727 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
Building an Ontology-Based Framework for Tourism Recommendation Services
The tourism product has an intangible nature in that customers cannot physically evallfate the
services on offer until practically experienced. This makes having access to ;credible;"i\nd
authentic information about tourism products before the actual experience very valuable. An
Ontology being a formal, explicit specification of concepts of a domain provides a viable
platform for the development of credible knowledge-based tourism information services. In this
paper, we present an approach aimed at enabling assorted intelligent reco=endations services
in tourism support systems using ontologies. A suite of tourism ontologies was developed and
engaged to enable a prototypical e-tourism system with various knowledge-based
reco=endation capabilities. A usability evaluation of the system yields encouraging results as
a demonstration of the viability of our approach
Automating Software Customization via Crowdsourcing using Association Rule Mining and Markov Decision Processes
As systems grow in size and complexity so do their configuration possibilities. Users of modern systems are easy to be confused and overwhelmed by the amount of choices they need to make in order to fit their systems to their exact needs. In this thesis, we propose a technique to select what information to elicit from the user so that the system can recommend the maximum number of personalized configuration items. Our method is based on constructing configuration elicitation dialogs through utilizing crowd wisdom.
A set of configuration preferences in form of association rules is first mined from a crowd configuration data set. Possible configuration elicitation dialogs are then modeled through a Markov Decision Processes (MDPs). Within the model, association rules are used to automatically infer configuration decisions based on knowledge already elicited earlier in the dialog. This way, an MDP solver can search for elicitation strategies which maximize the expected amount of automated decisions, reducing thereby elicitation effort and increasing user confidence of the result. We conclude by reporting results of a case study in which this method is applied to the privacy configuration of Facebook
Managing smart cities with deepint.net
In this keynote, the evolution of intelligent computer systems will be examined. The need for human capital will be emphasised, as well as the need to follow one’s “gut instinct” in problem-solving. We will look at the benefits of combining information and knowledge to solve complex problems and will examine how knowledge engineering facilitates the integration of different algorithms. Furthermore, we will analyse the importance of complementary technologies such as IoT and Blockchain in the development of intelligent systems. It will be shown how tools like "Deep Intelligence" make it possible to create computer systems efficiently and effectively. "Smart" infrastructures need to incorporate all added-value resources so they can offer useful services to the society, while reducing costs, ensuring reliability and improving the quality of life of the citizens. The combination of AI with IoT and with blockchain offers a world of possibilities and opportunities
Learning AI with deepint.net
This keynote will examine the evolution of intelligent computer systems over the last years, underscoring the need for human capital in this field, so that further progress can be made. In this regard, learning about AI through experience is a big challenge, but it is possible thanks to tools such as deepint.net, which enable anyone to develop AI systems; knowledge of programming is no longer necessary
The role of the AIoT and deepint.net
AIoT is a term, also known as intelligence of things, which refers to the new wave of the
future of technology that combines two major platforms, very present in today's market:
Artificial Intelligence (AI) and the Internet of things (IoT). As IoT devices will generate
large amounts of data, Artificial Intelligence is going to be functionally necessary to deal
with these huge volumes if we are to have any chance of making sense of the data. This
whole process will be called connected intelligence. To take this step forward and
definitively enter the era of Intelligence of Things, we will need to enable to a greater or
lesser part these cognitive and executive capacities towards objects. To do this, we are
going to talk more and more about the concept of Edge Computing (or “edge computing”),
which is nothing more than the ability to process data, analyze situations, evaluate
possible scenarios and make decisions from the object itself and not from a server
hundreds or thousands of miles away
Intelligent Models in Complex Problem Solving
Artificial Intelligence revived in the last decade. The need for progress, the growing processing capacity and the low cost of the Cloud have facilitated the development of new, powerful algorithms. The efficiency of these algorithms in Big Data processing, Deep Learning and Convolutional Networks is transforming the way we work and is opening new horizons
Neuro-Symbolic Recommendation Model based on Logic Query
A recommendation system assists users in finding items that are relevant to
them. Existing recommendation models are primarily based on predicting
relationships between users and items and use complex matching models or
incorporate extensive external information to capture association patterns in
data. However, recommendation is not only a problem of inductive statistics
using data; it is also a cognitive task of reasoning decisions based on
knowledge extracted from information. Hence, a logic system could naturally be
incorporated for the reasoning in a recommendation task. However, although
hard-rule approaches based on logic systems can provide powerful reasoning
ability, they struggle to cope with inconsistent and incomplete knowledge in
real-world tasks, especially for complex tasks such as recommendation.
Therefore, in this paper, we propose a neuro-symbolic recommendation model,
which transforms the user history interactions into a logic expression and then
transforms the recommendation prediction into a query task based on this logic
expression. The logic expressions are then computed based on the modular logic
operations of the neural network. We also construct an implicit logic encoder
to reasonably reduce the complexity of the logic computation. Finally, a user's
interest items can be queried in the vector space based on the computation
results. Experiments on three well-known datasets verified that our method
performs better compared to state of the art shallow, deep, session, and
reasoning models.Comment: 17 pages, 6 figure
DeepTech - AI Models in Engineering Solutions
Artificial Intelligence revived in the last decade. The need for progress, the growing
processing capacity and the low cost of the Cloud have facilitated the development of new,
powerful algorithms. The efficiency of these algorithms in Big Data processing, Deep
Learning and Convolutional Networks is transforming the way we work and is opening new
horizons. Thanks to them, we can now analyse data and obtain unimaginable solutions to
today’s problems. Nevertheless, our success is not entirely based on algorithms, it also
comes from our ability to follow our “gut” when choosing the best combination of algorithms
for an intelligent artefact. It's about approaching engineering with a lot of knowledge and
tact. This involves the use of both connectionist and symbolic systems, and of having a full
understanding of the algorithms used. Moreover, to address today’s problems we must
work with both historical and real-time data. We must fully comprehend the problem, its
time evolution, as well as the relevance and implications of each piece of data, etc. It is also
important to consider development time, costs and the ability to create systems that will
interact with their environment, will connect with the objects that surround them and will
manage the data they obtain in a reliable manner
Efficient Deployment of DeepTech AI Models in Engineering Solutions
The blockchain system, appeared in 2009 together with the virtual currency bitcoin, is a record of
digital transactions based on a huge database in which all financial operations carried out with
electronic currency are registered. The Blockchain (or chain of blocks) is a shared database that
works as a book for the record of purchase-sale operations or any other transaction. It is the
technological base of the operation of bitcoin, for example. It consists of a set of notes that are in a
shared online database in which operations, quantities, dates and participants are registered by
means of codes. By using cryptographic keys and being distributed by many computers (people),
it presents security advantages against manipulation and fraud. A modification in one of the
copies would be useless, but the change must be made in all the copies because the database is
open and public
- …