Six Shortcomings of Current LLMs I Expect From AGI

Large language models (LLMs) have made significant leaps in recent years. The amazing capabilities of ChatGPT & co have revived the discussion around the emergence of a General Artificial Intelligence. AGI aims to match human intelligence in every way. And may not even be so far from that. Already today, LLMs can write essays, compose music, and even generate art. Yet, when we look at the spectrum of intelligence, there’s still an obvious gap between today’s models and human intelligence. Even defining AGI is a hard task that has been subject to heavy discurs. In this article, I will thefeore share a more personal perspective on the shortcomings of current LLMs and what I would personally anticipate from an AGI. These are the elements that I believe will signal AGI’s arrival.

Also:

What is AGI?

My current understanding of Artificial General Intelligence is that it represents the hypothetical ability of an AI system to understand, learn, and apply knowledge in a way that is indistinguishable from a human being. This means an AGI would be able to perform any intellectual task that a human can, with equal or better proficiency. For super-human intelligence we can also call it an “Artificial Super Intelligence”.

An AGI is the kind of intelligence that would allow a machine to reason in complex situations, make judgments under uncertainty, and plan for the long term. Some also argue an AGI will only be achieve if it has consciousness. While this may sound like the stuff of science fiction, the steps toward AGI are being taken in the real world today.

At the forefront of this ambitious journey is OpenAI, the organization behind ChatGPT. OpenAI’s mission is to steer the development of General Artificial Intelligence towards benefiting all of humanity, encapsulating a vision that melds technological advancement with ethical stewardship. Next, let’s look at some shortcoming of current models that may provide clues on what we may encounter on the road towards AGI.

OpenAI’s vision for the future of AGI: https://openai.com/about

Shortcoming of Current LLMs

Despite their groundbreaking achievements, we identified six key areas where current AI falls short of the nuanced capabilities expected from Artificial General Intelligence (AGI):

  • Empathy in Writing
  • Contextual Understanding of Visuals
  • Conceptual Synthesis
  • Genuine Learning Memory
  • The Art of Silence
  • Visual Ideation

Let’s look at the six capabilities more in detail.

Showing Empathy

The first shortcoming I see is that current models are not good at showing emphaty. Current AI can simulate empathy, much like a well-rehearsed actor. But true empathy requires an understanding that goes beyond algorithms and pattern recognition—it’s about connecting on a human level. The AGI of the future would be capable of this genuine emotional engagement, providing comfort or joy that feels truly sincere. For example, an AGI should be capable of dynamically adjust tone (and voice) and break out from its current pattern to create a bond during a conversation.

Contextual Understanding

Today’s LLMs can describe what they see in a picture, but understanding the story behind the image is another matter. AGI will not only describe but comprehend the scenes, recognizing the emotions, the history, and the nuances that a human might intuitively understand. The latest GPT-4 Vision model shows some sparks in this capability. It is capable of understanding how different objects in an image are related. For example, when there is water coming into a house, it can recognize that this is an undesired state. However, there are limitations. For example, the model struggles to tell if an object is in front or in the back of an image, and it struggles to assess how far objects are from each other. So there is still room for improvement.

Conceptual Synthesis

The capability for Conceptual Synthesis in AGI transcends the mere blending of existing ideas. It embodies the ability to conduct research independently, apply concepts to novel problems, and, crucially, develop entirely new concepts autonomously. This advanced synthesis is not just about rehashing known information but about pushing the boundaries of innovation and knowledge.

AGI’s prowess in this area would mean that it could dive into the vast sea of human knowledge, identify gaps or opportunities for advancement, and forge new paths in science, technology, arts, and beyond. For instance, in the realm of medical research, AGI could uncover connections between disparate studies and datasets, proposing novel treatments or uncovering previously unknown disease mechanisms. In environmental science, it might develop unique strategies for sustainability that combine ecological science, urban planning, and renewable energy technologies in ways no human or current AI has conceived.

Furthermore, AGI’s ability to autonomously develop new concepts means it could theoretically initiate its own research projects without human guidance, identifying areas of potential breakthrough and dedicating resources to explore them. This level of initiative and creativity could significantly accelerate the pace of innovation, potentially solving complex global challenges more rapidly than current human-led efforts.

The implications of such capabilities are profound. They suggest a future where AGI partners with humans not merely as tools or assistants but as co-creators and innovators, contributing original ideas and solutions that are currently beyond human conception. This partnership could redefine the landscape of research and development, making what we now consider science fiction into science fact.

Genuine Learning Memory

AGI will remember interactions not as data points but as experiences, learning from them in a way that is dynamic and evolving. This means an AGI could continue a conversation from weeks ago, recall past emotions, and build upon previous ideas, creating a continuity of intelligence that today’s LLMs can’t achieve.

The Art of Silence

The power of well-timed words is undeniable, yet the value of strategic silence holds equal weight. Unlike current models like ChatGPT, which operate on a prompt-response basis without discernment on when to speak, AGI will master the art of silence. It will understand when providing a listening ear outweighs the need for immediate advice, recognizing the moments when simply being present is more beneficial than any verbal input. AGI’s sophistication will extend beyond the automatic generation of responses to include the ability to discern the appropriate moments for engagement, effectively timing its interactions to align with the nuanced dynamics of human communication. This evolution marks a significant departure from the current limitations, showcasing AGI’s capacity for judgment and empathy in conversation.

Visual Ideation

Current language models have the capability to generate images; however, they often face challenges when it comes to explaining complex concepts through diagrams and sketches. For instance, when tasked with illustrating Michael E. Porter’s Five Forces model, the results provided by Dall-E highlighted some of these limitations.

Prompt to ChatGPT: “Illustrate 5 forces from Michael E. Porter”

What ChatGPT proposed
The actual illustration of 5 Forces
The actual illustration of 5 Forces

ChatGPT’s response included an attempt to visually represent the concept, followed by an actual illustration of the Five Forces model. This experience underscores the current gap in language models’ ability to convey intricate ideas visually in a manner that is both intuitive and informative.

The advent of Artificial General Intelligence (AGI) is expected to revolutionize this aspect. AGI will possess the capability to not only illustrate complex concepts in ways that are easily understandable but also to innovate by creating diagrams and sketches that dynamically incorporate its own ideas. This will significantly enhance our ability to bridge the divide between abstract theories and their tangible representations, thereby enriching our comprehension of complex subjects.

Summary

This article embarked on a journey through the current landscape of LLMs and their progression towards the much-anticipated goal of AGI. We examined key areas where today’s AI technologies, including notable examples like ChatGPT, fall short of the comprehensive capabilities expected from AGI. These areas include empathic writing, contextual understanding of visuals, innovative conceptual synthesis, genuine learning memory, the nuanced art of silence, and the ability to create and interpret complex visual ideation.

It’s important to recognize that the six capabilities outlined are not exhaustive. The path to achieving AGI is likely to uncover additional prerequisites and challenges that we have yet to consider. As we continue to advance, our understanding of what constitutes true artificial general intelligence will evolve, revealing new frontiers of knowledge and technology.

Looking ahead, the journey toward AGI is both exciting and uncertain. Based on the current pace of innovation and the challenges that lie ahead, I personally believe that we are approximately 3-5 years away from realizing the first AGI. This timeframe allows for the development of not only the technical capabilities but i wonder wether it is enough for humanity to prepare for the implications. There is now an urgent need to develop the ethical frameworks necessary to ensure that AGI benefits all of humanity. As we move forward, it’s crucial that we continue to engage in thoughtful dialogue and collaboration across disciplines to navigate the complexities of this next great leap in artificial intelligence.

Sources and Further Reading:

Author

  • Florian Follonier

    Hi, I am Florian, a Zurich-based Cloud Solution Architect for AI and Data. Since the completion of my Ph.D. in 2017, I have been working on the design and implementation of ML use cases in the Swiss financial sector. I started this blog in 2020 with the goal in mind to share my experiences and create a place where you can find key concepts of machine learning and materials that will allow you to kick-start your own Python projects.

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