In Part 2 of the 3 series David Shrier takes us through the value of persistence in developing Cognitive AI capabilities & the future of human-machine hybrid systems.
In the first part of this series, we talked about the importance of cognitive flexibility in the context of digital disruption. The human factor is what can keep humans competitive in the face of AI displacement. We discussed the importance of #1 applied learning as well as #2 reflection.
In this article, we will look at #3, the value of persistence in developing these capabilities, #4 , the importance of learning from each other, and #5, the benefits of creative exploration. We will also peer briefly into the future of human machine hybrid systems.
#3 Sustained and Gradual Change
We live in a global culture that demands instant results. Particularly, as new technologies emerge and as the economic instability of the COVID era disrupts companies and countries, people are expecting more – faster. However, the typical human brain needs to put information together slowly over time.
Thinking in new directions requires not only a fact-based mental shift around a new idea, but also an emotional one, due to our embedded cognitive biases like loss aversion (“I don’t want to give up my old idea”) and the mere exposure principle (“the idea I see every day is more comfortable than a new, unfamiliar idea”).Corporate strategists and organizational consultants talk about ‘mindset shift,’ but what they mean is overcoming a fear of new information and a fear of uncertainty, with a willingness to adjust behavior.
As you train your brain into greater flexibility, you will find that you can assimilate new information more rapidly In the beginning, however, you will be working for small, daily improvements, rather than massive, step-function change. It’s worth keeping track of your progress to motivate your continued effort. If you are learning a new topic or set of ideas, try writing down at the beginning what you hope to learn and how you hope to use this knowledge. At the end of a month, look back (reflect!) and write down again what you learned in that month. It’s important to examine yourself as you do this, to engage in metacognition, and understand where you might be resisting taking in a new idea. Your brain will naturally tend towards inertia, and pushing forward with sustained change will help you acquire the ability to change, which is essential to cognitive flexibility.
#4 Peer Learning
We learn best, as it happens, from each other. Yet our systems of education don’t typically reflect this, for reasons of historical precedent. The contemporary approach to education grew out of an effort in the Middle Ages to mass-produce knowledge, originally in the form of ecclesiastical education (University of Oxford was founded 850 years ago on the site of an older church teaching centre that dated centuries prior). Ironically, Oxford (and Cambridge) are famous for their peer-and-mentor teaching system known as the ‘tutorial’, where students discuss ideas with each other under the guidance of a faculty member. Many of the rest of the world’s 25,000 colleges and universities favor large lecture-style education since this is more efficient for the university (one professor’s salary can scale to hundreds of students). Online learning, particularly in the COVID era, has only exacerbated the problem, opening up the potential for scaling to thousands or tens of thousands of students for that same one professor’s salary. The ‘sage on the stage’ model, however, suffers from the passive learning and cognitive inflexibility we talked about in the section on practice. It’s no wonder that MOOC-style learning from edX and Coursera only has 3% of students completing courses they start. We’ve been able to achieve closer to 99% completion rates with our Esme Learning classes by putting students into small groups and cohorts, carefully architecting their group interactions and learning journeys, and adding an artificial intelligence ‘coach’ to their peer interactions to drive a dramatic and tangible improvement in results.
#5 Creative Exploration
Carl Sagan famously described the loss of inquisitiveness that the modern primary educational system drives. When he would go speak to 5 or 6 year olds, they would fire questions at him like ‘What is the birthday of the world?’ and ‘What is a dream?’ but by the time they reach 17 years old they become “leaden and incurious,” as he puts it. This spirit of mental fearlessness is integral to the act of creative exploration, which in part requires reconnecting with the spirit of curiosity and inventiveness that we all have as young children.
All great innovation comes from some form of creative exploration, which also helps to open new cognitive pathways and make it easier to obtain new knowledge in the future. As a general principle, find ways to introduce play into your day, even if it’s something as innocuous as doodling on a whiteboard while thinking about a problem.
More specifically in the learning and innovation context, see how you can break out of narrow lines of thought.Try different tools and approaches than what you have been using, to frameshift your perspective and open up new ideas. Don’t be afraid to fail – the famous Marshmallow Challenge around teams and collaboration is dominated by kindergarteners (who outperform MBA students dramatically) because they don’t focus on status, or prior work – they dive right in and begin seamlessly experimenting and playing. See how you can inject a bit of play, and failure and experimentation, into your daily problem-solving.
It is the domain of creativity that will remain dominated by human beings for at least several more decades, since it is one area where machine intelligence has yet to achieve breakthroughs.
Finding a New Human-Machine Balance
The principles I’ve outlined here are useful not only in a learning context, but also in your everyday work. They will help you stave off obsolescence as the Fourth Industrial Revolution takes hold, and higher-order tasks are assumed by artificial intelligences. Most importantly, if these are directed against new kinds of human+AI hybrid systems, the tools of cognitive flexibility will make you a much more effective knowledge worker and a more dynamic partner for your AI assistant.
As I was working on my new book, Augmenting Your Career: How to Win at Work in the Age of AI (Little, Brown 2021) I had a chance to play around with GPT-3, a new AI tool from Elon Musk-backed Open AI, and compare it with its predecessor of a year or two prior, GPT-2. The newer system, GPT-3, did a pretty good job of helping me write one of the chapters in my book, which shows me that unless I set up my game, I could be replaceable by AI within the next decade. It’s not there yet – in fact, Artificial General Intelligence (AGI) is still a few years away, and it remains a domain that a well-trained human brain can maintain superiority in, with a little practice.
There is another model of what the future of work, and of AI, would look like. If we can harmonize tools like Siri, GPT-3, and the Google type-ahead function that keeps trying to finish my sentences of this article – and if we can harness all of this machine intelligence in a different way as we learn and as we work – we may be able to unlock a new form of intelligence that lets us achieve things that we can’t conceive of today, to form a better society and shape a better world.
A critical building block of this new future is the human brain, and human ingenuity. The road to harnessing this human intelligence together with machine intelligence is walked by improving on what humans do best, and maximizing our potential for cognitive flexibility. So see where you can play creatively, look for opportunities to practice and reflect, find ways to engage in the daily work to sustain your shift to a more flexible mind, and talk to your peers as you work through problems and pioneer new ideas. And soon, you will find how your AI assistants can also accelerate your journey on this path.
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