Bibliographic record
Abstract
A topic of central interest to the autonomic computing community is how to manage computing or other self-adaptive systems in dynamic environments.In years past, I had advocated an approach in which highlevel goals are expressed in the form of utility functions, and optimization and/or feedback control techniques are used in conjunction with system models to adjust resources and tuning parameters to maximize utility.After outlining and illustrating the general concepts behind this idea, I will point out a significant flaw that the autonomic computing community (including myself) had ignored historically.Then, I will introduce my recent work on embodied Artificial Intelligence agents, which are somewhat like Alexa, Siri, and other voice-driven assistants, with two major differences.First, they are designed to operate in the business realm, where they assist humans with data analysis and decision making.Second, they interact with people multi-modally, using speech in conjunction with non-verbal modalities like pointing and facial expression.In the final third of my talk, I will explain why I believe embodied AI agents can solve the fundamental flaw of utility-based autonomic computing and speculate about how autonomic computing can contribute to the growth of embodied AI, especially given recent AI advances such as ChatGPT. BioJeffrey O. Kephart is a distinguished research scientist who currently leads research on embodied AI systems at IBM Research in Yorktown Heights, New York, USA.He is known in various academic circles for his work on computer virus epidemiology and immune systems, electronic commerce agents, and data center energy management, but to the ACSOS community he is best known for his leadership and research in founding autonomic computing as an academic discipline, for which he was awarded the rank of IEEE Fellow in 2013.His 2003 IEEE Computer paper on "The Vision of Autonomic Computing" has been cited over 8500 times.In 2004, Kephart co-founded the International Conference on Autonomic Computing, which recently merged with SASO to become ACSOS.Kephart's work has been featured in Scientific American, The New York Times, Wired, Forbes, The Atlantic Monthly, Discover Magazine, and comparable publications, and he has co-authored over 200 papers and 75 patents.He graduated from Princeton University with a BS in electrical engineering (engineering physics) and received his PhD from Stanford University in electrical engineering, with a minor in physics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.590 | 0.363 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".