The time for talking about the rise of the machines is over. They have risen. Or more accurately, humans have brought them to life to amplify and accentuate work that men and women are doing. That is my view: machines, Artificial Intelligence algorithms, big data, neural networks, are here to help. The debate over human vs. machine is silly. No thinking human — or thinking machine for that matter — is looking for all of humanity to be swept into the bin of history, while C-3PO and R2-D2 run our planet. Now, before you write me off as loony as those who see advances in Machine Learning and Artificial Intelligence as a precursor to some sort of apocalypse, I grant you that there is a reason for caution — watchfulness even.
Any great transformation brings with it unintended consequences. But on balance, many of those tectonic shifts the industrial revolution, the rise of the horseless carriage, radio and television, the semiconductor, the personal computer, the internet, the smartphone have produced far greater upside than downside. The most productive way — quite literally — to look at increasing automation is to view the model not as a human vs. machine, but as human-plus-machine. I don’t expect this piece, nor a hundred more like it will put an end to the argument. Powerful voices have raised dramatic concerns about where artificial intelligence could ultimately lead.
Elon Musk, of Tesla and SpaceX, has warned of “summoning the demon” and the rise of “an immortal dictator.” Stephen Hawking presented the possibility of the end of the human race, thanks to advanced artificial intelligence. Bill Joy, a co-founder of Sun Microsystems, a wildly successful Silicon Valley company until it wasn’t, concluded that given the power of machines, “the future doesn’t need us.” And it never fails: when something goes wrong with computer-aided technology, be it a plane crash, a self-driving car accident or Amazon Prime delivering jalapeño loaf instead of sourdough bread, the machine often takes the blame, rightfully or not. Those are some pretty smart people worrying about our automated future. And I don’t pretend to be able to see clearly and far into the future. But framing the debate as an either/or, a black or white, a one or a zero, is illogical. Instead, the way to think about AI is human-plus-machine.
Machines and humans are very good at many things. But they are not very good at the same things. Give the nod to machines on memory, optimization, objectivity, speed, and scale. Humans are your go-to for common sense, intuition, empathy and making generalizations. And let’s not forget that for now machines still rely on humans with the domain expertise to build the models that allow machines to learn. The human element, in fact, means that machines remain fallible and subject to bias.
Microsoft’s Tay chatbot was one of the most public and notorious examples. But again, the upside outweighs the downside. How does this pairing of human and machine play itself out in the real world? Medicine, banking, transportation, entertainment, government, airport security, sports, schools, in-home care. The areas of our lives not touched by a combination of humans and machines are by now probably fewer than those that are. Cynthia Breazeal, in a talk at one of Tom Friedman’s Global Technology Forums, explained the state of the machine and human relations this way: “The new, new thing is how it really engages with you, not just as a tool, but as a partner,” she said of robots pitching in to manufacture, teach and care for the ill. “Now, you’re side-by-side, doing something together, and I think that’s a profound change. To me, that’s a more enlightened view. It’s not a replacement. It’s extending human capabilities.”
Think about something we do every day something I bet you’ve done since you started reading this piece an internet search. Back at the dawn of readily accessible internet search, Yahoo was the search engine of choice. The company employed an army of “surfers” who sifted through thousands of URLs submitted by website owners and members of the public. It was the surfers’ job to decide which URLs were worthy of inclusion in the Yahoo directory and how they should be categorized. “It was pretty wild,” says Steve Berlin, who was Yahoo employee No. 14 and the company’s first full-time surfer. “Basically, everyone was given a list of hundreds of sites and every day they were given a new list or every week they were given a new list. Everyone had their own specialties.” Then along came Google, of which perhaps you’ve heard. Google had another idea: Let the machines do it. Machines designed and tuned by humans. It’s a good thing Google came along. When Berlin was surfing the web for Yahoo, there were approximately 258,000 websites, according to Internet Live Stats.
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As of this writing, there are about 1.7 billion sites. That’s a lot of surfing. The way machines are changing the way we work — and the way we work with them — can be seen by diving into one industry that has been transformed by AI. The retail industry worldwide will spend more on artificial intelligence this year than will any other business sector, according to IDC. Total 2019 retail spending on AI: £4.5 billion. Artificial intelligence has reshaped every aspect of retailing as it has been applied to every point along a buyer’s consumption journey. The power of machines is at the center of providing a top-quality customer experience, something that is becoming a key differentiator in the age of Amazon and high consumer expectations. Retailers turn to data to spot trends and confirm their instincts on trends they have already spotted. Marketers lean on machines to help personalize shopping experiences and to improve the search experience on digital sites.
E–commerce operations professionals increase the speed with which they fill orders by handing off payments, fraud detection, inventory tracking and fulfillment to constantly learning machines. Customer service departments deploy armies of chatbots to handle basic customer questions, freeing up humans for nuanced questions and more serious complaints. But delivering the ideal customer experience doesn’t come from a machine alone. When it comes to detecting fraudulent online orders, we know that machines alone are not up to the task of protecting retailers while ensuring that customers enjoy a friction-free buying experience. In a given order, machines can be confronted with a combination of factors they have not seen before, potentially raising a red flag that leads to an order being declined. But a human — relying on experience, intuition and the ability to generalize — might build a narrative that explains that the unusual order was undoubtedly placed by a legitimate customer, meaning the order is shipped.
In the first case, a legitimate customer has been denied an order that by all rights he or she should have received. The result is a terrible customer experience and an insulted customer — all because a machine knew what it knew. In physical stores, machines can help build a better experience. Neal Kaiser of Mi9 Retail said clothes sellers have turned to big data and algorithms to better understand their customers’ intent and desires. Store assistants can use the data to consider what a customer was interested in online and to recall what the customer was after during his or her last visit to the store. And in the end, it is humans who are delivering what the customer wants.
“They want that service,” Kaiser says. “They want that warm and fuzzy feeling when you’re engaging with them and you need the technology to know really what they’re doing, not just in disparate systems, but to kind of bring it all together. You need to engage and you’re not just selling a product anymore. It’s all about the experience with the customer.” As I said at the beginning: Machines and humans each have their own strengths. The best systems, the most efficient organizations, the most profitable businesses, understand that and wisely assign tasks to one or the other. For the record, when it comes to warm and fuzzy, it’s the humans that win — every time.