The emergence of large language models (LLMs) has sparkedthe discussion on Artificial Superintelligence (ASI), a hypothetical AI system surpassing human intelligence. Though ASI is still hypothetical and far from current AI capabilities, existing alignment methods struggle to guide such advanced AI ensure its safety in the future. It is essential to discuss the alignment of such AI now. Superalignment, the alignment of AI at superhuman levels of capability systems with human values and safety requirements, aims to address two primary goals: scalability in supervision to provide high-quality guidance signals and robust governance to ensure alignment with human values. In this survey, we review the original scalable oversight problem and corresponding methods and potential solutions for superalignment. Specifically, we introduce the Figure 1: Challenges from the perspectives of supervision challenges and limitations of current alignment and governance. While supervision perspective paradigms in addressing the superalignment focuses on providing high-quality guidance signals for problem. Then we review scalable oversight enhancing system competence, governance perspective methods for superalignment. Finally, we discuss emphasizes aligning the behavior of advanced aI with the key challenges and propose pathways human values to prevent harmful outcomes.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Intelligence in an artificial intelligence (AI) system can be measured in mainly 4 ways: Breadth: Most systems we know to be intelligent such as the human brain have broad capabilities. A child learns a lot of tasks such as walking, talking and many more things. An AI system that should be considered intelligent should also have such similar broad capabilities. The so-called strong AI system should be able to learn any task without any modification directly to its source code by human engineers. But we all know about the no free lunch theorem which states that an algorithm that is good at a particular set of tasks pays for that by performing poorly on the other remaining set of tasks.

Artificial intelligence (AI) has the potential to surpass human intelligence (HI) in the near future. Despite huge advances in technology, artificial intelligence is still far from complete. Human intelligence aims to learn from experience and adapt to new environments via different cognitive processes, while artificial Intelligence (AI) aims to mimic and surpass human actions and behaviour. Human intelligence is measured by either a group or individually administered test. It only takes a few minutes after meeting someone for most of us to judge how smart, competent, or quick-witted we think they are.

The mention of artificial intelligence usually sparks conversations around the singularity and robots revolting against humans, but what really is AI and how is it used today? Outside of the realm of science fiction, AI is becoming more and more prevalent in our society. The technology can be discreet, such as a mobile phone voice-controlled assistant, or more obviously, such as self-driving cars. Artificial intelligence programs are widely used over a number of industries. The technology can be used to help doctors to analyse images, control robots in automotive manufacturing and even translate for Amazon's home assistant, Alexa.

In March this year, AlphaGo, a machine created by Google's artificial intelligence (AI) arm, DeepMind, trounced Mr Lee Sedol, a grandmaster at Go, the ancient Chinese game. AlphaGo used cutting-edge AI to beat a player acknowledged to be one of the greatest ever. For Go aficionados, the game will never be the same again, just as chess was changed when IBM's Deep Blue beat then world champion Garry Kasparov in 1997. That year, it was widely thought that while machines could master chess, beating the world's best at Go - a far more complex game with near-infinite variations of play - was still several decades away. Deep Blue used brute-force calculation and sheer computing power to beat the reigning world champion.