The activity is called “asks” and “offers”: At any given moment, there is something you need to know, learn or accomplish. Simultaneously, you represent collections of experiences and knowledge that could help someone else achieve his or her goals. The problem is that if you walk into a room of 100 people, the chance that somebody in that room has what you need is 4 percent. And given how social networks function and people interact in crowds, the chance of connecting with any of those four people is the equivalent of hitting the lottery.
With the explosion of digital data and global social knowledge systems, it seems that learning will soon become obsolete. The presumption is that what you need to know can be made available when you need to know it. But more data and information does not equate to more knowledge, and the knowledge that anybody needs to know (the translation of data, information and experience into something that can be learned and used) is still contained, mostly, in the heads of humans.
This means that whom you know (or whose knowledge you can access) still matters. In his book “The Tipping Point,” Malcolm Gladwell describes “connectors” as people who are known for their extensive social networks and their ability to make connections in the “ask and offer” marketplace. But the world has changed since his book was published, and the value that connectors once provided has now become the network’s greatest weakness. Hyper-connected humans cannot manage the asks and offers at digital speeds. Whereas the prior model was phone calls, in-person meetings and letters, the digital era moves at the speeds of email, tweets and LinkedIn requests. Whereas you once depended on humans to learn what you needed to know in order to achieve your goals, the gridlock of a new era has left you with the shortcomings of the internet as the source of all wisdom.
In his book “Too Big to Know,” David Weinberger continually makes the claim that “the smartest person in the room is the room itself.” In the digital age, personal social networks expand knowledge networks instantaneously. This makes the room the smartest person in it. But Weinberger fails to make a critical connection: The “smart room” can make the people in it smarter.
Artificial intelligence, or AI, is already predicted to launch the fourth industrial revolution. As with prior industrial revolutions, new forms of social, education and economic systems are likely to emerge. In that process, people will be displaced as automation, productivity and smart systems create mass extinction events for certain careers while new ones only slowly emerge. But a new possibility, or at least a parallel one, is that instead of taking our jobs, AI is about to make us better at our jobs.
Access to knowledge is the foundation of learning, and networks represent knowledge. By this logic, a larger and more interconnected network means more learning. But humans are lousy at building good social networks. They are limited by their ability to manage a finite number of inputs and outputs. This limitation is where AI plays a role in the future of learning.
Instead of stealing your job and your reason to think, AI can recognize where in social and organizational networks information cannot flow or where super-connected individuals are preventing information from flowing freely. AI can build new connections and bridges for knowledge and learning so that the knowledge landscape becomes more efficient and productive.
In his book “Where Good Ideas Come From,” Steven Johnson explains it this way: “A good idea is a network. A specific constellation of neurons – thousands of them – fire in sync with each other for the first time in your brain, and an idea pops into your consciousness. A new idea is a network of cells exploring the adjacent possibility of connections that they can make in your mind. This is true whether the idea in question is a new way to solve a complex physics problem, or a closing line for a novel, or a feature for a software application. If we’re going to try to explain the mystery of where ideas come from, we’ll have to start by shaking ourselves free of this common misconception: an idea is not a single thing. It is more like a swarm.”
Johnson makes the case that innovation, representing all forms of humanity’s greatest achievements, has been the result of serendipitous network connections.
The future of learning is not less learning but real-time learning, enabled by intelligent digital networking that can manage an infinite number of asks and offers. AI can connect humans in their exact moment of need, with another human who has that specific knowledge, in an instance.
