The bottleneck of LLMs is that it is actually impossible to label all knowledge?
DeepLearning/LLMs are ultimately efficiency problem of data production?
Our brain is not only learning from rewards, it is learning from failures as well, in fact more frequently than we think. The same principle is true for Artificial Intelligence and However, most of the time, we are not giving AI enough chance to fail (it is ok to make mistakes and fail.). We are only trying to teach them the perfect way to do stuff. Just like our sons being different from us, so are the machines, their way of doing things could be much different. It is good to point them in the right direction, however we should be giving them a chance of trial and error.
The question touches on several important aspects of machine learning, particularly in the context of large language models and deep learning frameworks. Let's see the key points:
1. Learning to Think vs. Memorizing Correct Answers:
- Adaptability: The real world is complex and ever-changing, which means the "correct answers" can also change over time. By learning to think, AI models can adapt to new situations, contexts, and evolving information, which is not possible if they only memorize static answers.
- Generalization: AI models that learn to think are better at generalizing from past examples to new, unseen scenarios. Memorizing answers is limiting, as it's impossible to anticipate and store every possible question or situation the model might encounter.
- Creativity and Problem Solving: Learning to think allows AI models to engage in more creative and complex problem-solving tasks, going beyond predefined answers. This is crucial in areas like research, where novel solutions are often needed.
2. The Challenge of Labeling All Knowledge:
- Scale and Diversity: The sheer volume and diversity of human knowledge make it impractical, if not impossible, to label and memorize all of it. Even if we could, this knowledge is continually expanding and evolving.
- Context Dependence: Many questions don't have universal "correct answers." Context, cultural nuances, and subjective perspectives often play a significant role in determining what an appropriate answer might be.
3. Deep Learning and LLMs as an Efficiency Problem in Data Production:
- Efficiency in Learning: One of the main advantages of deep learning and LLMs is their ability to learn efficiently from large datasets. They can extract patterns, understand language structures, and build knowledge from the data they are trained on, making them efficient in learning compared to manual data entry or rule-based systems.
- Data Quality and Bias: The efficiency of data production also brings challenges. The quality of the output of LLMs heavily depends on the quality of the input data. Biases in the training data can lead to biased outputs, highlighting the need for careful dataset curation.
- Continuous Learning: Unlike static databases, LLMs can continue learning and adapting. New data can be incorporated, allowing the model to stay current and improve over time, though this is a complex process that involves retraining and fine-tuning.
In conclusion, while memorizing correct answers might seem straightforward, it's not feasible or desirable for complex, real-world applications where adaptability, generalization, and problem-solving are required. Deep learning and LLMs, through their ability to learn and think in a more human-like manner, address these challenges more effectively, with their own set of limitations.
Ok so su can code replies
If "Hello world": reply("...")
if not "Hello world": reply("...")
Or you can let ML learn:
if I say "Hello world" it will reply with something. If I will say not "Hello world" it will reply with something. But what if I will say not not or not not not or it isn't... we have infinite options so labeling them all for ML seems to be less energy requiring. No?
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