The text deals with the emergence of a specific Jewish counterculture in the wake of the civil rights movement and the anti-war movement in the USA in the late 1960s and early 1970s. Martin Buber’s philosophy of dialogue, Erich Fromm’s humanism and the religious existentialism of Abraham Joshua Heschel had a significant impact on the ideas of the young Jewish generation and influenced their striving for a renewal of Jewish life. Although the three Jewish thinkers differed in their forms of (...) expression, they had in common a relentless critique of existing state of affairs and fought for a just society. Whether it be Buber’s communitarian society, Fromm’s humanist socialism or Heschel’s free society imbued with religion – they offered answers to the frustrated young generation and pointed the way to the “spiritual revolution” they longed for. Thus they became prophets for them. (shrink)

Artificial social intelligence (ASI) agents have great potential to aid the success of individuals, human–human teams, and human–artificial intelligence teams. To develop helpful ASI agents, we created an urban search and rescue task environment in Minecraft to evaluate ASI agents’ ability to infer participants’ knowledge training conditions and predict participants’ next victim type to be rescued. We evaluated ASI agents’ capabilities in three ways: (a) comparison to ground truth—the actual knowledge training condition and participant actions; (b) comparison among different ASI (...) agents; and (c) comparison to a human observer criterion, whose accuracy served as a reference point. The human observers and the ASI agents used video data and timestamped event messages from the testbed, respectively, to make inferences about the same participants and topic (knowledge training condition) and the same instances of participant actions (rescue of victims). Overall, ASI agents performed better than human observers in inferring knowledge training conditions and predicting actions. Refining the human criterion can guide the design and evaluation of ASI agents for complex task environments and team composition. (shrink)