In this new blog post, I present my perspective on the future of #AI and an approach to scale to human-like intelligence: focus on socio-cultural *environments*

👉developmentalsystems.org/ecological_nic…
1/n Discussing with M. Eppe, I first explain how developmental and evolutionary theories of human cognition should further inform artificial intelligence. Through this, I argue why the concept of #AGI is not well founded

Our new #Corl18 paper:

How curiosity-driven autonomous goal setting enables to discover
independantly controllable features of environments

pdf: arxiv.org/abs/1807.01521

Blog: openlab-flowers.inria.fr/t/discovery-of…

Colab: colab.research.google.com/drive/176q8pns…

#machinelearning #AI #NeuralNetworks Imagine a robot perceiving a scene through low-level pixels.

It has no prior knowledge of its body (it only knows it can send numerical
numbers as motor commands that produce unknown movements of unknow body parts)

How many random seeds are needed to compare #DeepRL algorithms?

Our new tutorial to address this key issue of #reproducibility in #reinforcementlearning

PDF: arxiv.org/pdf/1806.08295…

Code: github.com/flowersteam/rl…

Blog: openlab-flowers.inria.fr/t/how-many-ran…

#machinelearning #neuralnetworks Algo1 and Algo2 are two famous #DeepRL algorithms, here tested
on the Half-Cheetah #opengym benchmark.

Many papers in the litterature compare using 4-5 random seeds,
like on this graph which suggests that Algo1 is best.

Is this really the case?