InfoWorld — In the land of IT, the one thing you can count on is a slick vendor presentation and a whole lot of hype. Eras shift, technologies change, but the sales pitch always sounds eerily familiar.

In virtually every decade there's at least one transformational technology that promises to revolutionize the enterprise, slash operational costs, reduce capital expenditures, align your IT initiatives with your core business practices, boost employee productivity, and leave your breath clean and minty fresh.

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Today, cloud computing, virtualization, and tablet PCs are vying for the hype crown. At this point it's impossible to tell which claims will bear fruit, and which will fall to the earth and rot.

Looking back with 20/20 hindsight, however, it's easy to see how we got sucked into believing claims that simply weren't true -- whether they were about the promise of artificial intelligence or the practicality of ERP.

Here then are six technologies -- five old, one new -- that have earned the dubious distinction of being the hype kings of their respective eras. Think about them the next time a sales team comes knocking on your door.

1. Artificial intelligenceEra: Late 1950s to present

The pitch: "Some day we will build a thinking machine. It will be a truly intelligent machine. One that can see and hear and speak. A machine that will be proud of us." -- Thinking Machines Corp., year unknown

Once upon a time, machines were going to do our thinking for us. And then, of course, they'd grow tired of doing humanity's bidding and exterminate us.

The good news? We haven't been offed by the machines (yet). The bad news is that artificial intelligence has yet to fully deliver on its promises. Like, for example, in 1964, when researchers at the newly created Stanford Artificial Intelligence Lab assured the Pentagon that a truly intelligent machine was only about a decade away. Guess what? It's still at least 10 years away.

Several times in the decades since, companies have tried to build an industry around AI computers -- largely supported by government research dollars -- only to run headfirst into an "AI winter" when the funding dried up.

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