Have you ever considered delivering a project that utilizes Semantic Technology or a Graph Database to validate your business case? One reason that drives people away from the direct application of such technology is that it is often considered too technical and hard to implement. While somewhat true, this is also misguided. There is simply a lack of organized, consistent resources focused on practical knowledge. This is one challenge we want to address with Ontotext's live, online training.

What do disease diagnostics, language learning, and image recognition have in common? All depend on the organization of collective intelligence: data ontologies. In this episode of the a16z Podcast, guests Luis von Ahn, founder of reCaptcha and Duolingo, Jay Komarneni, founder of HumanDX, a16z General Partner Vijay Pande, and a16z Partner Malinka Walaliyadde break down what data ontologies are, from the philosophical (Wittgenstein and Wikipedia!) to the practical (a doctor identifying a diagnosis), particularly as they apply to the field of healthcare and diagnosis. It is data ontologies, in fact, that enable not only human computation -- but that allow us to map out, structure, and scale knowledge creation online, providing order to how we organize massive amounts of information so that humans and machines can coordinate in a way that both understand.

As a popular tool for producing meaningful and interpretable models, large-scale sparse learning works efficiently when the underlying structures are indeed or close to sparse. However, naively applying the existing regularization methods can result in misleading outcomes due to model misspecification. In particular, the direct sparsity assumption on coefficient vectors has been questioned in real applications. Therefore, we consider nonsparse learning with the conditional sparsity structure that the coefficient vector becomes sparse after taking out the impacts of certain unobservable latent variables. A new methodology of nonsparse learning with latent variables (NSL) is proposed to simultaneously recover the significant observable predictors and latent factors as well as their effects. We explore a common latent family incorporating population principal components and derive the convergence rates of both sample principal components and their score vectors that hold for a wide class of distributions. With the properly estimated latent variables, properties including model selection consistency and oracle inequalities under various prediction and estimation losses are established for the proposed methodology. Our new methodology and results are evidenced by simulation and real data examples.

Real-world complex networks describe connections between objects; in reality, those objects are often endowed with some kind of features. How does the presence or absence of such features interplay with the network link structure? Although the situation here described is truly ubiquitous, there is a limited body of research dealing with large graphs of this kind. Many previous works considered homophily as the only possible transmission mechanism translating node features into links. Other authors, instead, developed more sophisticated models, that are able to handle complex feature interactions, but are unfit to scale to very large networks. We expand on the MGJ model, where interactions between pairs of features can foster or discourage link formation. In this work, we will investigate how to estimate the latent feature-feature interactions in this model. We shall propose two solutions: the first one assumes feature independence and it is essentially based on Naive Bayes; the second one, which relaxes the independence assumption assumption, is based on perceptrons. In fact, we show it is possible to cast the model equation in order to see it as the prediction rule of a perceptron. We analyze how classical results for the perceptrons can be interpreted in this context; then, we define a fast and simple perceptron-like algorithm for this task, which can process $10^8$ links in minutes. We then compare these two techniques, first with synthetic datasets that follows our model, gaining evidence that the Naive independence assumptions are detrimental in practice. Secondly, we consider a real, large-scale citation network where each node (i.e., paper) can be described by different types of characteristics; there, our algorithm can assess how well each set of features can explain the links, and thus finding meaningful latent feature-feature interactions.

When was the last time your insurance company realized you had a life change, like getting married or buying a house or having a baby, and designed a policy just for you? And presented it to you without being asked? Without encroaching on personal privacy, this is the vision of insurance in the future: personalized, affordable and adaptable. Artificial intelligence (AI) can help insurance companies understand their customers in powerful new ways and be proactive -- and competitive -- about serving their needs. You may have heard the terms analytics, advanced analytics, machine learning and AI.

Fast forward to 2017 I have spent 100's of hours working on Deep learning projects and the technology has become more and more accessible due to several advancements in software(ease of usage -- Keras, PyTorch), hardware(GPU becoming commercially viable for someone like me sitting in India -Not still cheap), availability of data, good books and MOOCS. After completing the 3 most popular MOOCS in deep learning from Fast.ai, deeplearning.ai/Coursera In this post I talk about 5 aspects of each course which will help you decide. I came across this course when reading an article in kddnudgets . For the first time I heard about Jeremy Howard, searched about him in Wikipedia and was impressed .

Artificial Intelligence and Machine Learning are terms that are currently filling the air in the tech world and across a range of industries, as plans for massive disruption begin to take shape. Both technologies are set to be central to the future, particularly in critical fields such as cybersecurity, and the financial services. Technology is developing at a gallop, and an ever widening skills gap is causing concern, driving the need for technology to fill in for the lack of human skills. However, it is common that the two terms are haphazardly thrown together under the banner of automation, or simply used together without distinction. Because of this, CBR is setting out to find out the main differences, what they are best applied to, and who is standing out in each of the popular spaces.

When I started learning deep learning I spent two weeks researching. I selected tools, compared cloud services, and researched online courses. In retrospect, I wish I could have built neural networks from day one. That's what this article is set out to do. You don't need any prerequisites.

Google's AlphaGo may have unseated Ke Jie as the Go world champion but it's no smarter than a kindergartner. A study published Saturday showed Google's artificial intelligence technology scored best out of 50 systems that Chinese researchers tested against an AI scale they created, although it's still no smarter than a six year old, CNBC reported Monday. AI systems have developed so quickly that it's been able to act as an assistant, take an exam and even outperform us at strategy games.

I've read more than my fair share of articles about all-things AI, including ones on machine learning and deep learning, and two things are clear: (1) my brain is about to short-circuit faster than a vintage robot, and (2) more often than not, the media uses these terms interchangeably even though they're not the same thing. First of all: know that machine learning and deep learning are related and that both fall under the AI umbrella. Think of the three as concentric circles: deep learning is a type of machine learning, machine learning is part of AI, and AI encompasses the entire field of study. In a nutshell, machine learning is an automated process that uses data and math (algorithms) to uncover ("learn") new information without human intervention. Because the machine continuously "self-learns" (or "self-trains"), humans don't need to write code for each process along the way (huge time saver!).