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pierredv : ml   16

AI for Weather Forecasting – In Retail, Agriculture, Disaster Prediction, and More
"This article will look at how big data and machine learning are transforming weather forecasting and what it means for businesses and governments. In this article we’ll explore:

How companies and government agencies are using AI to improve weather forecasting (including IBM, Panasonic, and the US Government)
Sector-specific machine learning applications for improving business performance (including Retail, Agriculture, Transportation)

Weather forecasting is a strong fit for machine learning. The incredible volume of relevant information — historical data and real-time data — that can be analyzed is simply too great for any group of unaided humans to even begin to process on their own. "

"GE Current has installed smart street lights in several cities that can monitor things like light, humidity, and air quality."

"Panasonic has been working on its own weather forecasting model for years, and it stepped up its effort with the purchase of AirDat in 2013. The company makes TAMDAR, a speciality weather sensor installed on commercial airplanes. "

"According to IBM, 90 percent of crop losses are due to weather events and 25 percent of weather-related crop losses could be prevented by using predictive weather modeling."
TechEmergence  AI  ML  weather  forecasting  IBM  Panasonic  satellite  GE  IoT  NOAA  Monsanto  agriculturevideo 
november 2018 by pierredv
A Blueprint for the Future of AI - Brookings Oct 2018
Each of the papers in this series grapples with the impact of an emerging technology on an important policy issue, pointing out both the new challenges and potential policy solutions introduced by these technologies.
Brookings  AI  ML  robotics 
october 2018 by pierredv
Radio Frequency-Activity Modeling and Pattern Recognition (RF-AMPR) | 2018
"OBJECTIVE: The PMW 120 Program Office desires a Radio Frequency Activity Modeling and Pattern Recognition (RF-AMPR) capability to perform pattern recognition, anomaly detection, and improved clustering of radio frequency (RF) signals. Specifically, it shall consist of a parametric RF classifier, a generative model of activity in the local electromagnetic environment, a machine learning-based anomaly detection method, and an RF data-clustering algorithm that classifies data that would otherwise be discarded by the parametric classifier."

"DESCRIPTION: Current automated RF data analysis and information discovery methods necessitate discarding significant volumes of sensor data as “non-analyzable”. This SBIR topic seeks to apply machine learning methodologies to better characterize this discarded data, enabling a more complete understanding of RF activity present in a specific environment."

"Anomaly classification shall include “known unknowns”, radio frequency events that are outliers of known classes, and “unknown unknowns”, anomalous RF events that represent new devices or activities."
SBIR  DoD  RF  spectrum  machine-learning  anomaly-classification  ML  AI 
october 2018 by pierredv
Introduction to Local Interpretable Model-Agnostic Explanations (LIME) - O'Reilly Media
A technique to explain the predictions of any machine learning classifier.
By Marco Tulio RibeiroSameer SinghCarlos Guestrin
August 12, 2016

"In "Why Should I Trust You?" Explaining the Predictions of Any Classifier, a joint work by Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin (to appear in ACM's Conference on Knowledge Discovery and Data Mining -- KDD2016), we explore precisely the question of trust and explanations."

"Because we want to be model-agnostic, what we can do to learn the behavior of the underlying model is to perturb the input and see how the predictions change."

"We used LIME to explain a myriad of classifiers (such as random forests, support vector machines (SVM), and neural networks) in the text and image domains."
ML  AI  neural-nets  explanation  prediction 
september 2018 by pierredv
Telecoms AI ecosystems: increasing automation in processes - Analysys Mason Jul 2018
Operators have been aiming to use automations driven by artificial intelligence (AI) to improve processes for a long time, but the effort involved has limited the number of instances in which intelligence can be applied. The introduction of AI ecosystems means that AI and other analytics techniques can be used to model processes and apply optimisation algorithms. Moreover, where processes require dynamic optimisation, machine learning (ML) and deep learning (DL) tools can automatically reoptimise processes as they change.
This report:

looks at the shift that is underway within the analytics market as telecom vendors develop telecoms-specific solutions
explains what the above shift is, the reasons behind the change and the implications for communications service providers (CSPs) and vendors
provides profiles of significant vendors in the market and a comparison of their approaches and tools.
AnalysysMason  AI  ML  telecoms 
september 2018 by pierredv
Army turns to artificial intelligence to counter electronic attacks - aug 2018
"A team of eight engineers from Aerospace Corp. won a $100,000 Army prize by correctly detecting and classifying the greatest number of radio frequency signals using a combination of signal processing and artificial intelligence algorithms"
SpaceNews  AI  ML  spectrum  USArmy  competition  signal-classification 
august 2018 by pierredv
RSPG to examine role of machine learning | PolicyTracker aug 2018
"There is growing interest in the application of artificial intelligence (AI) to spectrum: Google thinks it could replace propagation modelling; a leading consultancy has described the telecoms sector as a "perfect opportunity" and AI will be one of the forthcoming study areas for the EU's spectrum advisory group. "
PolicyTracker  AI  ML  RSPG  spectrum 
august 2018 by pierredv
AI researchers allege that machine learning is alchemy | Science | AAAS, May 2018
Via John Helm

"Speaking at an AI conference, Rahimi charged that machine learning algorithms, in which computers learn through trial and error, have become a form of "alchemy." Researchers, he said, do not know why some algorithms work and others don't, nor do they have rigorous criteria for choosing one AI architecture over another. Now, in a paper presented on 30 April at the International Conference on Learning Representations in Vancouver, Canada, Rahimi and his collaborators document examples of what they see as the alchemy problem and offer prescriptions for bolstering AI's rigor."

"The issue is distinct from AI's reproducibility problem... It also differs from the "black box" or "interpretability" problem in machine learning"

"Without deep understanding of the basic tools needed to build and train new algorithms, he says, researchers creating AIs resort to hearsay, like medieval alchemists."
ML  machine-learning  AI  ScienceMag 
may 2018 by pierredv
Microsoft continues its quest to bring machine learning to every application | Ars Technica May 2018
We've been tracking Microsoft's work to bring its machine learning platform to more developers and more applications over the last several years. What started as narrowly focused, specialized services have grown into a wider range of features that are more capable and more flexible, while also being more approachable to developers who aren't experts in the field of machine learning.
ArsTechnica  Microsoft  ML 
may 2018 by pierredv
Deep learning - Yann LeCun, Yoshua Bengio & Geoffrey Hinton | Nature May 2015
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
machine-learning  ML  AI  NatureJournal 
april 2018 by pierredv
Machine Learning for Performance Prediction in Mobile Cellular Networks - IEEE Computational Intelligence Magazine ( Volume: 13, Issue: 1, Feb. 2018 )
Janne Riihijarvi ; Petri Mahonen

In this paper, we discuss the application of machine learning techniques for performance prediction problems in wireless networks. These problems often involve using existing measurement data to predict network performance where direct measurements are not available. We explore the performance of existing machine learning algorithms for these problems and propose a simple taxonomy of main problem categories. As an example, we use an extensive real-world drive test data set to show that classical machine learning methods such as Gaussian process regression, exponential smoothing of time series, and random forests can yield excellent prediction results. Applying these methods to the management of wireless mobile networks has the potential to significantly reduce operational costs while simultaneously improving user experience. We also discuss key challenges for future work, especially with the focus on practical deployment of machine learning techniques for performance prediction in mobile wireless networks.
machine-learning  ML  automation  AI  IEEE  cellular  spectrum 
april 2018 by pierredv
CRFS publishes White Paper on Machine Learning — CRFS, Dec 2017
Via Dale Hatfield, March 2018

"Machine Learning and RF Spectrum Intelligence Gathering"

"Many applications that are central to RF spectrum intelligence gathering require some sort of pattern recognition. For example, to classify a signal by type we need to identify the particular pattern associated with the modulation, while to recognise that there is an interesting signal present in received data, we need to distinguish between pattern and noise.

In this White Paper, we explore how machine learning techniques can be applied to these applications of signal classification and anomaly detection to deliver faster and more effective performance to customers."
CRFS  signal-processing  enforcement  AI  ML  machine-learning  spectrum 
april 2018 by pierredv
I-Connect007 :: Article The Radio Frequency Spectrum + Machine Learning = a New Wave in Radio Technology
" there now is a need to apply ML to the invisible realm of radio frequency (RF) signals, according to program manager Paul Tilghman of DARPA’s Microsystems Technology Office. To further that cause, DARPA today announced its new Radio Frequency Machine Learning Systems (RFMLS) program."

“What I am imagining is the ability of an RF Machine Learning system to see and understand the composition of the radio frequency spectrum – the kinds of signals occupying it, differentiating those that are ‘important’ from the background, and identifying those that don’t follow the rules,” said Tilghman.

"stand up an RF forensics capability to identify unique and peculiar signals amongst the proverbial cocktail party of signals out there"

"The RFMLS program features four technical components that would integrate into future RFML systems:"
= feature learning
= attention and saliency
= autonomous RF sensor configuration
= waveform synthesis
machine-learning  ML  DARPA  enforcement  forensics  mirror-worlds  *  spectrum 
september 2017 by pierredv
AT&T Labs working to combine drone video footage with artificial intelligence monitoring | FierceWireless
"AT&T Labs is researching ways to take video footage from a drone flying around an AT&T cell tower and use AI to analyze that footage for signs of rust, corrosion or other defects on the tower. The result would save AT&T the time and money it takes to have engineers physically climb cell towers to do inspections (thanks to the drone), as well as the time and money it takes to have engineers review video from a drone inspecting a tower (thanks to the artificial intelligence)."

"Pregler’s drone team has been looking at ways to use drones to provide cell coverage in areas where the carrier doesn’t currently offer service, or where it needs to provide extra coverage."

"AT&T’s helicopter drone also carries a long tether cable that connects it to a box on the ground."
AT&T  FierceWireless  drones  AI  ML  cellular 
may 2017 by pierredv
The Future of Spectrum Sharing with Machine Learning –
DARPA sees artificial intelligence’s machine learning enabling on-the-fly sharing of spectrum at “machine” timescales. It is announcing a 3-year competition, starting in 2017, to identify the best collaborative – not competitive – noble ways to manage spectrum beyond current band assignments and static allocation schemes.
DARPA  Elena-Neira  AI  spectrum  spectrum-sharing  ML 
september 2016 by pierredv

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