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Machine Learning


Toward the use of neural networks for influenza prediction at multiple spatial resolutions

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Mitigating the effects of disease outbreaks with timely and effective interventions requires accurate real-time surveillance and forecasting of disease activity, but traditional health care–based surveillance systems are limited by inherent reporting delays. Machine learning methods have the potential to fill this temporal “data gap,” but work to date in this area has focused on relatively simple methods and coarse geographic resolutions (state level and above). We evaluate the predictive performance of a gated recurrent unit neural network approach in comparison with baseline machine learning methods for estimating influenza activity in the United States at the state and city levels and experiment with the inclusion of real-time Internet search data. We find that the neural network approach improves upon baseline models for long time horizons of prediction but is not improved by real-time internet search data. We conduct a thorough analysis of feature importances in all considered models for interpretability purposes.



Senior Engineer, Computer Vision and Machine Learning

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The companyOutrider, the pioneer in autonomous yard operations for logistics hubs, helps large enterprises improve safety and increase efficiency. The only company exclusively focused on automating all aspects of yard operations, Outrider eliminates manual tasks that are hazardous and repetitive. Outrider's mission is to drive the rapid adoption of sustainable freight transportation by deploying zero-emission systems. Outrider is a private company backed by NEA, 8VC, and other top-tier investors. For more information, visit www.outrider.ai


Prevent Underfitting and Overfitting for your model

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Machine learning and deep learning algorithms are mainly based on statistics. Matching new, unseen data, to the ones we already learned from. This is why there are two typical problems when you train a neural network for a deep learning application. We will take the example of a classifier in this article, but these problems apply to all types of machine learning tasks. The first one is called underfitting, where your model is too simple to represent your data.


AI system-on-chip runs on solar power

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AI is used in an array of useful applications, such as predicting a machine's lifetime through its vibrations, monitoring the cardiac activity of patients and incorporating facial recognition capabilities into video surveillance systems. The downside is that AI-based technology generally requires a lot of power and, in most cases, must be permanently connected to the cloud, raising issues related to data protection, IT security and energy use. CSEM engineers may have found a way to get around those issues, thanks to a new system-on-chip they have developed. It runs on a tiny battery or a small solar cell and executes AI operations at the edge--i.e., locally on the chip rather than in the cloud. What's more, their system is fully modular and can be tailored to any application where real-time signal and image processing is required, especially when sensitive data are involved.


What Google's AI-designed chip tells us about the nature of intelligence

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This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. In a paper published in the peer-reviewed scientific journal Nature last week, scientists at Google Brain introduced a deep reinforcement learning technique for floorplanning, the process of arranging the placement of different components of computer chips. The researchers managed to use the reinforcement learning technique to design the next generation of Tensor Processing Units, Google's specialized artificial intelligence processors. The use of software in chip design is not new. But according to the Google researchers, the new reinforcement learning model "automatically generates chip floorplans that are superior or comparable to those produced by humans in all key metrics, including power consumption, performance and chip area."


Sr. Data Engineer

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Role Description: The Rackspace FinOps group enables cloud users to align their cloud technology adoption with their business strategies. We advise many of the world's largest AWS, GCP, Azure, and other Cloud consumers on topics ranging from cloud architecture to organizational governance to cloud economics, driving efficient cloud adoption and usage for our clients. The FinOps Data Engineer role is an exciting opportunity to build solutions that will help us and our clients turn complex multi-cloud cost and performance datasets into actionable insights. This is an opportunity to make an impact on a fast-growing team. You'll be instrumental in creating new and better analytics and ML solutions, and generally innovate to drive new value for our clients.


Interdisciplinary Centre For Artificial Intelligence

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The Interdisciplinary Centre for Artificial Intelligence was established in the year 2019 in the Faculty of Engineering and Technology after the approval by the Executive Council, Aligarh Muslim University. One of the major objectives of the establishment of this Centre is to promote interdisciplinary research and development activities in Artificial Intelligence and its allied fields including Machine learning, Data Analytics, Natural language processing, etc. The faculty members attached to this Centre are from different fields of Engineering and Science with international exposure and a good publication record. The Centre aims to conduct masters and doctoral research programmes in the area of Artificial Intelligence. The Centre is poised to prepare the students to demonstrate technical competence in their profession by applying knowledge of contemporary advances in AI for providing practical and innovative solutions.


What Google's AI-designed chip tells us about the nature of intelligence

#artificialintelligence

In a paper published in the peer-reviewed scientific journal Nature last week, scientists at Google Brain introduced a deep reinforcement learning technique for floorplanning, the process of arranging the placement of different components of computer chips. The researchers managed to use the reinforcement learning technique to design the next generation of Tensor Processing Units, Google's specialized artificial intelligence processors. The use of software in chip design is not new. But according to the Google researchers, the new reinforcement learning model "automatically generates chip floorplans that are superior or comparable to those produced by humans in all key metrics, including power consumption, performance and chip area." And it does it in a fraction of the time it would take a human to do so. The AI's superiority to human performance has drawn a lot of attention.


How can insurance companies prepare for rapid AI-related changes? - i-Brokers

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Deep learning (DL) tools like convolutional neural networks which contain millions of simulated neurons structured in layers enable artificial intelligence (AI) to imitate the learning, reasoning, perception and problem solving of the human brain. This technological change is revolutionizing every dimension of the insurance industry. While insurers, suppliers, insurance brokers and consumers are getting better at implementing DL and AI tools for the improvement of reasoning and productivity, cost minimization and the enhancement of the customer experience, this technological shift will be even faster. In this blog post, we are going to share with you four aspects insurers need to focus on and how they can get prepared so as to get the most out of this tech-driven transformation. The number of connected consumer devices will drastically rise in the near future.