“Automation v Augmentation: the two forms of technology atwork”
Henri Stephanou
Université Paris Panthéon-Sorbonne, IHPSTIn the past ten years, the rapid progress of AI has reignited the debate on thepotential of technology to automate full spans of business activities and its direconsequences on employment. This possibility has been demonstrated in many
physical places, such as data centers, warehouses, and factories. However, experts
are more nuanced. Full automation has its own, daunting, challenges, and somerather claim that the future of ``smart industry'' lies in increased agility enabled
by a new alliance between human and machine, sealed by AI. This debate raises
the question of what is automation in the first place, a term which is notoriously
difficult to define.
In this talk, we will consider a few examples of automation over time, startingwith Jacquard's loom in the early 19th century to fully automated warehouses atthe end of the 20th century, in order to show that automation should not be defined
relatively to the human tasks which they replace, but relatively to the problems
they solve. Automation is a specific way to solve a problem through the operation ofa machine. It requires the problem to be solved /systematically/, a term which wewill explicate in detail.
Automation has however strong conditions, as it requires the problematic
situation itself to behave systematically. This is why it works best in closed,
carefully designed environments such as a factory. Open situations, such as alawsuit, are until now the domain of traditional human problem solving. In thatcase, the human agent keeps the central stage in solving the problem at stake, andtechnology is only here to facilitate her performance of some tasks, -- for example alegal database search -- i.e. to /augment/ her. /Automation/ and /augmentation/ arethe two basic modes by which machinery can be put to work, because they arebased on two fundamentally different approaches to problem solving.
However what we believed were open situations have been regularly redefined to
become systematic ones. How can this happen? Our thesis is that, whereas typicalmachines operate through rigid mechanisms that can only solve a narrow set ofproblems,computers

havemechanisms

whichareeasilyreconfigurable(``programmable''). The progress of computability theory and software design has
made machines increasingly /flexible/ to solve ever larger classes of problems --from the rigid mechanisms of the industrial revolution to algorithmic behaviour,then to concurrent interaction, and now to adaptive, inductive ``learning''.
Is there a limit to this trend? AI is about the automation of problem-solving
itself ,and therefore it could in theory expand vastly the scope of automation.
However, AI is today mostly presented as an augmentation technology (a``co-pilot"), and not an automation one. We suggest three reasons for thislimitation : First, the need of AI to be complemented by traditional computational
tools (such as compilers), which will still face the same traditional difficulties.
Second, the data collection cost for achieving systematicity, which will make it inmany cases unprofitable. Third, the fact the human meanings evolve over time.
Language is a chaotic system which evolution cannot be predicted, because it
reflects the evolving and intersecting conversations, actions and practices ofcommunities of people.