Artificial intelligence (AI) is quickly becoming the backbone of many software features and services we use everyday. Anytime you search for specific image in Google Photos or read through Facebook's (NASDAQ:FB) NewsFeed, you're using a form of AI software and algorithms. These are just small AI examples, but there are much larger artificial intelligence trends that are changing how we drive our cars, use energy, answer questions, and connect with others. The companies behind these AI trends are pushing the technology forward like never before, and they (and their investors) could see major benefits just around the corner. Driverless cars are coming, there's no debating that.

Since the early days of the current Big Data and Data Science movements, many data scientists have been devoted to applications of those data collections and advanced algorithms to solving societal problems. Subsequently, informal groups (e.g., meetups) and entire organizations have coalesced around particular approaches (e.g., data hackathons; and online competitions), or particular societal needs (e.g., DataKind.org; The range and depth of those applications have a tremendous reach: from human health to ocean health, from pet shelters to sheltering persons in need, from healthy lifestyle change to climate change, from smart cities to smart farms, from precision law enforcement to precision medicine, from improving commuter traffic to stopping human trafficking. The world is data now. Data are the new asset to fuel insightful discoveries, smarter decisions, essential innovations, and better outcomes.

The sale of sex has shifted online, as pimps are now using underground websites, social media and mobile apps to conduct business. A study found that pimps are adopting new technologies and employing online marketing strategies that are generating an annual income of about 75,000 to 100,000. Following interviews with 71 pimps, researchers discovered that these procurers hide their illegal services under massages or dates on websites in order to communicate with clients. The sale of sex has shifted online, as pimps are now using underground websites, social media and mobile apps to conduct business. Michigan State University and Loyola University Chicago in interviewed 71 pimps to determine how their marketing decisions are influenced by police enforcement of online prostitution.

Imagine the difference between a buffalo stampede and a cheeseburger. Both are tasty sources of protein. The difference lies in their requisite culinary tools. Predictive Analytics (PA) is the buffalo stampede of quantitative research: data is big, fast, and shaggy. Interactive Analytics (IA) is a cheeseburger: structured, convenient, and easy to grill.

Nick Triantos spent the past year trying to commercialize the technology being developed at SRI International, the Stanford research offshoot that helped invent the Internet and Siri, among other things. Now he has joined Ignition Partners at its new office in Los Altos, where he says he will be able to help create companies from a broader range of innovation. Triantos said his focus will be on business-focused startups, particularly ones working in artificial intelligence, cybersecurity and augmented and virtual reality. But he doesn't expect that will include startups in the currently hot space of chatbots, despite his background with voice recognition and machine learning at SRI. The following Q&A about these and other topics has been edited for length and clarity. What was your role at SRI and why are you leaving there? Unfortunately, not many people know about SRI.

Correction: Because of incorrect data initially provided in the Narrative Science report, a previous version of this article reported that 56% of survey respondents were planning to deploy AI technologies within the next two years. The study also found that a lack of data science talent is currently the biggest barrier to adopting AI, with 59% of respondents naming it as the primary obstacle to getting value out of the Big Data available to them. A recent study from Square Root showed that despite spending up to 20 hours per week collecting, analyzing and reporting on data, nearly one in three companies fail to act on their collected data. The Narrative Science study also found that 61% of the respondents who have an innovation strategy are using AI to identify opportunities in data that would be otherwise missed. "One of the major compelling differences in this year's survey compared to last year is the changing perception of AI technologies," said Stuart Frankel, CEO of Narrative Science in an an announcement.

These are lecture notes that are based on the lectures from a class I taught on the topic of Spectral Graph Methods at UC Berkeley during the Spring 2015 semester.

Determinantal point processes (DPPs) are an elegant model for encoding probabilities over subsets, such as shopping baskets, of a ground set, such as an item catalog. They are useful for a number of machine learning tasks, including product recommendation. DPPs are parametrized by a positive semi-definite kernel matrix. Recent work has shown that using a low-rank factorization of this kernel provides remarkable scalability improvements that open the door to training on large-scale datasets and computing online recommendations, both of which are infeasible with standard DPP models that use a full-rank kernel. In this paper we present a low-rank DPP mixture model that allows us to represent the latent structure present in observed subsets as a mixture of a number of component low-rank DPPs, where each component DPP is responsible for representing a portion of the observed data. The mixture model allows us to effectively address the capacity constraints of the low-rank DPP model. We present an efficient and scalable Markov Chain Monte Carlo (MCMC) learning algorithm for our model that uses Gibbs sampling and stochastic gradient Hamiltonian Monte Carlo (SGHMC). Using an evaluation on several real-world product recommendation datasets, we show that our low-rank DPP mixture model provides substantially better predictive performance than is possible with a single low-rank or full-rank DPP, and significantly better performance than several other competing recommendation methods in many cases.

Finding the most effective way to aggregate multi-subject fMRI data is a long-standing and challenging problem. It is of increasing interest in contemporary fMRI studies of human cognition due to the scarcity of data per subject and the variability of brain anatomy and functional response across subjects. Recent work on latent factor models shows promising results in this task but this approach does not preserve spatial locality in the brain. We examine two ways to combine the ideas of a factor model and a searchlight based analysis to aggregate multi-subject fMRI data while preserving spatial locality. We first do this directly by combining a recent factor method known as a shared response model with searchlight analysis. Then we design a multi-view convolutional autoencoder for the same task. Both approaches preserve spatial locality and have competitive or better performance compared with standard searchlight analysis and the shared response model applied across the whole brain. We also report a system design to handle the computational challenge of training the convolutional autoencoder.

Outlier detection amounts to finding data points that differ significantly from the norm. Classic outlier detection methods are largely designed for single data type such as continuous or discrete. However, real world data is increasingly heterogeneous, where a data point can have both discrete and continuous attributes. Handling mixed-type data in a disciplined way remains a great challenge. In this paper, we propose a new unsupervised outlier detection method for mixed-type data based on Mixed-variate Restricted Boltzmann Machine (Mv.RBM). The Mv.RBM is a principled probabilistic method that models data density. We propose to use \emph{free-energy} derived from Mv.RBM as outlier score to detect outliers as those data points lying in low density regions. The method is fast to learn and compute, is scalable to massive datasets. At the same time, the outlier score is identical to data negative log-density up-to an additive constant. We evaluate the proposed method on synthetic and real-world datasets and demonstrate that (a) a proper handling mixed-types is necessary in outlier detection, and (b) free-energy of Mv.RBM is a powerful and efficient outlier scoring method, which is highly competitive against state-of-the-arts.