Computer Science is a remarkable field that allows people to build incredible tools – but at the end of the day, those tools we build are still just tools. Personally, I'm not interested in building mechanisms unless they are used to reduce human suffering. As a PhD student in Computer Science, I have the flexibility to set up my own projects and work on efforts that I find both intellectually stimulating and personally meaningful. My research focuses on cyber-security, machine learning and natural language processing – all fields that have produced amazing tools and insights. My goal and desire is to both add to these fields, and use their instruments to analyze and root out the dark corners where traffickers and child abusers spend their time on the Internet.

Just days after purported test footage of Pokémon Go surfaced online, The Pokémon Company has released the first screenshots from its augmented-reality, along with additional details about gameplay. Announced in September, the smartphone game will allow Pokémon Trainers to track down and capture pocket monsters in a real-world setting. When paired with a Bluetooth-powered, Poké Ball-like device called Pokémon Go Plus, the app will notify players that there's untamed Pokémon in the vicinity. Once you've spotted the pocket monster using the AR app, you'll be able to throw a virtual Poké Ball to catch the creature. Players will also be able to engage other Pokémon Trainers in battle.

As the Primary season progresses there's been no end of political pundits backpedaling and mea-culpa-ing over their previous inability to predict the rise of Donald Trump to become the frontrunner in the GOP. From Charles Krauthammer admitting that it was wrong to laugh at The Donald to innumerable others, both liberal and conservative, wishing they'd take Trump seriously, it seems like just about everyone in the Predictive Class will be dining on roast crow this Easter. But why did they get things so wrong? Was it because they assumed that he'd "crash and burn" like John Podhoretz did? Was it because they assumed that he couldn't win because Republican voters hated him, as implied by Patrick Murray of Monmouth University when releasing early poll results in June of 2015?

Dave and Matt discuss the recent Hot Topic of A.I. Should we create an A.I? Is A.I a form of life?

A group of logistics entrepreneurs is launching a Silicon Valley-style accelerator for supply-chain technology startups, aiming to draw new investment and talent to the freight business. The founders say they will run the Dynamo program alongside a 12 million logistics venture fund in Chattanooga, Tenn. The accelerator will have a 3 million budget raised from the fund and other partners to put toward startups focused on logistics-oriented technologies such as autonomous-truck operations, drones and software. The startups will participate in a three-month program that will include testing their ideas with businesses in the Chattanooga area. The city has a growing manufacturing base, and is close to distribution hubs.

Top Machine Learning & Data Mining Books - for this post, we have scraped various signals (e.g. We have combined all signals to compute a Quality Score for each book and publish the list of top Machine Learning and Data Mining books. The readers will love the list because it is data-driven & objective. This book is very well rated on Amazon website and is written by three professors from USC, Stanford and University of Washington. The three authors: Gareth James, Daniela Witten, & Trevor Hastie all have backgrounds in statistics.

An Iranian charged with hacking the computer system that controlled a New York dam used a readily available Google search process to identify the vulnerable system, according to people familiar with the federal investigation. The process, known as "Google dorking," isn't as simple as an ordinary online search. Yet anyone with a computer and Internet access can perform it with a few special techniques. Federal authorities say it is...

I've recently switched (from my own custom NN code) to theano and its been a marvel to have the benefits of a flexible optimized graph but I often find its a bit of a black box. It can do a lot of very nice things but I always worry that they might cost me in performance. E.g. is it more efficient to vectors in order to do fewer larger matrix multiplications (e.g. in the units of an LSTM or a GRU) does is the loss in memory locality or shuffling stuff around in memory outweigh the gains of larger matrix multiplications? I still have figured out how to efficiently implement attention with minibatches. I realize I can try to profile and test this stuff (and I do) but I am wondering where people normally go to get this kind of info on theano?

Collaborative filtering is the process of filtering for information using techniques involving collaboration among multiple agents. Applications of collaborative filtering typically involve very large data sets. This article covers some good tutorials regarding collaborative filtering we came across in Python, Java and R. Crab engine aims to provide a rich set of components from which you can construct a customized recommender system from a set of algorithms. The tutorial is from official documentation of Crab. This article presents an implementation of the collaborative filtering algorithm, that filters information for a user based on a collection of user profiles.

The problem of recovering a structured signal $\mathbf{x} \in \mathbb{C}^p$ from a set of dimensionality-reduced linear measurements $\mathbf{b} = \mathbf {A}\mathbf {x}$ arises in a variety of applications, such as medical imaging, spectroscopy, Fourier optics, and computerized tomography. Due to computational and storage complexity or physical constraints imposed by the problem, the measurement matrix $\mathbf{A} \in \mathbb{C}^{n \times p}$ is often of the form $\mathbf{A} = \mathbf{P}_{\Omega}\boldsymbol{\Psi}$ for some orthonormal basis matrix $\boldsymbol{\Psi}\in \mathbb{C}^{p \times p}$ and subsampling operator $\mathbf{P}_{\Omega}: \mathbb{C}^{p} \rightarrow \mathbb{C}^{n}$ that selects the rows indexed by $\Omega$. This raises the fundamental question of how best to choose the index set $\Omega$ in order to optimize the recovery performance. Previous approaches to addressing this question rely on non-uniform \emph{random} subsampling using application-specific knowledge of the structure of $\mathbf{x}$. In this paper, we instead take a principled learning-based approach in which a \emph{fixed} index set is chosen based on a set of training signals $\mathbf{x}_1,\dotsc,\mathbf{x}_m$. We formulate combinatorial optimization problems seeking to maximize the energy captured in these signals in an average-case or worst-case sense, and we show that these can be efficiently solved either exactly or approximately via the identification of modularity and submodularity structures. We provide both deterministic and statistical theoretical guarantees showing how the resulting measurement matrices perform on signals differing from the training signals, and we provide numerical examples showing our approach to be effective on a variety of data sets.