The widespread adoption of machine learning models in different applications has given rise to a new range of privacy and security concerns. Among them are'inference attacks', whereby attackers cause a target machine learning model to leak information about its training data. However, these attacks are not very well understood and we need to readjust our definitions and expectations of how they can affect our privacy. This is according to researchers from several academic institutions in Australia and India who made the warning in a new paper (PDF) accepted at the IEEE European Symposium on Security and Privacy, which will be held in September. The paper was jointly authored by researchers at the University of New South Wales; Birla Institute of Technology and Science, Pilani; Macquarie University; and the Cyber & Electronic Warfare Division, Defence Science and Technology Group, Australia.
Google has devised a machine learning (ML) model that predicts disk failures with 98 per cent accuracy. The idea is to reduce data recovery work when disks actually fail. According to a Google blog by technical program manager Nitin Agarwal and AI engineer Rostam Dinyari, Google has millions of hard disk drives (HDDs) under management, some of which fail. "Any misses in identifying these failures at the right time can potentially cause serious outages across our many products and services." When a disk in Google's data centres encounters non-fatal problems, short of an actual crash, then data is (drained) read from the drive. The drive is then disconnected from production use, they apply diagnostics and it is fixed and returned to production.
If you have built Deep Neural Networks before, you might know that it can involve a lot of experimentation. In this article, I will share with you some useful tips and guidelines that you can use to better build better deep learning models. These tricks should make it a lot easier for you to develop a good network. You can pick and choose which tips you use, as some will be more helpful for the projects you are working on. Not everything mentioned in this article will straight up improve your models' performance.
Machine Learning is a popular topic in Information Technology in the present day. Machine Learning allows our computer to gain insight from data and experience just as a human being would. In Machine Learning, programmers teach the computer how to use its past experiences with different entities to perform better in future scenarios. Machine Learning involves constructing mathematical models to help us understand the data at hand. Once these models have been fitted to previously seen data, they can be used to predict newly observed data.
Has anyone ever worked on a machine learning model for "queues"? Suppose there is a bakery: the bakery has has "n" people working, "m" people in line" and "q" orders that they are currently working on. The bakery is interested in making a machine learning model that predicts how long a customer will have to wait before the customer's order is ready and how long will the next customer have to wait before they can place an order. Has anyone ever come across a machine learning model which can predict waiting and processing times? I have seen examples online where people try fitting exponential distributions to historical waiting times and see how well they fit, as well as trying different m/m/k combinations... but has anyone ever come across an instance where machine learning algorithms (e.g.
IBM Developer Advocates Anam Mahmood and Sidra Ahmed conducted a workshop on 3rd February. Their goal was to show how everyone can easily use Jupyter Notebooks in IBM Watson Studio to run small pieces of code that process your data and immediately show you the results of your computation in an interactive environment and quickly build machine learning models. The session was divided into two sections. The first half of the workshop was led by Sidra, where she welcomed the audience and talked about the agenda. She then explained to them about Data Science, Artificial Intelligence, Machine Learning, and Deep Learning.
Machine learning adoption exploded over the past decade, driven in part by the rise of cloud computing, which has made high performance computing and storage more accessible to all businesses. As vendors integrate machine learning into products across industries, and users rely on the output of its algorithms in their decision making, security experts warn of adversarial attacks designed to abuse the technology. Most social networking platforms, online video platforms, large shopping sites, search engines and other services have some sort of recommendation system based on machine learning. The movies and shows that people like on Netflix, the content that people like or share on Facebook, the hashtags and likes on Twitter, the products consumers buy or view on Amazon, the queries users type in Google Search are all fed back into these sites' machine learning models to make better and more accurate recommendations. It's not news that attackers try to influence and skew these recommendation systems by using fake accounts to upvote, downvote, share or promote certain products or content.
LOS ALAMOS, N.M., April 22, 2021--A new machine-learning model that generates realistic seismic waveforms will reduce manual labor and improve earthquake detection, according to a study published recently in JGR Solid Earth. "To verify the efficacy of our generative model, we applied it to seismic field data collected in Oklahoma," said Youzuo Lin, a computational scientist in Los Alamos National Laboratory's Geophysics group and principal investigator of the project. "Through a sequence of qualitative and quantitative tests and benchmarks, we saw that our model can generate high-quality synthetic waveforms and improve machine learning-based earthquake detection algorithms." Quickly and accurately detecting earthquakes can be a challenging task. Visual detection done by people has long been considered the gold standard, but requires intensive manual labor that scales poorly to large data sets.
With the fire hose of imagery that's streaming daily from a variety of sensors, the need for using artificial intelligence (AI) to automate feature extraction is only increasing. The ability to train more than a dozen deep learning models on geospatial datasets and derive information products has been available using the ArcGIS API for Python or ArcGIS Pro, and users can scale up processing using ArcGIS Image Server. Esri is taking AI to the next level with ready-to-use geospatial AI models in the ArcGIS Living Atlas of the World. Initially, three models have been made available. Two of the models use satellite imagery.
We all know how quick was the Fourth Hokage. Known as Yellow Flash in all the five great nations whose rasengan made us all go "woooah". Well, we all can do the same with our deep learning models as well. This counters one of the biggest issue we face while doing deep learning projects, "LOW FPS". Passing frames from multiple videos or stream sources to our deep learning model causes slowdown and it is annoying as hell.