From: jimruttshow8596
Current Large Language Models (LLMs), which are primarily Transformer Nets trained to predict the next token in a sequence and then tuned for specific functions [00:04:38], are not expected to achieve full human-level Artificial General Intelligence (AGI) on their own [00:04:49]. While they can perform many amazing and useful functions and might even pass the Turing Test [00:05:01], their architecture presents fundamental challenges to advanced reasoning and creativity [00:32:12].
Hallucination Problem
A notable limitation of LLMs is their tendency to “hallucinate” [00:09:32]. This means they can generate factually incorrect information or make up non-existent papers and entities [00:09:42]. While external probes and techniques, such as running multiple queries and calculating entropy or paraphrasing prompts, can filter out many hallucinations [00:11:06], this does not indicate genuine understanding. Humans avoid hallucinations through a “reality discrimination function” and reflective self-modeling [00:12:12], a capability LLMs currently lack [00:12:40].
Limitations in Multistep Reasoning
LLMs struggle with complex multi-step reasoning, which is crucial for tasks like writing original science papers [00:30:04].
- Scientific and Mathematical Discovery: Current LLMs are not able to “create Einstein” or invent concepts like quantum gravity or supercomputing [00:31:30]. They typically do not come up with surprising hypotheses to experts [00:36:19]. While they can “turn the crank” on advanced mathematics and flesh out theories from initial ideas [00:38:48], they cannot yet perform science as well as a master’s student or a professional scientist [00:36:46]. They require an “original seed of an idea from the humans” and curation at multiple stages [00:39:09].
- Lack of Deep Judgment: LLMs possess “no deep judgment” [00:39:17]. They cannot determine if a mathematical definition is “interesting” or a “dead end” [00:40:04], a discernment that experienced mathematicians make based on “math aesthetics” [00:40:18].
Limitations in Creativity
The natural state of LLM output is often described as “banal” [00:31:12], “derivative, and imitative” [00:33:20].
- Original Artistic Creativity: LLMs struggle with original artistic creativity, such as inventing new musical genres [00:30:14] or creating “incredible new genres of music” [00:29:50]. While clever prompting can move their output “way outside of its centers” [00:34:36], it generally doesn’t reach the level of a great creative human [00:34:39]. For example, while they can generate a decent blues guitar solo [00:35:35], they are not comparable to musicians like Jimmy Hendrix or Allan Holdsworth, who invented new chords, scales, and emotionally evocative music [00:35:21].
Architectural Roots of Limitations
These shortcomings are attributed to the fundamental architecture of LLMs:
- They primarily recognize “surface level patterns” in the data they are fed [00:32:33], creating a “humongous well-weighted well-indexed library of surface level patterns” [00:32:40].
- There is no clear evidence that they are learning abstractions in the same way humans do [00:32:47]. Human abstraction is guided by “agentic nature,” such as survival and reproduction, leading to the development of heuristics to navigate complex environments [00:42:18].
- LLMs are focused on predicting the next token in a sequence [00:57:11]. The LLM itself is a program, not a sequence, making it unsuited for “recognizing patterns in itself and recursing” [00:57:17].
Approaches to Evolving AI Architectures
To address these limitations, several innovative approaches in AI research are being explored:
- Increased Recurrence: Introducing more recurrence into Transformer networks, similar to LSTMs, could help generate more interesting abstractions [00:46:46].
- Alternative Training Methods: Exploring methods like predictive coding-based training instead of or in conjunction with backpropagation, especially for highly recurrent networks, is a promising area [00:47:36].
- Hybrid Architectures:
- Combining LLMs with other systems like AlphaZero (for planning and strategic thinking) [00:48:17] and neural knowledge graphs [00:48:21], where LLMs might serve as an integration hub [00:05:22].
- Integrating LLMs as supporting actors within a self-modifying, self-rewriting weighted labeled metagraph, which serves as the central hub for knowledge representation and cognitive operations (e.g., OpenCog Hyperon) [00:55:07]. This approach focuses on reflection, recognizing patterns in its own mind, and is naturally suited for logical reasoning and evolutionary programming [00:56:54].
These directions aim to overcome the inherent limitations of current AI architectures by building systems capable of deeper abstraction, multi-step reasoning, and genuine creativity, moving beyond mere surface pattern recognition [00:32:33].