Human-level intelligence or animal-like abilities?

95 citations recorded by the source corpus.

What just happened in artificial intelligence and how it is being misunderstood.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Learning First-Order Symbolic Representations for Planning from the Structure of the State Space (2019, background): “Learners, and in particular deep learners, have achieved considerable success but result in black boxes that do not have the flexibility, transparency, and generality of their model-based counterparts [26, 27, 32, 12, 17].”
Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution (2018, methodology): “Darwiche (2017) used “function-fitting,” for it amounts to fitting data by a complex function defined by the neural network architecture.”
Deep Nets: What have They Ever Done for Vision? (2018, background): “Several recent papers (Darwiche, 2018; Marcus, 2018) have critiqued Deep Nets from the perspectives of machine reasoning and cognitive science, arguing that though Deep Nets are useful as a tool they will need to be combined with alternative approaches in order to achieve human level intelligence.”
Model-free, Model-based, and General Intelligence (2018, methodology): “Explanation and accountability also require models: we build explanations and make predictions using models [Darwiche, 2017; Lake et al., 2017; Pearl, 2018].”
Three Modern Roles for Logic in AI (2020, background): “Quot-ing [30]: “We need a new generation of AI researchers who are well versed in and appreciate classical AI, machine learning, and computer science more broadly while also being informed about AI history.”