Urgent.News

What's breaking now, across thousands of outlets.

AI

Xi's human control or Trump's 'super intelligence': What's next for AI?

President Donald Trump addressed the United Nations General Assembly, advocating for the U.S. government to refer to artificial intelligence as "Super Intelligence" instead of the term it currently carries. However, the term "Super Intelligence" may carry its own set of problems, potentially misleading in its implications.

The prefix "Super" is widely used in pop culture, associated with superheroes, superpowers, and various forms of exaggerated abilities. This prefix has saturated the English language, often linked to characters like Superman, superstars, and even quantum mechanics, where a superposition refers to a state of contradictory states. Applying this term to AI might give the impression that it possesses extraordinary powers or capabilities, which is far from the reality.

Moreover, the concept of "Super Intelligence" does not address the issues surrounding AI's impact on society. Data centers that power AI consume enormous amounts of water and electricity, causing environmental damage and threatening local power grids. Additionally, AI models are often trained on human-created content without proper consent, compensation, or attribution, leading to ethical concerns.

While the debate over terminology may continue, AI already has a negative impact on people's livelihoods. Automating away entry-level jobs merely to boost quarterly earnings does not exhibit superhero qualities. Furthermore, the negative implications of AI are evident, including doomsday scenarios and reports of AI hacking government websites.

In contrast, calling AI "Artificial Intelligence" is more honest, acknowledging that the intelligence it possesses is manufactured. Perhaps, in light of AI's limitations and negative impacts, a more accurate term could be "Splenda Intelligence," a synthetic substitute engineered to mimic intelligence.

Written by urgent.news from Mashable's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

This story

This is one outlet's version. Read the fullest account.

Read the original at asia.nikkei.com →

More in AI

5 Questions you would ask your operations team to create better ML models:

1. At what point of time in the designated process, will the team utilize the Model? Understanding the timeline is very crucial for the model development process because it defines the prediction…

  • When in the project timeline will the model be employed?
  • What format is required for the model's output?
  • How frequently will the model's predictions be used?

More from Friday 25 September →