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The most obvious counter-argument is that the amount of things we can do with AI keeps expanding. People were incredulous that computer chess programs could bea

The most obvious counter-argument is that the amount of things we can do with AI keeps expanding. People were incredulous that computer chess programs could beat humans in the 1980s. Now they can beat us at basically any board game including Go, do image classification, and we have some early prototypes of self-driving cars. AI hasn't mastered common-sense reasoning yet. That's likely going to come last, but the amount of things AI can understand is set to only expand IMO.

TheOtherHobbes 5y ago
Define "understand." I think you may be confusing automated processing with communicable abstracted insight. If this isn't obvious consider the difference between producing an AI that can play chess, producing an AI that learns to play chess, and producing a research program that produces an AI that can play chess and summarises all the resulting developments and insights.
- naasking 5y ago
  It's not clear whether there is a difference in kind between those behaviours rather than merely a difference in degree of complexity.
The_rationalist 5y ago
Well I am not defending the paper thesis but no it's time to realize that we are in a new AI winter where progress has stopped. Sure we can make accuracy progress on tasks that were underesearched before, moreover we do make extremely slow (and with increasingly diminishing returns) accuracy gains on core tasks. But the diminishing returns are diminishing fast to the point that progress in terms of applications has stopped for core AI tasks such as NLU. However there is still some hope as the vast majority of papers bring an innovation but almost never attempt to merge/synergize with other papers innovations. If human resources where allocated at merging the top 10 papers on a given task, I'm sure it would lead to a major accuracy improvement.
ben_w 5y ago
One thing I’ve long noticed is that “common sense” is analogous to a stopped clock, in that it’s its only correct when it happens to also be a different form of reasoning such as deductive, inductive, or abductive reasoning. Things called “common sense” but which are not also a different kind of knowledge are mere cultural shibboleths, and vary from wrong (fan death) to opinion (Shakespeare is good). The traditional examples of common sense knowledge given when introducing the topic of A.I. are sufficiently imprecise to only be true given further common sense interpretation. For example: “things fall when you let go of them” unless they’re buoyant, or they fly, or they’re already on the ground, or you were in free-fall when you let go — these exceptions won’t really surprise anyone, and yet it’s both more compact and more accurate to say Σf=ma, f_g=G(m_1)(m_2)/r^2 etc.

TheOtherHobbes 5y ago

Define "understand." I think you may be confusing automated processing with communicable abstracted insight. If this isn't obvious consider the difference between producing an AI that can play chess, producing an AI that learns to play chess, and producing a research program that produces an AI that can play chess and summarises all the resulting developments and insights.

naasking 5y ago
It's not clear whether there is a difference in kind between those behaviours rather than merely a difference in degree of complexity.

The_rationalist 5y ago

Well I am not defending the paper thesis but no it's time to realize that we are in a new AI winter where progress has stopped. Sure we can make accuracy progress on tasks that were underesearched before, moreover we do make extremely slow (and with increasingly diminishing returns) accuracy gains on core tasks. But the diminishing returns are diminishing fast to the point that progress in terms of applications has stopped for core AI tasks such as NLU. However there is still some hope as the vast majority of papers bring an innovation but almost never attempt to merge/synergize with other papers innovations. If human resources where allocated at merging the top 10 papers on a given task, I'm sure it would lead to a major accuracy improvement.

ben_w 5y ago

One thing I’ve long noticed is that “common sense” is analogous to a stopped clock, in that it’s its only correct when it happens to also be a different form of reasoning such as deductive, inductive, or abductive reasoning. Things called “common sense” but which are not also a different kind of knowledge are mere cultural shibboleths, and vary from wrong (fan death) to opinion (Shakespeare is good). The traditional examples of common sense knowledge given when introducing the topic of A.I. are sufficiently imprecise to only be true given further common sense interpretation. For example: “things fall when you let go of them” unless they’re buoyant, or they fly, or they’re already on the ground, or you were in free-fall when you let go — these exceptions won’t really surprise anyone, and yet it’s both more compact and more accurate to say Σf=ma, f_g=G(m_1)(m_2)/r^2 etc.