Road to AGI: Is it even possible?
Breaking down definitions for AGI and arguments for and against scaling
AGI, or Artificial General Intelligence, has finally entered the zeitgeist. It has been a scifi trope for decades, and a niche interest for futurists like Ray Kurzweil. The father of modern computing, Alan Turing, speculated on machine intelligence in his “Computing Machinery and Intelligence” paper from 1950. This is where we get the synonymous Turing Test from.
After several decades of repeating cycles of hype and winter, the surprising superhuman success of AlphaGO in 2016 sparked renewed mainstream interest in what’s possible with AI. Even that hype somewhat fizzled as these abilities were ultimately limited to a few board games. AI was successful only in its lesser form of Machine Learning, which had some commercial value in recommending which brand of toothbrush to buy on Amazon, and which Korean soap opera to watch next on Netflix.
This is why the unreasonable success of the transformer architecture and next-token prediction that powers ChatGPT has changed the conversation entirely. We’ve blown past the Turing Test so hard and fast that we never really even stopped to wonder. There are new and wondrous capabilities seemingly every other month now and the goalpost is moving with it.
