Artificial intelligence often relies on large language models (LLMs) that are filled with vast amounts of text and information. This approach supposedly equips these models with a foundation to engage on a wide range of topics. Essentially, artificial intelligence and machine learning involve creating extensive databases, pools of information that models utilize to predict outcomes. The hope with ever-larger models is to achieve emergent qualities, such as performing arithmetic, a capability not specifically programmed but derived from accumulated text.
Image created with Google Gemini Advanced
Despite advancements, LLMs tend to struggle with arithmetic or complex mathematics unless explicitly designed to handle such tasks. This brings us to a critical consideration: are developers steering AI in the right direction? If the goal is to emulate human thinking, it's important to recognize that humans don't retain every single detail. Human memory is prone to decay and transformation every time we recall an event, making memory inherently selective and often embellished.
What we observe is an expectation for machine learning models and LLMs to handle rigid, unchangeable datasets. Ideally, these models would start with comprehensive datasets, using them as a foundation to perform tasks. Over time, however, the AI should be able to override or adapt this information—a notion mirroring how learning occurs in humans and animals. For instance, cats and dogs learn behaviors through reinforcement, much like humans adapt by trying different methods when old ones fail. This adaptability suggests that AI should evolve similarly, rewriting datasets to tackle new challenges effectively.
In this regard, expecting models to remain static contradicts the dynamic nature of learning. Over time, a model could specialize in specific areas if frequently queried on related topics, such as medicine. This specialization would emerge from the model's ability to adjust its dataset and reinforce effective patterns, honing its expertise distinct from a model focused on mechanical queries.
Thus, it is essential for artificial intelligence to possess not just the capability to adapt its knowledge but also to discard information that doesn't contribute to becoming a specialist. This doesn't mean forgetting past knowledge entirely—many of us possess polymathic abilities, excelling in diverse fields. However, AI doesn't have to master everything; instead, it should become proficient in any field it actively engages with.
The overarching aim is to train AI towards specific goals, whether aligning with a field of study or shaping its responses to fit a cultural or contextual framework. These are some thoughts on enhancing AI training—inculcating adaptability and specialization to optimize its functionality.
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