BACKGROUND Deep learning has the potential to transform health care; however, substantial expertise is required to train such models. We sought to evaluate the utility of automated deep learning software to develop medical image diagnostic classifiers by health-care professionals with no coding--and no deep learning--expertise. METHODS We used five publicly available open-source datasets: retinal fundus images (MESSIDOR); optical coherence tomography (OCT) images (Guangzhou Medical University and Shiley Eye Institute, version 3); images of skin lesions (Human Against Machine [HAM] 10000), and both paediatric and adult chest x-ray (CXR) images (Guangzhou Medical University and Shiley Eye Institute, version 3 and the National Institute of Health [NIH] dataset, respectively) to separately feed into a neural architecture search framework, hosted through Google Cloud AutoML, that automatically developed a deep learning architecture to classify common diseases. Sensitivity (recall), specificity, and positive predictive value (precision) were used to evaluate the diagnostic properties of the models. The discriminative performance was assessed using the area under the precision recall curve (AUPRC).

These days you'll be hard-pressed to find someone who hasn't interrogated Siri (or Alexa), enjoyed the movie Netflix suggested, or fallen victim to purchasing that additional item Amazon recommended--all of which are only possible due to artificial intelligence. AI has been a field of study as far back as the 1950s, but advances have skyrocketed in recent years. These days AI is everywhere and has increasingly become part of all of our everyday lives. Thanks to AI, once tedious tasks are now simple, single-click activities. And as technology becomes even more pervasive, it will only continue to impact our personal and professional lives.

Learning to rank is an important problem in machine learning and recommender systems. In a recommender system, a user is typically recommended a list of items. Since the user is unlikely to examine the entire recommended list, partial feedback arises naturally. At the same time, diverse recommendations are important because it is challenging to model all tastes of the user in practice. In this paper, we propose the first algorithm for online learning to rank diverse items from partial-click feedback. We assume that the user examines the list of recommended items until the user is attracted by an item, which is clicked, and does not examine the rest of the items. This model of user behavior is known as the cascade model. We propose an online learning algorithm, cascadelsb, for solving our problem. The algorithm actively explores the tastes of the user with the objective of learning to recommend the optimal diverse list. We analyze the algorithm and prove a gap-free upper bound on its n-step regret. We evaluate cascadelsb on both synthetic and real-world datasets, compare it to various baselines, and show that it learns even when our modeling assumptions do not hold exactly.

The term "artificial intelligence" has been around since 1956, and yet many journalists are unfamiliar with its history and impact on the world today, even as its influence grows everywhere, including on how we gather and report the news. The next massive open online course (MOOC) in Spanish, and the Knight Center's first in partnership with Microsoft, will familiarize students with the foundations of artificial intelligence (AI) and how it impacts the news industry. "Artificial Intelligence: How to cover AI and understand its impact on journalism," will run from Oct. 22 to Nov. 25, 2018 and will be taught by Sandra Crucianelli, a veteran instructor for Knight Center MOOCs and a member of the International Consortium of Investigative Journalists (ICIJ). "The course will be a wonderful opportunity for those who have not yet become familiar with artificial intelligence technologies," Crucianelli said. "We will be sharing definitions, but also analyzing applications, examples and there also will be online discussions.

Hey guys, I'm looking to make an application that uses neural text to speech for my Python program. I'm not sure what open source SOTA is like, would love to get some reference repositories to check out, especially if they have demos.

Data management and data analytics are two critical fundamental resources that should be used by Thai enterprises and tech startups to develop innovative services backed by artificial intelligence (AI) technology, instead of working to develop intelligent products or services to compete with global players. Chai Wutiwiwatchai, research unit director of the National Electronics and Computer Technology Center (Nectec), said local enterprises can benefit from the many data sets held by state agencies through 20 ministries and the private sector. "Intelligent products and services driven by AI may not be easy to enter for local enterprises and startups, as there are too many global tech players and AI tech-embedded tools available for free in the market," he said. But the government must urgently digitise the existing data sets of all agencies, 70% of which are stored on paper and in portable document format (PDF) files. Speaking on the sidelines of the "AI Shapes the Future" forum last week, Mr Chai said innovative products and services embedded with AI tech have been increasingly accessible in the global market for four years, especially through popular use cases of image recognition, biometrics, cybersecurity and smart speakers.

Python is a dynamic modern object -oriented programming language. It is easy to learn and can be used to do a lot of things both big and small. Python is what is referred to as a high level language. That means it is a language that is closer to humans than computer. It is also known as a general purpose programming language due to it's flexibility.

Online learning may refer to the ones with batch size to be 1, but here I mean online reinforcement learning is the RL where the agent is updated at every timestep.

Recently rebooted GigaOm has started a new and excellent podcast Voices in AI, where host Byron Reese, @byronreese, himself a promiment author and speaker, talks with some of the leading minds in AI, Machine Learning, and Data Science. I have only had time to listen to 2 episodes so far and both have been really excellent - I cannot recommend this podcast series highly enough. A bunch of episodes have already been released and here are the first 6 episodes which should keep you glued to your headphones! Episode 1: A Conversation with Yoshua Bengio In this episode, Byron and Yoshua talk about knowledge, unsupervised learning, how the brain learns, creativity, and machine translation. Episode 5: A Conversation with Daphne Koller In this episode, Byron and Daphne talk about consciousness, personalized medicine, and transfer learning. I am very honored to be included in Episode 11: A Conversation with Gregory Piatetsky-Shapiro, where Byron and I talk about consciousness, jobs, data science, and transfer learning.

I would recommend that you start with Introduction to Statistical Learning with R (usually shortened as ISLR). A lot of people have adapted the examples to Python if you google a bit and it's an excellent book that hides just enough complexity to not be overwhelming. Plus, once you have a good understanding of all of it, you can either graduate to the more extensive version (Elements of Statistical Learning, usually shortened as ESL) for a more rigorous treatment of the same thing, or choose to go for something different like Bishop's Pattern Recognition and Machine Learning. ISLR is free as a pdf and has a corresponding MOOC. ESL doesn't, but is also free on the author's website.