Lucidea’s Lens: Knowledge Management Thought Leaders
Part 117 – David Weinberger

David Weinberger is Fellow, Senior Researcher, and member of the Fellows Advisory Board at Harvard’s Berkman Klein Center for Internet & Society. He writes and speaks about how AI is changing how we think about ourselves and our world—from morality to reality itself.  

David was the co-director of the Harvard Library Innovation Lab, focusing on the future of libraries. He is a former writer-in-residence at Google AI groups and the editor of the “Strong Ideas” book series for MIT Press. He writes the “Perspective on Knowledge” column for KMWorld Magazine. He has been a philosophy professor, journalist, strategic marketing consultant to high tech companies, Internet entrepreneur, advisor to several presidential campaigns, and a Franklin Fellow at the US State Department. 

David is an author and speaker about the effect of technology on ideas. He explored the effect of the Internet and AI on knowledge, on how we organize our ideas, and on the core concepts by which we think about our world. His work focuses on how technology—particularly the internet and machine learning— is changing our ideas. He has written books about the effect of machine learning’s complex models on business strategy and sense of meaning; order and organization in the digital age; the networking of knowledge; the Net’s effect on core concepts of self and place, and the shifts in relationships between businesses and their markets.  

Here are definitions for five of David’s specialties: 

• Artificial intelligence (AI): the capacity of a computer to perform operations analogous to learning and decision making in humans, as by an expert system. 

• Knowledge: Plato’s definition: Knowledge is the set of beliefs that are true and that we are justified in believing. Knowledge results from a complex process that is social, goal-driven, contextual, and culturally-bound. We get to knowledge – especially ‘actionable’ knowledge – by having desires and curiosity, through plotting and play, by being wrong more often than right, by talking with others and forming social bonds, by applying methods and then backing away from them, by calculation and serendipity, by rationality and intuition, by institutional processes and social roles. Every single thing we know is part of a larger system of knowledge. If you know the water is boiling in your tea kettle because you hear its whistle, then you also know that water is a liquid, flames heat things, things can transmit heat to other things, and so on, until your entire knowledge framework has been drawn in. 

• Machine learning (ML): giving computers the ability to learn without being explicitly programmed; a method of data analysis that automates analytical model building; using algorithms that iteratively learn from data to find hidden insights without being explicitly programmed where to look. 

• Networked Knowledge: The value of a web of ideas comes from the differences among the participants in that web. If everybody’s saying the same thing, there’s negative value in networking them. Knowledge contains difference, rather than knowledge being that from which all disagreement has been driven, that which has been settled once and for all. Knowledge exists in networks that contain disagreement and difference. 

• Understanding: seeing things connected into larger contexts. Understanding has degrees: you can understand a little about something or understand a lot. whereas knowledge is generally binary: you know, or you don’t know. Knowing without understanding is useless. Understanding is the goal of knowing. Understanding is an enhancement of knowledge: knowledge + connection. 

David created the following content. I have curated it to represent his contributions to the field. 

Books by David Weinberger

 

AI Has a Revolutionary Ability to Parse Details. What Does That Mean for Business? with Michele Zanini 

AI as an idea is making the world visible to us in its ever-changing specificity and details: an overwhelming riot of particulars, each related to all else in a landscape of creative chaos and emergence, finding patterns beyond our comprehension. In short, it’s a world in which everything is an exception. 

Our economic growth will spring from this new ability to engage with the particulars — the specific differences among people, things, and situations (despite its new challenges to values such as fairness, transparency, and autonomy). Leaders who are aware of this change are best positioned to harness the possibilities. 

As a philosophically-trained writer about technology, and as a researcher and advisor on innovative management practices, we’re already seeing these patterns across the business world. Here are four quick examples. 

Strategy 

Much of the value of high-level strategies comes from their being constants in a changing world. A strategy looks ahead a year, five years, even 10 years, and formulates a top-down vision that can be so grand only because it is so vague. 

A long-term strategy is thus primed to miss the small signals — the particulars — that foretell a change in the landscape of emerging risks and opportunities. This includes the flapping of butterfly wings, most of which are irrelevant, but some of which are instances of the Butterfly Effect, ready to set off a cascade that can result in a tornado aimed at your business. 

That’s why if businesses are going to heed thinkers such as Rita McGrath who implores them to look for “transient advantage,” they need to be hyper-alert to the weak signals that portend strong changes. That’s right in AI’s wheelhouse as manifested by companies such as Zignal Labs and Dataminr that generate billions of data points from their daily scans of thousands of sources, listening for the tornadoes foreshadowed in gently beating wings. 

That’s some of what AI can do as a tool. As an idea, it wakes us up to the idea of those wing flaps and sends us looking for them. We see this already in companies that empower the expertise distributed throughout their ranks. An always-on, real-time conversational space open to all (in particular those at the edges of the organization) can enable people to talk about what’s interesting to them, such as an incipient trend and how it might affect the industry. This platform might be the first place to note small signals, collaboratively make sense of them, and potentially usher in a strategic shift. 

This is changing the sense of where the knowledge resides in the organization, from a cluster of designated experts, to something akin to a metaphorical “neural network” of transient conversations, many (though not all) initiated and put together by AI that’s connecting a wide and diverse set of people across every layer of the hierarchy. Some of the most valuable insights might well emerge when the system uncovers strong agreement — or constructive disagreement — between people in functions that often don’t see eye to eye, such as sales and R&D, or finance and HR.  

Talent management 

In traditional talent management, people are recruited based on their credentials, prior experience, and other visible tokens of their skills. Once hired, the development of these employees is typically guided by generic competency models that are similar across companies within the same industry. The outcome: a one-size-fits-all view of people’s capabilities and interests.  

AI can change that. It can uncover important skills that might not fit into the established checkboxes, such as handling exceptions well or being open to criticism. From this it can connect people to work together on a project, form internal interest groups, or strengthen and expand social bonds.  

For example, the Guider learning and development platform matches mentees and mentors using AI that considers over 100 different factors based on users’ behaviors, rather than asking them to check off a handful of skills they’re interested in. It also is able to give detailed — specific! — reports that can guide the mentor-mentee interactions and relationship. 

In the light of the idea of AI, even bigger changes are in store. Rather than thinking about our careers in terms of jobs with formal descriptions that draw on traditional categories, we might begin to think of ourselves as AI sees us: unique bundles of capabilities and interests, ready to participate in opportunities we could not foresee.  

Leadership roles 

A January 2023 paper by faculty at the Center for Strategic Leadership at the U.S Army War College predicts that AI will “directly influence the organizational structure of militaries.” For example, AI could “identify the soldier with the best situational awareness, put him or her in charge of the unit, and assign the rest of the team to supporting roles.” 

If this is how authority should move in direct response to the particularities of a life-and-death situation, why not in less-fraught circumstances? 

Indeed, perhaps AI will put together emergent hierarchies — dynamic and situation-relevant — that enable power to fluidly move to the people who can add unique value in a specific situation, irrespective of their formal credentials or position in the chain of command. Perhaps this is a way in which AI — rightly criticized for its tendency toward bias — could be used to empower people who don’t look or act according to the traditional norms of leadership. 

Supply chain management 

Through the 1990s, supply chains — however complex — still had to be simple enough to be understood and governed by human brains. Now AI as a tool is already beginning to transform container terminal operations, logistics workflow, inventory replenishment, demand forecasting, routing, and just about every other element of supply chain management by taking in vast amounts of data and re-coordinating logistics in real time. 

For example, Haier, one of the the world’s largest appliance makers, uses an internet platform, COSMOPlat, to connect its millions of customers and tens of thousands of vendors around the globe. COSMOPlat interprets hundreds of thousands of customer inputs, which are quickly translated into design specs. These are then bid out to its vast supplier network. COSMOPlat can make unexpected connections, such as tapping a refrigerator insulation specialist to create a vibration-reducing material for washing machines. COSMOPlat even integrates its member companies’ networks to manage distribution and logistics, based on their strengths in each territory. 

AI as an idea is thus letting us see supply chains as what they have always been: spontaneously coordinated loose networks of suppliers and partners. That affects how the whole business should be run in ways that go far beyond just which bots are managing its supply chain. Or, as Zhang Ruimin, the founder of Haier, has put it, the company should become a self-adaptive “rainforest”: complex, emergent, wildly interdependent, and in the service of the particulars of every situation.