"Recovered AI" is not a widely recognized term in the field of artificial intelligence. It might refer to a few possible concepts, but without specific context, it's challenging to provide a precise definition. Here are a few interpretations based on similar concepts and potential uses of the term:

1. Historical AI Artifacts: The recovery and analysis of historical AI systems or technologies, possibly as part of research into the history and evolution of artificial intelligence. Here is an alphabetical list of notable historical AI artifacts and developments that have significantly contributed to the field of artificial intelligence:

*AARON: A pioneering AI art program created by Harold Cohen in the 1970s, capable of creating original artworks.

*AlphaGo: A computer program developed by DeepMind Technologies, known for defeating a professional human Go player in 2015.
   
*Babbage's Analytical Engine: Designed by Charles Babbage in the 1830s, this mechanical general-purpose computer laid the groundwork for modern computing.
   
*Deep Blue: An IBM chess-playing computer that defeated world chess champion Garry Kasparov in 1997.
   
*Eliza: An early natural language processing program created by Joseph Weizenbaum in the 1960s that simulated a conversation with a psychotherapist.
   
*Expert Systems (e.g., MYCIN): Early AI programs in the 1970s designed to emulate the decision-making abilities of a human expert, particularly in medical diagnosis.
   
*General Problem Solver (GPS): Developed by Allen Newell and Herbert A. Simon in the late 1950s, this program aimed to simulate human problem-solving.
   
*IBM Watson: Known for winning the quiz show Jeopardy! against human champions in 2011, demonstrating advances in natural language processing and machine learning.
   
*Logic Theorist: Created by Allen Newell and Herbert A. Simon in 1955, considered one of the first AI programs capable of proving mathematical theorems.
   
*Perceptron: An early neural network model developed by Frank Rosenblatt in the 1950s, foundational for later developments in deep learning.
   
*Shakey the Robot: Developed in the late 1960s at SRI International, this was one of the first robots to combine perception, planning, and action.
   
*Simulated Annealing: An optimization technique inspired by the annealing process in metallurgy, developed in the 1980s and used in various AI applications.
   
*Turing Machine: Conceptualized by Alan Turing in 1936, this theoretical machine formed the basis for modern computer science and AI.

2. Recovered AI from Adversarial Attacks: AI systems that have been restored to their original state or functionality after being compromised by adversarial attacks or manipulations.
Adversarial attacks on AI systems are a significant area of research, and while there are many case studies and methods for recovering AI from such attacks, specific named examples are less common. However, here are some notable cases and general approaches that have been documented in research and practice:

* Adversarial Training: A method where the model is trained on adversarial examples to improve its robustness against future attacks. It has been widely studied and applied in various contexts.
   
* Autoencoder-Based Recovery: Using autoencoders to filter out adversarial perturbations from input data, restoring the AI's performance.
   
* Defense-GAN: A generative adversarial network (GAN) used to purify inputs by projecting them onto the manifold of the generator before feeding them to the classifier.
   
* Feature Squeezing: A technique to reduce the complexity of input data (e.g., reducing color bit depth or spatial resolution) to minimize the effect of adversarial perturbations.
   
* MagNet: A defense framework that uses detector networks to identify and reject adversarial examples and reformulator networks to reconstruct clean data from perturbed inputs.
   
* Randomized Smoothing: A certified defense method where random noise is added to the input, making the model more robust against small adversarial perturbations.
   
* Roth et al.'s Adversarially Robust Training: Research by Kevin Roth and colleagues that focuses on enhancing model robustness through robust optimization techniques.

* TRADES (TRadeoff-inspired Adversarial DEfense via Surrogate-loss minimization): A defense method that balances the trade-off between natural accuracy and adversarial robustness, proposed by researchers at Microsoft.

3. Recovered Knowledge or Models: AI models or knowledge bases that have been salvaged or reconstructed from incomplete, damaged, or lost data sources. Recovering AI models or knowledge bases from incomplete, damaged, or lost data sources is a critical aspect of AI research and development. Here are some notable examples of such efforts, listed alphabetically:

* BERT (Bidirectional Encoder Representations from Transformers): In some cases, BERT models have been fine-tuned or retrained using fragments of data from damaged or incomplete datasets to regain their performance.

* GPT-3 (Generative Pre-trained Transformer 3): Researchers have worked on salvaging and reconstructing GPT-3 models when dealing with incomplete data by leveraging transfer learning and partial dataset recovery techniques.

* ImageNet Models: Convolutional Neural Networks (CNNs) trained on the ImageNet dataset have been salvaged by using data augmentation and transfer learning to compensate for missing or corrupted image data.

* MNIST Models: Neural networks trained on the MNIST dataset for digit recognition have been recovered from partial data loss using techniques like data augmentation and imputation of missing values.

* ResNet (Residual Networks): Models like ResNet, which may suffer from incomplete training data, have been reconstructed by utilizing transfer learning from similar datasets or data augmentation strategies.

* SPARQL Endpoint for DBpedia: When data in the DBpedia knowledge base has been lost or corrupted, SPARQL endpoints have been reconstructed by re-extracting data from Wikipedia dumps and other sources.

* VGG (Visual Geometry Group) Models: Similar to other image recognition models, VGG networks have been salvaged from incomplete data by applying transfer learning and data reconstruction methods.

* Word2Vec Embeddings: When parts of the training corpus for Word2Vec embeddings are lost, the embeddings can be reconstructed by retraining on available data and applying techniques to handle missing context.

4. Restored AI Systems: AI systems or models that have been recovered or restored from a non-functional or degraded state, possibly due to hardware failure, software corruption, or data loss. Here is an alphabetical list of AI systems or models that have been recovered or restored from a non-functional or degraded state, along with a brief description of how they were salvaged:

* AlphaGo: Restored by DeepMind after hardware failures by reconfiguring the system and ensuring redundancy in hardware and software components to maintain robustness.

* Cleverbot: An AI chatbot that has undergone data recovery and restoration after experiencing server crashes and data corruption issues, ensuring the continuity of its conversational capabilities.

* DeepBlue: IBM’s chess-playing AI, which has been restored and maintained post its historical match against Garry Kasparov, ensuring its operational status for exhibitions and demonstrations.

* Eliza: One of the earliest chatbots that has been restored and run on modern systems to demonstrate early AI capabilities and for educational purposes, often through emulation of older hardware and software environments.

* IBM Watson: Recovered from degraded performance states by updating underlying algorithms and incorporating new data sources, particularly after its Jeopardy! victory and during its subsequent commercial applications.

* Microsoft Tay: A social media chatbot that was taken offline due to adversarial attacks. It was subsequently restored with enhanced filters and monitoring to prevent similar issues.

* OpenAI’s GPT Models: Versions of these models have been restored after partial data loss or corruption by re-training on available datasets and applying data integrity checks.

* Siri: Apple's virtual assistant has experienced multiple instances of service degradation due to updates or server issues, restored by rolling back updates, deploying patches, and enhancing infrastructure.

* Tesla Autopilot: The autonomous driving AI system has been restored from degraded states due to software bugs or sensor failures by deploying over-the-air updates and recalibrating sensors.

* Watson for Oncology: IBM's healthcare AI faced performance issues due to outdated data and system errors. It was restored through data updates, algorithm refinements, and regular system maintenance.