Authorities have said that before going to Pakistan, Farekh and Imam frequently watched videos promoting violent jihad, including online lectures by Anwar Al-Awlaki, the U.S.-born, Yemen-based militant preacher affiliated with al Qaeda in the Arabian Peninsula who was killed in a U.S. drone attack in 2011.

FinTech is a lucrative, yet quite saturated market. In order to stay competitive, businesses should keep track of the emerging trends and be able to capitalize on them before their competitors do. Artificial Intelligence is currently among the most promising FinTech trends. Leading financial brands such as Capital One, MasterCard, as well as hundreds of startups have set the pace for the adoption of virtual financial advisors. If you want to stay ahead of your competition or simply explore the opportunities for AI in fintech, this article is for you.

Voters have a right to keep their political beliefs private. But according to some researchers, it won't be long before a computer program can accurately guess whether people are liberal or conservative in an instant. All that will be needed are photos of their faces. Michal Kosinski – the Stanford University professor who went viral last week for research suggesting that artificial intelligence (AI) can detect whether people are gay or straight based on photos – said sexual orientation was just one of many characteristics that algorithms would be able to predict through facial recognition. Using photos, AI will be able to identify people's political views, whether they have high IQs, whether they are predisposed to criminal behavior, whether they have specific personality traits and many other private, personal details that could carry huge social consequences, he said.

We are drowning in content. Information in more readily available than ever before, but it has become overwhelming. How can employees find the content they need to be more productive and advance? In this webinar, we will discuss how artificial intelligence (AI) technology has been developed to solve the problem of content discovery by sifting through vast quantities of materials to make available the right information at the right time for the individual's needs.

Learn to build your own generative adversarial network using TensorFlow, with this free interactive tutorial, "General adversarial networks for beginners." If you ask a child to draw a cat, you'll learn more about the child than you will about cats. In the same way, asking neural networks to generate images helps us see how they reason about the information they're given. It's often difficult to interpret neural networks--that is, to relate their functioning to human intuition--and generative algorithms offer a way to make neural nets explain themselves. Neural networks are most commonly implemented as classifiers--models that are able to distinguish, say, an image of a cat from an image of a dog, or a stop sign from a fire hydrant.

What is Artificial Intelligence (AI)? Artificial Intelligence (AI) is the study of computer science focusing on developing software or machines that exhibit human intelligence. "AI is a broad topic ranging from simple calculators to self-steering technology to something that might radically change the future." Predictive systems– These AI are made to look at statistical data and form valuable conclusions. . Editing Software's– Here AIs suggest the ways that can be used to make pictures and texts more attractive. .

While having myself a strong mathematical background, I have developed an entire data science and machine learning framework (mostly for data science automation) that is almost free of mathematics, and known as deep data science. You will see that you can learn serious statistical concepts (including limit theorems) without knowing mathematics, much less probabilities or random variables. Anyway, for algorithms processing large volume of data in nearly real-time, computational complexity is still very important: read my article about how bad so many modern algorithms are and could benefit from some lifting, with faster processing time allowing to take into account more metrics, more data, and more complicated metrics, to provide better results. It looks like f(n), as n tends to infinity, is infinitely smaller than log n, log(log n), log(log(log n))), and so on, no matter how many (finite number of) nested log's you have.

Compared with artificial neural networks (ANNs), spiking neural networks (SNNs) are promising to explore the brain-like behaviors since the spikes could encode more spatio-temporal information. Although pre-training from ANN or direct training based on backpropagation (BP) makes the supervised training of SNNs possible, these methods only exploit the networks' spatial domain information which leads to the performance bottleneck and requires many complicated training skills. Another fundamental issue is that the spike activity is naturally non-differentiable which causes great difficulties in training SNNs. To this end, we build an iterative LIF model that is more friendly for gradient descent training. By simultaneously considering the layer-by-layer spatial domain (SD) and the timing-dependent temporal domain (TD) in the training phase, as well as an approximated derivative for the spike activity, we propose a spatio-temporal backpropagation (STBP) training framework without using any complicated technology. We achieve the best performance of multi-layered perceptron (MLP) compared with existing state-of-the-art algorithms over the static MNIST and the dynamic N-MNIST dataset as well as a custom object detection dataset. This work provides a new perspective to explore the high-performance SNNs for future brain-like computing paradigm with rich spatio-temporal dynamics.

We study the misclassification error for community detection in general heterogeneous stochastic block models (SBM) with noisy or partial label information. We establish a connection between the misclassification rate and the notion of minimum energy on the local neighborhood of the SBM. We develop an optimally weighted message passing algorithm to reconstruct labels for SBM based on the minimum energy flow and the eigenvectors of a certain Markov transition matrix. The general SBM considered in this paper allows for unequal-size communities, degree heterogeneity, and different connection probabilities among blocks. We focus on how to optimally weigh the message passing to improve misclassification.

In this paper, we propose and study opportunistic bandits - a new variant of bandits where the regret of pulling a suboptimal arm varies under different environmental conditions, such as network load or produce price. When the load/price is low, so is the cost/regret of pulling a suboptimal arm (e.g., trying a suboptimal network configuration). Therefore, intuitively, we could explore more when the load is low and exploit more when the load is high. Inspired by this intuition, we propose an Adaptive Upper-Confidence-Bound (AdaUCB) algorithm to adaptively balance the exploration-exploitation tradeoff for opportunistic bandits. We prove that AdaUCB achieves $O(\log T)$ regret with a smaller coefficient than the traditional UCB algorithm. Furthermore, AdaUCB achieves $O(1)$ regret when the exploration cost is zero if the load level is below a certain threshold. Last, based on both synthetic data and real-world traces, experimental results show that AdaUCB significantly outperforms other bandit algorithms, such as UCB and TS (Thompson Sampling), under large load fluctuations.