Summary:<\/em><\/strong>\u00a0 There are two definitions currently in use for AI, the popular definition and the data science definition and they conflict in fundamental ways.\u00a0 If you\u2019re going to explain or recommend AI to a non-data scientist, it\u2019s important to understand the difference.<\/em><\/p>\n \u00a0<\/p>\n For a profession as concerned with accuracy as we are, we do a really poor job at naming things, or at least being consistent in the naming.\u00a0 \u201cBig Data\u201d \u2013 totally misleading (since it incorporates velocity and variety in addition to volume).\u00a0 How many times have you had to correct someone on that?<\/p>\n So now we have Artificial Intelligence (AI) for which there are at least two competing definitions, the popular one and the one understood by data scientists.\u00a0 And that doesn\u2019t even account for the dozens of Venn diagrams trying to describe which is a subset of what and all basically in conflict.<\/p>\n I\u2019m sure by now you\u2019ve heard the old joke.\u00a0 What\u2019s the definition of AI?<\/p>\n When you\u2019re talking to a customer it\u2019s AI.<\/p>\n When you\u2019re talking to a VC it\u2019s machine learning.<\/p>\n When you\u2019re talking to a data scientist it\u2019s statistics.<\/p>\n It would be even funnier if it weren\u2019t true, but it is.<\/p>\n So it\u2019s a worthwhile conversation to go directly at these two definitions and see where they conflict, and where if anywhere they converge.<\/p>\n \u00a0<\/p>\n The Popular Definition<\/strong><\/span><\/p>\n This definition got underway 12 or 18 months ago and seems to have unstoppable momentum.\u00a0 In my opinion that\u2019s too bad since it\u2019s misleading in many respects.\u00a0 Gathered from a variety of sources and distilled here the popular definition of AI is:<\/p>\n Anything that makes a decision or takes an action that a human used to take, or helps a human make a decision or take an action.<\/em><\/p>\n The main problem with this is that it describes everything we do in data science including every technique of machine learning we\u2019ve been using since the 90s.<\/p>\n As I gathered up different versions of this to distill for you here it became apparent that there are four different groups promoting this meme.<\/p>\n \u00a0<\/p>\n The Data Scientist\u2019s Definition<\/strong><\/span><\/p>\n Those of us professionally involved in all these techniques know that a set of new or expanded techniques evolved over the last ten years.\u00a0 These included deep neural nets and reinforcement learning<\/u><\/em><\/a>.<\/p>\n These aren\u2019t radically new techniques since they grew out of neural nets that had been in our toolbox for a long time but blew up on the steroids of MPP (massive parallel processing brought by NoSQL Hadoop), GPUs, and vastly expanded cloud compute.<\/p>\n When you looked at these from the perspective of the AI founders like Turing, Goertzel, and Nilsson you could see these newly expanded capabilities as the eyes, ears, mouth, hands, and cognitive ability that started to add up to their vision of what artificial intelligence was supposed to be able to do.<\/p>\n \u00a0 Data scientists understand that the definition of AI as we practice it today is really a collection of the six unique techniques above, some more advanced toward commercial readiness than others.<\/p>\n \u00a0<\/p>\n Is There Any Common Ground<\/strong><\/span><\/p>\n It\u2019s narrow, but there is some common ground between these two definitions.\u00a0 That\u2019s primarily in the backstory for AI.\u00a0 The popular press has mostly represented that AI is something brand new but the correct way to look at this is<\/u><\/em> as an evolution over time<\/u><\/em><\/a>.<\/p>\n \u00a0 I think we all understand that we stand on the shoulders of those who came before.\u00a0 Even as far back as the 90\u2019s we were building hand crafted decision trees that we called expert systems to take the place of human decision making in complex situations.<\/p>\n Once you understand that the popular definition wants to include everything that makes a decision, then it\u2019s easy to see the progression through machine learning and Big Data into deep learning.<\/p>\n One place where the casual reader needs to be careful though is in understanding what elements of AI are commercially ready.\u00a0 Among the six techniques or technologies that make up AI, only CNNs and RNN\/LSTMs for image, video, text, and speech are at commercially acceptable performance levels.<\/p>\n What you may need to explain to your executive sponsors is that these six \u2018true\u2019 AI methods are still the bleeding edge of our capabilities.\u00a0 Projects based on these are high cost, high effort, and higher risk.\u00a0<\/p>\n The conclusion ought to be that there are many business solutions that can be based on machine learning without involving true AI methods.\u00a0 As more third party vendors create industry or process specific solutions using these new techniques this risk will become less, but that\u2019s not today.<\/p>\n For the rest of us, the conflict of definitions remains.\u00a0 When someone asks you about AI, you\u2019re still going to need to ask \u2018what do you mean by that\u2019.<\/p>\n \u00a0<\/p>\n \u00a0<\/p>\n Other articles by Bill Vorhies.<\/u><\/em><\/a><\/p>\n \u00a0<\/p>\n About the author:\u00a0 Bill Vorhies is Editorial Director for Data Science Central and has practiced as a data scientist since 2001.\u00a0 He can be reached at:<\/p>\n Bill@DataScienceCentral.com<\/a> or<\/span> Bill@Data-Magnum.com<\/a><\/p>\n \u00a0<\/span><\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<\/a>And look back at all the things we\u2019ve called ourselves since the late 90\u2019s.\u00a0 These names don\u2019t describe different outcomes or even really different techniques.\u00a0 We\u2019re still finding the signal in the data with supervised and unsupervised machine learning.<\/p>\n
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