人机结合:超越常规合作关系 Building Mind-Machine Combinations: Move Past Standard Partnerships



Building Mind-Machine Combinations: Move Past Standard Partnerships, with Andrew McAfee, Associate Director, MIT Center for Digital Business and Author, Machine, Platform, Crowd

The “standard partnership” between mind and machine is terminology Andrew McAfee and Erik Brynjolfsson use to describe the status quo in most companies, as of 2017. Machines are “essentially glorified spreadsheets”, as McAfee puts it. They store, sort, and send data. Humans are the “big brains” of most operations, looking for patterns and solutions in these data sets and sifting through ideas to find the good ones. The trouble is that even with training and experience, our intuition is flawed. So many forward-thinking companies are restructuring the partnership, finding better ways to use human intuition and machine thinking in conjunction.




Rethink the balance between minds and machines 重新思考人和机器之间的平衡

In our book, Machine, Platform, Crowd , Eric Brynjolfsson and I use this phrase the “standard partnership” over and over again to talk about the status quo in organizations, and the status quo in two ways. First of all, the status quo between minds and machines. And the standard partnership right now is the machines do all the record keeping, the transaction processing, they zip the information around the world. They’re glorified spreadsheets in a lot of ways. And then the job of the people, the jobs of the minds in the organization, is essentially to look at all that data that’s generated but then apply their good old-fashioned human judgment and intuition and experience on top of that. And there’s just way too much evidence about how buggy and glitchy and biased our human decision-making processes are to let that standard partnership endure. So the companies that we researched and that I came to respect a lot when we were writing the book are trying very hard to move past that standard partnership. And they’re trying not to get human minds totally out of the loop, but if it turns out that the algorithms, and the data, and the evidence are better and are making better decisions, we need to respect that and we need not to try to slap human judgment on top of it just because people feel good about making decisions. So the example that I like to use is, let’s say it turns out that computers become better at medical diagnostics than even highly trained human doctors. In that case, do we want to continue to let human doctors make the diagnosis just so they get to keep feeling good about themselves? I would say absolutely not if we want a healthier population. And if it turns out, like I think it will, that especially in an era of really, really powerful machine learning that the machines are better at that task than the minds, we really need to give a lot more of that work over to the machines.



Get away from managers as gatekeepers-of -ideas 不要把管理者当作想法的“守门人”

So part of the standard partnership is rethinking the balance between minds and machines, but there’s another aspect to it that I find really interesting. Another part of the standard arrangement inside companies is that part of the job of management is to act as a gatekeeper for ideas. And what managers at all levels do, or what they think their job is, is to listen to the people that report to them, think about the ideas that get teed up, and say yeah, that sounds right to me, we’re going to go do that, versus that doesn’t sound right to me, we’re actually not going to do that. And the reason that an idea sounds good or bad is because, again, of the manager’s intuition, judgment, experience – those all sound ok. Here are other reasons why one idea sounds good or bad. The implicit and explicit biases that the manager has, the blinders that they have on, the experiences they don’t have, and the glitches in their mental hardware. One thing that I don’t want to see continue is that we let glitches in the mental hardware of managers determine which ideas get to see the light of day and get a fair hearing inside of organizations. I want to get out of that business. And when we were researching the book we came across companies where it was clear to us that they were working really hard – the managers, the leaders of that organization were working very hard not to intervene and be good gatekeepers and say green light red light to different things, but to get out of that business as much as they could, and to say look, we hired you inside this organization because we trust you, we think you’re good at what you do, and because we value your ideas. What we want to do now is let you run with those ideas as long as they’re not going to be financially ruinous if they turn out not to be good, if we can do a smaller-scale, shorter-term experiment to figure out if this is a good idea or not, if you’re not going to trash the company’s brand or its reputation or get us in very serious legal trouble. Ok, put those parameters around it. But within that, the job of us, the managers and leaders of this organization, is not to gatekeep your ideas anymore. What we want to try to do instead is set up an experimental engine inside this company where you feed ideas into it, you iterate, you figure out which of those ideas are good ones or not. You double down on the good ones, you try to learn from the ones that didn’t work out and walk away from them, but you want a lot of different people pouring ideas into that experimental engine. And some of the companies that I came to respect a lot are saying, look, come one come all. Or they’re trying to get closer to that view of the world. And we’re not only going to let people whose job title says “innovator” or who work in the R&D lab to pour good ideas into that experimentation funnel. We’re going to try to open that up fairly broadly. And it’s difficult homework. It’s easy to say and hard to set up, but the companies that are accomplishing that are seeing a higher rate of good ideas, a higher rate of successful experiments, and they’re charging ahead more quickly into a better future, in every sense of the word.




Building Mind-Machine Combinations: Move Past Standard Partnerships, with Andrew McAfee, Associate Director, MIT Center for Digital Business and Author, Machine, Platform,Crowd.

Rethink the balance between minds and machines

• In status quo, organizations, machines are used for rote information processing. Human intuition, judgment, and experience are then applied on top. But human decision-making processes are too buggy and biased to let the status quo endure.

• When algorithms, data, and evidence work together to make better decisions, allow

them to lead. Avoid layering human judgment on top just because people feel good

about making decisions.

Get away from managers as gatekeepers-of -ideas

• Biases, blind spots, and lack of experience can lead managers to favor certain ideas over others. Don’t let the flawed mental hardware of one person determine which ideas get promoted.

• Allow employees to test their ideas within certain parameters, such that those ideas:

◦ won’t be financially ruinous

◦ lend themselves to small-scale, short-term experiments

◦ won’t degrade the brand or lead to legal trouble

• Set up an experimental engine into which employees feed their ideas. Iterate and double down on the good ideas. Learn from, and walk away from, the bad ones.



• 当今企业让机器机械式地处理信息,再让人凭直觉、判断、经验做出决策。但人的决策充满了缺陷和偏见,这种状况不应该持续下去。

• 当算法、数据和依据协同起来,能做出更好的决策时,就应该把它们放在决策的首位。不要仅仅因为人们自我感觉良好就让人类的判断决定最终选择。



• 允许员工在一定条件内测试他们的想法,条件如下:




• 建立一个试验性的引擎,让员工测试自己的想法。重复测试,加倍努力去实现好想法。从失败的想法中吸取教训,避免重蹈覆辙。