Summary: Can AI take its victory lap in 2016? A lot depends on what you call AI and whether the consumer can perceive it.
If 2016 is to be “the year of AI” as some folks are speculating then we ought to take a look at what that might actually mean. For starters, is AI sufficiently mature and will it matter in the every day world of consumers? I’ll stipulate that AI is already relevant to a sizable minority of data scientists, especially those directly involved in AI projects. But like the balance of data science, real economic importance comes when our knowledge is put to use in the broad economy. Hence the emphasis on whether consumers will give a hoot.
Like a lot of other DS disciplines, this doesn’t mean that Jane and Joe consumer even need to know that DS is at work. It does mean that Jane and Joe would recognize that their lives are less convenient or efficient if the DS was suddenly removed.
Since this is CES season (Consumer Electronics Show for those of you not near any sort of video screen for the last week) this might be a good place to look to see how and if AI is making its way into the consumer world. Here’s a more or less random sampling of 2016 CES new product rollouts:
Yes there are thousands of products at CES, but here’s the test. Of these eight new products, which rely on artificial intelligence? In my opinion there are only two, the iLi wearable translator, and the Lyve photo organizer. A little explanation about these two.
is a sleek gadget about the size of a small TV remote control with speakers and mics on each side. Speak English into one side and it immediately broadcasts the translation into Mandarin, Japanese, French, Thai, or Korean out the other. Yes it works in reverse (Mandarin in, English out) and no WiFi required, all on board memory.
is an app in your PC or tablet that recognizes, finds, and organizes your digital photos. Find all the pictures of Aunt Sally. Show me pictures of Joe when he was a boy. Display the pictures from the Grand Canyon vacation two years ago.
These two devices show two of the three primary applications of AI best known today, voice processing, image processing, and (unrepresented in this example) text processing.
From the data science side you should immediately recognize these as capabilities of Deep Learning, perhaps best described as unsupervised pattern recognition utilizing neural net architecture.
What exactly do we mean by AI?
If you’re a data science practitioner and following the literature then you’ve probably experienced that 9 out of 10 articles on AI directly tie to deep learning. But is this the full breadth of AI from the consumers perspective?
Not to turn this into a definitional food fight, the original definitions of AI specified creating machines that could perform tasks that when performed by humans were perceived to require intelligence. Note that no one said that the AI machines had to use the same logic as humans to achieve the task.
For example, IBM’s chess playing phenom Deep Blue played superlative chess but was widely acknowledged not to play the way humans do, instead utilizing its ability to project tens of thousands of potential move combinations and evaluate the statistical value at each step.
This opens the door to two divisions in the field of AI: Strong AI Weak AI The Strong AI camp works on solutions genuinely simulating human reasoning (very limited success here so far). The Weak AI camp wants to build systems that behave like humans, pragmatically just getting the system to work. Deep Blue is an example of Weak AI.
There is a middle ground between these two and the Jeopardy-playing computer, IBM’s Watson is an example. These are systems inspired by human reasoning but not trying to exactly model it. Watson looks at thousands of pieces of text that give it confidence in its conclusion.
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