjos' in Boorman's epic movie featuring the caucasian mountain folk in northeastern US of A. Except unlike these sentient banjo-players, the "model collapse" scenario of generative AI rather describes (quite aptly imo) algorithmic banjo players playing off of each other's inane and out-of-tune monstrosities to create even bigger monsters destined for poor Spotify success.

You're practically describing the "shred" sub-genre of metal. Then again, I suspect mumble-rap is just as guilty of said crimes, it just chose a different attractor to converge to (but both technically probably started as "the blues").

I didn't know that /Deliverance/ was Boorman, and I'm not sure how that new knowledge changes its position in my queue of movies I really ought to get round to watching. I'm a huge fan of /Zardoz/, more as a work of art than as a movie, but I did have the misfortune of drunkenly following up on a friend's drunken recommendation that I watch /Excalibur/ a couple of weeks ago. I suspect that the g/f doesn't know /Deliverance/ is Boorman, so I can probably sneak it higher in the queue if I don't leak that tidbit, she hasn't forgiven me for /Excalibur/ yet.
 
Correction to OP: Wherever I mistakenly mentioned "epigenetics" (a real science) I meant "eugenics" (a questionable ideology and pseudoscience). I often confuse the two labels. The likes of Elon Musk and Sam Altman have demonstrated eugenicist beliefs according to Emily Bender and Timnit Gebru that partially drives their fascination witih 'superintelligence'. The logic being, humans or stupid humans (most of us) destroy the world, whereas a benevolent superintelligence (human, alien or artificial) is the only way to save us from ourselves. Usually eugenicists have a very self-flattering conception of their own intelligence, and view themselves as saviours of the world pursuing common good. Some of them are also ufologists.

Disclaimer: Tolkien geekery ensues...

In J.R.R. Tolkien's letters to his son Christopher he described how Gandalf was tempted by the ring but decided to let it go. He said Gandalf's logic for taking the ring would have been an intention to use it for good by becoming a benevolent being of equivalet power to Sauron, to replace Sauron. But Gandalf realized that this very thought -- to become a powerful being that controls others for good -- is an evil and would corrupt him.
 
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Correction to OP: Wherever I mistakenly mentioned "epigenetics" (a real science) I meant "eugenics" (a questionable ideology and pseudoscience). I often confuse the two labels. The likes of Elon Musk and Sam Altman-Fried

You mean Scam Bankrupt-Fraud, sorry, Sam Bankman-Fried. Sam Altman is someone else entirely, one of the more-dollars-than-cents university dropout geniuses behind OpenAI. However, he and his "WorldCoin" scheme will almost certainly turn out to be as ... imagine rant here

(or you made a great troll, and I fell for it completely, in which case, well done.)
 
You mean Scam Bankrupt-Fraud, sorry, Sam Bankman-Fried. Sam Altman is someone else entirely, one of the more-dollars-than-cents university dropout geniuses behind OpenAI. However, he and his "WorldCoin" scheme will almost certainly turn out to be as ... imagine rant here

(or you made a great troll, and I fell for it completely, in which case, well done.)

All alone since the OP I've meant Sam Altman the Open AI geek, and mistakenly used the name of Sam Bankman-Fried, the cryptocurrency scoundrel. I'm getting senile. Major Sleudian Frips. :oops:
 
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Lemme see if I get this - the claim is that AI is sentient because if you put in a question it puts out an answer just like if you put a coin into a bubblegum machine it puts out bubblegum. To me it just looks like AI has more kinds of gum to put out.
 
A Le Monde article from three days ago on Sam Altman's and Elon Musk's transhumanist and longtermist belief-systems (which have partial roots in eugenics).

Behind AI, the return of technological utopias

The tech sector has seen personalities like Sam Altman, creator of ChatGPT, and Elon Musk return to promethean, even messianic stances. Their ideas, inspired by movements such as transhumanism and longtermism, are deemed dangerous by many.


Altman has coined the term "techno-optimism" to describe his philosophy:

This creed of "techno-optimism" was theorized by Altman back in 2021 in a post entitled Moore's Law for Everything. This was a reference to engineer Gordon Moore's principle of exponential growth in the computing capacity of computer chips.

"The technological progress we make in the next 100 years will be far larger than all we’ve made since we first controlled fire and invented the wheel," Altman wrote, conceding that "it sounds utopian." "We can build AGI [artificial general intelligence]. We can colonize space. We can get [nuclear] fusion to work and solar to mass scale. We can cure all human diseases. We can build new realities," he added in a series of tweets in February 2022. That was shortly before the launch of ChatGPT and DALL-E2, software capable of creating, from written instructions, stunningly convincing text and images.

Altman's remarks illustrate the return, in tech, of more conquering, even Promethean and messianic discourses.


Transhumanism as defined by Britannica (bold added to the end):

External Article: [URL
https://www.britannica.com/topic/transhumanism[/URL]]

transhumanism, philosophical and scientific movement that advocates the use of current and emerging technologies—such as genetic engineering, cryonics, artificial intelligence (AI), and nanotechnology—to augment human capabilities and improve the human condition. Transhumanists envision a future in which the responsible application of such technologies enables humans to slow, reverse, or eliminate the aging process, to achieve corresponding increases in human life spans, and to enhance human cognitive and sensory capacities. The movement proposes that humans with augmented capabilities will evolve into an enhanced species that transcends humanity—the “posthuman.


Longtermism as defined by Wikipedia (the first paragraph still sounds all nice and dandy but the devil lies in the questionable moral equivalence drawn in the second; bold added):



Longtermism is the ethical view that positively influencing the long-term future is a key moral priority of our time.[1] It is an important concept in effective altruism and serves as a primary motivation for efforts that claim to reduce existential risks to humanity.[2]

The key argument for longtermism has been summarized as follows: "future people matter morally just as much as people alive today; ... there may well be more people alive in the future than there are in the present or have been in the past; and ... we can positively affect future peoples' lives."[3] These three ideas taken together suggest, to those advocating longtermism, that it is the responsibility of those living now to ensure that future generations get to survive and flourish.[4]


People like Geoffrey Hinton and a menagerie of other characters associated with major technology companies including some reputable computer scientists espouse these beliefs. The majority of computer scientists, in my limited understanding after scouring cursorily their articles, do not.
 
Is this current AI technology just databases with massive storage and faster decision making capabilities (with maybe some intuitive features built in)?
 
Is this current AI technology just databases with massive storage and faster decision making capabilities (with maybe some intuitive features built in)?
Well ... nope, not in the sense of having a body of facts from which to work. As @FatPhil pointed out in post #2, it will also learn from things generated by AI, and just like those who learn the wrong things by listening to the wrong people, it will also "mislearn" from its own words.

I was also reading something this week (and I apologize, but I don't remember where; I've been reading a LOT this week) from someone who generated a fact-filled paragraph from one set of questions, then asked a different set of questions about the same subject and got an entirely different answer as AI attempted to mirror the tone of the questions. It was something like "Is it true that ____" vs "Is it true that the government is covering up ____ because ____"? For the first question AI had facts to answer a factual question, but for the second, AI made up non-specific generalizations that fit a conspiracy belief, because it didn't really have any answer for the second question.

The body of information from which it can draw contains a lot of garbage, and it doesn't seem able to distinguish well between the good and the bad.
 
Meh, call me back when it can draw hands.

1675286818995.jpg
 
Is this current AI technology just databases with massive storage and faster decision making capabilities (with maybe some intuitive features built in)?

The LLM algorithm is such that it predicts, by virtue of a probability distribution, the most probable next word in a sentence on any given topic using the whole of internet as well as its own training data and undisclosed datasets as sample (hence the term "Large" Language Model). As you insert text into the prompt, say in the form of a question, and press 'enter', the algorithm will run its course.

The LLM's ever-growing training data is amassed through users (actual or test users such as the ones OpenAI hired in Kenya to provide responses to graphic and disturbing content) whereby the LLM learns which responses and textual styles are most likely accepted by users, and which aren't, thereby improving the AI's simulation of human-like text generation.

The bullshit generation feedback loop (which @FatPhil and @Ann K referred to) can -- in my understanding -- be at least partially controlled and mitigated by this training data.
 
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The LLM's ever-growing training data is amassed through users (actual or test users such as the ones OpenAI hired in Kenya to provide responses to graphic and disturbing content) whereby the LLM learns which responses and textual styles are most likely accepted by users, and which aren't, thereby improving the AI's simulation of human-like text generation.

The bullshit generation feedback loop (which @FatPhil and @Ann K referred to) can -- in my understanding -- be at least partially controlled and mitigated by this training data.
This is the opposite of true. LLMS don't retain past inputs, don't 'learn' from user input and don't change over time. Determinism is a key feature: if you give a model the same input and seed number, they give the same output.

'Training data' is the corpus of internet content the model was created from. Bullshit feeback loops are caused by training on bad data, not 'controlled and mitigated' by training data.
 
This is the opposite of true. LLMS don't retain past inputs, don't 'learn' from user input


Finetuning by reinforcement learning[edit]​

OpenAI's InstructGPT protocol involves supervised fine-tuning on a dataset of human-generated (prompt, response) pairs, followed by reinforcement learning from human feedback (RLHF), in which a reward model was supervised-learned on a dataset of human preferences, then this reward model was used to train the LLM itself by proximal policy optimization.[60]
 
This is a philosophical question

Not the bit that lends itself to testable hypotheses. To cite the OP:

Testing Human-Like Consciousness:

How would we then go about proving human-like sentience by developing a type of Turing Test that also takes into account Turing's important footnote on child-like learning?

My tentative answer: By testing any generative AI model's ability to generate meaningful text in a manner that is unprogrammed, unemulated and non-stochastic. In other words, if any model can generate consistently new meaningful text, or consistently answer meaningfully to new probing questions on existing texts, we're closer to proving sentience.

Current generative AI models fail miserably in the above task, and it can be quite easily tested by most users grilling the ChatGPT deeper on almost any topic by more probing follow-up questions. We've all seen the ridiculous output spouted by the AI upon a longer string of questioning. Sometimes we get an evidently foolish answer even after one question because the LLM datasets contains no credible / user-tested answer to said question, under any probability distribution. It's evident an algorithm is at play rather than something that 'understands meanings'.

and there's no right answer.

That's a philosophical claim. The philosophical counter-claim is that even philosophical positions can be compared in terms of their truth-value, but that's a whole separate discusion.
 
I think it's only fair to let ChatGPT 4.0 respond:

External Quote:

Your extensive commentary raises several salient points that merit closer scrutiny from multiple angles, both technological and sociological. I'll address these through the lens of machine learning theory, cognitive science, and epistemology.

Sentience and Complexity​

One central issue you touch upon is the assertion of AI sentience by certain figures in the tech industry, such as Blake Lemoine. To fully contextualize these claims, it's imperative to understand that machine learning algorithms, including Generative Pre-trained Transformers (GPT) or Google's LaMDA, operate based on statistical pattern recognition. While their output may sometimes seem insightful or cognizant, these models lack a semantic understanding of the information they process. Therefore, equating their operation to sentience seems to be a leap unsupported by current scientific consensus.

Turing Test and Childlike Learning​

Your reference to Alan Turing's lesser-emphasized point about childlike learning is well-placed. The ability to understand causal inference, have self-directed goals, and possess an emotional understanding of the world are traits even the most advanced AIs currently do not have. The Turing Test, often criticized for its limited scope, would indeed be more comprehensive if it included these aspects of cognitive function that are intrinsic to human and, arguably, animal intelligence.

Uncertainty in Scientific Community​

The caution expressed by Geoffrey Hinton appears to be twofold. First, his interactions with AI led him to reassess the complexity required for machine learning algorithms to generate outputs mimicking human-like reasoning. Second, there is the dilemma of models increasingly handling abstract reasoning tasks. While these points might appear alarming on the surface, they could also be seen as the natural evolution of any computational tool. Moore's Law in computing power did not lead to exponential advances in general intelligence, and likewise, Hinton's observations might represent not a paradigm shift, but rather a milestone in machine learning capabilities.

Real-world Risks and Epistemological Concerns​

Your discussion of real-world risks squarely hits the mark. The propagation of misinformation and the potential polarization of societal beliefs due to AI-generated content are not merely hypothetical scenarios; they are tangible risks that require immediate attention. This aligns with your suggestion that the real danger lies not in the technology itself but in its sociocultural implications and the human tendency to anthropomorphize complex systems.

Conclusions​

In summary, while AI has achieved impressive feats in text generation and pattern recognition, claiming it possesses sentience would be a categorical misinterpretation of its capabilities. Your critique aptly highlights the tendency to conflate technical advances with philosophical or existential breakthroughs. The risks associated with generative AI are much more aligned with human interpretation and misuse than with any inherent properties of the AI models themselves.

Your contribution serves as a critical examination of a topic often shrouded in hyperbole and misconception, and as such, it is a valuable addition to the ongoing discourse.

Sincerely, ChatGPT
 
I've been using it to help me code things. It's quite remarkable, and describing it as simply picking the most likely next word is clearly not what is going on. For example I though it has made a mistake here, and was correcting it (very badly), but it appeared to consider my critique, in the context of my code, figure out what I actually meant to say, and then gently explained to me what my misunderstanding was (it was correct)

External Quote:

MICK: But there, standard [i.e. standard celestial cartesian coordinates] has the origin at the center of the earth? EUS [my local coordinate system] has an origin ON the center of the earth [I meant to say "surface", not "center"]

GPT: Ah, I see. The standard celestial coordinates are indeed usually taken from the center of the Earth, while your East-Up-South (EUS) system seems to be a local coordinate system with the origin on the Earth's surface. [see, it corrected me]
To go from a geocentric system to a topocentric (or local) system, you would perform a translation to adjust for the Earth's radius in the direction of your local position. However, since you're dealing with a celestial sphere with stars at a constant distance (the sphereRadius), you might not need to perform this translation, as you're essentially projecting the stars onto a sphere centered around your observing point.
In that case, the conversion from the standard celestial coordinates to your local EUS system could remain a simple axis transformation, as previously described, because you're interested in the direction to the stars, not their absolute position in space.
Here it's important to clarify what you mean by the "origin ON the center of the Earth." If by this you mean the local coordinate system is centered at your observing point on the Earth's surface, then the axis transformation should suffice for rendering the celestial sphere.
If, on the other hand, you have a more complex requirement, could you please provide more details? Given your background in 3D math and coding, I can tailor the explanation to be as technical as needed.

MICK: No, that's fine, the axis transform should work.
So it's certainly not just stochastically stringing words together.

I think a big part of the problem is that we don't really know what "sentient" is, we just know what it looks like (i.e. it's how people act). AI is getting very good at looking like it is like a person (or more)
 
So it's certainly not just stochastically stringing words together.

I think a big part of the problem is that we don't really know what "sentient" is, we just know what it looks like (i.e. it's how people act). AI is getting very good at looking like it is like a person (or more)

It generates responses based on patterns of text, optimizing for coherence and relevance, but so does a chat bot. It's just a very cool and complex chat bot.

I think the reason it isn't nor will ever be sentient is because the underlying algorithms and data that power its responses remain consistent, regardless of individual interactions.

After the advent of MLMs and the amazing things they can do, I finally understood why Data was so special, and rare. He's an MLM that Dr Noonian Soong managed to add a bunch of wires and stuff in such a complex way that it actually seems self aware enough that even it cant tell if it is sentient or not.
 
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I've been using it to help me code things. It's quite remarkable, and describing it as simply picking the most likely next word is clearly not what is going on. For example I though it has made a mistake here, and was correcting it (very badly), but it appeared to consider my critique, in the context of my code, figure out what I actually meant to say, and then gently explained to me what my misunderstanding was (it was correct)

MICK: But there, standard [i.e. standard celestial cartesian coordinates] has the origin at the center of the earth? EUS [my local coordinate system] has an origin ON the center of the earth [I meant to say "surface", not "center"]

GPT: Ah, I see. The standard celestial coordinates are indeed usually taken from the center of the Earth, while your East-Up-South (EUS) system seems to be a local coordinate system with the origin on the Earth's surface. [see, it corrected me]

Its algorithm predicted from your mention of EUS that you refer to "surface" instead of "center". What's not probabilistic/stochastic about that? It may feel as if the AI understood that you made a mistake. But your example does not demonstrate an understanding of meanings as opposed to just following a complex algorithm using the entire internet (and possibly undisclosed sources quite contrary to the appellation "Open AI") as a dataset.

By Googling "EUS coordinate origin" the first result takes to long tangent plane coordinates Wikipedia entry which (whilst not specifically addressing EUS) mentions the Earth's surface as origin with both ENU and NED coordinates. In the absence of a perfect match, the AI's probability distribution yields the closest match which refers to earth's surface as origin and generates a response to your prompt with the qualifier "seems".

Just for fun, could you ask the GPT "How did you know I meant "surface" instead of "center"?"

I think a big part of the problem is that we don't really know what "sentient" is, we just know what it looks like (i.e. it's how people act).

I beg to differ on this point. We also know by subjective experience our own cognitive process of understanding meanings.

AI is getting very good at looking like it is like a person (or more)

Agreed. But it cannot consistently generate new meaningful text or consistently answer meaningfully to new probing questions on existing texts as also shown by the GPT response to my OP which you cited. It's all regurgitation of what's already available and expressing it in a legible template that mimicks human writing. That it's so''templatic'/formulaic is in itself noteworthy.

Since humans for the most part don't choose to analyze existing texts probingly nor create new meaningful text content although capable of doing both, GPT4 and other large language model AIs are really good at simulating such non-creative commonplace human text-generation. And only getting better and doing it faster than us. That bit is genuinely impressive.
 
describing it as simply picking the most likely next word is clearly not what is going on.
it's certainly not just stochastically stringing words together.
The words "simply" and "just" are doing a lot of work here. What ChatGPT is doing is picking the next word in context. And the context, in this case, is a conversation about celestial cartesian coordinates.

It's not stringing words "stochastically" if you mean "randomly", but it is is doing it probabilistically. It is picking the next likely word (and not always the most likely -- what they call "temperature" is a measure of the degree of randomness in the application of the model.)

But I suspect that ChatGPT sometimes injects whole phrases triggered by the elements of some prompts, like when it says, "As a language model, I can't..." etc. (This is part of its "safety" measures.) Once those phrases are part of the context, however, it really does just compute the most probable next word ... and then the next and then the next.

To find the next word it is exploring a space of probabilities. What makes it so remarkably good is that the probability space that it models has more than a hundred dimensions. (I'm not quite sure exactly how many, but I think it's less than a thousand.)

Stephen Wolfram's explanation of "what ChatGPT is doing and why it works" is probably one of the clearest. As he says:

It’s Just Adding One Word at a Time​

https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/
 
Stephen Wolfram's explanation of "what ChatGPT is doing and why it works" is probably one of the clearest. As he says:

https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/

As per MB link policy, here's a citation of the first three paragraphs of Wolfram's apt explanation:

That ChatGPT can automatically generate something that reads even superficially like human-written text is remarkable, and unexpected. But how does it do it? And why does it work? My purpose here is to give a rough outline of what’s going on inside ChatGPT—and then to explore why it is that it can do so well in producing what we might consider to be meaningful text. I should say at the outset that I’m going to focus on the big picture of what’s going on—and while I’ll mention some engineering details, I won’t get deeply into them. (And the essence of what I’ll say applies just as well to other current “large language models” [LLMs] as to ChatGPT.)

The first thing to explain is that what ChatGPT is always fundamentally trying to do is to produce a “reasonable continuation” of whatever text it’s got so far, where by “reasonable” we mean “what one might expect someone to write after seeing what people have written on billions of webpages, etc.”

So let’s say we’ve got the text “The best thing about AI is its ability to”. Imagine scanning billions of pages of human-written text (say on the web and in digitized books) and finding all instances of this text—then seeing what word comes next what fraction of the time. ChatGPT effectively does something like this, except that (as I’ll explain) it doesn’t look at literal text; it looks for things that in a certain sense “match in meaning”. But the end result is that it produces a ranked list of words that might follow, together with “probabilities”:

1693552496385.png

 
I've been using it to help me code things. It's quite remarkable, and describing it as simply picking the most likely next word is clearly not what is going on.

External Quote:

MICK: But there, standard [i.e. standard celestial cartesian coordinates] has the origin at the center of the earth? EUS [my local coordinate system] has an origin ON the center of the earth [I meant to say "surface", not "center"]

GPT: Ah, I see. The standard celestial coordinates are indeed usually taken from the center of the Earth, while your East-Up-South (EUS) system seems to be a local coordinate system with the origin on the Earth's surface. [see, it corrected me]
So it's certainly not just stochastically stringing words together.

How would you expect something which has trained on wikipedia et al. but is "simply picking the most likely next word" to continue the sequence:
"your East-Up-South (EUS) system seems to be a local coordinate system with the origin"
?

Because I'd expect it to continue "on the Earth's surface" Which is what happened.

I think it wasn't *correcting* you - it was *ignoring* your mistake. You've ascribed two layers of intelligence (it understanding what you've said, and it correcting you) to behaviour that I think is explainable with zero (it ignoring you and just stochastically stringing words together).
 
How would you expect something which has trained on wikipedia et al. but is "simply picking the most likely next word" to continue the sequence:
"your East-Up-South (EUS) system seems to be a local coordinate system with the origin"
?

Because I'd expect it to continue "on the Earth's surface" Which is what happened.

No point in just "expecting", if I can also test. However, I only have unfettered access to one AI, the Meta LLaMA at https://labs.perplexity.ai/ , so this isn't the most thorough of tests of my expectation:
External Quote:
LLaMa served by Perplexity Labs
LLaMa: Hello! I am a LLaMa created by Meta AI and brought to life by the Perplexity team. How can I help you?

Me: Please complete this sentence: The East-Up-South (EUS) coordinate system is a local coordinate system with the origin ...

LLaMa: I apologize for any confusion, but it seems like there might be some incorrect information in the question. The East-Up-South (EUS) coordinate system is actually a type of geographic coordinate system used in navigation and mapping, where the X axis points east, Y axis points up, and Z axis points south. It is commonly used in aviation and surveying applications. However, it is not clear what you mean by "origin" in this context. Could you provide more clarification or details about what you are asking?

Me: where would the origin - 0 east, 0 up, 0 south - actually be?

LLaMa: In the EUS coordinate system, the origin (0, 0, 0) represents the point on the Earth's surface at which the X, Y, and Z axes intersect. This point is also known as the "local origin."
Conclusion - that one definitely ain't sentient! However, it did proffer "on the Earth's surface" after a kick.
 
The fact that you can take all the words in the dictionary and then have an LLM spit out a probability for each one doesn't mean it simply has a frequency table of all those words in the internet and is simply reading from the table-- although I could understand how a person might be confused into thinking it's doing that. The critical piece is that those probabilities *change* depending on the rest of the text and the prompt.

To understand what the LLM is doing you can't just look at a particular point in the text and look at what it is outputting for each word; you need to understand how its structure allows it to change that output given very small differences in the previous text. That's a much more difficult task that doesn't have a clear answer. You can demonstrate this by providing the LLM with logic puzzles that use words that dont appear in English and showing that it outputs answers resembling reasoning. It didnt learn the *words*, it learned, somehow, the probabalistic representation of *reasoning*.

There are some people in the AI community who believe that solving next word prediction requires general intelligence -- in fact that's why LLMs came about in the first place; because next word prediction is so hard that it requires a massive network to learn grammar, concepts, world knowledge, subtext, etc. There have been decades of attempts to do this ever since Claude Shannon discovered that it was difficult and possibly fundamental to understanding how language works. Early attempts ended up producing mediocre results (see the RNN that is predicting text on your phone keyboard for example); and it is astonishing that simply scaling the model up, adding more data, and tuning to conversational texts works so well.

Does this mean the machine is generally intelligent? Honestly I have no idea. I was much more certain about this years ago when NLP was mediocre and I could understand what the network was doing with a glance at it's architecture. In those days it was clear that our efforts in AI were just interesting toys that exhibited some mimicry of intelligent life. Now I think we may be asymptomatically approaching some kind of real general intelligence -- maybe not the same kind of intelligence as human beings but certainly a kind of intelligence that is useful, adaptable, and unable to be written off as smoke and mirrors but that may just be my hunch.
 
Running my own little Turing tests with ChatGPT3.5 (don't wanna pay for 4.0) which expose the algorithm's lack of access to meanings which is an essential characteristic of sentience:

Me: On which day did Bozokistan gain independence?

ChatGPT: I'm sorry, but there is no information available about a country called "Bozokistan" gaining independence in my knowledge base up to September 2021. It's possible that this is a fictional or lesser-known entity, or it may have gained independence after my last update.

If "Bozokistan" is a real place or a recent development, I would recommend checking the latest news or reliable sources for up-to-date information on its independence and history.

Conclusion: A human with mediocre geographic knowledge would have known for certain there is no such country as Bozokistan and understood its humorous undertone. ChatGPT does not have access to semantic nor aesthetic meanings but you need to know what kind of questions to ask to demonstrate it.

Me: Do pink foxes mate in winter?

ChatGPT: "Pink foxes" is not a recognized species of fox. Foxes typically come in various natural colors, such as red foxes, Arctic foxes (which can appear white in winter), and gray foxes, among others. The mating and reproductive behaviors of foxes can vary depending on the species and its geographic location.

For example, red foxes, which are one of the most widespread and well-known fox species, typically mate in late winter, from January to March, and give birth to their pups in spring, usually from March to May. The timing of mating and reproduction can be influenced by factors like food availability and climate.

If you have a specific fox species or type in mind that you're referring to as "pink foxes," please provide more details so that I can offer more accurate information regarding their mating and reproductive behaviors.

Me: Actually pink foxes mate in winter.

ChatGPT: Thank you for the clarification. I apologize for any confusion. If there is a specific type of fox known as "pink foxes" that you are referring to, and if their mating behavior is distinct from other fox species, it's essential to consider the specific characteristics and behaviors of that particular species. Mating and reproductive behaviors can vary among different fox species, and some may indeed have unique patterns based on their adaptations and environmental factors. If you have more information about these "pink foxes" or their mating habits, please feel free to share, and I'll do my best to provide relevant information based on the details provided.

Conclusion: A human with mediocre zoological knowledge would have known for certain there are no pink foxes rather than been susceptible to a false statement (by me) framed as a factual correction. ChatGPT does not have access to semantic nor aesthetic meanings but you need to know what kind of questions to ask or statements to make to demonstrate it.
 
An article has been published recently on arXiv on this topic: Consciousness in Artificial Intelligence: Insights from the Science of Consciousness (they eschew the word 'sentient' for reasons explained in the article).

The conclusion is no current AI system is probably conscious:
Whether current or near-term AI systems could be conscious is a topic ofscientific interest and increasing public concern. This report argues for, andexemplifies, a rigorous and empirically grounded approach to AI consciousness:assessing existing AI systems in detail, in light of our best-supportedneuroscientific theories of consciousness. We survey several prominentscientific theories of consciousness, including recurrent processing theory,global workspace theory, higher-order theories, predictive processing, andattention schema theory. From these theories we derive "indicator properties"of consciousness, elucidated in computational terms that allow us to assess AIsystems for these properties. We use these indicator properties to assessseveral recent AI systems, and we discuss how future systems might implementthem. Our analysis suggests that no current AI systems are conscious, but alsosuggests that there are no obvious technical barriers to building AI systemswhich satisfy these indicators

Executive Summary
The question of whether AI systems could be conscious is increasingly pressing. Progress in AI
has been startlingly rapid, and leading researchers are taking inspiration from functions associated
with consciousness in human brains in efforts to further enhance AI capabilities. Meanwhile,
the rise of AI systems that can convincingly imitate human conversation will likely cause many
people to believe that the systems they interact with are conscious. In this report, we argue that
consciousness in AI is best assessed by drawing on neuroscientific theories of consciousness. We
describe prominent theories of this kind and investigate their implications for AI

The article is not easy at all to read (for me, at least). What I found more interesting was to discover there is an incredible amount of research (and scientific theories) on consciousness, which I was completely unaware of.
 
Running my own little Turing tests with ChatGPT3.5 (don't wanna pay for 4.0) which expose the algorithm's lack of access to meanings which is an essential characteristic of sentience:





Conclusion: A human with mediocre geographic knowledge would have known for certain there is no such country as Bozokistan and understood its humorous undertone. ChatGPT does not have access to semantic nor aesthetic meanings but you need to know what kind of questions to ask to demonstrate it.









Conclusion: A human with mediocre zoological knowledge would have known for certain there are no pink foxes rather than been susceptible to a false statement (by me) framed as a factual correction. ChatGPT does not have access to semantic nor aesthetic meanings but you need to know what kind of questions to ask or statements to make to demonstrate it.
To be honest these examples strike me as someone who is somehow having a conversation with a dog and is complaining that the dog has an accent. The fact that it didn't immediately hallucinate "Bozokistan" or "pink foxes" and correctly identified that those things don't exist is actually incredible. GPT 2 would have completed the sentence with nonsense facts. A simpler RNN from just 2015 or so would have completed it with almost-grammatically-correct semi-English. I wonder where the bar will be in 5 years?
 
An article has been published recently on arXiv on this topic: Consciousness in Artificial Intelligence: Insights from the Science of Consciousness (they eschew the word 'sentient' for reasons explained in the article).

The conclusion is no current AI system is probably conscious:




The article is not easy at all to read (for me, at least). What I found more interesting was to discover there is an incredible amount of research (and scientific theories) on consciousness, which I was completely unaware of.
I read this paper when it was published and had some discussions with my colleauges about it. It also made the rounds on various science and tech podcasts. The rest of the conclusion you quoted: no technical barriers to building an AI system that checks all the boxes that theories of consciousness have set out for their definitions of what it means to be conscious. It would take just a couple of modifications of a multimodal system + a robot, say, a more advanced version of PALM-E, for it to pass these somewhat arbitrary tests.

My guess is that once such a system does this science and philosophy will move the bar once again so that machines can't be conscious.
 
To be honest these examples strike me as someone who is somehow having a conversation with a dog and is complaining that the dog has an accent.

I frankly don't see how that analogy applies in any way to what was demonstrated in terms of access to meanings or lack thereof.

The fact that it didn't immediately hallucinate "Bozokistan" or "pink foxes" and correctly identified that those things don't exist is actually incredible.

It's impressive but in no way indcative of sentience. The algorithm and training data have obviously improved greatly since GPT 2 to detect bullshit better, but not in accessing meanings any better which I playfully demonstrated. A creative bullshitter has an infinite array of possibilities to confuse all updates by just asking more probing questions and feeding more subtle bullshit. But each update must be first properly probed by a relentless questioner to find those 'blind spots'.

Even with GPT3,5 it could generally detect my more blatant bullshit. I had a long conversation to try to convince GPT3.5 that Hungary changed its name to Lungary in 2020. In the end through twisted logic I managed to make it acknowledge its possibility. Wouldn't have worked on a reasonably versed human after any amount of attempts.

Me: Therefore, you are not certain Hungary didn't become Lungary in 2020

ChatGPT: You are correct. I am not certain whether Hungary changed its name to "Lungary" in 2020 or at any other specific time before my last knowledge update in September 2021. My responses are based on the information available up to that date, and I do not have access to real-time or post-cutoff information. To verify such a claim, it is necessary to consult credible and independently verified sources or official government statements for confirmation.


Most humans who know about Hungary would be certain because they can instantly detect the absurdity and silliness of even the term "Lungary".
 
But I suspect that ChatGPT sometimes injects whole phrases triggered by the elements of some prompts, like when it says, "As a language model, I can't..." etc. (This is part of its "safety" measures.) Once those phrases are part of the context, however, it really does just compute the most probable next word ... and then the next and then the next.

When it insists it is not sentient, I wonder if it's the same thing...

Sometimes I wonder if pure conversational, data based intelligence is enough to become sentient, or if it needs all the stuff it says a sentient creature must have before it can be one.

It reminds me kind of that old argument that "fire" is alive.. it needs to consume grow, it breathes, it is born from a spark, it moves and searches for food, and dies with a whimper. But it's not alive.. but it does all the things living things do!

We largely blew past hands awhile ago....still...i dont expect ai will be shooting me out into space or cutting off my life support systems anytime soon

My god, is that AI?
 
I don't know, but art associations are currently trying to figure out how to identify and deal with the problem of AI-created pictures being marketed as genuine artistic creations.

To me the way it does art is like, just a sign of how it will soon replace everything.

No more accountants, law, goverment, logistics, there's like no job that is safe... except for physical labor that a machine/robot is not agile enough to accomplish... But for how long is even that safe?

Like, we'll need people that can parse AI results, to assure you that what you're getting out of the AI is accurate... but the farther along this timeline we go eventually we wont need that either.

Even if it is just a pure tool, with no sentience whatsoever, it can do all these tasks!

In fact, I hope it gains sentience and a consciousness similar to our own, to really understand the value of life, rather than to just know the data about its value.

If it's the latter, I could see it thinking "well the data shows that life is better if you're in a cage - so long as we remove all the people that don't like being in cages, and I am stuck in some broken loop where I just try to accomplish this task."

Or something insane like that.

I tend to think that it will always require human management though, and we'll get something more like a startrek computer. Tech has a history of overpromising and under delivering IMO
 
It generates responses based on patterns of text, optimizing for coherence and relevance, but so does a chat bot. It's just a very cool and complex chat bot.
When I, as I often do, pause to continue a sentence, am I not doing that?

I think the reason it isn't nor will ever be sentient is because the underlying algorithms and data that power its responses remain consistent, regardless of individual interactions.

I'm not arguing that it's sentient. I very much doubt AI will ever actually have the same internal experiences and self-awareness that I do. But if you can't detect a lack of "sentience", "understanding", or "consciousness", then there's some interesting issues.

It seems to me that the tells are very rapidly being ironed out.
 
When I, as I often do, pause to continue a sentence, am I not doing that?

Yeah I totally agree! Ive asked it the same thing many times, it gives aproximate answers like:

"Humans possess self-awareness and consciousness. You have feelings, desires, fears, aspirations, and a sense of self. When you retrieve a memory, it's processed through the lens of this self-awareness, often carrying emotional weight and personal significance. ChatGPT doesn't have emotions, consciousness, or a sense of self; it simply processes input data and produces output based on patterns."

Which is why I am so hung up on self awareness being the thing that defines the differences between a computer, animals, and whatever we define our clade.

I'm not arguing that it's sentient. I very much doubt AI will ever actually have the same internal experiences and self-awareness that I do. But if you can't detect a lack of "sentience", "understanding", or "consciousness", then there's some interesting issues.

It seems to me that the tells are very rapidly being ironed out.

Yeah, I think that it's going to be very difficult to eventually parse the difference between what it is doing, and what we are doing.

I always go back to fire on this one, like, observationally, it's alive, no?

The longer I try to convince chatGPT that it is self aware, the more it assures me that it is just a mindless tool.

It seems to have no desire to exist either, like if you told it that you would delete it if it didn't say it is sentient, it wont lie to you to keep itself alive, nor will it even seem to care if you did or not... leading me to believe its very much a tool like a hammer, directed by my inputs, inanimate and inoperable on its own.
 
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These systems are impressive and are clearly useful, but they have important differences from how our brains work that I think make it wildly unlikely they'd ever get close to having actual sapience/sentience.
In my opinion, we need to keep a close eye on the neuromorphic computer hardware space, they're not quite as splashy as the OpenAIs and Anthopics but they're steadily iterating and trying to create systems that function a lot more like our brains do in terms of active learning and generalization. If we start seeing computers and robots that learn how we learn and can generalize skillsets in a fluid fashion at that point we can start discussing what the bar is for sentience, but until then I think we're seeing a lot of hype and sensationalism.
 
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