Descrizione del progetto
Macchine intelligenti per padroneggiare situazioni sconosciute
Nonostante i notevoli progressi compiuti dall’IA e dalle reti neurali, questi sistemi presentano capacità limitate rispetto all’intelligenza biologica. I sistemi di IA sono progettati e ottimizzati da esperti, mentre i sistemi biologici sono auto-organizzati attraverso un programma genetico più ridotto e sono dotati fin dalla nascita di capacità comportamentali più diversificate. Il progetto GROW-AI, finanziato dall’UE, intende creare macchine con maggiore adattabilità e intelligenza generale attraverso una combinazione di vita artificiale, neurobiologia e apprendimento automatico. Inoltre, analizzerà il potenziale della crescita algoritmica per la comprensione e la creazione di intelligenza.
Obiettivo
"Despite major advances in the field of artificial intelligence, especially in the field of neural networks, these systems still pale in comparison to even simple biological intelligence. Current machine learning systems take many trials to learn, lack common-sense, and often fail even if the environment only changes slightly. The enormous potential of autonomous machines remains unfulfilled and we still lack robots to fill our dishwashers or go on autonomous search-and-rescue missions. The grand goal of GROW-AI is to create machines with a more general intelligence, allowing rapid adaption in unknown situations. In stark contrast to current neural networks, whose architectures are designed by human experts and whose large number of parameters are optimized directly, evolution does not operate directly on the parameters of biological nervous systems. Instead, these nervous systems are grown and self-organize through a much smaller genetic program that produces rich behavioral capabilities right from birth and the ability to rapidly learn. Neuroscience suggests this ""genomic bottleneck"" is an important regularizing constraint, allowing animals to generalize to new situations. However, currently there does not exist a solution to creating a similar system artificially. We address this challenge with two ambitious ideas. First, we will learn genomic bottleneck algorithms instead of manually designing them, exploiting recent advances in memory-augmented deep neural networks that can learn complex algorithms. In addition, we will co-optimize task generators that provide the agents with the most effective learning environments. Taking inspiration from the fields of artificial life, neurobiology, and machine learning, we will investigate if algorithmic growth is needed to understand and create intelligence. If successful, this project will greatly improve the autonomy of machines and significantly increase the range of real-world tasks they can solve."
Campo scientifico (EuroSciVoc)
CORDIS classifica i progetti con EuroSciVoc, una tassonomia multilingue dei campi scientifici, attraverso un processo semi-automatico basato su tecniche NLP.
CORDIS classifica i progetti con EuroSciVoc, una tassonomia multilingue dei campi scientifici, attraverso un processo semi-automatico basato su tecniche NLP.
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Parole chiave
Programma(i)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Argomento(i)
Meccanismo di finanziamento
HORIZON-ERC - HORIZON ERC GrantsIstituzione ospitante
2300 Kobenhavn
Danimarca