The Road to Artificial General Intelligence: Large Language Models and the Race Towards AGI

Have you ever wondered what tasks must be accomplished before we reach Artificial General Intelligence (AGI)? In this article, I explore the idea that AGI is closer than we think, potentially arriving within the next five to ten years. I will examine the current state of large language models like GPT-4, the role of data and compute in their development, the limitations and concerns of these models, and the ethical implications of achieving AGI.

?What is AGI?

?Artificial General Intelligence (AGI) is a form of artificial intelligence that possesses the ability to understand, learn, and perform tasks across a wide range of domains at a level equal to or surpassing human capabilities. AGI, unlike narrow or specialized AI, can adapt, reason, and learn autonomously without being limited to a specific task or function. With AGI, an AI system can exhibit independent decision-making, problem-solving, and creative thinking, much like human intelligence. In essence, AGI embodies the concept of a machine that can perform any intellectual task that a human being can do, demonstrating versatility and adaptability across various domains.

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What is a Large Language Model (LLM)?

?A large language model is a type of artificial intelligence program that has been trained to understand and generate human-like text. It learns from a massive amount of written text data and uses this knowledge to respond to questions, write sentences, or even create entire articles. The model's main goal is to predict what word comes next in a given sentence or context, which helps it generate human-like responses. Think of it as a really advanced version of auto-complete for your keyboard, but with the ability to understand the context and generate more complex sentences.

?GPT-4 is an advanced version of the Generative Pre-trained Transformer (GPT) series, which are large language models developed by OpenAI. These models use artificial intelligence to understand and generate human-like text. GPT-4 exhibits remarkable capabilities across a variety of domains and tasks, such as mathematics, coding, vision, medicine, law, psychology, and more.?It was released on March 14, 2023, and has been made publicly available in a limited form via ChatGPT Plus, with access to its commercial API being provided via a waitlist1.?

?Large language models, such as GPT-4, Turing NLG, and Switch Transformer, are becoming increasingly advanced, with some researchers believing they may already possess AGI capabilities.

?Here is what OpenAi reported about GPT-4:

?In its recent GPT-4 technical report, OpenAi launched the large-scale, multimodal model capable of accepting both image and text inputs and producing text outputs. And stated that despite being less capable than humans in many real-world scenarios, GPT-4 demonstrated human-level performance on several professional and academic benchmarks, including passing a simulated bar exam with a top 10% score.

?OpenAi defined GPT-4 as a Transformer-based model pre-trained to predict the next word in a document. The post-training alignment process improved the model's factuality and adherence to the desired behavior. A core component of the project was developing infrastructure and optimization methods that behaved predictably across a wide range of scales, enabling accurate prediction of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4.

?Here is a summary of the capabilities section of the technical report:

1. GPT-4 is more reliable, creative, and able to handle more nuanced instructions than GPT-3.5.
2. GPT-4 was tested on various benchmarks, including simulating exams that were originally designed for humans.
3. GPT-4 performed well on these exams, often outscoring the majority of human test takers.
4. GPT-4 also outperformed previous large language models and most state-of-the-art systems on traditional NLP benchmarks, such as MMLU.
5. GPT-4 demonstrated strong performance in other languages besides English, surpassing the state-of-the-art in 24 of the 26 languages considered.

Limitations and Concerns of GPT-4

?While GPT-4 exhibits remarkable capabilities across various domains and tasks, it is essential to consider its limitations and concerns. For instance, GPT-4 may inherit biases from its training data, affecting its output and decision-making. Furthermore, despite its ability to generate human-like text, it may lack common sense or struggle with the reasoning in some situations, especially when faced with ambiguous or conflicting information.

It is very apparent from the report and findings that GPT-4 is already exhibiting human-like performance in so many tasks. In fact, a recent paper from the startup company Anthropics suggests that large language models are already computationally universal, meaning that they can mimic any computation that a universal computer can perform. According to the authors, these models are only limited by access to an unbounded external memory. Anthropics is so concerned by the accelerating progress of these models that they delay publishing research on their capabilities in order to avoid advancing the rate of AI progress.

?One of the most important factors in improving large language models is data. The more data a model has access to, the more it can learn and improve. However, there may be limits to the rate of improvement that can be achieved through data alone. For example, a recent graph from a paper on Microsoft's new Llama model shows that performance gains level off after a certain point as more tokens are added to the model. Additionally, some tasks, such as social interaction question answering and natural language questions, still present a challenge for even the most advanced models.

So, if data isn't everything, what other factors can we consider when it comes to achieving AGI? Anthropics suggests that compute may be a rough proxy for progress.

?What "Compute" means?

?In the AI context, compute refers to the amount of computational resources, such as processors, memory, and networking, that are used to train or run AI models. Compute is one of the key factors that drive the advance of AI, along with data and algorithmic innovation. Compute is usually measured in terms of FLOPS (floating point operations per second), which indicates how fast a system can perform calculations. Compute can also be measured in terms of cost, energy consumption, or carbon footprint. Compute is a critical component for developing and deploying AI systems that are capable, scalable, and efficient.

?For example, GPT-4 has more flops than GPT-3, because it has more parameters and more data.?GPT-4 has around 175B-280B parameters, while GPT-3 has 175B parameters?12.?GPT-4 uses 45GB of training data, while GPT-3 uses 17GB of training data?34.

?According to Anthropics's theory, the capability jump from GPT-2 to GPT-3 resulted mostly from a 250-time increase in compute. They predict that?a 1,000-time increase over the next five years could result in a capability jump that is significantly larger than any we've seen before. This could potentially result in human-level performance across most tasks, and may even signal the arrival of AGI.

?Is AGI a Well-Defined Concept?

The concept of AGI remains elusive, with some experts arguing that it is already here, while others believe it is still many years away. The subjective nature of certain tasks complicates the definition of AGI. The near-term future of AGI could see only niche tasks remaining out of reach, leading many to perceive AGI as already achieved.

?Ethical Implications and Responsible AGI Development

As we advance toward AGI, it is crucial to consider the ethical implications of these technologies. Companies like Microsoft, OpenAI, and Google stand to profit significantly from large language models, but at what cost? Anthropics suggest rewarding models based on good processes rather than expedient outcomes. Many researchers have called for increased transparency and accountability in the development and deployment of these technologies to ensure responsible AGI development.

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In conclusion, the journey towards Artificial General Intelligence (AGI) is marked by rapid advancements in AI research, with large language models like GPT-4 showing remarkable capabilities across various domains and tasks. Although some evidence suggests that AGI could potentially be achieved within the next five years, the timeline remains uncertain, and the concept of AGI itself is not well-defined. There are several limitations and concerns associated with large language models, such as biases inherited from training data and struggles with common sense reasoning in ambiguous situations.

?As we progress towards AGI, it is crucial to address the ethical implications surrounding these technologies. Ensuring responsible AGI development requires increased transparency and accountability from major players like Microsoft, OpenAI, and Google. Collaboration among researchers, policymakers, and other stakeholders is essential in order to harness the power of AGI for the betterment of society while mitigating potential risks.

?Ultimately, the development of AGI is a defining narrative of our time, and it is essential for all of us to remain informed and engaged. By fostering a responsible approach to AI research and addressing the limitations and ethical concerns associated with large language models, we can work together to shape the future of AGI and its potential impact on our world.

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