Brain-computer interfaces might have inspired works of science fiction, but the technology behind them is real and quickly developing. Companies like Synchron and Neuralink are racing to build a model that they can commercialize. Lauren and Mike speak with WIRED's Emily Mullin to discuss why Synchron's model is standing out, and the promises and limitations of these interfaces. Write to us at uncannyvalley@wired.com. You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link.

Is nothing strange that the technological industry is looking to create more automated solutions that help make different decisions (recommendations, projections, estimates and smart decisions makers) supported by Machine Learning. To generate these solutions involves a great deal of previous and post process just for Machine Learning to acquire the data, process it, store it, train models, monitor and deploy them and to retrain them, just to name a few. As I commented on a previous post, I work at an intelligence logistic company called www.simpliroute.com The problem that it's tried to be solved with Machine Learning is to better the input required by the VRP algorithm -Rich VRP. A relevant point is the travel times between points, key information to establish a good route planning.

As I commented on a previous post, I work at an intelligence logistic company called www.simpliroute.com The data analysis phase and previous features to be ingested in bigquery were analyzed by smaller segments (a sample of the large quantity of GPS that exist, in ideal situations such as no rain, protests, numerous variations and in just one sector). Travel time estimation and prediction using GPS data developed by: Javiera Morales Benza, Cristian Cortés, Victor Gonzalez, and Alvaro Echeverria. To work with 3TB of RAM in distributed systems with a controlled budget is a task that requires expertise and care (calculated from dataproc with mllib), specially when your models have only processed samples, and the dataset don't know the time series generated by the intake of GPS. Decisions were made for the next phase, all achieved by analytics generation, data cleaning pipelines and a lot of bigquery analysis (trial and error takes on a key role in the cost here).

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This article presents matrix backpropagation algorithms for the QR decomposition of matrices $A_{m, n}$, that are either square (m = n), wide (m < n), or deep (m > n), with rank $k = min(m, n)$. Furthermore, we derive novel matrix backpropagation results for the pivoted (full-rank) QR decomposition and for the LQ decomposition of deep input matrices. Differentiable QR decomposition offers a numerically stable, computationally efficient method to solve least squares problems frequently encountered in machine learning and computer vision. Software implementation across popular deep learning frameworks (PyTorch, TensorFlow, MXNet) incorporate the methods for general use within the deep learning community. Furthermore, this article aids the practitioner in understanding the matrix backpropagation methodology as part of larger computational graphs, and hopefully, leads to new lines of research.