Automated Machine Learning (AutoML) has been described as a "quiet revolution in AI" that is poised to dramatically change the data science landscape by using AI to automate many of the time-consuming aspects of applying Machine Learning to real-world problems. Academic researchers, startups, and tech giants alike have begun developing AutoML methods and tools ranging from simple open source prototypes to industry-scale software products. Yet beyond all the hype and vague tech jargon, many are left wondering: What is AutoML, really? In this talk, I will draw from my AutoML research experience to discuss the benefits of AutoML and highlight some promising future directions of the field, including Python packages and other existing tools that offer AutoML solutions.
Researchers at the University of Wollongong, Deakin University, Monash University and Kyushu University have developed a framework that could be used to build a smart, AI-powered agile project management assistant. Their paper, pre-published on arXiv, has been accepted at the 41st International Conference on Software Engineering (ICSE) 2019, in the New Ideas and Emerging Results track. "Our research was driven by our experience working in and with the industry," Hoa Khanh Dam, one of the researchers who carried out the study, told TechXplore. "We saw the real challenges in running agile software projects and the serious lack of meaningful support for software teams and practitioners. We also saw the potential of AI in offering significant support for managing agile projects, not only in automating routine tasks, but also in learning and harvesting valuable insights from project data for making predictions and estimations, planning and recommending concrete actions."
Artificial Intelligence is starting to deliver on its promise, but widespread adoption is essential to help drive the UK economy, says IBM's Bill Kelleher There is little doubt about the transformative benefit of AI. CBI research from last year shows that business leaders see the adoption of AI and other technologies as vital for increasing levels of productivity across the economy. This is echoed by a recent IBM Institute for Business Value study of 5,000 C-Suite executives, which revealed that 82 per cent of enterprises are now either implementing or considering an AI solution. This year Prime Minister Theresa May also called out her commitment to AI. Her vision is for the UK to become the best place in the world for businesses developing and deploying AI to start, grow and thrive.
In this blog post, we will talk about deep learning: its use and business implications. We will then give an overview of the R&D efforts that Qubole is conducting in this area with respect to GPU support and distributed training. The last few years have seen considerable interest in artificial intelligence (AI) and machine learning (ML), specifically in the area of deep learning. These terms are used interchangeably and very often confused with each other. Let's take a look at the relationship between these technologies and the reason for the popularity of deep learning.
This book is an introductory overview of Ethem's detailed text on ML. The text itself has gotten mostly mixed or bad reviews due to a lot of math and algorithms notated without a lot of detailed explanations, however, this is a general reader intro and doesn't go into math, algos in detail, trees, Bayesian logic or even pseudocode, it is more an up to date overview of the field as it exists at this writing. Alpaydin's expensive text, btw, is also available in a very inexpensive Asian edition here on Amazon if you want to brave that difficult book without a lot of investment (Introduction To Machine Learning 3Rd Edition). The present volume is sortof a "ML for Dummies" only updated for the current craze with big data management. There is a lot of history and background that an experienced ML person will find too basic, but as a High School intro or general interested reader intro it is excellent.
Despite their high predictive performance, many machine learning techniques remain black boxes because it is difficult to understand the role of each feature and how it combines with others to produce a prediction. However, users need to understand and trust the decisions made by machine learning models, especially in sensitive fields such as medicine. For this reason, there is an increasing need of methods able to explain the individual predictions of a model, that is, a way to understand what features made the model give its prediction for a specific instance. We have a neural network (the machine learning model) trained as an image classifier. This model would give a probability (let's say 0.98) that a cat appears in the picture (an observation), so we could say that "our model predicts that this is a cat with a probability of 0.98".
This project contains an overview of recent trends in deep learning based natural language processing (NLP). It covers the theoretical descriptions and implementation details behind deep learning models, such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and reinforcement learning, used to solve various NLP tasks and applications. The overview also contains a summary of state of the art results for NLP tasks such as machine translation, question answering, and dialogue systems. There are various ways to contribute to this project. Refer to the issue section of the GitHub repository to learn more about how you can help.