Tomomi Inada's resignation as defense minister ended a tenure that often made reporters wonder if her transgressions had more to do with ignorance than with incompetence. It would be wrong to associate her failures with her sex, though there were some in the media who harped on her fashion sense or supposed emotional instability as indications that she wasn't suitable for the job. Inada didn't actively discourage these indications. In June, she addressed the second plenary session of the International Institute of Strategic Studies' Shangri-La Dialogue in Singapore, where she expressed in English how privileged she felt to "share the podium" with other defense ministers, namely Marise Payne of Australia and Sylvie Goulard of France, saying that "We belong to the same gender … the same generation and, most importantly, we are all good looking." As mentioned in a June 14 article in the Huffington Post, Mayumi Mori, the Asahi Shimbun Singapore correspondent, noted that Inada was obviously making a joke "to relieve tension," and that there were a few chuckles in the hall.

This is a continuation of a personal project. In which I am trying to generate chemical molecules using neural networks. In this new project I have used wgan with 1D CNN to generate chemical molecules. This model learns faster than my other previous models and create molecules which are visually similar to SMILES with only one hour GPU training. The logs for the training is also uploaded in my github page.

The newspapers have a scoop today - it seems that artificial intelligence (AI) could be out to get us. "'Robot intelligence is dangerous': Expert's warning after Facebook AI'develop their own language'", says the Mirror. Similar stories have appeared in the Sun, the Independent, the Telegraph and in other online publications. It sounds like something from a science fiction film - the Sun even included a few pictures of scary-looking androids. So, is it time to panic and start preparing for apocalypse at the hands of machines? While some great minds - including Stephen Hawking - are concerned that one day AI could threaten humanity, the Facebook story is nothing to be worried about.

Maybe you have seen this image on the left, or some variation on it. It is shared quite frequently by people on social media who have not much else to share but infographics, and fluff pieces written by journalists. There are just a couple of things wrong here, in my opinion. Here's the deal: Starting from the top I think it is crazy to say that artificial intelligence must mean computers reasoning as humans. First of all reasoning as any other animal would be more than acceptable from my point of view, and secondly reasoning as something completely separate from any biological entity can still be considered intelligence.

The advertising industry faces major challenges. One of them is the industry's widespread difficulty targeting ads effectively. Too many ads are seen or heard by people who are not interested in the products or services being advertised. Frequently, advertisers don't know whom the correct people are to target or how to reach them. A second challenge is how to deliver ads that engage consumers and convey to them the experience of a product or service.

With the deluge of deep learning libraries and software innovation in the field over the last few years, it is an exciting time to be working on machine learning problems. Most of the libraries available evolved from fairly specialized computational code for large dense problems such as image classification into general frameworks for neural-network-based models offering marginal support for sparse models. At Netflix, our machine learning scientists deal with a wide variety of problems across a broad spectrum of areas: from tailoring TV and movie recommendations to your taste to optimizing encoding algorithms. A subset of our problems involve dealing with extremely sparse data; the total dimensionality of the problem at hand can easily reach tens of millions of features, even though every observation may only have a handful of non-zero entries. For these cases, we felt the need for a minimalist library that is specifically optimized for training shallow feedforward neural nets on sparse data in a single-machine, multi-core environment.

Movie studios have a long tradition of testing out new films to see how audiences react before launching them in wide release. But with their latest research innovation, Disney is taking it to a whole new level. Now as you're settling in to watch the latest Disney blockbuster, the movie could also be watching you. And while this could signal an exciting new era of responsive storytelling in which movies are shaped around our likes and dislikes in real time, it also raises some red flags about yet another frontier in personal data collection. At a conference in July, Disney Research presented a new process called factorized variational autoencoders (FVAEs).

Both industry and community must develop effective mechanisms to filter bias as well as negative sentiment in the data that AI learns from - ensuring AI does not perpetuate stereotypes. Users build a relationship with AI and start to trust it after just a few meaningful interactions. With trust, comes responsibility and AI needs to be held accountable for its actions and decisions, just like humans. Technology should not be allowed to become too clever to be accountable. We don't accept this kind of behaviour from other'expert' professions, so why should technology be the exception.

Amazon announced today that users of its streaming service Prime Music, which is free with a Prime membership, and its subscription-based Amazon Music Unlimited can now ask Alexa to find tunes appropriate for various activities. As of now, over 500 different activity-based requests are supported including music for meditation, partying and even "getting pumped." The new feature is available immediately to users with Alexa-enabled devices. The new voice controls were geared towards activities that have been requested most often by Alexa users and listeners of Amazon's music streaming services. In the announcement, the company said that 27 percent of all activity requests come from users who want to relax.

I know this might sound like a "google this for me question" but bare with me (I googled it). I'm working with a highly imbalanced data set where the minority class accounts for 1.5% of the total set. This leads to poor predictive performance by most models when nothing is done to address the problem because most algorithms will minimize cost on the majority class, to the detriment of the minority class, when training so as to decrease overall cost. So far I've tried out ANNs,RFs,XGBs, and SVMs and have found that XGB and RF outperform the others in this particular problem, so the remaining post will be about RF and XGB. I've tried penalizing classification on the minority class much more than the majority class to try to fix the imbalance on an algorithmic level but I've found undersampling and then training on the resulting data set to be more effective in my case.