HN Theater @HNTheaterMonth

> 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.

Source: https://youtu.be/ZyUFy29z3Cw?t=4m42s

⬐ computerex

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/