2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) / Institute of Electrical and Electronics Engineers.

Format:

Book

Author/Creator:

Institute of Electrical and Electronics Engineers, issuing body, author.

Language:

English

Subjects (All):

Ubiquitous computing--Congresses.

Ubiquitous computing.

Physical Description:

1 online resource

Other Title:

2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events

Place of Publication:

Piscataway, New Jersey : IEEE, 2021.

Summary:

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. MACHINE and AI justifiably attract the attention and interest not only of the wider scientific community and industry, but also society and policy makers. However, even the most powerful (in terms of accuracy) algorithms such as deep learning (DL) can give a wrong output, which may be fatal. Due to the opaque and cumbersome model structure used by DL, some authors started to talk about a dystopian "black box" society. Despite the success in this area, the way computers learn is still principally different from the way people acquire new knowledge, recognise objects and make decisions. People do not need a huge amount of annotated data. They learn by example, using similarities to previously acquired prototypes, not by using parametric analytical models. Current ML approaches are focused primarily on accuracy and overlook explainability, the semantic meaning of the internal model representation, reasoning and its link with the problem domain. They also overlook the efforts to collect and label training data and rely on assumptions about the data distribution that are often not satisfied. The ability to detect the unseen and unexpected and start learning this new class/es in real time with no or very little supervision is critically important and is something that no currently existing classifier can offer. The challenge is to fill this gap between high level of accuracy and the semantically meaningful solutions. The most efficient algorithms that have fuelled interest towards ML and AI recently are also computationally very hungry - they require specific hardware accelerators such as GPU, huge amounts of labeled data and time. They produce parametrised models with hundreds of millions of coefficients, which are also impossible to interpret or be manipulated by a human. Once trained, such models are inflexible to new knowledge. T.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

1-66540-424-8