English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Autonomous lifelong development and learning is a fundamental capability ofhumans, differentiating them from current deep learning systems. However, otherbranches of artificial intelligence have designed crucial ingredients towardsautonomous learning: curiosity and intrinsic motivation, social learning andnatural interaction with peers, and embodiment. These mechanisms guideexploration and autonomous choice of goals, and integrating them with deeplearning opens stimulating perspectives. Deep learning (DL) approaches madegreat advances in artificial intelligence, but are still far away from humanlearning. As argued convincingly by Lake et al., differences include humancapabilities to learn causal models of the world from very little data,leveraging compositional representations and priors like intuitive physics andpsychology. However, there are other fundamental differences between current DLsystems and human learning, as well as technical ingredients to fill this gap,that are either superficially, or not adequately, discussed by Lake et al.These fundamental mechanisms relate to autonomous development and learning.They are bound to play a central role in artificial intelligence in the future.Current DL systems require engineers to manually specify a task-specificobjective function for every new task, and learn through off-line processing oflarge training databases. On the contrary, humans learn autonomously open-endedrepertoires of skills, deciding for themselves which goals to pursue or value,and which skills to explore, driven by intrinsic motivation/curiosity andsocial learning through natural interaction with peers. Such learning processesare incremental, online, and progressive. Human child development involves aprogressive increase of complexity in a curriculum of learning where skills areexplored, acquired, and built on each other, through particular ordering andtiming. Finally, human learning happens in the physical world, and throughbodily and physical experimentation, under severe constraints on energy, time,and computational resources. In the two last decades, the field ofDevelopmental and Cognitive Robotics (Cangelosi and Schlesinger, 2015, Asada etal., 2009), in strong interaction with developmental psychology andneuroscience, has achieved significant advances in computational

Autonomous development and learning in artificial intelligence and robotics: Scaling up deep learning to human--like learning