WHEN the first printed books with illustrations started to appear in the 1470s in the German city of Augsburg, wood engravers rose up in protest. Worried about their jobs, they literally stopped the presses. In fact, their skills turned out to be in higher demand than before: somebody had to illustrate the growing number of books. Fears about the impact of technology on jobs have resurfaced periodically ever since. The latest bout of anxiety concerns the arrival of artificial intelligence (AI).

OK, so here it is - the newest version of Neptune, a tool for building and deploying machine learning models. Run things in the cloud with a single command line neptune send, track your models with charts, compare & reproduce your previous models. We give you $100 for cloud computing in Google Cloud (we charge per second, so it's a lot of computing power). Let us know if you find Neptune helpful for your work (whether business projects, Kaggle competitions, some side projects or hands-on learning deep learning). We would be excited to hear your feedback, so we can keep improving Neptune.

Nearly ten years after the debut of Half-Life 2: Episode Two, the world is still waiting for Valve to deliver the final episode in the trilogy, but we may have to settle for something else. Just a few months after the last of the game's writers left Valve, and 21 years to the day since the company started, lead writer Marc Laidlaw has posted "Epistle 3" to his personal website (it's overloaded and inaccessible now, but you can view it on Archive.org). I guess fanfic is popular, even a genderswapped snapshot of a dream I had many years ago. With some light editing to change a few key names --"Gertrude Freemont, Ph.D" -- the post lays out a plotline for an Episode Three that never appeared. It's hard to say if it will give gamers any closure after all this time, but you can also check it out with the names corrected on Pastebin.

Well, Half-Life 3 is finally here. Or is it Half-Life 2: Episode 3? Hard to say, but in any case, it arrived exactly as expected: A whispered surprise, traveling in the wee hours of the night by word of mouth alone. Instead, longtime Valve writer Marc Laidlaw (ex-Valve writer as of last year) seems to have posted the hypothetical plot to a never-going-to-be-made Episode 3 on his blog. It's thinly disguised as "Epistle 3," and the character names and genders have been tweaked too. This forms the basis of Laidlaw's current deflection, which is that it's "a genderswapped snapshot of a dream I had many years ago." He also refers to it as "fanfic."

I've been working (in collaboration with others) on this R package accSDA, (accelerated sparse discriminant analysis), which is now available on CRAN. The research behind this has mostly been application of alternative optimization approaches, which are considerably faster than the traditional approach (old version available as an R package sparseLDA). I am currently looking for feedback on the package. What problem does this package solve? This algorithm solves supervised classification problems with multiple classes, where the number of variables can be considerably larger than the number of samples, i.e. p n problems.

Commonsense reasoning is in principle a central problem in artificial intelligence, but it is a very difficult one. One approach that has been pursued since the earliest days of the field has been to encode commonsense knowledge as statements in a logic-based representation language and to implement commonsense reasoning as some form of logical inference. This paper surveys the use of logic-based representations of commonsense knowledge in artificial intelligence research.

As a new way of training generative models, Generative Adversarial Nets (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data. However, it has limitations when the goal is for generating sequences of discrete tokens. A major reason lies in that the discrete outputs from the generative model make it difficult to pass the gradient update from the discriminative model to the generative model. Also, the discriminative model can only assess a complete sequence, while for a partially generated sequence, it is non-trivial to balance its current score and the future one once the entire sequence has been generated. In this paper, we propose a sequence generation framework, called SeqGAN, to solve the problems. Modeling the data generator as a stochastic policy in reinforcement learning (RL), SeqGAN bypasses the generator differentiation problem by directly performing gradient policy update. The RL reward signal comes from the GAN discriminator judged on a complete sequence, and is passed back to the intermediate state-action steps using Monte Carlo search. Extensive experiments on synthetic data and real-world tasks demonstrate significant improvements over strong baselines.

Future generations of blockbuster movies might be determined based on the ability to re-shape content during the film based on viewers' reactions. Disney Research is already tracking reactions of audiences through a neural network it has developed which is helping the company quantify how a film is working on a granular scale. While studios have used test audiences to preview early cuts of films for years and would make changes based on that feedback, the difference with today's methods is the amount of data that can be analyzed. It is expected that these sentiment-analysis cameras would also make its ways into other experiences such as at the parks or restaurants. Disney's board of directors has some heavy tech players such as Sheryl Sandberg, COO of Facebook, Jack Dorsey, founder of Twitter, and John Chen, CEO of Blackberry, so there's no doubt the entertainment icon will continue to be a leader in using machine learning and big data to enhance the customer experience.

Note: This review was originally published July 3, 1991. The film is being re-released in 3D. And yes, without a doubt, they will come. He is the gifted James Cameron, the consensus choice as the action director of his generation. What he's built is "Terminator 2: Judgment Day," the most eagerly awaited film of the summer and one of the most expensive (officially, $88 million and counting) ever made.

This is an interesting problem I have not had before to do with a project I am working on. So far the data structures of my features have been basic discrete or continuous numbers or categories. Now I find myself needing to add a feature that has a treelike data structure. Googling to try and find out about how to input a tree like data structure as a feature to my model just has things come up to do with a decision tree model type, but not answering my question. Does any have any experience/insight on how to use tree like data as a feature to their existing model that already has categorical and continous numerical features?