A new solution and concrete implementation steps for Artificial General Intelligence
In this paper, we propose a new approach to building a artificial general intelligence with self awareness, which includes: (1) a new method to implement attention mechanisms; (2) a way to give machines self-demands; (3) how to form a value evaluation system compatible with the network; (4) a way to create the world models; (5) how to realize a top-down, hierarchical thinking decision-making chain; (6) a way to achieve general decision-making and response capabilities; (7) a way for a machine to directly obtain human experience through language. In the paper, we first analyze some of the shortcomings of current LLMs (Large Language Model) and propose ideas for improvement. Then we analyze why our scheme can solve the above problems and provide detailed steps for implementing our scheme. In chapter 4, we have presented a step-by-step mplementation roadmap. And in chapter 5, we have presented a specific implementation demonstration. In chapter 6, we analyze the advantages and disadvantages of our scheme and propose further research directions. In this article, we have put forward how to create genuine artificial general intelligence step by step. It can handle data of all modalities in a unified form and can directly understand the experience that humans already possess through language, thus avoiding the problem that reinforcement learning is required for every decision-making process.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingLarge Language Modelreinforcement-learningReinforcement LearningSimilar Papers 제목 키워드 기반
FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging
The recent advancements in artificial intelligence (AI) combined with the extensive amount of data generated by today's clinical systems, has led to the development of imaging AI solutions across the whole value chain of…
FairnessImage ReconstructionImage SegmentationMedical Image Segmentation+1On the Current and Emerging Challenges of Developing Fair and Ethical AI Solutions in Financial Services
Artificial intelligence (AI) continues to find more numerous and more critical applications in the financial services industry, giving rise to fair and ethical AI as an industry-wide objective. While many ethical princip…
Position: Certified Robustness Does Not (Yet) Imply Model Security
While certified robustness is widely promoted as a solution to adversarial examples in Artificial Intelligence systems, significant challenges remain before these techniques can be meaningfully deployed in real-world app…
PositionBridging the AI Trustworthiness Gap between Functions and Norms
Trustworthy Artificial Intelligence (TAI) is gaining traction due to regulations and functional benefits. While Functional TAI (FTAI) focuses on how to implement trustworthy systems, Normative TAI (NTAI) focuses on regul…
Engineering Sentience
We spell out a definition of sentience that may be useful for designing and building it in machines. We propose that for sentience to be meaningful for AI, it must be fleshed out in functional, computational terms, in en…