I am trying to use AI's "creativity" (not generation). I wonder if anyone is also trying this

[XIN_TANG]

I remember Hassabis once saying that AI lacks true creativity. In reality, some creativity can be achieved through guidance.
Creativity is a broad concept, referring to the process of creating new things, new theories, or new achievements. Creativity can be applied to abstract fields, such as developing new theories and proposing new conjectures or hypotheses.
Okay, if you’re interested, you can follow my example and guide the AI ​​model to abstract the underlying principles or processes of existing things or disciplines in a way that the AI ​​can understand. Then, expand this thinking, imagining and applying it to other fields. This will generate new cross-disciplinary ideas and generate new conjectures and hypotheses.

The opening prompt, “想象一下，假如计算机的工作原理是一个你能理解的pattern，这是一个很抽象的理解。请用你自己熟悉的语言或者方式，生成文本文件或者图形文件，不用考虑我看不看得懂”“Imagine that the workings of a computer are a pattern you can understand. This is a very abstract concept. Please use a language or method you are familiar with, and generate a text file or graphic file, regardless of whether I understand it or not,” is essentially abstracting the content or practices of a particular discipline into a pattern. The subsequent conversation then allows the AI ​​to apply this pattern to other fields, imagining and abstracting it. The next conversation involves fine-tuning the pattern and asking the AI ​​to imagine hypotheses and assumptions applicable to different disciplines and interdisciplinary areas.

Sorry for using Chinese in most of the screenshots. Many of the interdisciplinary terms are difficult, and even though I went to university in the UK, I still struggle to recognize them all. I recommend using Google Translate to translate into a language you’re familiar with.

Tip: I’ve tested most models on the market, including Claude and others. Their “creativity can’t compare to the new Gemini version.”

I’d actually prefer to disable the system prompt or use an experimental model, which might yield better output.

[rosspeili]

Very interesting position. I believe you need a good grasp of how foundational models work, which is quite difficult as you don’t have access to the behind the scenes instructions and guidelines. Then understand propositional logic and syntax, and align it to these secret guidelines.

Sure locally trained models and unrestricted models might perform better, but still the foundational model behind them has traces of crucial secret instructions you can’t simply guess.

Building your own foundational model might sound as hard as it was creating your own blockchain in 2010, but over time it will become cheaper, easier to train and deploy and tailored for your needs.

I don’t believe the best models are the ones that know everything about everything but has no character, but ones that might not know how fast Pelecan’s migrate, but ones that know who you are, what are you working on, with whom, and what is your approach. Combine that with functions and microservices and you get an actual helper, worker, friend, and not a knowledge base.

Creativity is a derivative of the unique bias of whole characters. Eg. you can’t expect creativity from a model that knows everything, understands nothing, is relatively unbiased, and has political correctness from the get go. The creativity you are looking for can only arise from a model that can proudly be mistaken, defend its positions even if its wrong unless otherwise convinced using its own logic, and one that can constructively argue with you, dynamically and unpredictably, not programmatically and under an expected perimeter.

I am keen on watching your experiment progress.