Uploaded April 2021 | Updated September 2026, 1 day ago
Recently I read an article about why computers wont make themselves smarter. The claim of the article was as reads, the singularity will not come. I thing that there were quite a few flaws with the article though, so lets discuss them!
A quick background for people that aren't familiar with the concept of the singularity (or AI singularity), it's the idea that there will be a point at which computer intelligence is able to self-recursively improve, which will lead to an explosion in artificial intelligence.
Link to the article: newyorker.com/culture/annals-of-inquiry/why-computers-wont-make-themselves-smarter
The AI Singularity
Recently I read an article about why computers wont make themselves smarter. The claim of the article was as reads, the singularity will not come. I thing that there were quite a few flaws with the article though, so lets discuss them!
A quick background for people that aren't familiar with the concept of the singularity (or AI singularity), it's the idea that there will be a point at which computer intelligence is able to self-recursively improve, which will lead to an explosion in artificial intelligence.
Link to the article: newyorker.com/culture/annals-of-inquiry/why-computers-wont-make-themselves-smarter
The AI Singularity
![Self-Supervised RL - Learning Without Data [Zero to Paper]](https://i.ytimg.com/vi/CDKsa06xU0o/mqdefault.jpg "Self-Supervised RL - Learning Without Data [Zero to Paper]
Inverse Reinforcement Learning with Natural Language Goals (LangGoal IRL) offers a way to do sample-efficient IRL and a way to generalize using self-supervised learning. The paper is novel and is a step forward for general AI and ML algorithms. Though it has its cons, I think it is one of the better papers out there that cover RL, IRL, NLP, and generalization.
Zero to Paper playlist: https://www.youtube.com/playlist?list=PL_49VD9KwQ_ONxENRk11jFEI3_pqAwaug
Inverse Reinforcement Learning video: https://www.youtube.com/watch?v=qo355ALvLRI
Paper covered: https://arxiv.org/pdf/2008.06924.pdf")
