Michael Jacob

Gopal P. Sarma, Sunny D. Bhatt, Michael Jacob et al.

In this report, we review the current state of methodologies to forecast the arrival of artificial general intelligence, assess their reliability, and analyze the implications for strategy and policy. We synthesize diverse forecasting approaches, document significant limitations in existing methods, and propose a research agenda for developing more-robust forecasting infrastructure. The report does not endorse a specific forecast or scenario but rather provides a framework for interpreting forecasts under conditions of deep uncertainty. We experimented with an iterative approach to human and artificial intelligence collaboration for this report. The primary drafting of the text was performed by large language models (GPT 5.1, Gemini 3 Pro, and Claude 4.5 Opus), with human researchers providing direction, peer review, fact-checking, and revision.

Parham Pourdavood, Michael Jacob, Terrence Deacon

This paper proposes a novel conceptualization of Large Language Models (LLMs) as externalized informational substrates that function analogously to DNA for human cultural dynamics. Rather than viewing LLMs as either autonomous intelligence or mere programmed mimicry, we argue they serve a broader role as repositories that preserve compressed patterns of human symbolic expression--"fossils" of meaningful dynamics that retain relational residues without their original living contexts. Crucially, these compressed patterns only become meaningful through human reinterpretation, creating a recursive feedback loop where they can be recombined and cycle back to ultimately catalyze human creative processes. Through analysis of four universal features--compression, decompression, externalization, and recursion--we demonstrate that just as DNA emerged as a compressed and externalized medium for preserving useful cellular dynamics without containing explicit reference to goal-directed physical processes, LLMs preserve useful regularities of human culture without containing understanding of embodied human experience. Therefore, we argue that LLMs' significance lies not in rivaling human intelligence, but in providing humanity a tool for self-reflection and playful hypothesis-generation in a low-stakes, simulated environment. This framework positions LLMs as tools for cultural evolvability, enabling humanity to generate novel hypotheses about itself while maintaining the human interpretation necessary to ground these hypotheses in ongoing human aesthetics and norms.

Michael Jacob, Zack Zheng

In situ self-report is widely used in human-computer interaction, ubiquitous computing, and for assessment and intervention in health and wellness. Unfortunately, it remains limited by high burdens. We examine unlock journaling as an alternative. Specifically, we build upon recent work to introduce single slide unlock journaling gestures appropriate for health and wellness measures. We then present the first field study comparing unlock journaling with traditional diaries and notification based reminders in self report of health and wellness measures. We find unlock journaling is less intrusive than reminders, dramatically improves frequency of journaling, and can provide equal or better timeliness. Where appropriate to broader design needs, unlock journaling is thus an overall promising method for in situ self report.