Associative memory as driver for innovation
23/07/24 08:45
Understanding Large Language Models: Thoughts on Their Creation, Functionality, and the Rise of Smaller Models
Large language models (LLMs) have primarily been created using brute force approaches, leveraging vast amounts of data and computational power. Their emergent capabilities have surprised many within the field, prompting organizations such as OpenAI to release their models publicly at an early stage, responding to the unexpected effectiveness of these systems.
Despite their success, our understanding of LLMs' internal workings remains incomplete. I posit that these models operate analogously to the associative memory processes inherent in the human brain. Just as our brains form connections based on experiences and knowledge—where one idea or concept can evoke another—neural networks, through their architecture and training on vast datasets, mimic this associative thinking. The underlying mechanisms of LLMs enable them to draw inferences and generate text based on patterns recognized during training, akin to how humans recall related memories or concepts when prompted with a stimulus.
This analogy not only illustrates the potential for processing language but also highlights gaps in our comprehension of how these models function. Accepting that LLMs mirror some aspects of human associative memory opens numerous avenues for exploration. It suggests that there is significant potential for improvement—this includes optimizing larger models and, importantly, developing smaller, more efficient architectures that can achieve similar degrees of performance.
In recent trends supporting this shift, OpenAI has released GPT-4o mini, a smaller variant of GPT-4o. This model has made headlines as the best small model available, even surpassing competitive models like Anthropic's Claude 3 Haiku and Google's Gemini Flash on some benchmarks. Remarkably, this efficiency comes at a fraction of the cost, priced at just 15 cents per million input tokens and 60 cents per million output tokens, demonstrating a powerful trend towards creating AI models that are not only smaller but also faster and cheaper.
The movement towards smaller models reflects the reality that early AI systems were underoptimized, leading to excessively high operational costs. As noted, GPT-4o mini is more than 60% cheaper than its predecessors while delivering significantly improved performance. This rapid evolution reaffirms that the AI industry can produce models that, once considered exorbitantly priced, could have been optimized much earlier.
The associative memory aspect of these models may play a crucial role in their efficiency. By drawing on a vast web of relationships between data points, LLMs can respond more swiftly and accurately, reflecting the way human memory retrieves relevant information based on associative links. This capability allows smaller models to take advantage of increased efficiency, focusing on the most relevant connections rather than necessitating the sheer computational weight of their larger counterparts.
As we consider these advancements, we must acknowledge a changing landscape wherein a few key players—often referred to as FAANG (Facebook, Apple, Amazon, Nvidia, Google) plus Microsoft—control not only the most powerful models but also the essential distribution channels through which these technologies reach consumers. This reality highlights a critical aspect of today's AI market: it is not a democratization of technology that is emerging, but rather a consolidation of power among a select few companies.
OpenAI, the primary developer of models like GPT-4o, operates within this ecosystem, standing alongside competitors such as Google and Anthropic, who similarly control both large and smaller AI models. Their dominance raises questions about the future of competition in the AI field. While many hoped for a shift that would allow smaller companies to unseat these giants, the reality is that these established entities have proven resilient in maintaining their positions.
The mechanisms through which AI is now integrated into everyday technology further cement this trend. Major companies like Microsoft and Google are capitalizing on their existing distribution channels, seamlessly incorporating AI models into widely used services like Office and Workspace. As such, consumers are unlikely to turn to alternative, smaller providers for AI solutions; instead, they will engage with the technology offered by the leading players, reinforcing the oligopolistic structure of the market.
I conclude that LLMs signify a remarkable convergence of technological advancement and market dynamics. While there remains a path for innovation and optimization— which includes the development of powerful yet cost-efficient smaller models like GPT-4o mini—the surrounding industry landscape is increasingly dominated by large corporations. The real challenge moving forward will be navigating these complexities, ensuring that advancements in AI technology reflect the associative processes of human cognition, and ultimately providing benefits to a broader array of stakeholders, rather than perpetuating existing hierarchies.
Large language models (LLMs) have primarily been created using brute force approaches, leveraging vast amounts of data and computational power. Their emergent capabilities have surprised many within the field, prompting organizations such as OpenAI to release their models publicly at an early stage, responding to the unexpected effectiveness of these systems.
Despite their success, our understanding of LLMs' internal workings remains incomplete. I posit that these models operate analogously to the associative memory processes inherent in the human brain. Just as our brains form connections based on experiences and knowledge—where one idea or concept can evoke another—neural networks, through their architecture and training on vast datasets, mimic this associative thinking. The underlying mechanisms of LLMs enable them to draw inferences and generate text based on patterns recognized during training, akin to how humans recall related memories or concepts when prompted with a stimulus.
This analogy not only illustrates the potential for processing language but also highlights gaps in our comprehension of how these models function. Accepting that LLMs mirror some aspects of human associative memory opens numerous avenues for exploration. It suggests that there is significant potential for improvement—this includes optimizing larger models and, importantly, developing smaller, more efficient architectures that can achieve similar degrees of performance.
In recent trends supporting this shift, OpenAI has released GPT-4o mini, a smaller variant of GPT-4o. This model has made headlines as the best small model available, even surpassing competitive models like Anthropic's Claude 3 Haiku and Google's Gemini Flash on some benchmarks. Remarkably, this efficiency comes at a fraction of the cost, priced at just 15 cents per million input tokens and 60 cents per million output tokens, demonstrating a powerful trend towards creating AI models that are not only smaller but also faster and cheaper.
The movement towards smaller models reflects the reality that early AI systems were underoptimized, leading to excessively high operational costs. As noted, GPT-4o mini is more than 60% cheaper than its predecessors while delivering significantly improved performance. This rapid evolution reaffirms that the AI industry can produce models that, once considered exorbitantly priced, could have been optimized much earlier.
The associative memory aspect of these models may play a crucial role in their efficiency. By drawing on a vast web of relationships between data points, LLMs can respond more swiftly and accurately, reflecting the way human memory retrieves relevant information based on associative links. This capability allows smaller models to take advantage of increased efficiency, focusing on the most relevant connections rather than necessitating the sheer computational weight of their larger counterparts.
As we consider these advancements, we must acknowledge a changing landscape wherein a few key players—often referred to as FAANG (Facebook, Apple, Amazon, Nvidia, Google) plus Microsoft—control not only the most powerful models but also the essential distribution channels through which these technologies reach consumers. This reality highlights a critical aspect of today's AI market: it is not a democratization of technology that is emerging, but rather a consolidation of power among a select few companies.
OpenAI, the primary developer of models like GPT-4o, operates within this ecosystem, standing alongside competitors such as Google and Anthropic, who similarly control both large and smaller AI models. Their dominance raises questions about the future of competition in the AI field. While many hoped for a shift that would allow smaller companies to unseat these giants, the reality is that these established entities have proven resilient in maintaining their positions.
The mechanisms through which AI is now integrated into everyday technology further cement this trend. Major companies like Microsoft and Google are capitalizing on their existing distribution channels, seamlessly incorporating AI models into widely used services like Office and Workspace. As such, consumers are unlikely to turn to alternative, smaller providers for AI solutions; instead, they will engage with the technology offered by the leading players, reinforcing the oligopolistic structure of the market.
I conclude that LLMs signify a remarkable convergence of technological advancement and market dynamics. While there remains a path for innovation and optimization— which includes the development of powerful yet cost-efficient smaller models like GPT-4o mini—the surrounding industry landscape is increasingly dominated by large corporations. The real challenge moving forward will be navigating these complexities, ensuring that advancements in AI technology reflect the associative processes of human cognition, and ultimately providing benefits to a broader array of stakeholders, rather than perpetuating existing hierarchies.