Best of arXiv.org for AI, Machine Learning, and Deep Learning – September 2019 - insideBIGDATA

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Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a "thumbs up" icon.


Democratized image analytics by visual programming through integration of deep models and small-scale machine learning

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Deep learning1 has revolutionized the field of biomedical image analysis. Conventional approaches have used problem-specific algorithms to describe images with manually crafted features, such as cell morphology, count, intensity, and texture. Feature learning with deep convolutional neural networks is implicit, and training the network usually focuses on particular tasks, such as breast cancer detection in mammography2, subcellular protein localization3, or plant disease detection4. Training a deep network usually requires a large number of images, which limits its utility. For example, the classifier for plant disease detection by Mohanty et al.4 was trained on 54,306 images of diseased and healthy plants, and the yeast protein localization model by Kraus et al.3 was inferred from 22,000 annotated images, but not everyone who could benefit from image analysis has so many well-annotated images.


Machine Learning with SQL

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Python (and soon JavaScript with TensorFlow.js) is a dominant language for Machine Learning. There is a way to build/run Machine Learning models in SQL. There could be a benefit to run model training close to the database, where data stays. With SQL we can leverage strong data analysis out of the box and run algorithms without fetching data to the outside world (which could be an expensive operation in terms of performance, especially with large datasets). This post is to describe how to do Machine Learning in the database with SQL.


Future membership with machine learning and discounting - QUANTLABS.NET

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I got an email regarding the future of memberships about machine learning and discounting. After looking at the latest stuff from Binance, I feel I want to put more effort into crypto vs FX. Machine learning is the future and only an open source language like Python can deliver this. No retail platform can support open source libraries like Tensor Flow 2. How has Metatrader 4 Expert Advisor worked out for you? That is like $333 per year which includes 5 courses and a 10 year archive of my research, this will be priced well into the thousands compared to discretionary trading guru places like Warrior Trading.


The Future of AI/Machine Learning - Entrepreneurship Association

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Are you curious about AI/Machine Learning and the future of this tech in start-ups and big business? Come listen to Emma Zohner and her research background in this field as well as her view on the future of AI/machine learning.


'Artificial Intelligence and Internet of Things will rule future'

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Artificial Intelligence (AI) and the Internet of Things (IoT) will decide how agriculture, businesses, and industries work in the future, said V. Neethi Mohan, Chairman of Young Entrepreneur School, here on Saturday. He was addressing a session on the impact of technology on business, during the second edition of Sangamam 2019, a conference on emerging digital technologies for entrepreneurs, organised by Digit-All, a forum of Tamil Nadu Chamber Foundation. To meet the global food requirement, which is expected to double by 2050, agriculture needs technological intervention, said Mr. Neethi Mohan. "Artificial Intelligence will help in precision agriculture, which will determine the nutrient and moisture level in the soil. Using AI, a drone survey is done to identify where there is a need for the use of pesticides. We need to equip our farmers to efficiently utilise the technology," he said.


10 Trends of Artificial Intelligence (AI) in 2020

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While AI is still in its infancy, there is no doubt that it will have a positive impact on education, security, human resources, poverty eradication, health, and science. The possibilities for business improvement that can result from AI trends will only continue to expand in 2020. If you're interested in adopting AI technology for your business, then you might need some help getting started. Contact us and let's set you on your way to business innovation.


Math Vs Coding: Data Science - WebSystemer.no

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I will be sharing my perspective on which is actually more sought after in the current industry. Let me ask you one question. If you were the tech lead of data science, and there already has a lot of Ph.D. people working for you, at the same time, you would like to expand your team. You have two candidates in mind, one is better in coding and one is better in math concept, which candidate will you prefer? There is no right or wrong answer to this question, but from what I observed, usually, they will prefer the ones who have better skills in coding.


A Look At The Investment In Self-Driving Cars: Who Has Spent The Most?

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The development of driverless car technology is on the rise, and automakers are investing millions and billions to be the first to market with their lineup of autonomous vehicles. But which company has made the largest investment in self-driving cars? Here's a look at what some of the top companies have invested in their driverless vehicle programs so far. The investment into the autonomous vehicle industry has reached over $100 billion, with the leader in spending investing more than half of this number, according to a report by Leasing Options. The report indicated that Volkswagen is driving the charge when it comes to driverless technology with an investment of $54.2 billion and 57 percent share in total industry investment of self-driving cars.


Theoretical Foundations of Artificial General Intelligence - Programmer Books

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This book is a collection of writings by active researchers in the field of Artificial General Intelligence, on topics of central importance in the field. Each chapter focuses on one theoretical problem, proposes a novel solution, and is written in sufficiently non-technical language to be understandable by advanced undergraduates or scientists in allied fields. This book is the very first collection in the field of Artificial General Intelligence (AGI) focusing on theoretical, conceptual, and philosophical issues in the creation of thinking machines. All the authors are researchers actively developing AGI projects, thus distinguishing the book from much of the theoretical cognitive science and AI literature, which is generally quite divorced from practical AGI system building issues. And the discussions are presented in a way that makes the problems and proposed solutions understandable to a wide readership of non-specialists, providing a distinction from the journal and conference-proceedings literature.