Traditional artificial neural networks take inspiration from biological networks, using layers of neuron-like nodes to pass information for processing. More realistic models include spiking in the neural network, capturing the electrical characteristics more closely. However, a large proportion of brain cells are of the glial cell type, in particular astrocytes which have been suggested to play a role in performing computations. Here, we introduce a modified spiking neural network model with added astrocyte-like units in a neural network and asses their impact on learning. We implement the network as a liquid state machine and task the network with performing a chaotic time-series prediction task. We varied the number and ratio of neuron-like and astrocyte-like units in the network to examine the latter units effect on learning. We show that the combination of neurons and astrocytes together, as opposed to neural- and astrocyte-only networks, are critical for driving learning. Interestingly, we found that the highest learning rate was achieved when the ratio between astrocyte-like and neuron-like units was roughly 2 to 1, mirroring some estimates of the ratio of biological astrocytes to neurons. Our results demonstrate that incorporating astrocyte-like units which represent information across longer timescales can alter the learning rates of neural networks, and the proportion of astrocytes to neurons should be tuned appropriately to a given task.

Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, documents, and analyzes radio content in real-time. While our framework is generally applicable, we showcase the efficacy of WavePulse in a collaborative project with a team of political scientists focusing on the 2024 Presidential Elections. We use WavePulse to monitor livestreams of 396 news radio stations over a period of three months, processing close to 500,000 hours of audio streams. These streams were converted into time-stamped, diarized transcripts and analyzed to track answer key political science questions at both the national and state levels. Our analysis revealed how local issues interacted with national trends, providing insights into information flow. Our results demonstrate WavePulse's efficacy in capturing and analyzing content from radio livestreams sourced from the Web. Code and dataset can be accessed at \url{https://wave-pulse.io}.

Computerized Adaptive Testing (CAT) provides an efficient and tailored method for assessing the proficiency of examinees, by dynamically adjusting test questions based on their performance. Widely adopted across diverse fields like education, healthcare, sports, and sociology, CAT has revolutionized testing practices. While traditional methods rely on psychometrics and statistics, the increasing complexity of large-scale testing has spurred the integration of machine learning techniques. This paper aims to provide a machine learning-focused survey on CAT, presenting a fresh perspective on this adaptive testing method. By examining the test question selection algorithm at the heart of CAT's adaptivity, we shed light on its functionality. Furthermore, we delve into cognitive diagnosis models, question bank construction, and test control within CAT, exploring how machine learning can optimize these components. Through an analysis of current methods, strengths, limitations, and challenges, we strive to develop robust, fair, and efficient CAT systems. By bridging psychometric-driven CAT research with machine learning, this survey advocates for a more inclusive and interdisciplinary approach to the future of adaptive testing.

Columbia University professor and robotics engineer Hod Lipson knows the importance of artificial intelligence (AI) on a global level. "It permeates everything we do, from the stock market, from predicting the weather to what product you're going to buy," he said Wednesday during the second day of the virtual Ai4 2020 conference. AI falls into the category of an exponential technology, meaning it accelerates with time. Both biopharma and med-tech companies are increasingly pulling the technology into their business operations, working on programs that can assist in everything from drug discovery and clinical trial recruitment to precision diagnostics and patient compliance efforts. Computing power has doubled every 20 months or so for the past 120 years, Lipson said, moving from mechanical instruments to graphics processing units (GPUs) today.

With the release of Qlik Sense Business on Tuesday, Qlik extended the reach of its cloud-first capabilities. The offering replaces Qlik Sense Cloud Business, which the analytics and business intelligence vendor, based in King of Prussia, Pa., debuted in 2015. In addition, Qlik rolled out Qlik Sense September 2019, the latest update of its central BI product. Qlik Sense Business is a SaaS offering built on third-generation BI capabilities -- augmented intelligence and machine learning. It differs from Qlik Sense Cloud Business by removing limits on the number of users, connecting more seamlessly to Qlik Sense Enterprise and providing expanded AI and machine learning capabilities.

Elsewhere, I have suggested that there are three junctures at which any data mining project may go the most wrong: 1. problem definition, 2. data acquisition and 3. model validation (see the Data Mining and Predictive Analytics Web log). Data acquisition is a superset of statistical sampling, and the text by Lohr is highly recommended for this topic. Model validation is well explained in the literature: see, for instance, Weiss and Kulikowski. Problem definition involves understanding the business problem and mapping an appropriate technical solution to it. This may not be as simple as it sounds, and it is easy to be naïve about the best way to construct a technical solution which most naturally solves the given problem.

In Deep Learning, a well-known approach for training a Deep Neural Network starts by training a generative Deep Belief Network model, typically using Contrastive Divergence (CD), then fine-tuning the weights using backpropagation or other discriminative techniques. However, the generative training can be time-consuming due to the slow mixing of Gibbs sampling. We investigated an alternative approach that estimates model expectations of Restricted Boltzmann Machines using samples from a D-Wave quantum annealing machine. We tested this method on a coarse-grained version of the MNIST data set. In our tests we found that the quantum sampling-based training approach achieves comparable or better accuracy with significantly fewer iterations of generative training than conventional CD-based training. Further investigation is needed to determine whether similar improvements can be achieved for other data sets, and to what extent these improvements can be attributed to quantum effects.