[fat-chunk]

I work in AI, specifically in the field of deep learning applied to computer vision. Not trying to discredit your comment, but can you provide some sources indicating that the type of models being trained which you have mentioned are approaching the notion of a general agent?

I haven't read much of the literature around deep learning, mostly only what has been applied to computer vision. But from what I understand, the general consensus is that the current crop of state-of-the-art deep learning models are very good at performing specific tasks (machine translation, object recognition in images, etc.), but are not so good at generalising across multiple fields. This seems more relevant to the domain of reinforcement learning (which does indeed include deep learning), which has proven to be a very difficult problem to solve.

[ilaksh]

Having an agent with sensory and motor terminals in diverse contexts gradually trained on increasingly complex knowledge and tasks is key I believe. Like I mentioned see Deep Mind's work. Also see field of AGI which does exist (for example search 'AGI-16 intelligence' on youtube).

I am working on a webpage to try to break down why I think this is coming so fast.

https://www.youtube.com/watch?v=BP7vhBaBDyk&t=4589s General Reinforcement Learning

https://www.youtube.com/watch?v=T9eSVYLSSrs The Emotional Mechanisms in NARS

https://www.youtube.com/watch?v=eVrflIw6sGg&t=4056s AGI-15 Keynote by Jürgen Schmidhuber - The Deep Learning RNNaissance

[argonaut]

Don't read too much into titles.

AGI(-16) is a cognitive science / philosophy conference (and not particularly high impact). Similarly, NIPS is not really about neural information processing.

[ilaksh]

I'm not reading into titles. I watched a lot of the videos. Don't dismiss it on your superficial evaluation of titles or prejudice about the conference.

AGI is in fact a developed field with key insights into general intelligence. You should study it.

[nl]

Argonaut works in the field. I'm pretty sure (s)he knows what NIPS is and has thought about AGI some too.

[argonaut]

I comment a lot on machine learning but I don't actually work in the field actively. I did some research in college.

There aren't many active researchers/experts commenting on HN (better things to do), which is IMO a big issue with the ML-related discussion quality on HN (it's basically 90% futurism/speculation).

[Florin_Andrei]

The current AIs are all "base level". There's no "meta" level processing there.

The human brain is full of meta upon meta levels. Networks on top of other networks, and so on. Abstractization. Correlation between different domains. Feedback loops. There will be no AGI until we start building that kind of architectures.

We're currently only making individual Lego bricks. We haven't started to assemble them yet.

[ilaksh]

See the field of AGI.

Networks on top of other networks -- about any recursive neural network could be in that category. Also there are multiple projects exploring metalearning on deep NNs where it learns the network topology, i.e. learning how to learn.

Abstraction in single domains is very commonly achieved with various types of hierarchy in NN and non-NN systems. Correlation between different domains has fewer examples simply because most AI systems do not deal with multiple domains, but they do exist.

Feedback loops are common and fundamental to AI and simpler non-AI control systems.