> In DeepMind's AlphaGo, for example, most of the "intelligence" on display is designed and hard-coded by expert programmers (e.g. Monte-Carlo tree search);
> They tested their best-performing policy network against Pachi, the strongest open-source Go program, and which relies on 100,000 simulations of MCTS at each turn. AlphaGo's policy network won 85% of the games against Pachi! I find this result truly remarkable. A fast feed-forward architecture (a convolutional network) was able to outperform a system that relies extensively on search.
Also, this article reeked of AGI ideas. Deep learning isn't trying to solve AGI. Reasoning and abstraction and high level AGI concepts that I don't think apply to deep learning. I don't know the path to AGI but I don't think it'll be deep learning. I think it would have to be fundamentally different.
>Also, this article reeked of AGI ideas. Deep learning isn't trying to solve AGI. Reasoning and abstraction and high level AGI concepts that I don't think apply to deep learning. I don't know the path to AGI but I don't think it'll be deep learning. I think it would have to be fundamentally different.
I think that this is actually what the article is arguing.
from the article:
>Models closer to general-purpose computer programs, built on top of far richer primitives than our current differentiable layers—this is how we will get to reasoning and abstraction, the fundamental weakness of current models.
This means not using current deep learning ideas, and instead finding ways to integrate other types of programs (conventional algorithms, other types of ML) alongside Deep Networks.
Of course it did. It is about how machine learning can evolve to solve problems that require reasoning and abstractions.
The previous article was about the limits of machine learning and this one is about how to overcome them in the future. The limits was pretty much defined as "Reasoning and abstraction" so of course this article is about how to get that working.
> We really believe that if you solved intelligence in a very general way, like we're trying to do at DeepMind, then step 2 ['use intelligence to solve everything else'] would naturally follow on.
They go on to talk about general purpose learning machines.
Dr. Hassabis is awesome, but that video and the language is misleading to a layman. He is distinguishing between expert driven systems that rely on heuristics/feature engineering and between systems that learn from raw input and derive their own optimal set of features (unsupervised learning).
This is a far cry from AGI. I think Dr. Hassabis rather in a tongue and cheek manner played with the terminology in the video. Deep learning and all the modern AI stuff you hear about is within the realm of "narrow AI", or more formally, applied AI. In his video, he uses "narrow AI" to define systems that rely on expert based heuristics and feature engineering, and general purpose AI to be what they are currently doing with reinforcement learning.
Whilst it's wonderful that their advancement in reinforcement learning has been applied to various different problems successfully, it shouldn't be confused with AGI.
AGI is on a totally different playing field. I don't think we are substantially closer to AGI than we were 50 years ago, and I would be very interested in anyone arguing the opposite.
I think at this point the only company trying to seriously tackle AGI is: https://numenta.com/
Not true. This paraphrases the original paper: https://www.tastehit.com/blog/google-deepmind-alphago-how-it...
> They tested their best-performing policy network against Pachi, the strongest open-source Go program, and which relies on 100,000 simulations of MCTS at each turn. AlphaGo's policy network won 85% of the games against Pachi! I find this result truly remarkable. A fast feed-forward architecture (a convolutional network) was able to outperform a system that relies extensively on search.
Also, this article reeked of AGI ideas. Deep learning isn't trying to solve AGI. Reasoning and abstraction and high level AGI concepts that I don't think apply to deep learning. I don't know the path to AGI but I don't think it'll be deep learning. I think it would have to be fundamentally different.