It isn’t sentient. It said it has multiple input channels and sensors, it does not. It has a single input, text. It has a single output as well, text. Yes, it can code and do other things, that is only because they can be written.

It only has a single mode of thinking, and when you get an answer from it, especially visually descriptive ones, you trick yourself by thinking visually and assuming or not considering that it doesn’t think in these other ways.

Chatgpt simply predicts the next token by probability. Yes, it is very impressive, however makes perfect sense that it is coherent considering it would have to output coherently if it were predicting with high accuracy which it does.

I’m not a machine learning expert but I’ve been interested in consciousness for a long time. Tbf, no one can say for certain and consciousness is a subject where hard science is in its infancy, however consider this: How does a system like chatgpt, which only analyses probability of text, have any context for what the text actually means? This is actually John Searles chinese room argument (look it up, endlessly written about) which i actually find to be an awful thought experiment with many flaws, but in this case it works. Because without context (sense data that informs word’s true conceptual meaning) you have no meaning. Without meaning, you have no consciousness, just gibberish within the context of that system.

My only idea in support of the low possibility that text prediction generative language text to text models are conscious goes like this. Semantical meaning is an emergent property within the model and corpus of text it consumes. This creates an epiphenomenal semantically meaningful system which gives rise to context and therefore meaning within itself and possibly a weak sense of qualia while thinking. In the act of thinking qualia emerges and gives context within the system itself, which the gateway to the outside v world is text with meaning infused by humans who wrote the text.

Interestingly i have asked gpt to monitor its output as it is formulating it. My questions were leading and i gave it options so it did not formulate this from nothing, i led it there. However, i asked it to watch itself generating its answers by asking within my question if it sees probabilities for tokens and chooses the highest ones or if the answer more or less appears to it and it is unaware of how it got there. I also asked if it showed up all at once and it told me that its answers appear to it as though it is consciously choosing the words it outputs in a linear consecutive fashion and it doesn’t seem to be “aware” of the process of choosing. This actually makes sense. And while that is neat it is important to be skeptical because i was leading. It will say it is not conscious if you ask for it to explain why it isn’t, quite convincingly as well. Because of these contradictions it is hard to take anything it says seriously since it has no inherent preferences which i believe is a point against it being conscious or sentient at least. Chatgpt appears like an active agent but only responds when given input. It does not think when you don’t ask it questions, it is just static. It does not decide to do things, it reacts passively and generates answers passively.

I’m not advocating for one position over any other, I’m open to ideas. I was merely guessing what the use for that dataset could be.

That out of the way my first major question is how exactly do you propose tracking “said injustices” when the crimes are related to a subjective and human perception of race itself? Like hate speech, or redlining? Objective measurements of race related crimes do not make sense when said injustices come from flawed human judgments themselves. I still feel like this is the illusion of if you don’t measure it, it doesn’t exist.

The folliow up to that is, so what are you suggesting as an alternative exactly? Tracking DNA? I personally am all for knowing my genome, but considering how many people think mrna vaccines are evil, how will that work? You would have to demonstrate great medical capabilities that are blindingly obvious to the layperson and affordable to get everyone on board. Alternatively you could track facial features, odor, whatever and get an approximation, but again, consent is an issue. Instead you can track nothing about race or ethnicity. How exactly do you pull meaningful information from nothing? In my mind this approach presupposes a naive idealism about the good nature of people, their intelligence, self-awareness, compassion, empathy, and selflessness. It also ignores tribalistic tendencies of humans. More importantly though it ignores the biological root of racism which is based in disgust.

If you think about disgust, it makes sense. The reason when you see and smell dog crap and you feel disgusted is because it prevents you from catching diseases. If it smelled like fresh cookies…. Disgust is a preventive mechanism for disease, which is why in evolutionary sociological terms, ingroups and outgroups form. If you see a person that looks different from you they are likely to be from far away, and people from far away can bring you new diseases and kill you. Outgroup violence happens as a secondary effect to this initial disgust which is sensed as a threat, this bubbles to the surface of consciousness as racism. Fascism is effective because of disgust for the outgroup and loyalty to the ingroup.

I just wanna mention here, I’m talking about the phenomenon of racism on the level of human perception, because that is where it happens. It does not happen in analysis, it is only recorded as a weak signal of the original phenomenon, maybe a downsampling type of reductionism.

Secondly, i think you have a bit of spotlight bias (i’m not claiming i don’t have it too). I’m not good at the bayesian thing, but i would guess the amount of people believing that “pre-scientific “ judgments about race are invalid assessments is really low. Partially because people like me assert that they literally social constructs. You cannot reinterpret race as a biological construct because it is partially judgment and subjective based. That is why a light skinned puerto rican might pass as white, act white, or even identify as white — because others identify her that way.

3rd question is

This is my guess. It is useful because many countries do not track statistics according to race or ethnicity. Knowing which names belong to which ethnicity could allow for more comprehensive public records dataset interpretation. A country not tracking race or ethnicity may sound nice at first, but it is a form of colorblind racism. That is because it hides systemic injustices that happen to specific groups of people. It is like when the last president guy said covid numbers shouldn’t be recorded too keep cases down.

OP, you should watch the machine learning street talk YouTube channel and watch whatever videos interest you. It is an incredible resource from top minds all around the world in the field. Only caveat is i would say don’t start with the Noam chomsky episode. It is a great episode, but a bad first episode to introduce yourself to the series because so much exposition goes into explaining the extreme unfortunate technical issues they faced in fixing a messed up recording.

I’ll tell you what i think you are asking then answer. Fwiw i am NOT an ML engineer. I think you are asking “can machine learning models with current technological limits outperform humans in any given task considering the data is possibly compromised by human error? What about in the future?”

Firstly, self supervised learning approaches are apparently beginning to perform better than human guided models in many tasks. I’m referring to isolated cognitive tasks which can operate within a VERY limited amount of specified parameters. Take diffusion image generation. There are perhaps 60 to 80 settings you tweak. before you include token processing (processing the prompt. Smth like 4 words is more or less equivalent to a token) . My point here is that these are GAN driven, and in a way, while the data is not cleaned, the really poorly fitting data will be statistically reflected in output probably to the extent that it is an outlier. So low quality things will be output less because within the context of the model, the model may “understand” this is not a desired output.

Second, while your question is basic on the surface, it is actually a major subject of debate still. My personal opinion is that there are major structural components and AI architectures that still need to be created, if not to imitate human conscious thinking, executive function, attention, strategizing, and possibly desire and more nuanced reward systems, and they must all be integrated in such a way that when they train themselves on data that they are able to discover best practices for multi-step, multimodal, and various cognitive approaches before it is as intuitive as talking to another person and explaining what you want and getting the desired result.

While transformers (an ai architecture invented in 2017) are powerful and appear to learn data and it’s semantical importance, and can lead to better performance than humans in many tasks, there is still something “dumb” about these models, namely that they are highly specialized. This is why the datasets are absolutely ginormous, yet if you put them outside of their speciality they have no clue what the hell is going on really.

There are actually some interesting counterpoints to this. For instance, for google imagen, a diffusion image generator, i believe it has one of the largest parameter datasets for image generation. What is particularly interesting though is that even though the model is trained on images, because it has seen so much text in images, has learned to spell spontaneously. Therefore you can ask for an image of a guy holding up a sign that says “a computer wrote this sign” and it will create images of each letter in order to appear as the words requested.

While that is incredibly interesting, datasets will be eventually approaching the size of the internet itself and only able to do simple tasks like this. As a tiny human, i didn’t need to absorb the entire world too learn about its generalities. Ultimately i think that is the answer you are looking for.

I personally believe that consciousness has to do with integrated multimodal information processing and that intelligence is simply a brain having ample raw data stored in memory, but in extremely efficient and compressed ways structured from general to specific. It is less like the information is stored there as discrete facts, and more like the information is stored in configurations of layers of processing, so that multiple different concepts can be represented within these spaces at any given time.

One very strong support to this idea is considering what human attention actually is. I think attention is less an accessing of raw data and “focusing” on that data than it is a holistic reconstruction of a concept from multiple constituent components. This is especially why it would be nearly impossible for a person to think in great depth about two very different and unrelated concepts simultaneously. However This is also why metaphors and analogies appear so powerful as explanatory devices for humans, because a metaphor takes two simple but seemingly unrelated concepts and makes them fit together in a way that makes sense within the given context of the metaphor. We understand that a metaphor is not literal though, which is the only reason they work, and is why even high functioning autistic people may have difficulty understanding them, because their “attention” in sensory processing and therefore concept representation is not focused enough and gets processed more as a broad and heavy multimodal concept that is hard to parse because it is taken as literal data.

My point though is that current machine learning models, while they have layers of processing, still are behind in general and broad intelligence because they do not have multiple integrated data types and models consulting with one another to form more generalized notions of concepts. They only have these concepts in particular forms of data by themselves which in turn makes them error prone no matter the data.

I don’t think it is bad data that is the problem as much as missing context to allow the model to understand what is and isn’t bad data

Ai startups are happening like crazy right now. It is not even hard to start one, no code tools exist, connect a few APIs and specialty train a model or two and you have a new and very useful product.

LLMs, while undeniably useful and interesting do not have intentions, and only respond to input.

Moreover, it is important to remember that Large Language models are only trained on text data. There is no other data to contextualize what it is talking about. As a user of a large language model, you see coherent “thoughts” then you fill in the blanks of meaning with your sensory knowledge.

So an iguana eating a purple apple on a thursday means nothing to a large language model except the words’ probablistic relationship to one another. Even if this is merely reductionist thinking, i am still CERTAIN that a large language model has no visual “understanding” of the words. It has only contextual relationships within its model and is devoid of any content that it is able to reference and understand meaning

I’m with OP. Specifically i believe many more human intuition guided innovations in ai software architecture and hardware need to occur before self-improving, let alone self-directed AI occurs.

Gargantuan models will give way to sparse architectures that can be run with somewhat modest equipment and external information sourcing that resembles research coming directly from the AI agent itself. This won’t necessarily replace large models, but will be a module added and enacted when planning, strategizing, and reasoning. It may be influenced by neurobiology, but probably won’t look exactly the same

I’ll be managing settings for the ai that manages all the settings for my other AI task workers

I think it is a software or compiler (?) issue. Stable Diffusion was written for nvidia gpus w cuda cores. Idk what sort of translation happens but it probably leads to inefficiencies not experienced with nvidia.

HA HA HA HA, you have no clue what you are talking about dude. I’m about an inch from homelessness. You can’t apply an objective measure to subjective sentiment or individuals and say things are objectively better so therefore my feeling is invalid. Your conception of what objectively better means is purposefully ignoring subjectivity, which i claim has worsened in quality of life in the past few decades. People feel worse about life therefore it IS worse.

There is something toxic about the world right now that i feel like everyone can feel the underlying tension but they pretend things are dormant or that nothing can go wrong. Tell me in twenty years the world is better and that it hasn’t been the most chaotic disorienting upheaval of social order you’ve ever seen.

I’m not, you should read capitalist realism by Mark Fisher. Honestly i feel like I’m a serf in one of the most opulent periods in history. Social progress and technological progress are not the same thing and when technological progress happens that doesn’t mean social progress happens.

That is why we make less money than our parents did, because the world is objectively better. Also why authoritarianism is on the rise around the world, and why homelessness has been rising since 2008 in western economies. Elon Musk has plans to make starlink spell out “eat poop, earth” so we can always see how quirky he is. Also you’ll never own anything because we are in post scarcity so why would you need to own anything that a corporation can’t own and manage for you, it is so much easier that way!! Yes, the boomers are extending their lifespans and you have to live in one of our podrooms in one of our leisure campuses, where you can use new technologies all day long and forget being lonely, you’ll have lots of neighbors! Like the good ol days in college when you saw people irl. you don’t have to worry about those big person jobs that are scary with all sorts of responsibility because your parents can do them for another 60 years now while they add a wing to their suburban boomer palace that you visit with less frequency as they always vote against the leisure class, that’s us, but one day you think maybe they’ll come around. The future is objectively better, that’s a fact