{"id":103070,"date":"2023-05-23T11:12:40","date_gmt":"2023-05-23T15:12:40","guid":{"rendered":"https:\/\/www.shortform.com\/blog\/?p=103070"},"modified":"2023-05-28T13:17:56","modified_gmt":"2023-05-28T17:17:56","slug":"reliance-on-technology","status":"publish","type":"post","link":"https:\/\/www.shortform.com\/blog\/reliance-on-technology\/","title":{"rendered":"A Surprising Consequence of Society&#8217;s Reliance on Technology"},"content":{"rendered":"\n<p>Is society&#8217;s reliance on technology really all that great? How do data and technology fail to make accurate predictions?<\/p>\n\n\n\n<p>It&#8217;s assumed that, with technological advancements, machines can help us do anything. This includes <a href=\"https:\/\/www.shortform.com\/blog\/no-one-can-predict-the-future\/\">predicting the future<\/a>. But, in actuality, they&#8217;re more wrong than ever.<\/p>\n\n\n\n<p>Continue reading to learn how our reliance on technology prevents us from making correct predictions.<\/p>\n\n\n\n<!--more-->\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-we-trust-too-much-in-data-and-technology\"><strong>We Trust Too Much in Data and Technology<\/strong><\/h2>\n\n\n\n<p>One of the challenges of making good predictions is a lack of information. By extension, the more information we have, our predictions should be better, at least in theory. By that reasoning, today\u2019s technology should be a boon to predictors\u2014we have more data than ever before, and thanks to computers, we can process that data in ways that would have been impractical or impossible in the past. However, <em>The Signal and the Noise<\/em> by Nate Silver argues that data and computers both present their own unique problems that can hinder predictions as much as they help them, and it&#8217;s all because of society&#8217;s heavy reliance on technology.<\/p>\n\n\n\n<p>For one thing, Silver says, having more data doesn\u2019t <em>inherently<\/em> improve our predictions. He argues that <strong>as the total amount of available data has increased, the amount of signal (the useful data) hasn\u2019t<\/strong>\u2014in other words, the proliferation of data means that there\u2019s more noise (irrelevant or <a href=\"https:\/\/www.shortform.com\/blog\/misinterpreting-data\/\">misleading data<\/a>) to wade through to find the signal. At worst, this proportional increase in noise can lead to convincingly precise yet faulty predictions when forecasters inadvertently build theories that fit the noise rather than the signal.<\/p>\n\n\n\n<p>(Shortform note: In <a href=\"https:\/\/www.shortform.com\/app\/book\/everybody-lies\/1-page-summary\" target=\"_blank\" rel=\"noreferrer noopener\"><em>Everybody Lies<\/em><\/a>, data scientist Seth Stephens-Davidowitz goes into more detail about why too much data can actually be a bad thing. He explains that <a href=\"https:\/\/www.shortform.com\/app\/book\/everybody-lies\/1-page-summary#drawbacks-when-data-gets-in-the-way\" target=\"_blank\" rel=\"noreferrer noopener\">the more data points you have, the greater your chances of detecting a <em>false<\/em> signal<\/a>\u2014an apparent correlation that occurs due to chance. For example, if you flip 1,000 coins every day for two years, record the results for each coin, and record whether the stock market went up or down each day, it\u2019s possible that through pure luck, one of the coins will appear to \u201cpredict\u201d the stock market. And the more coins you flip, the likelier you are to find one of these \u201clucky\u201d coins because more coins means more chances to generate randomness that seems predictive.)<\/p>\n\n\n\n<p>Likewise, Silver warns that <strong>computers can lead us to baseless conclusions when we overestimate their capabilities or forget their limitations<\/strong>. Silver gives the example of the 1997 chess series between grandmaster Garry Kasparov and IBM\u2019s supercomputer Deep Blue. Late in one of the matches, Deep Blue made a move that seemed to have little purpose, and afterward, Kasparov became convinced that the computer must have had far more processing and creative power than he\u2019d thought\u2014after all, he assumed, it must have had a good reason for the move. But in fact, Silver says, IBM eventually revealed that the move resulted from a bug: Deep Blue got stuck, so it picked a move at random.<\/p>\n\n\n\n<p>According to Silver, Kasparov\u2019s overestimation of Deep Blue was costly: He lost his composure and, as a result, the series. To avoid making similar errors, Silver says you should <strong>always keep in mind the respective strengths and weaknesses of <a href=\"https:\/\/www.shortform.com\/blog\/humans-and-machines\/\">humans and machines<\/a><\/strong>: Computers are good at brute-force calculations, which they perform consistently and accurately, and they excel at solving finite, well-defined, short- to mid-range problems (like a chess game). On the other hand, Silver says, computers aren\u2019t flexible or creative, and they struggle with large, open-ended problems. Conversely, humans can do several things computers can\u2019t: We can think abstractly, form high-level strategies, and <a href=\"https:\/\/www.shortform.com\/blog\/how-to-look-at-the-bigger-picture\/\">see the bigger picture<\/a>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Machine Intelligence Revisited<\/strong><\/h3>\n\n\n\n<p>With the proliferation of <a href=\"https:\/\/www.nytimes.com\/article\/ai-artificial-intelligence-chatbot.html\" target=\"_blank\" rel=\"noreferrer noopener\">large language model<\/a> (LLM) AIs such as <a href=\"https:\/\/www.shortform.com\/blog\/what-can-you-do-with-chatgpt\/\">ChatGPT<\/a> and Bing Chat, computing has changed radically since <em>The Signal and the Noise<\/em>\u2019s publication\u2014and as a result, it\u2019s easier than ever to forget the limitations of computer intelligence. LLMs are capable of complex and flexible behaviors such as holding long conversations, conducting research, and generating text and computer code, all in response to simple prompts given in ordinary language.&nbsp;<\/p>\n\n\n\n<p>In fact, these AIs can seem so lifelike that in 2022, <a href=\"https:\/\/www.scientificamerican.com\/article\/google-engineer-claims-ai-chatbot-is-sentient-why-that-matters\/\" target=\"_blank\" rel=\"noreferrer noopener\">a Google engineer became convinced that an AI he was developing was sentient<\/a>. Other experts dismissed these claims\u2014though the engineer\u2019s interpretation was perhaps understandable in light of interactions in which <a href=\"https:\/\/www.euronews.com\/next\/2023\/02\/18\/threats-misinformation-and-gaslighting-the-unhinged-messages-bing-is-sending-its-users-rig\" target=\"_blank\" rel=\"noreferrer noopener\">AIs have declared their humanity, professed their love for users, threatened users, and fantasized about world domination<\/a>.<\/p>\n\n\n\n<p>Given these surprisingly human-like behaviors, we might be left wondering, like Kasparov with Deep Blue, whether these programs can actually <em>think<\/em>. According to AI experts, they can\u2019t: Though LLMs can effectively emulate human language, <a href=\"https:\/\/www.nytimes.com\/2023\/03\/28\/technology\/ai-chatbots-chatgpt-bing-bard-llm.html\" target=\"_blank\" rel=\"noreferrer noopener\">they do so not through a deep understanding of meaning but through an elaborate matching operation<\/a>. In essence, when you enter a query, the program consults a massive database to find statistically likely words and phrases to respond with. (Some observers have likened this to the <a href=\"https:\/\/mind.ilstu.edu\/curriculum\/searle_chinese_room\/searle_chinese_room.html\" target=\"_blank\" rel=\"noreferrer noopener\">Chinese Room thought experiment<\/a>, in which a person with no understanding of Chinese conducts a written exchange in that language by copying the appropriate symbols according to instructions.)&nbsp;<\/p>\n\n\n\n<p>That\u2019s why, despite AI\u2019s impressive advances, many experts recommend that the best way forward isn\u2019t to replace people with AI\u2014it\u2019s to combine the two, much as Silver recommends, in order to maximize the strengths of both. Interestingly, Kasparov himself promotes this approach, arguing that <a href=\"https:\/\/hbr.org\/2021\/03\/ai-should-augment-human-intelligence-not-replace-it#:~:text=Kasparov%20lost%20a%20game%20from%20an%20IBM,simply%20as%20an%20individual%20effort%20but\" target=\"_blank\" rel=\"noreferrer noopener\">the teamwork between specific humans and specific machines is more important than the individual capabilities of each<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Is society&#8217;s reliance on technology really all that great? How do data and technology fail to make accurate predictions? It&#8217;s assumed that, with technological advancements, machines can help us do anything. This includes predicting the future. But, in actuality, they&#8217;re more wrong than ever. Continue reading to learn how our reliance on technology prevents us from making correct predictions.<\/p>\n","protected":false},"author":14,"featured_media":34709,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[160,25],"tags":[1032],"class_list":["post-103070","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-science","category-statistics","tag-the-signal-and-the-noise","","tg-column-two"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v24.3 (Yoast SEO v24.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>A Surprising Consequence of Society&#039;s Reliance on Technology - Shortform Books<\/title>\n<meta name=\"description\" content=\"Technology is amazing, but it&#039;s terrible at making predictions. 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