Microsoft's quest to build computing systems that understand the world around them doesn't end with the company's Project Oxford machine-learning technology. Researchers at the Redmond, Wash., software maker are also developing systems that mimic how humans pull information from the things they see. "When a person is asked about something in a photo, they're taking in a lot of details--a lot of words--to answer questions about it," blogged Microsoft spokesperson Athima Chansanchai. "Now, a team of Microsoft researchers, together with colleagues from Carnegie Mellon University, has created a system that uses computer vision, deep learning and language understanding to analyze images and answer questions the same way humans would." Together, the researchers created a model that "applies multi-step reasoning to answer questions about pictures," said Chansanchai.

Humans do not reboot their understanding of language each time we hear a sentence. Given an article, we grasp the context based on our previous understanding of those words. One of the defining characteristics we possess is our memory (or retention power). Can an algorithm replicate this? The first technique that comes to mind is a neural network (NN).

Artificial intelligence (AI) refers to computer systems that perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision making or translation. More recently, AI has often revolved around the use of algorithms. An algorithm is a set of instructions that a computer can execute. Owing to such advanced technologies, farmers today can get watering assistance to make production more efficient; flu epidemics can be predicted, thereby enabling better medical preparation; intelligent surgical robotics can assist doctors; and, in Norway, a more balanced gender representation in the public media landscape is possible when AI tracks how much airtime is given to females on the Norwegian news programme'Dagsnytt 18'. These are merely a few examples of areas that benefit from AI and robot technology. Other less positive examples of AI include efforts to build applications, such as a'gaydar', which is an algorithm that researchers claim will reveal a person's sexual orientation; facial recognition systems that are biased towards white males, like Google's deep-learning algorithm, which consequently classified a picture of a black woman as a gorilla; or China's social credit system, whereby the Chinese government uses AI and big data as a tool to socially control its citizens.

Coronary heart disease (CHD) is one of the leading causes of death in the UK; it's responsible for more than 66,000 deaths each year, and it's estimated that 2.3 million people in the UK are currently living with the disease. Imaging is an important part of diagnosing people and managing treatment with a range of cardiovascular diseases, and the NHS uses Echo, MRI and CT scans to do this. A CT scan is used to do a coronary CT angiography, which involves taking images of the heart blood vessels and using the CT scan with some dye contrast into the blood vessels. This enables doctors to see if blood vessels are narrowing, causing patients to have chest pain or angina. The test has been around for the last 10 or 15 years but has increased in usage as radiation doses have decreased dramatically.

Deep learning (DL) is a data science term that has become a hot buzzword in tech, business and marketing. Along with artificial intelligence (AI) and machine learning (ML), deep learning is a technology that is now coming into its own, and together, all three are poised to begin ushering in massive changes to practically every industry on the planet. Sounds like a pretty big deal, right? It is – so let's try and get our heads around what deep learning is, does, and aims to achieve. To do so, we need to understand the differences between AI, ML and DL.

In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics. As data science is a broad discipline, I start by describing the different types of data scientists that one may encounter in any business setting: you might even discover that you are a data scientist yourself, without knowing it. As in any scientific discipline, data scientists may borrow techniques from related disciplines, though we have developed our own arsenal, especially techniques and algorithms to handle very large unstructured data sets in automated ways, even without human interactions, to perform transactions in real-time or to make predictions. To get started and gain some historical perspective, you can read my article about 9 types of data scientists, published in 2014, or my article where I compare data science with 16 analytic disciplines, also published in 2014. I also wrote about the ABCD's of business processes optimization where D stands for data science, C for computer science, B for business science, and A for analytics science.

The LF Deep Learning Foundation is now accepting proposals for the contribution of projects. The LF Deep Learning Foundation, a community umbrella project of The Linux Foundation with the mission of supporting artificial intelligence, machine learning and deep learning open source projects, is working to build a self-sustaining ecosystem of projects. Having a clear roadmap for how to contribute projects is a first step. Contributed projects operate under their own technical governance with collaboration resources allocated and provided by the Governing Board of the LF Deep Learning Foundation. Membership in the LF Deep Learning Foundation is not required to propose a project contribution.

The shockingly fast sell-out of the 2018 Neural Information Processing Systems (NIPS) conference put industry on notice: artificial intelligence is poised to break out of the hype cycle and become an important element in strategic business planning. A lot has been written about AI over the last year, but as the many papers and presentations from NIPS showed, much of what has been offered for public consumption has been superficial and, in some cases, misleading. AI is neither a simple panacea poised to solve all our business problems, nor is it a looming evil ready to usher in a dystopian future. It is a powerful tool that can aid decision making in a fast-moving, digital business climate--and there's ample evidence that it's already happening. Among the lucky few who were able to secure credentials to the conference, and as machine learning practitioners who have spent the better part of their careers involved with AI and machine learning, we came away with a look into the future of AI.

Neural networks are trained using stochastic gradient descent and require that you choose a loss function when designing and configuring your model. There are many loss functions to choose from and it can be challenging to know what to choose, or even what a loss function is and the role it plays when training a neural network. In this post, you will discover the role of loss and loss functions in training deep learning neural networks and how to choose the right loss function for your predictive modeling problems. Loss and Loss Functions for Training Deep Learning Neural Networks Photo by Ryan Albrey, some rights reserved. A deep learning neural network learns to map a set of inputs to a set of outputs from training data. We cannot calculate the perfect weights for a neural network; there are too many unknowns.

Online social platforms have been the battlefield of users with different emotions and attitudes toward each other in recent years. While sexism has been considered as a category of hateful speech in the literature, there is no comprehensive definition and category of sexism attracting natural language processing techniques. Categorizing sexism as either benevolent or hostile sexism is so broad that it easily ignores the other categories of sexism on social media. Sharifirad S and Matwin S 2018 proposed a well-defined category of sexism including indirect harassment, information threat, sexual harassment and physical harassment, inspired from social science for the purpose of natural language processing techniques. In this article, we take advantage of a newly released dataset in SemEval-2018 task1: Affect in tweets, to show the type of emotion and intensity of emotion in each category. We train, test and evaluate different classification methods on the SemEval- 2018 dataset and choose the classifier with highest accuracy for testing on each category of sexist tweets to know the mental state and the affectual state of the user who tweets in each category. It is a nice avenue to explore because not all the tweets are directly sexist and they carry different emotions from the users. This is the first work experimenting on affect detection this in depth on sexist tweets. Based on our best knowledge they are all new contributions to the field; we are the first to demonstrate the power of such in-depth sentiment analysis on the sexist tweets.