Developmental AI is a bootstrapping approach where embodied AIs start with innate competences and learn by interacting with the world. They develop abilities in small steps along a bio-inspired trajectory. However, developmental AIs have not yet reached the abilities of young children. In contrast, mainstream approaches for creating AIs have led to valuable AI systems and impressive feats. These approaches include deep learning and generative approaches (e.g., large language models) and manually constructed symbolic approaches. Manually constructed AIs are brittle even in circumscribed domains. Generative AIs are helpful on average, but they can make strange mistakes and not notice them. They sometimes lack common sense and social alignment. This position paper lays out prospects, gaps, and challenges for augmenting AI mainstream approaches with developmental AI. The ambition is to create data-rich experientially based foundation models and human-compatible, resilient, and trustworthy AIs. This research aims to produce AIs that learn to communicate, establish common ground, read critically, consider the provenance of information, test hypotheses, and collaborate. A virtuous multidisciplinary research cycle has led to developmental AIs with capabilities for multimodal perception, object recognition, and manipulation. Computational models for hierarchical planning, abstraction discovery, curiosity, and language acquisition exist but need to be adapted to an embodied learning approach. They need to bridge competence gaps involving nonverbal communication, speech, reading, and writing. Aspirationally, developmental AIs would learn, share what they learn, and collaborate to achieve high standards. The approach would make the creation of AIs more democratic, enabling more people to train, test, build on, and replicate AIs.

This is an integrative review that address the question, "What makes for a good explanation?" with reference to AI systems. Pertinent literatures are vast. Thus, this review is necessarily selective. That said, most of the key concepts and issues are expressed in this Report. The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems). The Report expresses the explainability issues and challenges in modern AI, and presents capsule views of the leading psychological theories of explanation. Certain articles stand out by virtue of their particular relevance to XAI, and their methods, results, and key points are highlighted. It is recommended that AI/XAI researchers be encouraged to include in their research reports fuller details on their empirical or experimental methods, in the fashion of experimental psychology research reports: details on Participants, Instructions, Procedures, Tasks, Dependent Variables (operational definitions of the measures and metrics), Independent Variables (conditions), and Control Conditions.

The Brain Makers: Genius, Ego, and Greed in the Quest for Machines That Think, Harvey P. Newquist, Sams Publishing, Indianapolis, Indiana, 1994, 488 pp., $24.95, ISBN 0-672-30412-0. Newquist is a business reporter who covered the field during the 1980s when academic researchers went commercial in one of the 1980's smaller speculative bubbles. His book begins with a history spanning Babbage to Turing to Minsky, McCarthy, Newell, Simon, Samuel, and others at the 1956 Dartmouth meeting and moves on to the 1980s, where the real story begins. Good, if glib, descriptions of people, places, and events are punctuated by technical explanations ranging from poor to inane. Because I am a little slow, it took me a quarter of the book to recognize a journalist with an attitude.

Reports from some of these first projects make up the majority of the book, with the balance of the book providing an overview of neuroinformatics. The book's foreword provides interesting history and perspective on the incubation of neuroinformatics. The preface and first two chapters of the book explain neuroinformatics and the motivation for it. As with so many other fields, there has been an information explosion in neuroscience research. Data are produced by tens of thousands of investigators in hundreds of journals.

In Greed in the Quest for Machines That the first wave of AI businesses were addition, when expert systems began Think, Harvey P. Newquist, Sams Publishing, researchers, sneering dominates over to be written in The author's aversion to places away they could implement their applications from the executive suite distorts the in house at a lower cost. Gold Hill, and other took root as an academic companies founded by Ed Feigenbaum. Inc., marketing a symbolic mathematics because pioneering companies making who covered the field during the small assembly robots and industrial program that was once a 1980s when academic researchers vision systems failed just as the robots minor product. Teknowledge was went commercial in one of the 1980's became essential to manufacturing reduced to a small division. Alan Newell's world-leading of traditional companies now use AI begins with a history spanning Babbage but unmarketed reasoning program techniques in house for such things to Turing to Minsky, McCarthy, research at Carnegie Mellon University, as geological exploration, financial Newell, Simon, Samuel, and others at conducted vigorously through the decision making, medical advice, factory the 1956 Dartmouth meeting and 1980s, is dismissed.

To more precisely evaluate the implications of a neuronal model.

To more precisely evaluate the implications of a neuronal model.

Reifsnider A number of learning models have recently been proposed which involve calculations of temporal differences (or derivatives in continuous-time models).