| 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). |
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.
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.
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.