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Here's how neuroscience can protect AI from cyberattacks


Here’s how neuroscience can protect AI from cyberattacks
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Deep learning has come a long way since the days it could only recognize hand-written characters on checks and envelopes. Today, deep neural networks have become a key component of many computer vision applications, from photo and video editors to medical software and self-driving cars.
Roughly fashioned after the structure of the brain, neural networks have come closer to seeing the world as we humans do. But they still have a long way to go and make mistakes in situations that humans would never err.
These situations, generally known as adversarial examples, change the behavior of an AI model in befuddling ways. Adversarial machine learning is one of the greatest challenges of current artificial intelligence systems. They can lead machine learning models failing in unpredictable ways or becoming vulnerable to cyberattacks. ....

Joel Dapello , Tiago Marques , James Dicarlo , David Cox , Watson Ai Lab , Ludwig Maximilian University , University Of Munich , Phrma Foundation Postdoctoral , University Of Augsburg , Postdoctoral Fellow , தியகோ மார்க்ஸ் , ஜேம்ஸ் திக்கறிலோ , டேவிட் காக்ஸ் , வாட்சன் ஐ ஆய்வகம் , லுட்விக் மாக்ஸிமிலியன் பல்கலைக்கழகம் , பல்கலைக்கழகம் ஆஃப் முனிச் , ப்ர்ம அடித்தளம் போஸ்ட்‌டாக்டொரல் , பல்கலைக்கழகம் ஆஃப் அஉஃஸ்புர்க் , போஸ்ட்‌டாக்டொரல் சக ,