and persistence that underpin it (Zurn and Shankar 2020).
Two different ways of thinking about how to advance have long existed. One line of thought traces its lineage to the ancient fascination with automata and, more generally, to the smooth functioning of the machines that have fuelled technological revolutions, with their automated production lines devoted to increasing efficiency and lowering costs. This is where all the promises of automation enter, couched in wild technological dreams and imaginaries. Deep Learning algorithms will continue to equip computers with a statistical ‘understanding’ of language and thus expand their ‘reasoning’ capacity. There is confidence among AI practitioners that work on ethical AI is progressing well. The tacit assumption is that the dark side of digital technologies and all the hitherto unresolved problems will also be sorted out by an ultimate problem-solving intelligence, a kind of far-sighted, benign Leviathan fit to manage our worries and steer us through the conflicts and challenges facing humanity in the twenty-first century.
The other line of thinking insists that theoretical understanding is necessary and urgent, not only for mathematicians and computational scientists, but also for developing tools to assess the performance and output quality of Deep Learning algorithms and to optimize their training. This requires the courage to approach the difficult questions of ‘why’ and ‘how’, and to acknowledge both the uses and the limitations of AI. Since algorithms have huge implications for humans it will be important to make them fair and to align them with human values. If we can confidently predict that algorithms will shape the future, the question as to which kinds of algorithms will do the shaping is currently still open (Wigderson 2019).
Understanding also includes the expectation that we can learn how things work. If an AI system claims to solve problems at least as well as a human, then there is no reason not to expect and demand transparency and accountability from it. In practice, we are far from receiving satisfactory answers as to how the inner representations of AI work in sufficient detail, let alone an answer to the question of cause and effect. The awareness begins to sink in that we are about to lose something connected to what makes us human, as difficult to pin down as it is. Maybe the time has come to admit that we are not in control of everything, to humbly concede that our tenuous and risky journey of co-evolution with the machines we have built will be more fecund if we renew our attempt to understand our shared humanity and how we might live together better. We have to continue our exploration of living forward while trying to understand Life backwards and linking the two. Prediction will then no longer only map the trajectories of living forward for us, but will become an integral part of understanding how to live forward. Rather than foretelling what will happen, it will help us understand why things happen.
After all, what makes us human is our unique ability to ask the question: Why do things happen – why and how?
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