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That\u2019s bad news for the humanists — we may run more complex code, but it looks like we’re just algorithms, too. For anyone ready to cry “free will,” yes, you get to decide whether to carry out that last thought that popped into your head.<\/P><\/p>\n
But why did it pop there? Algorithms.<\/P><\/p>\n
Psychologists since Sigmund Freud have known how to manipulate opinions and decisions by tapping into the autonomous logic that runs in our subconscious. The decision heuristics or biases that Nobel laureate Daniel Kahneman describes in his book Thinking Fast, Thinking Slow<\/EM> are essentially bugs in the code of our operating system that make us easily hackable by advertisers<\/A>.<\/P> The documentary also explains how the Thatcher-Reagan era institutionalized this approach to the masses, ultimately creating a consumer society oriented around our ability to express ourselves as individuals. Computer-driven advances in manufacturing fueled the lifestyle consumer movement of the 1980s, creating short runs of products that certain groups felt were aligned to their individual philosophies. The most famous and transparent example of this was Apple\u2019s \u201cCrazy Ones\u201d ad that marked Steve Jobs’ return to the company, and the subsequent explosion of iMac configurations.<\/P> The same kinds of ideas can be found in how manufacturers are using manufacturing artificial intelligence deciding to approach Industry 4.0.<\/P> \u2022 Generative design<\/A> harnesses computing power to automate the ideation process, allowing designers to identify the best of thousands of potential new design iterations.<\/P><\/p>\n \u2022 The digital twin<\/A> allows you to test products in manufacturing and in the hands of virtual end-users before you commit to building a physical product.<\/P><\/p>\n \u2022 Distributed manufacturing execution systems work with intelligent automation and robotics<\/A> to make unique, correct product configurations the first time.<\/P><\/p>\n \u2022 Collaboration platforms create a digital thread<\/A> that integrates each node in the value chain, which helps manufacturers easily answer why something is happening with their products.<\/P><\/p>\n \u2022 The Industrial Internet of Things combines with machine learning to compare predictions with reality and optimize the whole system.<\/P><\/p>\n However, a major pushback with this artificial intelligence in manufacturing has been that while consumers are already willing to personalize major purchases like a car, they likely won\u2019t put the energy into customizing more mundane items that drive the bulk of the economy: groceries, cosmetics, personal care items, personal electronic devices, home furnishings, apparel and footwear.<\/P> Millions of years of evolution may have given human algorithms a head start, but the race is far from over. Whereas we arguably hit peak processing power 100,000 years ago, computer algorithms can already beat doctors at diagnosing cancer and grandmasters at chess. These algorithms are really just getting started when it comes to understanding us.<\/P> With 150 “Likes,” it could outsmart your family. With 300 “Likes,” it could best a person’s spouse. Perhaps more astounding, the more objective algorithm beat the subject\u2019s own subjective self-evaluation.<\/P> \u201cIf humankind is indeed a single data-processing system, what is its output? Dataists would say that its output will be the creation of a new and even more efficient data-processing system, called the Internet-of-All-Things.\u201d<\/P><\/p>\n 1. Data from social media will combine with shopping history, and the Internet of Things data to offer predictive shopping support through a Siri-like assistant.<\/STRONG> Interactions like this may become the norm: \u201cHey Al, shall I get your favorite beer in? Your friends are coming over tomorrow and the fridge is empty. I better get a 6-pack of non-alcoholic beer for the designated driver, and Laura prefers wine.\u201d<\/P><\/p>\n 2. These assistants then transition to being an online agent that is a full proxy for the self.<\/STRONG> As we become confident that the assistant\u2019s algorithms match our own, we will delegate more and more decisions to her. This may include scheduling romantic dates, voting in elections and certainly full control over purchases. We will give our proxy access to our medical history, and real-time data such as heart rate and serotonin levels so her algorithms keep us warm, happy and healthy.<\/P><\/p>\n Once billions of agents are active, they will self-organize into powerful purchasing syndicates that will rapidly combine and recombine as viral trends sweep unhindered across the world. Any consumer business still making to stock will find themselves chronically out-of-whack with rapidly changing demands, and only fully digitalized manufacturers will survive and thrive.<\/P> If this all seems farfetched, then go speak to your kids. They believe in the freedom of data and have far fewer concerns about a future that unfolds along these lines. They\u2019re already online, sharing everything and establishing the foundation of the agents that will search, shop, order and vote on their behalf.<\/P><\/p>\n It\u2019s true that there are still many unknowns about how manufacturing artificial intelligence will work once it\u2019s fully implemented. <\/P><\/p>\n \u2022 Will giant online corporations like Amazon use their consumer access, cloud-processing power, and supply chain mastery to control our agents and manipulate humanity? Will they become brokers in a giant online marketplace, or will they disappear as technologies like the Blockchain<\/A> enable a global peer-to-peer economy?<\/P><\/p>\n \u2022 Will brand owners still manufacture their own products in fixed locations? Or, will intellectual property and manufacturing become disassociated as digitalized micro-factories gain the ability to make anything in close proximity to the consumer?<\/P><\/p>\n \u2022 What minimum lot size will be economically advantageous for each type of product? Will the dynamics of agent syndicates drive manufacturers toward truly individualized products? Or, will the large numbers of customers in the global market allow manufacturer\u2019s algorithms to negotiate more efficient non-unitary orders?<\/P><\/p>\n \u2022 Which \u2018language\u2019 will agents use to express requirements to manufacturers? Will there be standards that we set, or will machine learning quickly evolve their own semantics?<\/P><\/p>\n
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Since the 1950s, corporations have been catering to our encoded self-interest and selling to our unconscious desires. In his BBC documentary, The Century of the Self, Adam Curtis explained how Sigmund Freud\u2019s nephew and public relations innovator, Edward Bernays, manipulated the sentiments of the masses. In one of his more infamous campaigns, he pushed for making smoking in public acceptable for women by calling cigarettes \u201ctorches of freedom.\u201d<\/A><\/P><\/p>\n
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The message was clear, if contradictory: you are individuals. Don\u2019t follow the herd. Think differently. And if you buy one of these products, you show that you\u2019re an individual.<\/P><\/p>\nAlgorithms, artificial intelligence in manufacturing and Industry 4.0 <\/H3>
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Industry 4.0 promises exponential improvements in personalization as digitalization provides manufacturers the opportunity to dramatically increase the speed, flexibility and efficiency of their internal value chain.<\/P><\/p>\n![\"Artificial](\"http:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/19\/2019\/09\/Artificial-Intelligence-in-Manufacturing_1-1.jpg\" "\\"Artificial")
Intelligent automation and robotics is just one of the beneficial results for manufacturers as they implement digitalization.<\/span><\/span><\/P>
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Some would argue that we\u2019re already making enough stuff and that we just need better algorithms to match it to consumers, similar to the approach Amazon takes with customers and delivering goods.<\/P>
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But the moment biologists reduced us to algorithms, the wall between computers and humans crumbled, and our status as the pinnacle of evolution was placed in doubt.<\/P><\/p>\nManufacturing artificial intelligence and algorithms <\/H3>
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Researchers at Cambridge and Stanford have shown that with information on just ten Facebook “Likes,” a computer algorithm is more accurate than the average person’s colleague at predicting behavior<\/A>.<\/P><\/p>\n
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Futurists predict a rapid evolution of the algorithms that help guide our online shopping decisions, and even hypothesize that humans may become \u201can obsolete algorithm.\u201d<\/P>
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Yuval Noah Harari is a historian and author who has written about the relationship between history and biology. He recently published a book called Homo Deus: A Brief History of Tomorrow<\/EM>, in which he discusses the future of humanity and humanity\u2019s relationship with data<\/A>. He offers an interesting point about humankind\u2019s output:<\/P><\/p>\n
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We\u2019ve mainly focused on the consumer perspective. Because the mission of a corporation can be more easily codified than that of a person, it\u2019s probable that corporate algorithms in this kind of manufacturing artificial intelligence will quickly evolve, and vendors of industrial products, services and utilities will have their own agent-based marketplace.<\/P><\/p>\nLingering questions about artificial intelligence in manufacturing <\/H3>
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