Limits of Generative AI and Large Language Models

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TITLE: Limits of Generative AI and Large Language Models
AUTHOR: Eddie S. Jackson, MrNetTek
DATE: July 2, 2024 at 8:25 PM EST
RESEARCH: Google search, current news, books, Copilot.
EDITING: Grammarly

Artificial Intelligence (AI) has become a cornerstone of modern technology, with generative intelligence and large language models (LLMs) at the forefront of recent advancements. Despite the media hype surrounding these technologies (and, ohhhh, there is so much hype), it is important to distinguish between the capabilities of current AI systems and the theoretical constructs of strong AI, Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI). I explore the limitations of generative AI and LLMs, arguing that they represent a form of weak AI that will not evolve into strong AI or AGI. By examining the fundamental differences between human intelligence and AI, I try to provide a balanced perspective on the future of AI. It will always be my objective to separate myth from reality.

 

Understanding Generative Intelligence and LLMs


Definition and Mechanisms

Generative intelligence and large language models (LLMs) are at the cutting edge of modern AI research and application. These technologies are based on neural networks, a class of machine learning models inspired by the human brain’s structure and function. Neural networks, particularly deep learning models, are capable of learning patterns from vast amounts of data, enabling them to generate new content, make predictions, and recognize patterns. Sometimes the results are humanlike. Sometimes the results are unique, and inspiring. Sometimes the results are utter nonsense (hallucinations or fabrications).

LLMs, such as GPT-4, and now GPT-4o, use advanced deep learning techniques and natural language processing (NLP) to understand and generate human-like text. These models are trained on [very] large datasets, allowing them to generate coherent and contextually relevant responses. When I say large, I mean terabytes large. However, their understanding is superficial (at best), relying on statistical correlations rather than true comprehension (Google Bayesian statistics). Think of it as a parrot that can recite Shakespeare, but it has no idea who Romeo and Juliet are, or that they’re humans, or what humans even are (this is the nature of all GenAI that currently exists).


Examples and Applications

Large language models like GPT-4 have found applications across various fields:

  • Content Creation: LLMs can generate articles, stories, and poems, aiding writers and journalists in content production. It’s like having a ghostwriter who never sleeps and doesn’t demand royalties. Is this controversial? Indeed it is.
  • Customer Service: AI chatbots powered by LLMs can handle customer inquiries, providing quick and efficient responses. Imagine a customer service rep who never gets tired of answering the same question for the hundredth time.
  • Research: LLMs assist researchers by summarizing papers, generating hypotheses, and even suggesting experimental designs. It’s like having a research assistant who never needs coffee breaks.

Despite their impressive capabilities, these applications highlight the models’ reliance on pre-existing data and their limitations in understanding and reasoning. When I say limitations, I mean it in the most severe of ways.

 

Weak AI vs. Strong AI


Conceptual Differences

Weak AI, or narrow AI, is designed to perform specific tasks without possessing general cognitive abilities. These systems can excel in their designated areas, such as playing chess or recognizing faces, but they lack the flexibility and adaptability of human intelligence. Weak AI operates within predefined parameters and cannot generalize its knowledge to new, unrelated domains. It’s like a Swiss Army knife that can do many things but won’t help you write a novel.

In contrast, strong AI, or Artificial General Intelligence (AGI), would exhibit general cognitive abilities comparable to human beings. AGI would be capable of understanding, learning, and applying knowledge across a wide range of tasks and domains, much like a human. Strong AI remains a theoretical construct, with no existing AI systems demonstrating such capabilities. Think of it as the unicorn of the AI world—everyone talks about it, but no one has ever seen it; some people may even claim it exists, but it’s a tall tale.


Current State of AI

The current state of AI, including advanced LLMs, falls squarely and deeply under the category of weak AI. While these models can perform remarkably well in specific tasks, they do not possess general intelligence. Their “intelligence” is limited to pattern recognition and data-driven responses, lacking the depth of understanding and reasoning required for AGI. It’s like having a calculator that can solve complex equations, but the calculator can’t tell you why the chicken crossed the road. This is also the very reason AI can easily become confused, and even hallucinate answers. These hallucinations, also known as fabrications (Microsoft’s terminology), have not been resolved to date. And similarly to how humans begin to forget with the more they know, the larger the datasets, the greater the amount of hallucinations that appear in GenAI (this is why you see limitations being placed on contextual output). Not great, really.

 

The Limitations of Generative AI and LLMs


Lack of Understanding and Consciousness

One of the primary limitations of generative AI and LLMs is their lack of true understanding and consciousness. These models generate responses based on patterns in data, without any genuine comprehension of the content. They do not possess awareness, emotions, or the ability to understand context beyond statistical correlations. It’s like having a conversation with a very knowledgeable but completely emotionless robot—great for trivia night, not so much for heart-to-heart talks. I would like to point out that context is everything when it comes to thought, when it comes to intelligence. With AI, even the best statistical correlations will often provide a single limited context. I like to call this contextual frame referencing, meaning one frame of context, and only one frame of context is applied to a restricted output. Really think about what that means.


Dependence on Data

LLMs are heavily reliant on large datasets for training. Their performance and accuracy are directly tied to the quality and diversity of the data they are trained on.

This dependence introduces several limitations:

  • Biases: Training data often contains biases present in human language and society, which can be perpetuated by the models. This can lead to biased or prejudiced outputs. It’s like teaching a parrot to speak by only letting it listen to your grumpy uncle—don’t be surprised if it starts complaining about the weather all the time. On a serious note, this means that bias is amplified with AI. Take a second, and think about what that means. It’s not good.
  • Generalization: LLMs struggle to generalize beyond their training data. They may perform well on familiar tasks but fail to handle novel or unexpected scenarios effectively. It’s like a student who aces the practice tests but freezes during the actual exam because the questions are slightly different. LLMs do a great job with interpreting simple context, but the wheels on the bus fall off (so to speak) with any scenarios that have real depth.

 

Absence of Common Sense and Reasoning

LLMs lack common sense reasoning and the ability to perform tasks requiring deep understanding or abstract thinking. They can generate text that appears coherent and contextually appropriate but may fail in tasks requiring genuine comprehension, logical reasoning, or ethical judgment. This limitation becomes evident in complex problem-solving, where human intuition and reasoning are crucial. It’s like having a GPS (which almost all phones have now) that can tell you the fastest route but can’t warn you about the giant pothole ahead. Keep your eyes on the road, folks. AI can only do so much to help you.

 

The Myth of AGI and ASI


Defining AGI and ASI

AGI, or Artificial General Intelligence, refers to AI systems with general cognitive abilities comparable to human beings. AGI would be capable of understanding, learning, and applying knowledge across various domains, exhibiting flexibility and adaptability akin to human intelligence. I like to refer to this type of intelligence as broad-spectrum.

Artificial Superintelligence (ASI) represents an even more advanced stage, where AI surpasses human intelligence in all aspects, including creativity, problem-solving, and social intelligence. ASI remains a speculative concept, with profound implications for society and ethics. It’s like imagining a super-genius who can solve world hunger, write a symphony, and still have time to beat you at chess—all before breakfast. Welcome to the Matrix? Hopefully, not.


Challenges and Impossibilities

Achieving AGI and ASI faces several significant challenges:

  • Technical Challenges: Developing AI systems with general cognitive abilities requires breakthroughs in machine learning, neural networks, and understanding human cognition. Current technologies are far from achieving this level of sophistication. I like to think we’re at the Lego block brain-building stage. Legos can do a lot, but they’re a far cry from constructing the inner workings of a brain.
  • Ethical and Philosophical Challenges: The development of AGI and ASI raises profound ethical and philosophical questions about the nature of intelligence, consciousness, and the role of humans in a world with superintelligent entities. Imagine talking about AI and robot rights, while the most advanced GenAI can’t even count the letters in the word strawberry, or conversely, the fear of killer AI is spreading around the planet.
  • Safety and Control: Ensuring the safety and control of AGI and ASI poses significant challenges. Unchecked superintelligent AI could have unpredictable and potentially catastrophic consequences. What if we really saw the emergence of a Matrix-like AI? Would we be destined to the same path of enslavement? Things we should all consider.

 

Human Intelligence vs. Machine Intelligence

Human intelligence is characterized by unique aspects that AI cannot replicate:

  • Consciousness and Self-Awareness: Humans possess self-awareness, emotions, and subjective experiences, which are far beyond the reach of current AI.
  • Common Sense and Intuition: Humans use common sense, intuition, and ethical judgment to navigate complex situations, which AI lacks.
  • Adaptability and Creativity: Human intelligence is adaptable and creative, capable of innovative thinking and problem-solving across diverse domains.

All three of these points would be part of the broad-spectrum intelligence I mentioned earlier. We as humans really do take so much for granted when it comes to intelligence. Perhaps, it’s because we don’t fully understand our own cognitive abilities. This just further adds to the complexity of building AI systems that are equivalent to human-level intelligence. This also explains why all AI systems to date are weak AI.

 

The Role of AI in Society


Practical Applications

Despite their limitations, weak AI systems have practical applications that benefit society:

  • Healthcare: AI aids in diagnosing diseases, analyzing medical images, and personalizing treatment plans.
  • Finance: AI algorithms optimize trading strategies, detect fraud, and manage risk.
  • Education: AI-powered tools provide personalized learning experiences and assist teachers in managing classrooms.

Something interesting to note, I spent nine years working in healthcare IT, and nearly seventeen years in education IT (so far). It is easy for me to imagine just how practical AI can be in these fields.


Ethical Considerations

The deployment of AI systems raises several ethical considerations:

  • Privacy: The use of AI in data collection and analysis raises concerns about privacy and data security.
  • Bias: AI models can perpetuate biases present in training data, leading to unfair or discriminatory outcomes.
  • Impact on Employment: The automation of tasks by AI can lead to job displacement and economic inequality.


Future Directions

Future AI research should focus on improving weak AI systems and addressing their limitations:

  • Bias Mitigation: Developing techniques to identify and mitigate biases in AI models.
  • Explainability: Enhancing the transparency and explainability of AI decisions.
  • Ethical AI: Promoting the development and deployment of AI systems that align with ethical principles and societal values.

 

Final Thoughts

While GenAI and LLMs represent significant advancements in how we use AI, the technologies remain forms of weak AI, and the many inherent limitations will continue to persist in these platforms. The dream of achieving AGI or ASI remains a distant and arguably unattainable goal (great for movie scripts though). By understanding the true capabilities and, yes, limitations of current AI technologies, we can better navigate the future of AI, leveraging its strengths while also mitigating its risks. It is essential to approach AI with a balanced perspective, recognizing both its potential and its boundaries. As for AI taking everyone’s job? Not anytime soon.

 

 

Notes

 

Verify AI-Generated Content

Artificial General Intelligence

Artificial intelligence in healthcare: transforming the practice of medicine

Artificial intelligence in education

Understanding the different types of artificial intelligence

Computing Machinery and Intelligence (Alan Turing)

 

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