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How to tell if AI or machine learning is real

Galen Gruman | June 19, 2017
False and misleading claims abound that applications and cloud services are now smart. Here’s how to identify true artificial intelligence and machine learning.

But most of what marketers call machine learning is simply logic. Programmers have been using logic in software since Day 1 to tell programs and robots what to do. Sophisticated logic can provide multiple paths for the software or robot to take, based on parameters the logic is designed to process.

Today’s hardware can run very sophisticated logic, so applications and devices can appear to be intelligent and able to adjust on their own. But most don’t actually learn—if their developer didn’t anticipate a situation, they can’t adjust on their own to handle it through pattern-analysis-based trial and error as a true machine learning system can.

Even if true machine learning is in place, a machine learning system is bound by whatever parameters its logic has set it to “know”—unlike a true AI, it can’t discover new facts outside its programmed world, only learn to understand and interact with the programmed world on its own.


Snow job 2: The use of IoT or cloud technology makes it smart

Marketers like to take hot technology terms and sprinkle them on whatever they already have. Many don’t really understand what the terms mean, or they don’t care. They only want your attention. You can identify a snow job quickly by looking at the buzzword-to-detail ratio: If all you see are buzzwords and the technology “how” details are lacking, you know it’s the same old technology with new marketing applied.

Today, the internet of things and cloud computing are hot, so they’re often at the heart of that new marketing. Still, both can play a role in machine learning or AI systems (really, AI precursor systems), so it’s not the use of the terms that’s a red flag, but their flippant use.

IoT relies on both local and networked sensors and on a combination of local and server (cloud) logic—both analytics and actuators to do something from the analysis. Together, these allow devices to seem smart because they’re programmed to adjust automatically to various events they sense. For machine learning, they are great inputs for the learning part, as well as great outputs for the adjusted actions.

Cloud computing opens up processing and data storage capabilities undreamt of in the past. Devices don’t have to carry all that overhead with them; instead, they can offload to the cloud all that work—and the hardware to support it. This is how Apple’s Siri, Microsoft’s Cortana, and Google Now work: They send your speech to the cloud, which translates it and figures out a response, then sends it back to your phone. That way, you don’t have to carry a mainframe or datacenter on your pocket or keep it on your desk.


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