The 20-man project, led by Prof. Yishay Mansour of Tel Aviv University and Prof. Noam Nisan of the Hebrew University of Jerusalem, was launched at the International Conference on Learning Theory in Haifa, Israel, earlier this year.
Mansour has developed an algorithm based on machine learning, or “artificial intelligence,” to minimize the amount of virtual regret a computer program might experience.
“We are able to change and influence the decision-making of computers in real-time. Compared to human beings, help systems can much more quickly process all the available information to estimate the future as events unfold – whether it’s a bidding war on an online auction site, a sudden spike of traffic to a media website, or demand for an online product,” says Mansour.
Google, which is funding the research, hopes to use it to improve its own online technologies and businesses, such as its AdWords and Adsense advertising platforms.
Mansour adds that his algorithm will adapt to the situation at hand. Since Internet users are not predictable, the algorithm can in effect study and “learn” as it is running. After the task is finished, the results are “almost as if you knew all the variables in advance,” says Mansour.
“If the servers and routing systems of the Internet could see and evaluate all the relevant variables in advance, they could more efficiently prioritize server resource requests, load documents and route visitors to an Internet site, for instance,” says Mansour – an efficiency that Google finds very attractive.