An artificial intelligence expert has called for countries to ban so-called "killer robots" before activists' warnings against them become a reality. The Campaign to Stop Killer Robots recently released a short film, in which autonomous weapons are used to carry out mass killings with frightening efficiency, while people struggle to work out how to combat them. A United Nations panel discussed the issue last week, but next plans to meet next year. Toby Walsh, Scientia Professor of AI at UNSW Sydney, says he's "confident" that killer robots will be banned, but is worried that the decision could take a long time to make. "[The] arms race has happened [and] is happening today," he said at the UN, reports AFP.

The sky-high prices of now archaic technology have been revealed in a gallery of old newspaper and magazine adverts. The vintage ads promote bulky computers, phones, video players and camcorders at eye-watering prices. They include a 1956 promotion for a magnetic tape recording system priced at $2,675 (£2,020), around $24,000 (£18,000) today when considering inflation. A simple landline answering machine would have cost you $130 (£98) in 1971, one advert shows, which today would cost $780 (£590). Since the first personal smartphones and computers were released in the mid-1970s, rising competition and ever-advancing manufacturing technologies have meant the average price of the gadgets has plummeted.

Welcome to California Inc., the weekly newsletter of the L.A. Times Business Section. Good times in the Golden State: We learned Friday that California added 31,700 net jobs in October and the state unemployment rate fell to 4.9% from 5.1% a month earlier. The latest state jobs report follows a strong September, when employers boosted payrolls by a revised 50,300. In October, the leisure and hospitality sector, and the educational and health services sector, saw the largest gains. The Automobile Club of Southern California says this year's Thanksgiving holiday will be the busiest locally since 2007, with 3.87 million residents expected to get away for the long weekend.

There just happens to be a much better streamer available if you're willing to spend another $20. That would be the stellar Roku Streaming Stick, which for $50 includes a remote with TV volume and power controls, voice controls, and point-anywhere RF technology. If you plan to use your Roku for any decent stretch of time, you'll want those features. Still, the new Roku Express is more powerful than its predecessor, which in turn makes it passable enough to buy as a cheap gift, for use with a universal IR remote, or perhaps for TVs you don't intend to use very often. Compared to last year's model, it feels a little less like a compromise.

Video streaming has been popular for quite some time, but its growth seems to keep expanding. While it was initially popularized by platforms such as YouTube and then Facebook, today they are being joined by video-on-demand services such as Netflix, Amazon and Hulu. Recently a study suggested that as much as 70% of all online traffic consists of streaming video and audio, and it is placing a heavy demand on internet bandwidths across the world. Although internet speeds and bandwidth have grown over the years, the demand for videos with higher resolutions and bitrates has grown as well. To help optimize the usage of available bandwidth for streaming video, most streaming platforms use algorithms known as Adaptive Bitrate (ABR). Traditional ABR algorithms are either rate-based that vary the video quality based on connection speed, or buffer-based that attempt to constantly keep a certain percentage of the video pre-loaded as a buffer so that the stream is smooth.

I've been playing with several GAN implementations found on github, and the authors often claim the example is under- or overfitting. How is this assessed / how do they know since there is no "validation" set.

Bengaluru: Hewlett Packard Enterprise (HPE) today announced new purpose-built platforms and services capabilities to help companies simplify the adoption of Artificial Intelligence, with an initial focus on a key subset of AI known as deep learning. Inspired by the human brain, deep learning is typically implemented for challenging tasks such as image and facial recognition, image classification and voice recognition. To take advantage of deep learning, enterprises need a high performance compute infrastructure to build and train learning models that can manage large volumes of data to recognize patterns in audio, images, videos, text and sensor data. Many organizations lack several integral requirements to implement deep learning, including expertise and resources; sophisticated and tailored hardware and software infrastructure; and the integration capabilities required to assimilate different pieces of hardware and software to scale AI systems. To help customers overcome these challenges and realize the potential of AI, HPE is announcing the following offerings: • HPE's Rapid Software Development for AI: HPE introduced an integrated hardware and software solution, purpose-built for high performance computing and deep learning applications.

A Reddit study group for the free online version of the Stanford class "Machine Learning", taught by Andrew Ng. The purpose of this reddit is to help each other understand the course materials, not to share solutions to assignments. Please follow the Stanford Honor Code. I'm a new user to Reddit, how does this site work? I have a question about the (class / videos / quiz / homework), how can I get help?

Cher Horowitz's closet from the film "Clueless" had a futuristic computer system that helped her put together outfits. Back in 1995, the concept teased what it might be like to get dressed in the future. Technology has evolved a lot since then, but closets have been largely untouched by innovation. Now, that's starting to change. "If algorithms do their job well, people will spend less time thinking about what to wear," said Ranjitha Kumar, an assistant professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign.

This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.