5 research outputs found
Deep Learning -- A first Meta-Survey of selected Reviews across Scientific Disciplines, their Commonalities, Challenges and Research Impact
Deep learning belongs to the field of artificial intelligence, where machines
perform tasks that typically require some kind of human intelligence. Similar
to the basic structure of a brain, a deep learning algorithm consists of an
artificial neural network, which resembles the biological brain structure.
Mimicking the learning process of humans with their senses, deep learning
networks are fed with (sensory) data, like texts, images, videos or sounds.
These networks outperform the state-of-the-art methods in different tasks and,
because of this, the whole field saw an exponential growth during the last
years. This growth resulted in way over 10,000 publications per year in the
last years. For example, the search engine PubMed alone, which covers only a
sub-set of all publications in the medical field, provides already over 11,000
results in Q3 2020 for the search term 'deep learning', and around 90% of these
results are from the last three years. Consequently, a complete overview over
the field of deep learning is already impossible to obtain and, in the near
future, it will potentially become difficult to obtain an overview over a
subfield. However, there are several review articles about deep learning, which
are focused on specific scientific fields or applications, for example deep
learning advances in computer vision or in specific tasks like object
detection. With these surveys as a foundation, the aim of this contribution is
to provide a first high-level, categorized meta-survey of selected reviews on
deep learning across different scientific disciplines. The categories (computer
vision, language processing, medical informatics and additional works) have
been chosen according to the underlying data sources (image, language, medical,
mixed). In addition, we review the common architectures, methods, pros, cons,
evaluations, challenges and future directions for every sub-category.Comment: 83 pages, 22 figures, 9 tables, 100 reference
Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
Machine generated text is increasingly difficult to distinguish from human
authored text. Powerful open-source models are freely available, and
user-friendly tools that democratize access to generative models are
proliferating. ChatGPT, which was released shortly after the first preprint of
this survey, epitomizes these trends. The great potential of state-of-the-art
natural language generation (NLG) systems is tempered by the multitude of
avenues for abuse. Detection of machine generated text is a key countermeasure
for reducing abuse of NLG models, with significant technical challenges and
numerous open problems. We provide a survey that includes both 1) an extensive
analysis of threat models posed by contemporary NLG systems, and 2) the most
complete review of machine generated text detection methods to date. This
survey places machine generated text within its cybersecurity and social
context, and provides strong guidance for future work addressing the most
critical threat models, and ensuring detection systems themselves demonstrate
trustworthiness through fairness, robustness, and accountability.Comment: Manuscript submitted to ACM Special Session on Trustworthy AI.
2022/11/19 - Updated reference