𝗙𝗿𝗼𝗺 𝗔𝗚𝗜 𝘁𝗼 𝗔𝗦𝗜: 𝗔 𝗦𝘂𝗺𝗺𝗮𝗿𝘆 𝗼𝗳 𝗚𝗼𝗼𝗴𝗹𝗲 𝗗𝗲𝗲𝗽𝗠𝗶𝗻𝗱’𝘀 𝗟𝗮𝘁𝗲𝘀𝘁 𝗣𝗮𝗽𝗲𝗿

Most people talk about Artificial General Intelligence (AGI) with hype or fear. Artificial Superintelligence (ASI) is even harder to discuss.

A new Google DeepMind paper asks a better question. Instead of asking when AGI will arrive, it asks: What happens after AGI?

If we build AI that matches human capability, how does it keep improving until it outperforms entire groups of human experts?

Here is a simple breakdown for engineers and developers.

𝗧𝗵𝗲 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲

• AGI: An AI system that performs at the level of a median human across many tasks. It can learn, reason, and solve diverse problems. • ASI: An AI system that outperforms large groups of human experts across almost all important fields.

A model that beats a human at chess is not ASI. ASI is an artificial system that can outperform research labs and entire companies.

𝗪𝗵𝘆 𝗔𝗜 𝘀𝗰𝗮𝗹𝗲𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆 𝗳𝗿𝗼𝗺 𝗵𝘂𝗺𝗮𝗻𝘀

Humans have biological limits. We cannot copy ourselves or run a thousand versions of our minds in parallel. AI does not have these constraints.

AI offers several advantages:

  • Machine speed: AI processes data much faster than biological brains.
  • Reasoning scale: You can give a model more compute to think longer or run many instances in parallel.
  • Memory: AI can connect to massive databases and infinite context windows.
  • Duplication: You can run millions of copies of a capable AI researcher.

𝗧𝗵𝗲 𝗙𝗼𝘂𝗿 𝗣𝗮𝘁𝗵𝘄𝗮𝘆𝘀 𝘁𝗼 𝗔𝗦𝗜

  1. Scaling: Using more data, more compute, and better hardware.
  2. Algorithmic shifts: Finding new architectures, better memory systems, or more efficient learning methods.
  3. Recursive self-improvement: AI helping to research and build better AI. This creates a feedback loop.
  4. Multi-agent systems: Many specialized AI agents working together like a large organization.

𝗪𝗵𝗮𝘁 𝗰𝗼𝘂𝗹𝗱 𝘀𝗹𝗼𝘄 𝘂𝘀 𝗱𝗼𝘄𝗻?

Progress is not guaranteed. Several bottlenecks exist:

  • Data limits: We may run out of high-quality human data.
  • Physical limits: Scaling requires massive amounts of electricity, chips, and hardware.
  • Research difficulty: As AI matures, the problems to solve may get harder.
  • Human intervention: Safety, regulation, and politics can slow development.

𝗧𝗵𝗲 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆

AGI에서 ASI로의 전환은 단순한 연구 과제가 아닙니다. 이는 엔지니어링 측면의 도전 과제입니다. 여기에는 분산 시스템, 인프라, 그리고 복잡한 오케스트레이션이 수반될 것입니다.

개발자라면 다음 분야에 집중하십시오:

  • 신뢰할 수 있는 AI 에이전트를 구축하는 방법.
  • 다수의 에이전트를 조율하는 방법.
  • 장기적인 과제를 평가하는 방법.
  • 안전성과 제어력을 확보하는 방법.

출처: https://dev.to/marrouchi/from-agi-to-asi-a-summary-of-google-deepminds-latest-paper-2ndd