Accurate real time crime prediction is a fundamental issue for public safety, but remains a challenging problem for the scientific community. Crime occurrences depend on many complex factors. Compared to many predictable events, crime is sparse. At different spatio-temporal scales, crime distributions display dramatically different patterns. These distributions are of very low regularity in both space and time. In this work, we adapt the state-of-the-art deep learning spatio-temporal predictor, ST-ResNet [Zhang et al, AAAI, 2017], to collectively predict crime distribution over the Los Angeles area. Our models are two staged. First, we preprocess the raw crime data. This includes regularization in both space and time to enhance predictable signals. Second, we adapt hierarchical structures of residual convolutional units to train multi-factor crime prediction models. Experiments over a half year period in Los Angeles reveal highly accurate predictive power of our models.

How can we explain the predictions of a black-box model? In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. To scale up influence functions to modern machine learning settings, we develop a simple, efficient implementation that requires only oracle access to gradients and Hessian-vector products. We show that even on non-convex and non-differentiable models where the theory breaks down, approximations to influence functions can still provide valuable information. On linear models and convolutional neural networks, we demonstrate that influence functions are useful for multiple purposes: understanding model behavior, debugging models, detecting dataset errors, and even creating visually-indistinguishable training-set attacks.

In June on /r/MachineLearning we learned of funding to a popular (and essential) Python project, are treated to a collection of machine learning cheat sheets, see how deep learning is done on premium cable television, read about Andre Karpathy's new job, and are introduced to a new machine learning "IDE." This is good news for the project. For the first time ever, NumPy -- a core project for the Python scientific computing stack -- has received grant funding. The proposal, "Improving NumPy for Better Data Science" will receive $645,020 from the Moore Foundation over 2 years, with the funding going to UC Berkeley Institute for Data Science. The principal investigator is Dr. Nathaniel Smith.

O'Reilly and Intel Nervana are presenting the Artificial Intelligence Conference in San Francisco, September 17-20, 2017. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I speak with Naveen Rao, VP and GM of the Artificial Intelligence Products Group at Intel. In an earlier episode, we learned that scaling current deep learning models requires innovations in both software and hardware.

You don't have to be a data scientist to be fascinated by the world of machine learning, but a few travel guides might help you navigate the vast universe that also includes big data, artificial intelligence, and deep learning, along with a large dose of statistics and analytics. In this article, I'll look at three of the most popular machine learning libraries for Python. Released nearly a decade ago and primarily developed by a machine learning group at Université de Montréal, Theano is one of the most-used CPU and GPU mathematical compilers in the machine learning community. A 2016 paper, Theano: A Python framework for fast computation of mathematical expressions, provides a thorough overview of the library. "Several software packages have been developed to build on the strengths of Theano, with a higher-level user interface, more suitable for certain goals," the paper explains.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

With the advent of artificial general intelligence and self-designed intelligent programs, new and more intelligent AI will appear, rapidly creating ever smarter machines that will, eventually, surpass us. Artificial intelligence consists of large collections of connected computational units called artificial neurons, loosely analogous to the neurons in our brains. Mining the internet, social networks and Wikipedia, researchers have created large collections of images and text, enabling machines to classify images, recognise speech, and translate language. In my own AI research, for example, I apply deep neural networks to medical diagnostics, which has sometimes resulted in slightly better diagnoses than in the past, but nothing dramatic.

Deep neural networks (DNNs) have excellent representative power and are state of the art classifiers on many tasks. However, they often do not capture their own uncertainties well making them less robust in the real world as they overconfidently extrapolate and do not notice domain shift. Gaussian processes (GPs) with RBF kernels on the other hand have better calibrated uncertainties and do not overconfidently extrapolate far from data in their training set. However, GPs have poor representational power and do not perform as well as DNNs on complex domains. In this paper we show that GP hybrid deep networks, GPDNNs, (GPs on top of DNNs and trained end-to-end) inherit the nice properties of both GPs and DNNs and are much more robust to adversarial examples. When extrapolating to adversarial examples and testing in domain shift settings, GPDNNs frequently output high entropy class probabilities corresponding to essentially "don't know". GPDNNs are therefore promising as deep architectures that know when they don't know.

Deep learning massive success in almost every fields represents its ability to solve complex problems. The tradeoff between model complexity and accuracy is an important area of deep learning research. Very complex model with millions of parameters [8], [9] proved to the state of the art solution for many vision and natural language problems. A common way to measure the performance or generalizability of a deep learning model is to test it on a well discriminative validation/test set representing the variation of samples of the corresponding problem. Learning very complex model is a matter of the requirements of high computing power and huge dataset.

Earlier this month, CI&T had the honor of being the first Google Cloud partner to be featured in a series of events hosted by Google. Mars Cyrillo, CI&T's VP of the Machine Learning and Lucas Persona, CI&T Chief Digital Evangelist share their thoughts and use cases of AI, machine learning, and more and how to use them to solve business challenges.