Programmers are constantly contributing to my open source projects (all of my projects are open source, FYI). Some are volunteering their time, others are paid through Zerocracy. While I have worked with a lot of great developers over the years, I have also come across a number of people afflicted with what I call "hazardous enthusiasm." These people have energy and often the skills, but are overzealous and don't know how to break down their changes and deliver them incrementally. People afflicted with hazardous enthusiasm frequently want to tear down and rebuild the entire architecture or implement some other huge changes, often simply for the sake of doing so.

Google speaks 106 languages--or at least can understand queries in written form if not also oral form. When I watch someone interacting verbally with Google Assistant in languages other than English (my native tongue), I realize Google's language ability vastly exceeds my own. I have a modest ability to speak and understand German. I know a few phrases in Russian and French. But it suddenly strikes me that Google is usefully dealing with over 100 languages in written and oral form.

--Due to the drastic increase of mobile traffic, wireless caching is proposed to serve repeated requests for content download. T o determine the caching scheme for decentralized caching networks, the content preference learning problem based on mobility prediction is studied. We first formulate preference prediction as a decentralized regularized multi-task learning (DRMTL) problem without considering the mobility of mobile terminals (MTs). The problem is solved by a hybrid Jacobian and Gauss-Seidel proximal multi-block alternating direction method (ADMM) based algorithm, which is proven to conditionally converge to the optimal solution with a rate O (1 / k) . Then we use the tool of Markov renewal process to predict the moving path and sojourn time for MTs, and integrate the mobility pattern with the DRMTL model by reweighting the training samples and introducing a transfer penalty in the objective. We solve the problem and prove that the developed algorithm has the same convergence property but with different conditions. Through simulation we show the convergence analysis on proposed algorithms. Our real trace driven experiments illustrate that the mobility-aware DRMTL model can provide a more accurate prediction on geography preference than DRMTL model. Besides, the hit ratio achieved by most popular proactive caching (MPC) policy with preference predicted by mobility-aware DRMTL outperforms the MPC with preference from DRMTL and random caching (RC) schemes. As a promising technology for the fifth-generation (5G) wireless networks and beyond, proactive caching can alleviate the heavy traffic burden on backhaul links and reduce service delay, through proactively storing popular contents at base stations (BSs) and mobile terminals (MTs) [1]-[3]. With the limitation of storage memory, determining where and what to cache in content centric wireless networks becomes one of the main challenges in the design of proactive caching schemes. Among the various factors affecting the wireless caching design, involving the mobility of MTs and learning content preference are two critical challenges, which have attracted more and more research interest recently. A. background Current investigation on mobility aware wireless caching mainly includes two aspects: studying the impact of MT mobility on caching schemes [4]-[7], and optimizing the wireless caching schemes based on the mobility information of MTs Y u Y e, Ming Xiao and Mikael Skoglund are with the School of Electrical Engineering and Computer Science, Royal Institute of Technology (KTH), Stockholm, Sweden (email: yu9@kth.se,

Numerous learning methods for fuzzy cognitive maps (FCMs), such as the Hebbian-based and the population-based learning methods, have been developed for modeling and simulating dynamic systems. However, these methods are faced with several obvious limitations. Most of these models are extremely time consuming when learning the large-scale FCMs with hundreds of nodes. Furthermore, the FCMs learned by those algorithms lack robustness when the experimental data contain noise. In addition, reasonable distribution of the weights is rarely considered in these algorithms, which could result in the reduction of the performance of the resulting FCM. In this article, a straightforward, rapid, and robust learning method is proposed to learn FCMs from noisy data, especially, to learn large-scale FCMs. The crux of the proposed algorithm is to equivalently transform the learning problem of FCMs to a classic-constrained convex optimization problem in which the least-squares term ensures the robustness of the well-learned FCM and the maximum entropy term regularizes the distribution of the weights of the well-learned FCM. A series of experiments covering two frequently used activation functions (the sigmoid and hyperbolic tangent functions) are performed on both synthetic datasets with noise and real-world datasets. The experimental results show that the proposed method is rapid and robust against data containing noise and that the well-learned weights have better distribution. In addition, the FCMs learned by the proposed method also exhibit superior performance in comparison with the existing methods. Index Terms-Fuzzy cognitive maps (FCMs), maximum entropy, noisy data, rapid and robust learning.

Google says it has made it possible for a smartphone to interpret and "read aloud" sign language. The tech firm has not made an app of its own but has published algorithms which it hopes developers will use to make their own apps. Until now, this type of software has only worked on PCs. Campaigners from the hearing-impaired community have welcomed the move, but say the tech might struggle to fully grasp some conversations. In an AI blog, Google research engineers Valentin Bazarevsky and Fan Zhang said the intention of the freely published technology was to serve as "the basis for sign language understanding".

Analysis of log data generated by online educational systems is an essential task to better the educational systems and increase our understanding of how students learn. In this study we investigate previously unseen data from Clio Online, the largest provider of digital learning content for primary schools in Denmark. We consider data for 14,810 students with 3 million sessions in the period 2015-2017. We analyze student activity in periods of one week. By using non-negative matrix factorization techniques, we obtain soft clusterings, revealing dependencies among time of day, subject, activity type, activity complexity (measured by Bloom's taxonomy), and performance. Furthermore, our method allows for tracking behavioral changes of individual students over time, as well as general behavioral changes in the educational system. Based on the results, we give suggestions for behavioral changes, in order to optimize the learning experience and improve performance.

Machine learning algorithms find frequent application in spatial prediction of biotic and abiotic environmental variables. However, the characteristics of spatial data, especially spatial autocorrelation, are widely ignored. We hypothesize that this is problematic and results in models that can reproduce training data but are unable to make spatial predictions beyond the locations of the training samples. We assume that not only spatial validation strategies but also spatial variable selection is essential for reliable spatial predictions. We introduce two case studies that use remote sensing to predict land cover and the leaf area index for the "Marburg Open Forest", an open research and education site of Marburg University, Germany. We use the machine learning algorithm Random Forests to train models using non-spatial and spatial cross-validation strategies to understand how spatial variable selection affects the predictions. Our findings confirm that spatial cross-validation is essential in preventing overoptimistic model performance. We further show that highly autocorrelated predictors (such as geolocation variables, e.g. latitude, longitude) can lead to considerable overfitting and result in models that can reproduce the training data but fail in making spatial predictions. The problem becomes apparent in the visual assessment of the spatial predictions that show clear artefacts that can be traced back to a misinterpretation of the spatially autocorrelated predictors by the algorithm. Spatial variable selection could automatically detect and remove such variables that lead to overfitting, resulting in reliable spatial prediction patterns and improved statistical spatial model performance. We conclude that in addition to spatial validation, a spatial variable selection must be considered in spatial predictions of ecological data to produce reliable predictions.

Everyone's talking about the fast.ai Massive Open Online Course (MOOC) so I decided to have a go at their 2019 deep learning course Practical Deep Learning for Coders, v3. I've always known some deep learning concepts/ideas (I've been in this field for about a year now, dealing mostly with computer vision), but never really understood some intuitions or explanations. I also understand that Jeremy Howard, Rachel Thomas and Sylvain Gugger (follow them on Twitter!) are influential people in the deep learning sphere (Jeremy has a lot of experience with Kaggle competitions), so I hope to gain new insights and intuitions, and some tips and tricks for model training from them. I have so much to learn from these folks.

Natural Language Processing is fast becoming Artificial Intelligence's new frontier which we all are using on daily basis – Siri, Google search, chatbots, automatic translation are just some examples. NLP can offer much more within your organization. We can combine together with your existing business applications tailor-made solution to analyze text, understand the conclusions without human effort, turn unstructured data into tabular data and much more.

Increased deployment of Artificial Intelligence around the world has torn open a very public and heated debate. While AI is being used to do things like sentence criminals, determine who should be hired and fired, and assess what loan rate you should be offered, it's also being leveraged to protect against poaching, detect illnesses sooner and more accurately, and shed new insights into fighting climate change. As we continue to develop AmandaAI here at TTT, we increasingly involve ourselves in the field. And as the technology continues to advance, we will continue to take on more and more clients who want to incorporate AI into their software. Since we're helping to create an AI-enabled future, we have a responsibility to explore what exactly that means.