The machines might not ultimately win in "Terminator" movies, but they are prevailing in the never-ending battle against spam. In fact, Google claims machine learning has helped reduce unwanted e-mails in Gmail inboxes to less than 0.1 percent.

In the meantime, to help further reduce the odds of wanted e-mail being misidentified as spam, the search giant on Thursday launched new Gmail Postmaster Tools for legitimate, high-volume e-mail senders like banks and airlines.

Currently, only 0.05 percent of wanted e-mails end up in Gmail users' spam folders, according to Google. However, better analysis and artificial intelligence Relevant Products/Services can help reduce that figure even more, avoiding the need for people to go "Dumpster diving" for important e-mails buried among the spam, Google said.

The new Gmail Postmaster Tools are designed to help companies that send out large volumes of e-mails to customers "analyze your e-mail performance and help Gmail route it to the right place," according to Google. The tools provide qualified e-mail administrators with Google data Relevant Products/Services on delivery errors, spam reports, reputation and more to help them fine-tune their e-mail delivery.

Goal: 'Spam-Free Gmail'

Using, among other things, the data provided by Gmail users when they click the "Report spam" or "Not spam" buttons, Google's machine learning applications have enabled the company to steadily reduce the amount of unwanted e-mails landing in users' inboxes.

"When you click the 'Report spam' and 'Not spam' buttons, you're not only improving your Gmail experience right then and there, you're also training Gmail's filters to identify spam vs. wanted mail in the future," Gmail Product Manager Sri Harsha Somanchi said in a post on the Official Gmail Blog. "Ultimately, we aspire to a spam-free Gmail experience."

For example, Gmail's spam filter now uses an artificial neural network "to detect and block the especially sneaky spam -- the kind that could actually pass for wanted mail," Somanchi noted. Machine learning also helps reflect individual user's Gmail preferences by, for instance, allowing weekly e-mail newsletters to continue arriving in the inboxes of people who enjoy that kind of reading while filtering them out for those who don't.

Businesses Driving E-Mail Growth

E-mail volumes are growing "mainly due to its use for notifications (e.g., for online sales Relevant Products/Services), rather than simply as an interpersonal communication tool," according to Radicati Group's most recent e-mail statistics report, released in March.

That means for companies that send out such e-mails to customers, it's increasingly vital that those messages are not identified as spam and make it to recipients' inboxes. Those messages might include, for example, monthly statements from banks or ticket receipts from airlines.

Somanchi said the new Gmail Postmaster Tools will help verified high-volume senders, make certain such e-mails arrive to their intended recipients by giving them data to better diagnose "hiccups" and ensure best practices for delivery.

In the meantime, Google's machine learning capabilities are improving the ability of its spam filters to identify e-mail impersonators, "that nasty source of most phishing scams," Somanchi added. "Thanks to new machine learning signals, Gmail can now figure out whether a message actually came from its sender, and keep bogus e-mail at bay."