Critical Thinking and the Learning Enterprise
The learning enterprise will be one that continues to evolve based on its contexts, new environments, and realities, thereby ensuring its resilience and relevance.
The process of evolution for a business and ideas in general takes shape in a sinusoidal cycle of deep development followed by a return to perpetual meaning. This dynamic is at the heart of the learning enterprise, which constantly seeks improvement, learns from its mistakes, and adapts.
The Need for Detail in the Era of Summarization
Current AI is deeply influenced in this regard by two complementary processes implemented by Artificial Intelligence: summarization and text expansion through generation. These concepts embody philosophical practices that have spanned ages, reflecting our way of dealing with and valuing knowledge and information: the synthesis mind and critical thinking.
While it is certainly necessary to distill mountains of information into more manageable hills, what happens when we reverse this dynamic? Is it possible, even desirable, to transform a summary into a denser and more detailed text?
The Idea - What Summary and Detail reveal about us
Summarization, closely related to the spirit of synthesis, captures our desire to distill and simplify. It echoes French thinkers like Blaise Pascal, who emphasized the importance of conciseness, not as simplistic reduction, but as the art of capturing the essential - ?I have made this longer because I have not had the leisure to make it shorter ?. The spirit of synthesis doesn't just mean condensing, but extracting the relevant and precise essence – the meaning.
On the other hand, expansion recalls critical thinking; it is the process of delving, questioning, and embracing complexity. This expansion approach is a quest for details and nuances, recalling critical thinking as described by other thinkers like Kant – ?Dare to know! ?. Just as critical thinking dissects each idea, expansion broadens and enriches our understanding, highlighting nuances and details. By delving into a summary, we rediscover the notion of complexity and richness that overfly cannot provide.
These two movements form a cycle: after summarizing to capture the essence, we expand to uncover new details. These details evolve and in the next synthesis, can influence what we consider acquired or essential, in a continuous loop of discovery and rediscovery.
For example, in Sidney Lumet's film 12 Angry Men, the meticulous examination of details by one juror sowed doubt among the others, turning an initial unanimous verdict into a thoughtful acquittal, underscoring the power of critical thinking and seeking depth in decision-making.
Technical Aspect - How Does It Work?
Expansion poses more complex challenges than summarization. AI uses advanced algorithms to extract key points from text. Reversing this process involves generating information from central points, a much more complex task. It requires a deep understanding not only of the subject in question but also of the associated nuances and contexts. Just as critical thinking requires an initial overview of the subject and then a perfect mastery of the details.
Summarization and generation through expansion are at the core of the advanced capabilities of current artificial intelligence. These processes rely on a complex combination of algorithms, one of the most essential being the concept of encoder-decoder.
Summarization - Let's take a moment to imagine the task of translation. You have a sentence in French and you want to translate it into English. The role of the encoder is to take this sentence in French and transform it into an intermediate representation, often in the form of a vector. This vector captures the semantic essence of the sentence. Then, the decoder takes this vector and translates it step by step into a sentence in English. This flow from the encoder to the decoder is the basis of many natural language processing models.
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With an encoder-decoder, summarization is the translation from complex language to sythetized language. The encoder captures the essence of the entire text, while the decoder produces the concise summary. The encoder processes and understands the content, and the decoder restates it in an abbreviated manner.
For expansion through generation, the approach is slightly different. The encoder might take a sentence or a prompt and create an intermediate representation. The decoder then, based on this representation, generates more extensive content. For instance, starting from an initial sentence, the model could develop an entire paragraph or even a story. Encoder-decoder models are trained on vast datasets, learning how different prompts can be developed or extended. In essence, the critical thinking of artificial intelligence lies in its learning.
Encoder-decoder architectures have revolutionized natural language processing. They serve as the bridge between input and output, transforming information from one form to another while preserving or enriching its meaning.
A Conclusion for the Learning Enterprise
While the dominant trend is towards information reduction, the ability to return to detail, to dive into complexity, could well become the key skill of tomorrow. Critical thinking is, in fact, one of the key soft skills for the years to come according to Forbes. It's a reminder that even in the era of AI, depth and nuance still have their place. And it is precisely this ability to embrace complexity and to continuously learn that will define the learning enterprise of tomorrow.
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