Does the AI rift matter to marketers?
A new article on Marketing Land asks if neither option at the fork in the road of AI's progress holds promise, does that really matter for marketing?
Researchers seem split on the question of how to develop AI from here. Some back neural network modelling while others see traditional logic-based AI as the way forward. It's basically a case of whether or not we use the human brain as a model for developing the concept:
Symbolic AI works well for clearly defined problems, like mathematical calculations and chess, for example. It doesn't work so well when rules are not explicit - like recognizing patterns of understanding what images represent:
Does it really matter to marketers?
At the moment, given the limited capabilities of AI, probably not so much. However, in the future, we might want to use AI to free up time. That's because it will most likely work well for stuff like campaign optimization, personalization, and data management.
Researchers seem split on the question of how to develop AI from here. Some back neural network modelling while others see traditional logic-based AI as the way forward. It's basically a case of whether or not we use the human brain as a model for developing the concept:
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It's not quite as simple as that of course (with AI, it never is). But hopefully, it's not impossible to explain the difference. Symbolic AI. AI has its historical roots in a thought experiment published by Alan Turing, and known as the "Turing Test." Without diving into the detail, the test was supposed to provide a criterion for judging success in modeling human intelligence -- the mind. Successfully modeling intelligence was the chief goal of AI for decades. "Symbolic AI" refers to the widely held assumption that human intelligence is reducible to logical statements -- the kind which can be captured by symbolic logic. |
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Deep learning. An alternative approach to AI is sometimes mis-described as based on modeling networks in the human brain. Rather, it takes inspiration from how human neural networks are thought to work. Again, without going too deep, large, artificial networks of "nodes," trained on substantial data sets, learn to recognize statistical relationships in the data; and a feedback loop between the layers of nodes creates the possibility of self-correction. The sheer scale of processing, and the multiple layers of nodes, gives this approach the name "deep learning." It was precisely the scale which hindered the development of this approach. Until relatively recently, there wasn't enough data or enough computer power to make deep learning both practicable and cost-effective. But that changes, and that's why in recent years, we've seen rapid improvement, for example, in AI image recognition. |
At the moment, given the limited capabilities of AI, probably not so much. However, in the future, we might want to use AI to free up time. That's because it will most likely work well for stuff like campaign optimization, personalization, and data management.
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