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11582 مقال
Emergent tool use from multi-agent interaction
We’ve observed agents discovering progressively more complex tool use while playing a simple game of hide-and-seek. Through training in our new simulated hide-and-seek environment, agents build a series of six distinct strategies and counterstrategies, some of which we did not know our environment supported. The self-supervised emergent complexity in this simple environment further suggests that multi-agent co-adaptation may one day produce extremely complex and intelligent behavior.
Removing Coordinated Inauthentic Behavior From Iraq and Ukraine - meta.com
Removing Coordinated Inauthentic Behavior From Iraq and Ukraine meta.com
Understanding Updates to Your Device’s Location Settings - meta.com
Understanding Updates to Your Device’s Location Settings meta.com
Tesla | Apparel - Tesla Shop
Tesla | Apparel Tesla Shop
It’s Facebook Official, Dating Is Here - meta.com
It’s Facebook Official, Dating Is Here meta.com
Charting a Way Forward on Privacy and Data Portability - meta.com
Charting a Way Forward on Privacy and Data Portability meta.com
An Update About Face Recognition on Facebook - meta.com
An Update About Face Recognition on Facebook meta.com
ニュース | Teslaジャパン - Tesla
ニュース | Teslaジャパン Tesla
How Patents Drive Innovation at Facebook - meta.com
How Patents Drive Innovation at Facebook meta.com
Tesla Insurance - Tesla
Tesla Insurance Tesla
Insurance - Tesla
Insurance Tesla
Get Updates - Tesla
Get Updates Tesla
Model S Specs - Tesla
Model S Specs Tesla
Charging - Tesla
Charging Tesla
Model 3 – Sports Electric Sedan | Tesla Ireland - Tesla
Model 3 – Sports Electric Sedan | Tesla Ireland Tesla
Product safety and compliance in our store - About Amazon
Product safety and compliance in our store About Amazon
Careers - Tesla
Careers Tesla
Model X - Tesla
Model X Tesla
Get Updates - Tesla
Get Updates Tesla
Testing robustness against unforeseen adversaries
We’ve developed a method to assess whether a neural network classifier can reliably defend against adversarial attacks not seen during training. Our method yields a new metric, UAR (Unforeseen Attack Robustness), which evaluates the robustness of a single model against an unanticipated attack, and highlights the need to measure performance across a more diverse range of unforeseen attacks.