In the footsteps of robots replacing workmen, deep learning bots look to boot out knowledge workers overwhelmed by muddy data.
Faced with that perspective, should humans try to learn deeper and faster than clones, or should they learn from octopuses and their smart hands.
Machine Learning & The Economics of Clones
As illustrated by scan-reading AI machines, the spreading of learning AI technology in every nook and cranny introduces something like an exponential multiplier: compared to the power-loom of the Industrial Revolution which substituted machines for workers, deep learning is substituting replicators for machines; and contrary to power looms, there is no physical limitation on the number of smart clones that can be deployed. So, however fast and deep humans can learn, clones are much too prolific: it’s a no-win situation. To get out of that conundrum humans have to put their hand on a competitive edge, e.g some kind of knowledge that cannot be cloned.
Knowledge & Competition
Appraising humans learning sway over machines, one can take from Spinoza’s categories of knowledge with regard to sources:
- Senses (views, sounds, smells, touches) or beliefs (as nurtured by the supposed common “sense”). Artificial sensors can compete with human ones, and smart machines are much better if prejudiced beliefs are put into the equation.
- Reasoning, i.e the mental processing of symbolic representations. As demonstrated by AlphaGo, machines are bound to fast extend their competitive edge.
- Philosophy which is by essence meant to bring together perceptions, intuitions, and symbolic representations. That’s where human intelligence could beat its artificial cousin which is clueless when purposes are needed.
That assessment is bore out by evolution: the absolute dominance established by humans over other animal species comes from their use of knowledge, which can be summarized as:
- Use of symbolic representations.
- Ability to formulate and exchange representations of contexts, concerns, and policies.
- Ability to agree on stakes and cooperate on policies.
On that basis, the third dimension, i.e the use of symbolic knowledge to cooperate on non-zero-sum endeavors, can be used to draw the demarcation line between human and artificial intelligence:
- Paths and paces of pursuits as part and parcel of the knowledge itself. The fact that both are mostly obviated by search engines gives humans some edge.
- Operational knowledge is best understood as information put to use, and must include concerns and decision-making. But smart bots’ ubiquity and capabilities often sap information traceability and decisions transparency, which makes room for humans to prevail.
So humans can find a clear competitive edge in this knowledge dimension because it relies on a combination of experience and thinking and is therefore hard to clone. Organizations should make sure that’s where smart systems take back and humans take up.
Organization & Innovation
Innovation being at the root of competitive edge, understanding the role played by smart systems is a key success factor; that is to be defined by organization.
As epitomized by Henry Ford, industrial-era thinking associated innovation with top-down management and the specialization of execution:
- At execution level manual tasks were to be fragmented and specialized.
- At management level analysis and decision-making were to be centralized and abstracted.
That organizational paradigm puts a double restraint on innovation:
- On execution side the fragmentation of manual tasks prevents workers from effectively assessing and improving their performances.
- On management side knowledge is kept in conceptual boxes and bereft of feedback from actual uses.
That railing between smart brains and dumb hands may have worked well enough for manufacturing processes limited to material flows and subject to circumscribed and predictable technological changes. It didn’t last.
First, as such hierarchies necessarily grow with processes complexity, overheads and rigidity force repeated pruning. Then, flat hierarchies are of limited use when information flows are to be combined with material ones, so enterprises have to start with matrix organization. Finally, with the seamless integration of digital and material flows, perpetuating the traditional line between management and execution is bound to hamstring innovation:
- Smart tools may be able to perform a wide range of physical tasks without human supervision, but the core of innovation core as well as its front lines are where human and machines collaborate in processing a mix of material and information flows, both learning from the experience.
- Hierarchies and centralized decision-making are being cut out from feeders when set in networked business environments colonized by smart bots on both sides of corporate boundaries.
Not surprisingly, these innovation trends seem to tally with the social dimension of knowledge.
Learning from the Octopus
The AI revolution has already broken all historical records of footprint (everything is affected) and speed (a matter of years). Given the length of human education cycles, appraising the consequences comes with some urgency, beginning with the disposal of two entrenched beliefs:
- Knowledge should not be dealt with as a single and homogeneous corpus: by focusing on collective knowledge and decision-making humans can take the lead on cloned brains, whatever their learning capability.
- Cognitive and manual skills should not be considered separately: by combining actual experience, cooperation, and knowledge nurture, humans can remain the primary source of innovation.
At individual level the new paradigm could be compared to the nervous system of octopuses: each arm gets its brain and neurons, and so its own touch of knowledge and taste of decision-making.
On a broader (i.e enterprise) perspective, knowledge should be supported by two organizational layers, one direct and innovation-driven between trusted co-workers, the other networked and knowledge-driven between remote workers, trusted or otherwise.
- Ontologies & Enterprise Architecture
- Ontologies as Productive Assets
- New Year: 2016 is the One to Learn
- New Year: Regrets or Expectations ?
- Knowledge Architecture
- Abstractions & Emerging Architectures
- Agile Collaboration & Social Creativity
- The Finger & the Moon: Fiddling with Definitions
- Reinventing the wheel
- AlphaGo: From Intuitive Learning to Holistic Knowledge
- AlphaGo & Non-Zero-Sum Contests
- Operational Intelligence & Decision Making
- “Why scan-reading artificial intelligence is bad news for radiologists”, The Economist
- “The Mind of an Octopus”, Scientific American
- Yuval Noah Harari, “Sapiens: A Brief History of Humankind”
- Yuval Noah Harari, “Homo Deus: A Brief History of Tomorrow“