AI News, Artificial Intelligence and the Adversary artificial intelligence

Are You Ready For The Age Of Adversarial AI? Attackers Can Leverage Artificial Intelligence Too

Artificial intelligence (AI) has become the foundation of everyday technologies — including smartphones, cars, banking apps, home devices and more.

This is what we call adversarial AI or adversarial machine learning, and it should be a growing concern for businesses and consumers as algorithms become more advanced.

Research Shows The Possibilities Of Adversarial AI As noted in a March 2019 article (registration required) in MIT Technology Review, Dawn Song, professor and cybersecurity researcher at the University of California, Berkley, stated that adversarial machine learning could be used to attack just about any system built on the technology.

For instance, in one case they demonstrated how attackers could exploit machine learning algorithms designed to automate email responses to instead “spit out sensitive data such as credit card numbers.” Song demonstrated how computer vision systems in vehicles could be tricked by placing stickers on road signs, corrupting the dataset and tricking the algorithms powering autonomous cars into thinking stop signs were actually speed limits.

In the report, the researchers noted, “Just as software is prone to being hacked and infected by computer viruses, or its users targeted by scammers through phishing and other security-breaching ploys, AI-powered applications have their own vulnerabilities.

If the attacks on the Ukrainian power grid that resulted in power loss for more than 250,000 citizens were to happen to Israel and appeared to come from Iran, would it precipitate a physical response?

In my opinion, it’s possible that adversarial AI could play a role in influencing the outcome of the elections or enable fraud in other aspects of business and daily life.

Security experts and product developers need to factor in the potential for abuse when building AI models and harden those models to the extent possible.

By taking steps today to become more aware of how adversarial AI works, everyone can be in a better position to eliminate or reduce the risks.

An adversarial interpretation of information-theoretic bounded rationality

Accordingly, an agent maximizes a regularized expected utility known as the free energy, where the regularizer is given by the information divergence from a prior to a posterior policy.

It turns out that the optimal strategy of the adversary consists in choosing costs so as to render the decision maker indifferent among its choices, which is a definining property of a Nash equilibrium, thus tightening the connection between free energy optimization and game theory.

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