"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.
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