The plan to hold America's first sextech hackathon wasn't hatched by Silicon Valley brogrammers between swigs of beer and writing code. In an origin story far more fitting, the vision crystallized after a sexual education workshop held at a polyamorous, sex positive living communityin Brooklyn. "You were the ideas man and pulled it all together," Bryony Cole, The Future Of Sex podcast host, said to organizer Andriy Yaroshenko the morning of the innagural event, which was held at the ThoughtWorks office in Manhattan. The workshop where Cole and Yaroshenko met was at the Hacienda Villa housing community, which hosts similar events throughout the year, though they previously connected on Twitter. The pair formed a friendship over a mutual desire to change the way we talk and think about sex.

You might have noticed lot of excitement around AI at the moment, with the likes of Google, Facebook, Microsoft, and Amazon all vying to be the loudest voice promoting the newest buzzword. In some cases it is even being heralded as the Fourth Industrial Revolution. With AI-powered Go and chess champions, it seems like'the singularity' is fast approaching. Even Apple's recent WWDC keynotes saw software chief Craig Federighi casually announce new Core ML APIs aimed at attracting AI-conscious coders, APIs that will increase the use of things like facial recognition and semantic text cognition in future Apple products. To be clear though, AI isn't quite yet the stuff of science fiction - although SkyNet may actually exist, what we have today doesn't resemble the Terminators, or even 2001: A Space Odyssey's Hal, luckily for us all.

Designing effective exploration-exploitation algorithms in Markov decision processes (MDPs) with large state-action spaces is the main challenge in reinforcement learning (RL). In fact, the learning performance degrades with the number of states and actions in the MDP. However, MDPs often exhibit a low-dimensional latent structure in practice, where a small hidden state is observable through a possibly large number of observations. In this paper, we study the setting of rich-observation Markov decision processes (\richmdp), where hidden states are mapped to observations through an injective mapping, so that an observation can be generated by only one hidden state. While this mapping is unknown a priori, we introduce a spectral decomposition method that consistently estimates how observations are clustered in the hidden states. The estimated clustering is then integrated into an optimistic algorithm for RL (UCRL), which operates on the smaller clustered space. The resulting algorithm proceeds through phases and we show that its per-step regret (i.e., the difference in cumulative reward between the algorithm and the optimal policy) decreases as more observations are clustered together and finally, matches the (ideal) performance of an RL algorithm running directly on the hidden MDP.

Topic models such as Latent Dirichlet Allocation (LDA) have been widely used in information retrieval for tasks ranging from smoothing and feedback methods to tools for exploratory search and discovery. However, classical methods for inferring topic models do not scale up to the massive size of today's publicly available Web-scale data sets. The state-of-the-art approaches rely on custom strategies, implementations and hardware to facilitate their asynchronous, communication-intensive workloads. We present APS-LDA, which integrates state-of-the-art topic modeling with cluster computing frameworks such as Spark using a novel asynchronous parameter server. Advantages of this integration include convenient usage of existing data processing pipelines and eliminating the need for disk writes as data can be kept in memory from start to finish. Our goal is not to outperform highly customized implementations, but to propose a general high-performance topic modeling framework that can easily be used in today's data processing pipelines. We compare APS-LDA to the existing Spark LDA implementations and show that our system can, on a 480-core cluster, process up to 135 times more data and 10 times more topics without sacrificing model quality.

Traditional recommendation systems rely on past usage data in order to generate new recommendations. Those approaches fail to generate sensible recommendations for new users and items into the system due to missing information about their past interactions. In this paper, we propose a solution for successfully addressing item-cold start problem which uses model-based approach and recent advances in deep learning. In particular, we use latent factor model for recommendation, and predict the latent factors from item's descriptions using convolutional neural network when they cannot be obtained from usage data. Latent factors obtained by applying matrix factorization to the available usage data are used as ground truth to train the convolutional neural network. To create latent factor representations for the new items, the convolutional neural network uses their textual description. The results from the experiments reveal that the proposed approach significantly outperforms several baseline estimators.

We present a model for random simple graphs with a degree distribution that obeys a power law (i.e., is heavy-tailed). To attain this behavior, the edge probabilities in the graph are constructed from Bertoin-Fujita-Roynette-Yor (BFRY) random variables, which have been recently utilized in Bayesian statistics for the construction of power law models in several applications. Our construction readily extends to capture the structure of latent factors, similarly to stochastic blockmodels, while maintaining its power law degree distribution. The BFRY random variables are well approximated by gamma random variables in a variational Bayesian inference routine, which we apply to several network datasets for which power law degree distributions are a natural assumption. By learning the parameters of the BFRY distribution via probabilistic inference, we are able to automatically select the appropriate power law behavior from the data. In order to further scale our inference procedure, we adopt stochastic gradient ascent routines where the gradients are computed on minibatches (i.e., subsets) of the edges in the graph.

This blog was coauthored by Lewis Fishgold and Rob Emanuele. Aerial and satellite imagery gives us the unique ability to look down and see the earth from above. It is being used to measure deforestation, map damaged areas after natural disasters, spot looted archaeological sites, and has many more current and untapped use cases. At Azavea, we understand the potential impact that imagery can have on our understanding of the world. We also understand that the enormous and ever-growing amount of imagery presents a significant challenge: how can we derive value and insights from all of this data? There are not enough people to look at all of the images all of the time.

Sebastian Raschka is the author of the bestselling book "Python Machine Learning." As a Ph.D. candidate at Michigan State University, he is developing new computational methods in the field of computational biology. Sebastian has many years of experience with coding in Python and has given several seminars on the practical applications of data science and machine learning. Sebastian loves to write and talk about data science, machine learning, and Python, and he is really motivated to help people developing data-driven solutions without necessarily requiring a machine learning background. Why did I bother writing this? Well, here is one of the most trivial yet life-changing insights and worldly wisdoms from my former professor that has become my mantra ever since: "If you have to do this task more than 3 times just write a script and automate it."

Thirty five years ago having a PhD in computer vision was considered the height of unfashion, as artificial intelligence languished at the bottom of the trough of disillusionment. Back then it could take a day for a computer vision algorithm to process a single image. "The competition for talent at the moment is absolutely ferocious," agrees Professor Andrew Blake, whose computer vision PhD was obtained in 1983, but who is now, among other things, a scientific advisor to UK-based autonomous vehicle software startup, FiveAI, which is aiming to trial driverless cars on London's roads in 2019. Blake founded Microsoft's computer vision group, and was managing director of Microsoft Research, Cambridge, where he was involved in the development of the Kinect sensor -- which was something of an augur for computer vision's rising star (even if Kinect itself did not achieve the kind of consumer success Microsoft might have hoped). He's now research director at the Alan Turing Institute in the UK, which aims to support data science research, which of course means machine learning and AI, and includes probing the ethics and societal implications of AI and big data. So how can a startup like FiveAI hope to compete with tech giants like Uber and Google, which are also of course working on autonomous vehicle projects, in this fierce fight for AI expertise?

In late post-revolutionary France one man was tasked to map out the country. Gaspard de Prony, a mathematician and engineer, decided to approach the task by creating logarithmic and trigonometric tables. These tables, which would come to be known as Tables of de Prony, were destined to speed up the trigonometric calculations needed to complete these cartographic task. In handling the vast amounts of data, de Prony asked for help. His team was divided in three levels of hierarchy: besides a couple of highly skilled mathematicians, several mathematicians with less sophisticated skills, he also hired sixty to eighty hairdressers.