257,836 research outputs found
Can biological quantum networks solve NP-hard problems?
There is a widespread view that the human brain is so complex that it cannot
be efficiently simulated by universal Turing machines. During the last decades
the question has therefore been raised whether we need to consider quantum
effects to explain the imagined cognitive power of a conscious mind.
This paper presents a personal view of several fields of philosophy and
computational neurobiology in an attempt to suggest a realistic picture of how
the brain might work as a basis for perception, consciousness and cognition.
The purpose is to be able to identify and evaluate instances where quantum
effects might play a significant role in cognitive processes.
Not surprisingly, the conclusion is that quantum-enhanced cognition and
intelligence are very unlikely to be found in biological brains. Quantum
effects may certainly influence the functionality of various components and
signalling pathways at the molecular level in the brain network, like ion
ports, synapses, sensors, and enzymes. This might evidently influence the
functionality of some nodes and perhaps even the overall intelligence of the
brain network, but hardly give it any dramatically enhanced functionality. So,
the conclusion is that biological quantum networks can only approximately solve
small instances of NP-hard problems.
On the other hand, artificial intelligence and machine learning implemented
in complex dynamical systems based on genuine quantum networks can certainly be
expected to show enhanced performance and quantum advantage compared with
classical networks. Nevertheless, even quantum networks can only be expected to
efficiently solve NP-hard problems approximately. In the end it is a question
of precision - Nature is approximate.Comment: 38 page
Mathematical Challenges in Deep Learning
Deep models are dominating the artificial intelligence (AI) industry since
the ImageNet challenge in 2012. The size of deep models is increasing ever
since, which brings new challenges to this field with applications in cell
phones, personal computers, autonomous cars, and wireless base stations. Here
we list a set of problems, ranging from training, inference, generalization
bound, and optimization with some formalism to communicate these challenges
with mathematicians, statisticians, and theoretical computer scientists. This
is a subjective view of the research questions in deep learning that benefits
the tech industry in long run
Perceptions on the Use of Artificial Intelligence in Accounting: An Empirical Study among Accounting Professionals in Nigeria.
Artificial Intelligence has been widely discussed in accounting for some years now, this study examined the level of awareness and perceptions on the use of artificial intelligence in accounting among accounting professionals in accounting, it also examined if the individual
characteristics of accountants affect their perception on the use of artificial intelligence in accounting.
A random sample of 399 Accounting Professionals in Nigeria was used in the study. The study adopted a between group design, and an independent samples T-test, and one way between group Anova was used to test for the effect of accountantsâ characteristics on their perception. The
study found that there is a high level of awareness on the use of artificial intelligence among accounting professionals in Nigeria, but their knowledge is mainly theoretical, gotten from personal readings and the media. Overall, the accounting professionals have a positive view on the use of artificial intelligence in accounting with the majority showing support for the development of AI in accounting, and minimal worries about job displacement due to AI.
The results indicated that male accountants tend to hold a more favourable opinion of AI compared to female accountants, while accountants of different ages, level of education, years of experience, area of specialization, qualification status and professional bodies do not differ in their perceptions on the use of artificial intelligence in accounting. The results of the study also
highlighted the need for reform in accounting education and continuous personal development for accountants to adapt to emerging trends
Artificial intelligence projects in healthcare:10 practical tips for success in a clinical environment
There is much discussion concerning âdigital transformationâ in healthcare and the potential of artificial intelligence (AI) in healthcare systems. Yet it remains rare to find AI solutions deployed in routine healthcare settings. This is in part due to the numerous challenges inherent in delivering an AI project in a clinical environment. In this article, several UK healthcare professionals and academics reflect on the challenges they have faced in building AI solutions using routinely collected healthcare data.These personal reflections are summarised as 10 practical tips. In our experience, these are essential considerations for an AI healthcare project to succeed. They are organised into four phases: conceptualisation, data management, AI application and clinical deployment. There is a focus on conceptualisation, reflecting our view that initial set-up is vital to success. We hope that our personal experiences will provide useful insights to others looking to improve patient care through optimal data use
Behaviour understanding through the analysis of image sequences collected by wearable cameras
Describing people's lifestyle has become a hot topic in the field of artificial intelligence. Lifelogging is described as the process of collecting personal activity data describing the daily behaviour of a person. Nowadays, the development of new technologies and the increasing use of wearable sensors allow to automatically record data from our daily living. In this paper, we describe our developed automatic tools for the analysis of collected visual data that describes the daily behaviour of a person. For this analysis, we rely on sequences of images collected by wearable cameras, which are called egocentric photo-streams. These images are a rich source of information about the behaviour of the camera wearer since they show an objective and first-person view of his or her lifestyle
UtwĂłr pracowniczy powstaĹy z wykorzystaniem sztucznej inteligencji oraz informacji sektora publicznego
The reason for writing this article was the generation (by an algorithm called âQuakebotâ written
in programming language by the âLos Angeles Timesâ journalist, Ken Schwencke) of a press
release about the earthquake that was registered five miles from Westwood (California, USA) on
23 December 2013. This information was prepared on the basis of the USGS Earthquake Notification
Service message posted on the government website. The article formulates the view that the
personal nature of employment does not exclude the possibility of creating a work with the use of
artificial intelligence. In addition, it was found that the piece of work does not lose its legal character
even if it was created with the use of public sector information, as well as that it is acceptable to
re-use public sector information for commercial purposes, obtained through Artificial Intelligence
A Case for Machine Ethics in Modeling Human-Level Intelligent Agents
This paper focuses on the research field of machine ethics and how it relates to a technological singularityâa hypothesized, futuristic event where artificial machines will have greater-than-human-level intelligence. One problem related to the singularity centers on the issue of whether human values and norms would survive such an event. To somehow ensure this, a number of artificial intelligence researchers have opted to focus on the development of artificial moral agents, which refers to machines capable of moral reasoning, judgment, and decision-making. To date, different frameworks on how to arrive at these agents have been put forward. However, there seems to be no hard consensus as to which framework would likely yield a positive result. With the body of work that they have contributed in the study of moral agency, philosophers may contribute to the growing literature on artificial moral agency. While doing so, they could also think about how the said concept could affect other important philosophical concepts
Memristors -- from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing
Machine learning, particularly in the form of deep learning, has driven most
of the recent fundamental developments in artificial intelligence. Deep
learning is based on computational models that are, to a certain extent,
bio-inspired, as they rely on networks of connected simple computing units
operating in parallel. Deep learning has been successfully applied in areas
such as object/pattern recognition, speech and natural language processing,
self-driving vehicles, intelligent self-diagnostics tools, autonomous robots,
knowledgeable personal assistants, and monitoring. These successes have been
mostly supported by three factors: availability of vast amounts of data,
continuous growth in computing power, and algorithmic innovations. The
approaching demise of Moore's law, and the consequent expected modest
improvements in computing power that can be achieved by scaling, raise the
question of whether the described progress will be slowed or halted due to
hardware limitations. This paper reviews the case for a novel beyond CMOS
hardware technology, memristors, as a potential solution for the implementation
of power-efficient in-memory computing, deep learning accelerators, and spiking
neural networks. Central themes are the reliance on non-von-Neumann computing
architectures and the need for developing tailored learning and inference
algorithms. To argue that lessons from biology can be useful in providing
directions for further progress in artificial intelligence, we briefly discuss
an example based reservoir computing. We conclude the review by speculating on
the big picture view of future neuromorphic and brain-inspired computing
systems.Comment: Keywords: memristor, neuromorphic, AI, deep learning, spiking neural
networks, in-memory computin
Iris Codes Classification Using Discriminant and Witness Directions
The main topic discussed in this paper is how to use intelligence for
biometric decision defuzzification. A neural training model is proposed and
tested here as a possible solution for dealing with natural fuzzification that
appears between the intra- and inter-class distribution of scores computed
during iris recognition tests. It is shown here that the use of proposed neural
network support leads to an improvement in the artificial perception of the
separation between the intra- and inter-class score distributions by moving
them away from each other.Comment: 6 pages, 5 figures, Proc. 5th IEEE Int. Symp. on Computational
Intelligence and Intelligent Informatics (Floriana, Malta, September 15-17),
ISBN: 978-1-4577-1861-8 (electronic), 978-1-4577-1860-1 (print
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