Annotating and labeling datasets for machine learning problems is an expensive and time-consuming process for computer vision and natural language scientists. However, a new deep learning approach is being used to decode, localize, and reconstruct image and video captions in seconds, making the machine-generated captions more reliable and trustworthy. To solve this problem, researchers at the Machine Learning Center at Georgia Tech (ML@GT) and Facebook have created the first cyclical algorithm that can be applied to visual captioning models. The model is able to use the three-step processing during training to make the model more visually-grounded without human annotations or introducing additional computations when deployed, saving researchers time and money on their datasets. The algorithm employs attention mechanisms, an intuitive concept for humans, when looking at a photo or video.

Following the amazing turn in of redditors for previous lectures, we are organizing another free zoom lecture for the reddit community. In this next lecture Dr. Marc Eder will talk about his research - Maximizing Computer Visions's Field. This talk will introduce the emerging field of 360 computer vision, and provide an overview of the spherical distortion problem, highlighting how this distortion affects many of the highest profile problems in computer vision, from deep learning to structure-from-motion and SLAM. It will survey some of the existing work on the topic, and identify 3 guiding principles that drive a general solution to the problem. Finally, we will conclude with some opportunities for further research and some big picture takeaways from work thus far.

Offered by Stanford University. Artificial intelligence (AI) has transformed industries around the world, and has the potential to radically alter the field of healthcare. Imagine being able to analyze data on patient visits to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system -- such as social media, purchases made using credit cards, census records, Internet search activity logs that contain valuable health information, and you’ll get a sense of how AI could transform patient care and diagnoses. In this specialization, we'll discuss the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically. This specialization is designed for both healthcare providers and computer science professionals, offering insights to facilitate collaboration between the disciplines.

We decide on our careers long before we ever step foot in our workplace. We take cues from our family and dramatized media depictions of professionals who often look and act nothing like their real-life counterparts. Therefore, to solve the gender inequality in technical roles, we need to kickstart our efforts in college or even high school – when students are open-minded, and there is still time to make real change. Because later in life, for every dollar men earn – women earn 81 cents. One woman trailblazing change is Professor Daniela Rus, the first woman director of the Massachusetts Institute of Technology's Computer Science Artificial Intelligence Lab, or MIT CSAIL for short.

A lot of people seem to confuse the two terms in the context of machine learning. This post will try to clarify what we mean by the two, where each one is useful, and how they are applied. I personally understood it when I had a class called Intelligent Data and Probabilistic Inference (by Duncan Gillies) in my Master's degree at Imperial College London some years back. Here, I will present a couple of examples in order to intuitively understand the difference. You observe the grass in your backyard. You infer it has rained.

Attempts to render deep learning models interpretable, data-efficient, and robust have seen some success through hybridisation with rule-based systems, for example, in Neural Theorem Provers (NTPs). These neuro-symbolic models can induce interpretable rules and learn representations from data via back-propagation, while providing logical explanations for their predictions. However, they are restricted by their computational complexity, as they need to consider all possible proof paths for explaining a goal, thus rendering them unfit for large-scale applications. We present Conditional Theorem Provers (CTPs), an extension to NTPs that learns an optimal rule selection strategy via gradient-based optimisation. We show that CTPs are scalable and yield state-of-the-art results on the CLUTRR dataset, which tests systematic generalisation of neural models by learning to reason over smaller graphs and evaluating on larger ones. Finally, CTPs show better link prediction results on standard benchmarks in comparison with other neural-symbolic models, while being explainable. All source code and datasets are available online, at https://github.com/uclnlp/ctp.

Machine Reading Comprehension (MRC) aims to extract answers to questions given a passage. It has been widely studied recently, especially in open domains. However, few efforts have been made on closed-domain MRC, mainly due to the lack of large-scale training data. In this paper, we introduce a multi-target MRC task for the medical domain, whose goal is to predict answers to medical questions and the corresponding support sentences from medical information sources simultaneously, in order to ensure the high reliability of medical knowledge serving. A high-quality dataset is manually constructed for the purpose, named Multi-task Chinese Medical MRC dataset (CMedMRC), with detailed analysis conducted. We further propose the Chinese medical BERT model for the task (CMedBERT), which fuses medical knowledge into pre-trained language models by the dynamic fusion mechanism of heterogeneous features and the multi-task learning strategy. Experiments show that CMedBERT consistently outperforms strong baselines by fusing context-aware and knowledge-aware token representations.

Ethical concerns regarding artificial intelligence has led to increased self-scrutiny from the machine learning community. Algorithmic fairness has proved to be a challenging research area. For one, a broadly appealing definition of fairness has long eluded philosophers, social scientists, and, more recently, the machine learning community. Currently, finding mathematical formulations of fairness is an active area of research in algorithmic fairness [Dwork et al., 2012, Chouldechova, 2016, Joseph et al., 2016]. While it is easy to agree on what is unfair, it is much harder to agree on what, exactly, is fair. For instance, ProPublica's eponymous article on machine bias [Angwin et al., 2016] uncovered prejudice against African-Americans in COMPAS (Correctional Offender Management Profiling for Alternative Sanctions), a recidivism prediction tool developed by Northpointe. But it turns out if different fairness criteria are used than those used in the ProPublica investigation, COMPAS can be more favourably viewed [Dieterich et al., 2016, Corbett-Davies et al., 2017]. This type of dissent is unavoidable as several works have shown that certain fairness criteria cannot be simultaneously satisfied [Hardt et al., 2016, Kleinberg, 2018]. Leaving aside for now the debate over the correct definition of fairness, most works in algorithmic fairness are quite straightforward once a fairness measure is settled on.

Wondering how to make money online in 2020? Making money online is easier than ever – whether you're a student who wants to make a little side money every month, a blogger who wants to monetize their blog, or a would-be entrepreneur who wants to build a business online. Whatever your goal is, the possibilities are there: you just need to figure out what you can do and figure out the best plan to help you reach your goals – which is what I want to help you do with this guide. There are a lot of ways to make a little extra cash online, like completing surveys for a couple of $ (literally, a couple!) but my focus here is on strategies that can help you make real money. Viable options that will help you make either a few hundred dollars a month or even thousands, depending on what your goals are and how much work you're willing to put in. Because at the end of the day, it's up to you how much you want to make and how much time and effort you can invest in this project, depending on your workload and your objectives. And all you really need to get started is motivation, an Internet connection and (literally!) a few dollars. In this epic guide of over 21,000 words, you'll find 51 ways to make money online – there's something here for every skill and every knowledge level – start reading or just jump directly to the money-making strategies you're most interested in, by clicking on the links below: Join my free 15k-word email course on how to make money online and earn up to $10k in 90 days from the comfort of your sofa (or bed!) Disclaimer: Some of the links included in this guide are affiliate links on the basis of which I can earn a commission, at no additional cost to you. Please know that any software tools or services I recommend in this article are all tried and tested by me – I would never recommend something that I don't know for a fact, works. I'm going to get this right out of the way from the start: Get rich quick schemes are just what their names says – schemes. They sound good in theory (sometimes!) but the truth is, the only people that will get rich from them are those who are behind them. Achieving true success – both online and in real life – is a difficult and time-consuming process and there are very few exceptions to this rule. Even those who appear to have become rich overnight, if you look deeper, you'll see that there are months and even years of work behind their success, and oftentimes, even failure. As for starting to make money immediately? There are numerous ways to monetize your skills and knowledge and start making money online within a few days or weeks.

Across the U.S., more and more students are enrolling in introductory A.I. and machine learning classes, according to The A.I. Index 2019 Annual Report (PDF) produced by Stanford University. That's good news for students everywhere, because it means that more schools will inevitably begin offering this sort of coursework. It's also good for employers desperate for A.I. and machine learning specialists, because it means that pool of talent will likely expand over the next few years as these students enter the workforce. At Stanford itself, enrollment in the school's "Introduction to Artificial Intelligence" course has grown "fivefold" between 2012 and 2018, according to the report. That's not even the most rapid uptake: At the University of Illinois at Urbana-Champaign, an "Introduction to Machine Learning" course grew twelvefold between 2010 and 2018, with the largest part of that spike occurring after 2015.