Artificial intelligence (AI) development is constantly progressing, with groundbreaking advancements paving the way for more sophisticated AI applications in various fields. One notable development comes from Google DeepMind, introducing new methods that could potentially revolutionize the field of robotics by training generally intelligent AI systems.
While AI has excelled in mastering games like chess and Go due to their clearly defined rules, training AI models to navigate open-world games such as Minecraft poses a more significant challenge. These games offer vast choices and abstract objectives, closely resembling real-life scenarios. Mastering these complex games represents a crucial milestone in developing AI agents capable of real-world tasks like controlling robots and achieving artificial general intelligence.
Google DeepMind’s latest innovation, the Scalable Instructable Multiworld Agent (SIMA), showcases remarkable capabilities by playing nine different video games and virtual environments without prior exposure. What sets SIMA apart is its ability to analyze and interpret game video feeds to perform around 600 short tasks across diverse games, ranging from space exploration to problem-solving challenges.
To achieve such impressive performance, DeepMind’s researchers leveraged existing video and image recognition models to process game video data and train SIMA to correlate specific tasks with visual inputs. By employing a unique training method where individuals played games collaboratively while providing instructions and insights into their actions, SIMA learned to replicate and execute human-like maneuvers effectively.
Despite demonstrating proficiency in adapting to new games, SIMA falls short of attaining human-level performance. Researchers addressed this by implementing a training regimen where the AI model was trained on a set of games and then tested on unfamiliar ones, repeated iteratively to enhance adaptability.
While experts acknowledge the significance of AI agents generalizing skills across different games as a step towards developing generalist AI, SIMA’s current limitations involve being confined to short-term, specific tasks devoid of long-term planning. Expanding its capabilities to encompass more complex endeavors remains a challenging prospect.
It is essential to understand that for entities like DeepMind, the focus extends beyond gaming to revolutionize robotics and enhance AI systems» ability to perceive and interact with the physical world. Navigating 3D environments in games serves as a stepping stone towards creating AI agents capable of real-world applications, with implications extending far beyond the realm of gaming.
Vanlige spørsmål
Hva er SIMA?
SIMA, eller Scalable Instructable Multiworld Agent, er en kunstig intelligensmodell utviklet av Google DeepMind. Den kan spille ulike videospill og virtuelle miljøer ved å analysere videofeeden kun fra spillet.
Hvordan ble SIMA trent?
For å trene SIMA, benyttet forskerne ved DeepMind eksisterende modeller for videogenkjenning og bildeanalyse. De lot også individer spille videospill sammen, der en person instruerte den andre om trekk og handlinger. Denne dataen, sammen med selvrefleksjon over spillet, gjorde at SIMA kunne forstå hvordan menneskelige bevegelser relaterte seg til spesifikke oppgaver.
Hva er begrensningene til SIMA?
Selv om SIMA har vist evne til å tilpasse seg ukjente videospill, når den foreløpig ikke opp til menneskelig ytelse. I tillegg er kunnskapsområdet hovedsakelig begrenset til kortsiktige oppgaver som ikke krever langtidsplanlegging.
Hva er det endelige målet med denne forskningen?
DeepMinds forskning har som mål å utvikle AI-systemer som kan oppfatte og samhandle med den virkelige verden. Selv om spill brukes som testarena, er fokuset på å revolusjonere robotikk og skape AI-agenter som er i stand til å utføre oppgaver i den virkelige verden.
The source of the article is from the blog jomfruland.net