In the “standard partnership” between mind and machine, workers use computers to store, sort, and send data. Humans are the “big brains” of most operations, looking for patterns and solutions in these data sets, while the machines are essentially glorified spreadsheets. Human intuition is great at some things, less effective at others, while next-gen computer systems have the largely untapped power to fill in these gaps.
In this masterclass, MIT’s Andrew McAfee offers a comprehensive, practical approach to harnessing the true power of mind and machine within any organization.
What You'll Learn
How to thrive in the current business world
How to make decisions in the machine learning age
How to leverage machine learning to empower people
How to succeed using digital platforms
Andrew McAfee's image was originally posted by David Shankbone and the image has been changed. This file is licensed under the Creative Commons Attribution 2.0 Generic license.
1. Keep Score to Improve Decision Quality
In the original Star Trek, there was an ongoing tension/comedy routine between Bones, the starship Enterprise’s doctor who preferred to “go with his gut” in making medical decisions, and Spock, the highly logical half-Vulcan who subjected every decision to rigorous rational analysis. Bones was always frustrated by Spock’s cold, machinelike qualities. But he wasn’t always right.As machine learning becomes an increasingly powerful tool for problem solving and decision making, we need to do a better job keeping track of our own success and failure rates. In working with organizations, Andrew McAfee has observed that many fall short in “keeping score,” relying instead on reputation and past successes to decide who should be making which decisions.We often forget to tally the outcomes of employee judgments. How did that group do at predicting sales? Was that product actually a big success?Track the accuracy of human-based decisions to measure their success rates. This will help:re...
2. Move Past Standard Partnerships
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 machinesIn 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 be...
3. Reduce Error Rates with Human-Computer Teams
Both humans and computers are powerful and deeply flawed, but in very different ways. Taken separately, this is bad news for us both. Taken together, and handled with skill, it’s a major opportunity.Acknowledge that algorithms are flawedAlgorithms may generate biased results when mining limited data sets.Evaluate your data sets for potential biases before designing or applying an algorithm.Acknowledge that human judgment is flawedHuman minds are biased, glitchy, and inconsistent.Machine biases and human biases are not the same - they make different kinds of mistakes.Bring together human and machine thinking for more flawless cognition.
4. Welcome Technology as Your Colleague
In popular culture and the press, leaps in machine learning and artificial intelligence are typically met with two kinds of reactions: awe or terror. Both imply that these systems are so far beyond our comprehension that they are likely either to destroy us (by taking most of our jobs, for example) or catapult us into a dazzling, utopian future.In fact, says Andrew McAfee, after a long period of stagnation, machine-learning has made inspiring leaps in recent years, paving the way not for our robot overlords, but for intelligent machine colleagues to replace the slavishly literal systems that preceded them.Understand the history of machine learningHistorically, coders have told computers exactly what to do through explicit, detailed instructions.Machine learning is the concept of getting computers to learn things on their own, without human programming.Until recently, there was little success for machine learning.Raise your game with machinesThe success of AlphaGo has shed light on t...
5. Hire Using Evidence-Driven Processes
News flash: You are terrible at hiring. Your “gut feeling” about a candidate doesn’t correlate at all to that person’s likely success level at your company. Don’t feel bad—this is a universal human problem. Study after study has shown that unstructured interviews and “gotcha” brainteasers result in unreliable hiring decisions. Once you’re armed with that knowledge, the only thing that’s inexcusable is doing nothing about it.Don’t be swayed by feelingsToo often, decision-makers let their gut override a more formal analysis of the data.Research shows that intuition-based hiring practices fail to accurately determine which people are going to be a good fit for an organization.Most interviews encounter at least one of two failure modes:Interviewer tends to seek out and favor candidates who look and act like the interviewer.Interviewer tests candidates with brain teasers, which accomplishes little except making the interviewer feel smart.Learn from GoogleCommit to evaluating y...
6. Unlock the Value of Products by Building Digital Platforms
In spite of the massive successes of companies like Uber and Facebook, the power of digital platforms is still in its infancy. There are enormous opportunities for experimentation and learning as value shifts from products to those who can build intuitive, efficient platforms that connect people, providers, products, and more. But even in these early days, some compelling patterns have emerged.Lessons Learned with Apple’s iOSA digital platform will typically get introduced on top of an existing industry. For example, Apple’s iOS rests on top of the smartphone industry.Any platform needs two distinct groups of participants. iOS brings together app consumers and app developers. The platform sits in the middle and allows for easy connections between the two groups.As the two groups grow and stay connected, they make up a two-sided network. The two-sided network leads to a network effect - the more people who use the platform, the more valuable it becomes.Making the LeapMost industrie...
7. A Recipe for Success in the Second Machine Age
We’re in the middle of a monumental transition—one so rapid and widespread that it will leave many businesses behind, scratching their heads. This certainly isn’t the first time in human history we’ve been here, but the nature of change in the second machine age is different in kind from anything we’ve ever experienced before. Companies that thrive in this new era, says Andrew McAfee, will be those that reorient themselves toward machine, platform, and crowd.Recognize the new technological momentAbout 100 years ago, factories began replacing steam power with electric power. Factory owners agreed that electricity was the future of power.However, most established companies didn’t survive that transition. They underestimated how different electricity was from steam.Most companies simply swapped their steam engine for an electric one. Everything else about how the factory operated stayed the same.Innovative factory owners realized that electricity could be used to reimagine how th...