SI is the future of AI – just not in the way Trump thinks
With US President Donald Trump recently pushing the use of “superintelligence” (SI) instead of artificial intelligence (AI) in national discourse, the acronym has gained unprecedented attention. It is the right acronym but the wrong meaning. In my opinion, SI should stand for “self-intelligence”. It probably explains why academics, critics and even major AI companies are calling for a temporary…
The acronym “superintelligence” (SI) has recently gained considerable attention in national discourse, largely due to US President Donald Trump's advocacy. However, the term's adoption in this context is misguided. From an academic perspective, SI should stand for “self-intelligence,” representing a more accurate definition of the technology.
This shift in understanding is crucial, as current AI development is on an erroneous trajectory. The essence of SI lies in a machine's ability to independently acquire knowledge, determine actions, and navigate unpredictable environments.
AI development has progressed through three distinct stages: AI 1.0, AI 2.0, and AI 3.0. AI 1.0 represents the initial era, where machines were programmed to solve specific problems using predefined rules. While successful in automating tasks requiring human expertise, these systems remained confined to their programmed limitations.
AI 2.0 introduced the concept of machines learning from data, expanding their capabilities significantly. Notable examples include Google DeepMind's Gemini Robotics 2 and Nvidia's Cosmos 3, which combine vision, language, and action to perform complex tasks. These advancements have showcased the potential of AI in understanding and interacting with environments.
AI 3.0 signifies the next major leap, focusing on machines that can acquire intelligence autonomously. Unlike AI 1.0 and 2.0, SI does not imply deep consciousness or human-like "self". Instead, it refers to a machine's capacity to perceive its surroundings, acquire relevant knowledge, comprehend its meaning, make decisions, and execute appropriate actions.
This transition from machine learning to machine self-intelligence is critical, as it requires machines to not only recognize what they do not know but also acquire that knowledge safely and act accordingly. As machines gain autonomy, ensuring reliable decision-making becomes paramount, necessitating robust safeguards, human oversight, and the machine's ability to explain and rectify errors.
The future of AI lies in machines capable of recognizing their knowledge gaps, acquiring new information, and making informed, safe decisions.
Written by urgent.news from SCMP Tech's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
