Although color TV is an established technology, there are a number of longstanding problems for which neural networks may be suited. Impulse noise is such a problem, and a modular neural network approach is pre(cid:173) sented in this paper. The training and analysis was done on conventional computers, while real-time simulations were performed on a massively par(cid:173) allel computer called the Princeton Engine. The network approach was compared to a conventional alternative, a median filter. Real-time simula(cid:173) tions and quantitative analysis demonstrated the technical superiority of the neural system.

Reconstructing spectral functions from propagator data is difficult as solving the analytic continuation problem or applying an inverse integral transformation are ill-conditioned problems. Recent work has proposed using neural networks to solve this problem and has shown promising results, either matching or improving upon the performance of other methods. We generalize this approach by not only reconstructing spectral functions, but also (possible) pairs of complex poles or an infrared (IR) cutoff. We train our network on physically motivated toy functions, examine the reconstruction accuracy and check its robustness to noise. Encouraging results are found on both toy functions and genuine lattice QCD data for the gluon propagator, suggesting that this approach may lead to significant improvements over current state-of-the-art methods.

Mitigating the effects of disease outbreaks with timely and effective interventions requires accurate real-time surveillance and forecasting of disease activity, but traditional health care–based surveillance systems are limited by inherent reporting delays. Machine learning methods have the potential to fill this temporal “data gap,” but work to date in this area has focused on relatively simple methods and coarse geographic resolutions (state level and above). We evaluate the predictive performance of a gated recurrent unit neural network approach in comparison with baseline machine learning methods for estimating influenza activity in the United States at the state and city levels and experiment with the inclusion of real-time Internet search data. We find that the neural network approach improves upon baseline models for long time horizons of prediction but is not improved by real-time internet search data. We conduct a thorough analysis of feature importances in all considered models for interpretability purposes.

Prof. Shawn Graham's research profile will change your vision of what is to be an archeologist. Originally trained in the traditional study of Roman archaeology, Graham's scholarly career has evolved to focus on the sophisticated fields of digital archaeology, digital history, and the digital humanities. The Department of History's Graham is an active public voice on Twitter and a variety of open access channels like www.electricarchaeology.ca, where he shares, among other things, his research, experiments, ruminations on the modern academy, progressive teaching, and his thoughts and experiences using new learning technology. Woven through his writings are cleverly placed odes to popular art and culture which serve as useful tools to ground and situate the often complex and avant-garde ideas he presents. Notably, Graham is also the founder and editor of the innovative online space Epoiesen: A Journal for Creative Engagement in History and Archaeology.

An emotionally-competent computer agent could be a valuable assistive technology in performing various affective tasks. For example caring for the elderly, low-cost ubiquitous chat therapy, and providing emotional support in general, by promoting a more positive emotional state through dialogue system interaction. However, despite the increase of interest in this task, existing works face a number of shortcomings: system scalability, restrictive modeling, and weak emphasis on maximizing user emotional experience. In this paper, we build a fully data driven chat-oriented dialogue system that can dynamically mimic affective human interactions by utilizing a neural network architecture. In particular, we propose a sequence-to-sequence response generator that considers the emotional context of the dialogue. An emotion encoder is trained jointly with the entire network to encode and maintain the emotional context throughout the dialogue. The encoded emotion information is then incorporated in the response generation process. We train the network with a dialogue corpus that contains positive-emotion eliciting responses, collected through crowd-sourcing. Objective evaluation shows that incorporation of emotion into the training process helps reduce the perplexity of the generated responses, even when a small dataset is used. Subsequent subjective evaluation shows that the proposed method produces responses that are more natural and likely to elicit a more positive emotion.

A discussion of three major approaches to building smart machines - Classic AI, Simple Neural Networks, and Biological Neural Networks - and examples as to how each approach might address the same problem. We are frequently asked how we distinguish our technology from others. This task is made difficult by the fact that there is not an agreed vocabulary; everybody uses the above terms (and other associated terms) differently. In addition, the commonly understood meaning of some of these terms has evolved over time. What was meant by AI in 1960 is very different than what is meant today.

We return to the question of terminology that we started this post with. Our feeling is that the term "artificial intelligence" has been used in so many ways that it is now confusing. People use AI to refer to all three approaches described above, plus others, and therefore has become almost meaningless. The term "machine learning" is a more narrowly defined term for machines that learn from data, including simple neural models such as ANNs and Deep Learning. We use the term "machine intelligence" to refer to machines that learn but are aligned with the Biological Neural Network approach. Although there still is much work ahead of us, we believe the Biological Neural Network approach is the fastest and most direct path to truly intelligent machines. This blog entry was modified on Thu Mar 24 2016 to clarify the timing of neural network research.

We return to the question of terminology that we started this post with. Our feeling is that the term "artificial intelligence" has been used in so many ways that it is now confusing. People use AI to refer to all three approaches described above, plus others, and therefore has become almost meaningless. The term "machine learning" is a more narrowly defined term for machines that learn from data, including simple neural models such as ANNs and Deep Learning. We use the term "machine intelligence" to refer to machines that learn but are aligned with the Biological Neural Network approach. Although there still is much work ahead of us, we believe the Biological Neural Network approach is the fastest and most direct path to truly intelligent machines. This blog entry was modified on Thu Mar 24 2016 to clarify the timing of neural network research.

Much experimental study of real neural networks relies on the proper classification of extracellulary sampled neural signals (i.e.