Artificial intelligence - Revision history

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 '''Artificial intelligence''' ('''AI'''), sometimes called '''[[machine]] intelligence''', is [[intelligence]] demonstrated by [[machine]]s, in contrast to the '''[[natural]] intelligence'''<!--boldface per WP:R#PLA--> displayed by humans and other animals.  In [[computer science]]  AI research is defined as the study of "[[intelligent agent]]s": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.<ref name="Definition of AI"/> Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other [[human mind]]s, such as "learning" and "problem solving".{{sfn|Russell|Norvig|2009|p=2}}
-Why is artificial intelligence important?
+''''Why is artificial intelligence important?''''
-AI automates repetitive learning and discovery through data. But AI is different from hardware-driven, robotic automation. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks reliably and without fatigue. For this type of automation, human inquiry is still essential to set up the system and ask the right questions.
+*AI automates repetitive learning and discovery through data. But AI is different from hardware-driven, robotic automation. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks reliably and without fatigue. For this type of automation, human inquiry is still essential to set up the system and ask the right questions.
 AI adds intelligence to existing products. In most cases, AI will not be sold as an individual application. Rather, products you already use will be improved with AI capabilities, much like Siri was added as a feature to a new generation of Apple products. Automation, conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies at home and in the workplace, from security intelligence to investment analysis.
-AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that the algorithm acquires a skill: The algorithm becomes a classifier or a predictor. So, just as the algorithm can teach itself how to play chess, it can teach itself what product to recommend next online. And the models adapt when given new data. Back propagation is an AI technique that allows the model to adjust, through training and added data, when the first answer is not quite right.
+*AI adds intelligence to existing products. In most cases, AI will not be sold as an individual application. Rather, products you already use will be improved with AI capabilities, much like Siri was added as a feature to a new generation of Apple products. Automation, conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies at home and in the workplace, from security intelligence to investment analysis.
 AI analyzes more and deeper data using neural networks that have many hidden layers. Building a fraud detection system with five hidden layers was almost impossible a few years ago. All that has changed with incredible computer power and big data. You need lots of data to train deep learning models because they learn directly from the data. The more data you can feed them, the more accurate they become.
-AI achieves incredible accuracy through deep neural networks – which was previously impossible. For example, your interactions with Alexa, Google Search and Google Photos are all based on deep learning – and they keep getting more accurate the more we use them. In the medical field, AI techniques from deep learning, image classification and object recognition can now be used to find cancer on MRIs with the same accuracy as highly trained radiologists.
+*AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that the algorithm acquires a skill: The algorithm becomes a classifier or a predictor. So, just as the algorithm can teach itself how to play chess, it can teach itself what product to recommend next online. And the models adapt when given new data. Back propagation is an AI technique that allows the model to adjust, through training and added data, when the first answer is not quite right.
 AI gets the most out of data. When algorithms are self-learning, the data itself can become intellectual property. The answers are in the data; you just have to apply AI to get them out. Since the role of the data is now more important than ever before, it can create a competitive advantage. If you have the best data in a competitive industry, even if everyone is applying similar techniques, the best data will win.
 The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring "intelligence" are often removed from the definition, a phenomenon known as the [[AI effect]], leading to the quip in Tesler's Theorem, "AI is whatever hasn't been done yet."<ref>{{Cite web|url=http://people.cs.georgetown.edu/~maloof/cosc270.f17/cosc270-intro-handout.pdf|title=Artificial Intelligence: An Introduction, p. 37|last=Maloof|first=Mark|date=|website=georgetown.edu|archive-url=|archive-date=|dead-url=|access-date=}}</ref> For instance, [[optical character recognition]] is frequently excluded from "artificial intelligence", having become a routine technology.<ref>{{cite magazine |last=Schank |first=Roger C.  |title=Where's the AI |magazine=AI magazine |volume=12 |issue=4 |year=1991|p=38}}</ref> Modern machine capabilities generally classified as AI include successfully [[natural language understanding|understanding human speech]],{{sfn|Russell|Norvig|2009}} competing at the highest level in [[strategic game]] systems (such as [[chess]] and [[Go (game)|Go]]),<ref name="bbc-alphago"/> [[autonomous car|autonomously operating car]]s, and intelligent routing in [[content delivery network]]s and [[military simulations]].

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 <!-- DEFINITIONS -->
 '''Artificial intelligence''' ('''AI'''), sometimes called '''[[machine]] intelligence''', is [[intelligence]] demonstrated by [[machine]]s, in contrast to the '''[[natural]] intelligence'''<!--boldface per WP:R#PLA--> displayed by humans and other animals.  In [[computer science]]  AI research is defined as the study of "[[intelligent agent]]s": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.<ref name="Definition of AI"/> Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other [[human mind]]s, such as "learning" and "problem solving".{{sfn|Russell|Norvig|2009|p=2}}
 The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring "intelligence" are often removed from the definition, a phenomenon known as the [[AI effect]], leading to the quip in Tesler's Theorem, "AI is whatever hasn't been done yet."<ref>{{Cite web|url=http://people.cs.georgetown.edu/~maloof/cosc270.f17/cosc270-intro-handout.pdf|title=Artificial Intelligence: An Introduction, p. 37|last=Maloof|first=Mark|date=|website=georgetown.edu|archive-url=|archive-date=|dead-url=|access-date=}}</ref> For instance, [[optical character recognition]] is frequently excluded from "artificial intelligence", having become a routine technology.<ref>{{cite magazine |last=Schank |first=Roger C.  |title=Where's the AI |magazine=AI magazine |volume=12 |issue=4 |year=1991|p=38}}</ref> Modern machine capabilities generally classified as AI include successfully [[natural language understanding|understanding human speech]],{{sfn|Russell|Norvig|2009}} competing at the highest level in [[strategic game]] systems (such as [[chess]] and [[Go (game)|Go]]),<ref name="bbc-alphago"/> [[autonomous car|autonomously operating car]]s, and intelligent routing in [[content delivery network]]s and [[military simulations]].

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