Outsmarting AI. Brennan Pursell

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СКАЧАТЬ ReLUs in particular enable neural networks to obtain their results faster.

      “Recurrent neural networks,” “convolutional neural networks,” “deep belief networks,” “generative adversarial networks,” and even “feedforward multilayer perceptron deep networks” all rely on the software you just learned about. And none of them are worth anything without good quality data, and lots of it.

      Equipped with the basic math and software underlying AI, you can readily face down any aggressive sales associate who tries to persuade you that his or her AI thinks like a human, one smarter than you.

      As you have seen, the technical ideas behind AI have been around for decades, and their applicability in the workplace has soared in just the past few years. The amount of available raw data has exploded as more and more applications on more and more mobile devices collect data and share it on the Internet with service companies and their partners. We communicate and work increasingly through apps. The “Internet of Things” (IoT) is a volcano, disgorging data everywhere faster than anyone can measure. No one can stop it. “Smart” devices and sensors proliferate. Better hardware, from the individual device to the network systems, help to process and transfer that data faster than ever.

      AI systems are increasingly capable of recognizing images, processing human language, and managing information in structured and unstructured data through statistical procedures. (I will revisit what applied AI can do for your organization in chapter 3.)

      To sum it all up: Software and hardware put statistics on steroids—and it will get much, much more powerful over time.

      We need to use AI because, when the data are well-labeled and the procedures are correct, computers run great numbers of them at high speed and low cost. Computers are also immune to human errors such as prejudice, favoritism, distraction, and mood swings (although we all wonder sometimes, when our systems suddenly slow down, freeze up, or crash).

      Nonetheless, AI has three big problems: dependency, consistency, and transparency. Dependency refers to the machines’ need for large amounts of high-quality, correctly classified training data. “Garbage in, garbage out,” as we said earlier. Consistency is a problem because adjustments made to algorithms produce different end results, regardless of data completeness and quality. Different AI systems produce different results on the same darned data. Finally, and most importantly, transparency in neural network processes is limited at best. Backpropagation makes it extremely difficult to know why the network produces the result that it does. The chains of self-adjusting calculations get so long and complicated that they turn the AI system into a “black box.”^[15]

      Despite these problems, AI functionalities are improving all the time, and applications of AI technology are proliferating in just about every sector of the economy. For every human being that uses a smartphone, there is literally no avoiding it.

      1.

      See World Intellectual Property Organization, Technology Trends 2019: Artificial Intelligence. https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf. The executive summary can be downloaded from https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055-exe_summary1.pdf.

      2.

      The best book on AI mathematics is Nick Polson and James Scott, AIQ (New York: St. Martin’s Press, 2018).

      3.

      See https://www.netflixprize.com/community/topic_1537.html and https://www.netflixprize.com/assets/GrandPrize2009_BPC_BellKor.pdf. The irony is that Netflix never actually used the prize-winning algorithm, supposedly because of the excessive engineering expense involved. It has used other algorithms developed by the prize-winning team. Casey Johnston, “Netflix Never Used Its $1 Million Algorithm Due to Engineering Costs,” Ars Technica, April 16, 2012, https://www.wired.com/2012/04/netflix-prize-costs/.

      4.

      See Polson and Scott, ch. 2.

      5.

      Public domain image from Wikipedia. https://en.wikipedia.org/wiki/Regression_analysis.

      6.

      See Polson and Scott, ch. 2.

      7.

      P(H|D) = P(H) * P(D|H) / P(D). See Polson and Scott, ch. 3.

      8.

      See Polson and Scott, ch. 4.

      9.

      See Polson and Scott, ch. 5.

      10.

      An excellent book on this topic is Steven Finlay, Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data-Driven Technologies (Relativistic, 2nd ed., 2017).

      11.

      Quoted in Clive Cookson, “Huge Surge in AI Patent Applications in Past 5 Years,” Financial Times, January 31, 2019.

      12.

      Cathy O’Neil, Weapons of Math Destruction (New York: Broadway Books, 2016).

      13.

      Madhumita Murgia, “How to Stop Computers Being Biased: The Bid to Prevent Algorithms Producing Racist, Sexist or Class-Conscious Decisions,” Financial Times, February 12, 2019.

      14.

      We recommend Andrew Ng’s courses on machine learning, available for free through Coursera, the online learning platform that he founded. See https://www.coursera.org/.

      15.

      See interview with Erik Cambria in Kim Davis (ed.), The Promises and Perils of Modern AI (DMN, 2018) eBook. https://forms.dmnews.com/whitepapers/usadata/0218/?utm_source=DMNTP04302018&email_hash=BBFC7DC6CF59388D09E7DF83D3FE564F&spMailingID=19474637&spUserID=NTMyMTI1MTM4MTMS1&spJobID=1241853091&spReportId=MTI0MTg1MzA5MQS2. For Erik Cambria’s work on teaching computers emotional recognition through using semantics and linguistics, see http://sentic.net/.

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