本文探讨了将信息论中的概念，如压缩和潜在向量，应用于个人目标设定和自我改进。作者提出了一种创新的方法，借鉴AI训练中的原理，帮助用户梳理抽象的目标，将其分解为可操作的实际步骤。该方法旨在通过关键问题的引导，减少目标设定的不确定性和复杂性，挖掘“潜在价值”，从而为用户提供更精准的自我评估基础。作者已在个人实践、学术研究和应用开发方面进行了多年探索，并计划推出一款名为keyMetas的个人规划应用，以期帮助年轻人在康复过程中更有效地设定和实现目标。

💡 借鉴AI模型中的“潜在向量”概念，该方法旨在从用户观察到的数据（如目标表述）中推断出未被直接观测到的“潜在价值”，并将其与目标关联起来，帮助用户理解目标的深层结构。

🎯 针对个人目标设定的普遍难题，特别是对康复中的年轻人而言，目标常因抽象和压倒性而难以执行。该方法通过信息论的“费米估计”等概念，引导用户通过一系列关键问题，层层递进地缩小目标变量范围，找到目标的关键信息点。

🚀 该方法的核心在于通过提问来减少不确定性，鼓励用户寻找证据并进行估算和制图，从而将影响个人目标的数千个因素（如身体、情感、社交等）系统化，为自我评估建立更准确的基石。

🧠 作者将AI的自我教育和纠错机制类比于人类的自我提升，认为既然我们有方法纠正AI的偏见和错误，同样可以将这些方法应用于需要深度改进的人类个体，实现个人效能的提升。

🛠️ 作者已为此方法投入多年实践，包括个人应用、学术论文（涉及神经科学、信息工程）和一款分析应用，并计划开发名为keyMetas的应用，进一步测试和推广这一结合信息论与AI思想的个人目标设定工具。

Published on August 22, 2025 2:04 PM GMT

I'd like to find people interested in setting personal goals and analyzing moments using information theory. I'd like help analyzing the potential flaws of this method, and perhaps I could find colleagues. I'm developing an app that uses AI-inspired methods to study their reach in people seeking more significant improvements: young people in rehabilitation.

I'm working with young people in rehabilitation who often struggle with goal-setting because their objectives feel overwhelming and abstract. Traditional approaches ask them to define goals, but when you're dealing with addiction recovery, trauma, or behavioral issues, 'get my life together' isn't actionable.

 

This could help users find their goals' underlying structure ('latent values') and build practical steps. It applies information theory compression concepts to human motivation and satisfaction. I continue with an illustrated introduction to acclimatize the idea of ​​the method. The technical details, proofs, script, and shipping are available upon request.

If AIs are like our children and we educate them, why not apply that education to ourselves?

 

Context

The AIs were trained with our data and make mistakes similar to ours, such as bias, blackmail, and deception. That's why we've created incredible methods to correct them so they make fewer mistakes.

 

Why not apply some of these methods to people who need extreme improvements?

Motivation

 

I would like, in this proposal, seek characteristics that can minimize an individual's uncertainty and complexity, starting with personal goals.

The idea of this proposal is to apply an approach similar to that of latent vectors in AI models: inferring unobserved characteristics from observed data and correlating with them latent values.

If training an AI requires vectorizing uncertainty, why not offer similar methods to humans who need robust improvements. But how?

Methodology basis

 

Since a personal goal has seemingly infinite variables, one way out would be to find the most robust information: key factors of the goals. 

There are uncertainty-friendly methods that can help. For example, the Fermi estimate allows a less error-prone approximation of the number of piano tuners in England without having any precise numbers. Starting with mayor data and doing some questions to refining:

How many people are there likely to be in the world?

How many people are there in Europe?

How many are interested in music?

How many musicians?

How many piano players?

How many tuners?

 

 

How can an AI do that?

The idea is to ask key questions to reduce uncertainty and encourage user to seek evidence and make estimates and graphs. 

We can then apply similar methods to narrow down the thousands of factors—physical, developmental, emotional, informational, social—that shape an individual's goals and subsequently establish a more accurate basis for self-assessment in an app.

So, going from AIs to people, maybe something like:

Impact– Which of your goals would be most rewarding? Which of your goals would be the hardest?
With these references, compare the risks and rewards between your goals.

 
Address– Does your goal require more interventions in yourself or in the environment?
Identify where intervention would be most likely.

 
Scope– Does your goal require more dedication on a basic physical level or an emotional level? More emotional or intellectual? More emotional or social?
Determine the level of possibility/uncertainty of your goals:
 • Elemental
 • Emotional
 • Informational
 • Social

 
Latent value– As a result, the user will receive for the app the balance of the latent values necessary for your goals:

With this base, we could create models, evaluate moments with a life journal and reconstruct personas to adapt them to the world of overwhelming information.

 

"A noise can be broken down into notes and restructured into music…

 

…with a touch of proportion and structure...,

  

An image can be reconstructed from light, shadow, and color, becoming art…

 

…with a touch of proportion and structure..,

  

One's expectations and goals can be reformulated into motivation, satisfaction, and values,

 

…with a little bit of...,

  

In information theory, an AI can compress complex data to 5% and reconstruct it with 95% functional fidelity—these are latent vectors."

 

And could we apply some of that and reduce someone's personal goals a little bit to their latent values?

 

Conclusions

With that introduction, I'd like the interested person's help if I can use it as a good first step to create a personal planner for users to define goals using information theory, which I'm currently calling: keyMetas. 

What I've done so far:

I've been applying an evolution of this method to myself for eleven years in sheets.A graphic design thesis at the University of La Plata to disseminate the method.A moment analysis apk that analyze moments that I'm adapting to analyze goals.A postgraduate degree in neuroscience, with a thesis defense that received top marks in motivation and satisfaction factors.Four year study of information engineering

Next steps:

Launch app on Play StoreClinical tests and supervision

Each part of the project will be send separately:

Latent value model with its informational and evolutionary basisScientific study modelOpen source app on GitHub, along with the theoretical framework and scoring formulas.

Discuss