| Attribute | Unconscious Mind | Generative AI |
|---|---|---|
| Information Processing | Processes vast amounts of information rapidly and automatically, often without conscious awareness (From the first studies of the unconscious mind to consumer neuroscience: A systematic literature review, 2023) | Processes large datasets quickly, extracting patterns and generating outputs without explicit programming for each task (Deep Learning, 2015) |
| Pattern Recognition | Recognizes complex patterns in sensory input and past experiences, influencing behavior and decision-making (Analysis of Sources about the Unconscious Hypothesis of Freud, 2017) | Excels at identifying patterns in training data, forming the basis for generating new content or making predictions (A Survey on Deep Learning in Medical Image Analysis, 2017) |
| Creativity | Contributes to creative insights and problem-solving through unconscious incubation and associative processes (The Study of Cognitive Psychology in Conjunction with Artificial Intelligence, 2023) | Generates novel combinations and ideas by recombining elements from training data in unexpected ways (e.g., GANs in art generation) (Generative Adversarial Networks, 2014) |
| Emotional Processing | Processes emotional information rapidly, influencing mood and behavior before conscious awareness (Unconscious Branding: How Neuroscience Can Empower (and Inspire) Marketing, 2012) | Can generate text or images with emotional content based on patterns in training data, but lacks genuine emotions (Language Models are Few-Shot Learners, 2020) |
| Memory Consolidation | Plays a crucial role in memory consolidation during sleep, strengthening neural connections (The Role of Sleep in Memory Consolidation, 2001) | Analogous processes in some AI systems involve memory consolidation and performance improvement (In search of dispersed memories: Generative diffusion models are associative memory networks, 2024) |
| Implicit Learning | Acquires complex information without conscious awareness, as in procedural learning (Implicit Learning and Tacit Knowledge, 1994) | Learns complex patterns and rules from data without explicit programming, similar to implicit learning in humans (Deep Learning for Natural Language Processing, 2018) |
| Bias and Heuristics | Employs cognitive shortcuts and biases that can lead to systematic errors in judgment (Thinking, Fast and Slow, 2011) | Can amplify biases present in training data, leading to skewed outputs or decision-making (Mind vs. Mouth: On Measuring Re-judge Inconsistency of Social Bias in Large Language Models, 2023) |
| Associative Networks | Forms complex networks of associations between concepts, influencing thought and behavior (The associative basis of the creative process, 2010) | Creates dense networks of associations between elements in training data, enabling complex pattern completion and generation tasks (Attention Is All You Need, 2017) |
| Parallel Processing | Processes multiple streams of information simultaneously (Parallel Distributed Processing: Explorations in the Microstructure of Cognition, 1986)) | Utilizes parallel processing architecture (e.g., neural networks) to handle multiple inputs and generate outputs (Next Generation of Neural Networks, 2021) |
| Intuition | Generates rapid, automatic judgments based on unconscious processing of past experiences (Blink: The Power of Thinking Without Thinking, 2005) | Produces quick outputs based on learned patterns, which can appear intuitive but lack genuine understanding (BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2019) |
| Priming Effects | Unconscious exposure to stimuli influences subsequent behavior and cognition (Attention and Implicit Memory: Priming-Induced Benefits and Costs, 2016) | Training on specific datasets can “prime” generative AI to produce biased or contextually influenced outputs (AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias, 2018) |
| Symbol Grounding | Grounds abstract symbols in sensorimotor experiences and emotions (The Symbol Grounding Problem, 1990) | Struggles with true symbol grounding, relying instead on statistical correlations in text or other data (Symbol Grounding Through Cumulative Learning, 2006) |
| Metaphorical Thinking | Uses embodied metaphors to understand and reason about abstract concepts (Metaphors We Live By, 1980) | Can generate and use metaphors based on learned patterns but lacks deep understanding of their embodied nature (Deep Learning-Based Knowledge Injection for Metaphor Detection, 2023) |
| Dream Generation | Produces vivid, often bizarre narratives and imagery during REM sleep (The Interpretation of Dreams, 1900) | Some generative models can produce dream-like, surreal content (Video generation models as world simulators, 2024) |
| Cognitive Dissonance | Automatically attempts to reduce inconsistencies between beliefs and behaviors (A Theory of Cognitive Dissonance, 1957) | MoE architectures can handle a wider range of inputs without ballooning model size, suggesting potential for resolving conflicts between different AI components by synthesizing expert opinions into a coherent whole (Optimizing Generative AI Networking, 2024). |
The appearance of similarities between Generative AI and the unconscious mind do not mean there is any actual equivilance to be had. We gotta stop using the same terminology to describe generative AI as we do humans because they are not the same thing in the slightest. This only leads to further unintentional bias, and an increased likelihood of seeing connections with the unconscious mind that don’t actually exist.
Typically speaking, the appearance of similarities should be explored for functional similarities. Even in the world of prompting, a user gets demonstrably better results (in some contexts) by acting as-if they’re talking to a person. We can explore the similarities between interpersonal interaction and AI-interaction for overlap in effective modalities without claiming they’re equivalent.
We can explore the similarities between interpersonal interaction and AI-interaction for overlap in effective modalities without claiming they’re equivalent.
Who is “we”? The reality is that these grifters are constantly claiming equivalence. The term “AI” is itself a misleading equivalence. I would much rather not be part of that “we”.
“We” being anyone interested in scientific exploration, hence the papers linked in the post and comments.
It’s a lofty sentiment, but I don’t agree.
I think that in the future generative A.I. will be seen like the Turbo Button, Desktop Publishing Revolution and Information Superhighway of their day, ideas that over promised, under delivered and faded into obscurity. I suspect that Block Chain and Crypto Currencies will go the same way for similar reasons as outlined below.
Machine Learning is a useful tool to automatically generate a model for a multivariate system where traditional modelling is too complex or time consuming.
Generative models are attempting to take that to a whole new level but I don’t believe that it’s either sustainable nor living up to the hype generated by breathless reporting by ignorant journalists who cannot distinguish advanced technology from magic.
It’s not sustainable for a range of reasons. The most obvious is that the process universally disintegrates when it ingests content generated by the same process.
Furthermore, it doesn’t learn, specifically, the model doesn’t change until a new version is released, so it doesn’t gather new models whilst it’s being used.
And finally, it requires obscene amounts of energy to actually work and with the exponential growth of models, this is only going to get worse.
Source: I’m an ICT professional with 40 years experience
Thanks onno & @Hackworth@lemmy.world
… “the process universally disintegrates when it ingests content generated by the same process.”
For a Layman enthusiast like me, this sounds like people very isolated (i.e. in a station at the South Pole), go crazy after too much time alone.
It’s more like a chemical chain reaction that explodes than anything to do with human behaviour.
Here’s the paper onno’s alluding to, for reference.
Isn’t your comment more of a perspective on the public perception of AI, the missteps surrounding its implementation, and its current role - rather than an examination of the potential role (practically speaking) of generative AI in a more general AI model? As is the thrust of the post, generative AI will necessarily be part of a larger AI, in part to make up for its weaknesses, in part to utilize its strengths.
That said, generative AI isn’t nearly as endangered by generated training data as is commonly understood. Even if it were that bad, embodiment is rapidly changing the landscape. There are a ton of papers about how to use larger models to make smaller models more effective, using generative AI to improve generative AI along with efficiencies. Heck, novel efficiencies get developed almost as regularly as novel use cases. We’re always learning how to do more with less.
No.
I think that the current generative models are fundamentally flawed and won’t last the decade.
Which is why I don’t think that they’ll be thought of much at all beyond academic curiosity.
AI has been around for a long time and has had moments of high interest and low interest. The latter has been given the term “AI Winter.” It is possible that there will be another winter if there is a limitation that cannot be avoided for several years.
I can’t imagine willingly going back to before Adobe added Generative Fill to Photoshop. Gen AI will certainly remain more than an academic curiosity, at least until they can be replaced with something better.
For shits and giggles you should try to do a generative fill on an area already filled that way.
(After saving the work, quitting and relaunching the Photoshop.)
Oh man, it saves me so much time - but it is like working with a bipolar intern. Sometimes it pulls off these amazing fills that would’ve taken me hours to do by hand. And sometimes it has a fit trying to fill in a cloudy sky, and I just do it the old fashioned way. I’m pretty systematic about testing tools in general, and gen fill resists all attempts at building a reliable workflow. Ya really gotta switch up tactics to react as ya go, which can be fun when it’s not irritating. Plus I find surreal hallucinations hilarious, so it makes up for some of its behavior issues with entertainment value.
I think there were many unthinkable things achieved in the past that one can hope that we can find a way to train LLM-style “AI” a lot faster with way less recourses and thus achieve self trained assistants, that interact exactly as one expect from their personal helper.
🤷🏻♀️I guess we’ll see
Very few lemmy users have enough background to appreciate your work here.
¯|(ツ)/¯ mostly looking to spark discussion.
Technically, what you say is possible thanks to the phenomenon called emergence and it is possible that AI can emulate human minds in the future.
Thank you; your posit is apt.
Even if it doesn’t work the same way, humans anthropomorphing pattern detection will grapple on to it as “same function, so same thing”. As we slowly build general AI, other “things that don’t work that way” will be attached on to it until we have a full general AI whose brain works nothing like humans but has pieces that work in similar fashions.
Sort of like how 60 Watt LED light bulbs don’t use 60 Watts. “They produce the same about of light, so they must use the same amount of energy!”
On the flip side of that, the discoveries that accompanied attention transformers are changing the way we think about our own neurology:
And how we study it:
Artificial Intelligence shaping the future of neurology practice
I try not to be quick to assume that intuitions around similarities are correct or incorrect, but I trust they are worth exploring.
That’s actually very true, interesting point
No
I think you mean, “As a large language model…”
