The simple truth is that there are no simple truths!
The soft sciences are the hard ones, and the hard sciences are the easy ones!
Our Team has Found the True Original ELIZA!
Adam Gordon Bell, the host of [CoRecursive], interviewed me about the discovery of the original ELIZA for [a recent episode]! Minor errors non-withstanding Adam did an amazing job of turning what was basically a 2-hour ramble into a really interesting mystery story. [There's an errata on the ElizaGen.org page about the original ELIZA]
Recent Interesting Publications and Presentations:
→ Perini, T, et al. (2023) Weight Set Decomposition For Weighted Rank Aggregation: An Interpretable And Visual Decision Support Tool. Foundations of Data Science
→ Wasserman, A, et al. (in press) Virtual Trials: Causally-validated treatment effects efficiently learned from an observational brain cancer registry.
In Press in AI in Medicine (https://www.medrxiv.org/content/10.1101/2021.06.12.21258409v3)
→ Shrager, J, (2021) Practice Make Better: A Classroom Investigation of Practice Effects. J. College Science Teaching, 50(3), 17-22
→ Shrager, J, Shapiro, M, Hoos, W (2019) Is Cancer Solvable? Towards Efficient and Ethical Biomedical Science.
J Law Med and Ethics, 47 (2019): 362-368. DOI: 10.1177/1073110519876164
→ ELIZA in BASIC; Ch. 4 in Stefan Holtgen and Marianna Baranovska (Eds.)
Hello, I'm Eliza. http://www.computerarchaeologie.de
→ The Scientist as User; presentation in the User-Focused Beam Line Control and Monitoring
for X-ray Science workshop in the 2018 SSRL/LCLS Users' Meeting
→ Prototyping a precision oncology 3.0 rapid learning platform; Connor Sweetnam, et al.
BMC Bioinformatics, 2018, 19:341
→ A TedEd-Style video I explaining GCTA (Global Cumulative Treatment Analysis)
→ Molecular Tumor Boards: What they are; What they do; What they need.
(A video of a talk I gave at Microsoft Research Feb. 2015)
→ Rapid Learning for Precision Oncology (Nature Reviews Clinical Oncology, 2014)
→ Theoretical Issues for Global Cumulative Treatment Analysis (GCTA). (arXiv:1308.106)
→ Cancer: A computational disease that AI can cure (AI Magazine 2011)
→ The journey from child to scientist: Integrating cognitive development and the education sciences (APA Press 2012)
→ BioBIKE: A Web-based, programmable, integrated biological knowledge base (Nucleic Acids Research 2009)
Other Interesting or Useful Links:
→ Mnemotheque
→ ElizaGen -- The Eliza Genealogy Project
→ My Public Github Account
→ Gorilla Science
→ Diary of an Insane Cell Mechanic
Professional Bio:
Dr. Jeff Shrager is a CommerceNet Fellow and Adjunct Professor in the Symbolic Systems Program at Stanford. He holds BSE, MSE, and PhD degrees in Computer Science, Cognitive and Developmental NeuroPsychology from The University of Pennsylvania and Carnegie Mellon University, and has conducted post doctoral work in Cotnitive Neurosciene and Molecular Biology at the University of Pittsburgh Learning Research and Development Center, and the Carnegie Inst. of Washington, Dept. of Plant Biology. His work spans Artificial Intelligence and the Cognitive Sciences, including cognitive and developmental neuroscience, formal and informal science and math education, scientific computing, human learning and brain development, artificial intelligence, machine learning, molecular, microbial, and marine biology and genomics, bioinformatics, and graph dynamics. Dr. Shrager's current work focuses on how science works and how scientists think, and on building intelligent tools, agents, models, and platforms to support and improve scientific reasoning, and other aspects of the scientific process. He has authored, or co-authored, over one hundred peer-reviewed papers, and three books, and has co-founded two successful AI-based biomedical companies, one in drug discovery robotics and another in cancer informatics. Most recently he was co-founder, CTO, and Director of Engineering and Research of xCures, his third AI-based biomedical startup.
Extended research summary:
My work focuses primarily on how science works and how scientists think, and on building intelligent tools, agents, models, and infrastructure to support and improve scientific reasoning, and other aspects of the scientific process. Symbolic and sub-symbolic computation must co-operate to support flexible, robust learning and cognition. Symbolic-level "complex" learning and reasoning is often depicted by, for example, "book learning", the pinnacle of which is taken to be philosophical or scientific reasoning and discovery. Meanwhile, sub-symbolic "sensory-motor" or "perceptual" learning is depicted by, for example, learning to walk, cook, or drive. Through computational modeling, as well as laboratory and field research, I study how symbolic and the sub-symbolic/sensory-motor/perceptual computation co-operate in enabling flexible and robust learning and cognition in many areas. Especially interesting is early child development, during which period the brain is becoming organized, and the child is embedded in a rich cognitive and sensory-motor scaffold. My computational work in this area has produced several influential models of brain self-organization, as well as of how high level reason interacts with, and indeed relies upon, the sub-symbolic cognitive infrastructure, while, at the same time, the organization of the sub-symbolic sensory-motor systems is guided by higher level activity. Three projects stand out in triangulating my contributions in these areas: 1. My model (with David Klahr) of "instructionless learning" based on a process, called "commonsense perception", which combines symbolic and "perceptual" reasoning; 2. My model (with Mark Johnson) of cortical parcellation, which explains how the brain obtains its functional architecture, and which was a precursor to later "deep learning" architectures; and 3. My model (with Bob Sielger) of the development of arithmetic knowledge and strategic skill, which has been widely influential upon a generation of developmental modelers, as well as in educational science, and which my colleagues and I continue to evolve to encompass recent findings in Systems Neuroscience. I also apply my work to real-world, science, especially in biocomputing: I co-founded, and served as CTO and engineering lead for, two (slightly) successful scientific biocomputing companies, as well as having envisioned, created, and led the team that developed BioBike and BioDeducta, decade-long NASA and NSF-funded projects that built the world's first cloud-based "intelligent" scientific computing engine (a precursor to Wolfram Alpha). I have co-authored nearly a hundred peer-reviewed papers in areas such as machine learning, graph theory, developmental psychology, computational psychology, drug discovery, molecular biology, computational biology, privacy and computer security, and even in the philosophy of science.