In the cat-and-mouse game of the antispam industry, staying one step ahead of spammers is difficult because they constantly exploit the weaknesses of e-mail keyword filtering. But the newest artificial-intelligence filtering technology may adapt faster than the spammers can alter their messages.

Artificial intelligence techniques closely resemble the way our brains learn. Once we learn a skill, we use it to reason with. Using artificial intelligence to detect spam is done in the same way.

Natural-language processors serve as powerful artificial intelligence tools in the fight against spam. These processors, which actually are an array of complex algorithms, scan e-mail messages to discover the content of the messages. The algorithms are packaged into mail-filtering software, which generally sits outside a firewall or at an application service provider's network.

Artificial intelligence mail-filtering software accepts all in-bound e-mail traffic, routing legitimate traffic to a corporate SMTP server and flagging other messages as spam. Suspect e-mail is sent to a quarantine area where an administrator can view the contents to determine whether to discard it or pass it along.

Humans can quickly skim a message to judge if it is spam. Referencing keywords by their location in a sentence lets us understand the difference between "chicken breasts" as food and "bare breasts" as pornography. Similarly, natural-language algorithms break down messages into sentences and analyze their meaning.

With considerable processing effort, natural-language processing technology pieces together the meaning of messages by analyzing the words, sentences and paragraphs in the reverse order from which the algorithms originally took them apart.

Consider this e-mail example: "These delicious chicken breasts look good enough to eat - let's cook out tonight. If you can bring Bill, call me at work, 800-262-2222 x231. Oh, and check out the pictures from our last cookout at www.ophoto.com/2623/party_pictures>." A standard keyword analysis would flag the terms: breasts, look good, toll free number, Web site URL. But artificial intelligence analysis would determine the message was an invitation to dinner.

In the example, keyword-filtering technology picks out pieces of the sentence without really understanding its meaning. Its selective hearing incorrectly determines that the sentence is pornographic.