Jeff Shrager

Tyler Perini, Amy Langville, Glenn Kramer et al. · stanford

The problem of interpreting or aggregating multiple rankings is common to many real-world applications. Perhaps the simplest and most common approach is a weighted rank aggregation, wherein a (convex) weight is applied to each input ranking and then ordered. This paper describes a new tool for visualizing and displaying ranking information for the weighted rank aggregation method. Traditionally, the aim of rank aggregation is to summarize the information from the input rankings and provide one final ranking that hopefully represents a more accurate or truthful result than any one input ranking. While such an aggregated ranking is, and clearly has been, useful to many applications, it also obscures information. In this paper, we show the wealth of information that is available for the weighted rank aggregation problem due to its structure. We apply weight set decomposition to the set of convex multipliers, study the properties useful for understanding this decomposition, and visualize the indifference regions. This methodology reveals information--that is otherwise collapsed by the aggregated ranking--into a useful, interpretable, and intuitive decision support tool. Included are multiple illustrative examples, along with heuristic and exact algorithms for computing the weight set decomposition.

Jeff Shrager · stanford

The Logic Theorist (LT), created by Allen Newell, J. C. Shaw, and Herbert Simon in 1955-1956, is widely regarded as the first artificial intelligence program. While the original conceptual model was described in 1956, it underwent several iterations as the underlying Information Processing Language (IPL) evolved. Here I describe the construction of a new IPL-V interpreter, written in Common Lisp, and the faithful reanimation of the Logic Theorist from code transcribed directly from Stefferud's 1963 RAND technical report. Stefferud's version represents a pedagogical re-coding of the original heuristic logic into the standardized IPL-V. The reanimated LT successfully proves 16 of 23 attempted theorems from Chapter 2 of Principia Mathematica, results that are historically consistent with the original system's behavior within its search limits. To the author's knowledge, this is the first successful execution of the original Logic Theorist code in over half a century.

Rupert Lane, Anthony Hay, Arthur Schwarz et al.

ELIZA, created by Joseph Weizenbaum at MIT in the early 1960s, is usually considered the world's first chatbot. It was developed in MAD-SLIP on MIT's CTSS, the world's first time-sharing system, on an IBM 7094. We discovered an original ELIZA printout in Prof. Weizenbaum's archives at MIT, including an early version of the famous DOCTOR script, a nearly complete version of the MAD-SLIP code, and various support functions in MAD and FAP. Here we describe the reanimation of this original ELIZA on a restored CTSS, itself running on an emulated IBM 7094. The entire stack is open source, so that any user of a unix-like OS can run the world's first chatbot on the world's first time-sharing system.

Roussel Rahman, Jeff Shrager

Strategy Choice Theory (SCT)\footnote{``Strategy Choice Theory'', ``Distributions of Associations'', and ``Overlapping Wave Theory'' have been used to refer to this line of work, emphasizing different aspects.}\citep[e.g.,][]{siegler1984strategychoices, siegler2000rebirth} explains important aspects of children's arithmetic learning based upon principles including learning from developmentally naturalistic data, probabilistic representation, confidence-based retrieval, and the phase-like importance of scaffolding strategies, such as finger-counting. Here we recast SCT as a ``Small Math Model'' (SMM), employing a neural-network-based architecture analogous to LLMs. The SMM extends SCT to include counting practice\footnote{The original SCT model was pre-biased in accordance with the supposed experience of counting.}, symbol (number) embedding, and gated attention. Similar to earlier work, the SMM demonstrates constructive and destructive interference between counting and addition, and the ``wave-like'' use of finger-counting as sum recall improves. We plan to extend the SMM to later aspects of the decades-long SCT program, including adaptive strategy choice and eventually strategy discovery, providing a unified platform to investigate the understanding of numerical characteristics and relationships essential for mathematical reasoning -- as it can emerge in LLM-based agents.

Jeff Shrager

ELIZA, often considered the world's first chatbot, was written by Joseph Weizenbaum in the early 1960s. Weizenbaum did not intend to invent the chatbot, but rather to build a platform for research into human-machine conversation and the important cognitive processes of interpretation and misinterpretation. His purpose was obscured by ELIZA's fame, resulting in large part from the fortuitous timing of it's creation, and it's escape into the wild. In this paper I provide a rich historical context for ELIZA's creation, demonstrating that ELIZA arose from the intersection of some of the central threads in the technical history of AI. I also briefly discuss how ELIZA escaped into the world, and how its accidental escape, along with several coincidental turns of the programming language screws, led both to the misapprehension that ELIZA was intended as a chatbot, and to the loss of the original ELIZA to history for over 50 years.

Jeff Shrager

A demandance is a psychological "pull" exerted by a stimulus. It is closely related to the theory of "affordance". I introduce the theory of demandance, offer some motivating examples, briefly explore its psychological basis, and examine some implications of the theory. I exemplify some of the positive and negative implications of demandances for design, with special attention to young children and the design of educational products and practices. I suggest that demandance offers an approach to one of the persistent mysteries of the theory of affordance, specifically: Given that there may be many affordances in any particular setting, how do we choose which to actually act upon?

Jeff Shrager

Adaptive trials are now mainstream science. Recently, researchers have taken the adaptive trial concept to its natural conclusion, proposing what we call "Global Cumulative Treatment Analysis" (GCTA). Similar to the adaptive trial, decision making and data collection and analysis in the GCTA are continuous and integrated, and treatments are ranked in accord with the statistics of this information, combined with what offers the most information gain. Where GCTA differs from an adaptive trial, or, for that matter, from any trial design, is that all patients are implicitly participants in the GCTA process, regardless of whether they are formally enrolled in a trial. This paper discusses some of the theoretical and practical issues that arise in the design of a GCTA, along with some preliminary thoughts on how they might be approached.