I think I see what you're saying, but... The way the paper reads, the problem tackled is: "Given sample trajectories from Game of Life but no access to / knowledge of the source code, create a model that perfectly predicts the game's dynamic." However, by using n to set the problem difficulty (by using a terminal loss instead of a trajectory loss), the actual problem being tackled is: "Given sample state pairs {y[0], y[n]} spaced n steps apart... create a model that perfectly predicts the game's dynamic." The latter is clearly a much more difficult problem. I can agree that a narrow network may have difficulties with it (perhaps related to the lottery hypothesis).

Journalism is a vast industry. The never tiring sector needs more human power for jobs starting from field reporting to approving a copy and publishing it. Thousands of journalists are on the ground covering stories and doing live telecast across the globe. However, it is very rare for a normal news agency to think of bringing in Artificial Intelligence (AI) technologies or a robot to the functioning system. Even when well-established media house is on the process, small news agencies struggle to digest the fact that AI can aid them in a lot of ways.

Although Hollywood movies can lead you to believe that AI is an ominous thing, market researchers have nothing to fear and everything to gain from it. The new artificial intelligence technologies transform interaction and contribute to the hottest market research patterns, with everything from broad scale data processing to report production. Here are some ways in which AI will help market researchers achieve success in 2019 and beyond. Artificial intelligence is able to capture and interpret large quantities of data at a faster level than ever before and with greater precision. This helps market analysts to get a better view of their audience, not only what they want but why they like it.

Conditional sequence generation aims to instruct the generation procedure by conditioning the model with additional context information, which is a self-supervised learning issue (a form of unsupervised learning with supervision information from data itself). Unfortunately, the current state-of-the-art generative models have limitations in sequence generation with multiple attributes. In this paper, we propose a novel conditional hybrid GAN (C-Hybrid-GAN) to solve this issue. Discrete sequence with triplet attributes are separately generated when conditioned on the same context. Most importantly, relational reasoning technique is exploited to model not only the dependency inside each sequence of the attribute during the training of the generator but also the consistency among the sequences of attributes during the training of the discriminator. To avoid the non-differentiability problem in GANs encountered during discrete data generation, we exploit the Gumbel-Softmax technique to approximate the distribution of discrete-valued sequences. Through evaluating the task of generating melody (associated with note, duration, and rest) from lyrics, we demonstrate that the proposed C-Hybrid-GAN outperforms the existing methods in context-conditioned discrete-valued sequence generation.

Memes are graphics and text overlapped so that together they present concepts that become dubious if one of them is absent. It is spread mostly on social media platforms, in the form of jokes, sarcasm, motivating, etc. After the success of BERT in Natural Language Processing (NLP), researchers inclined to Visual-Linguistic (VL) multimodal problems like memes classification, image captioning, Visual Question Answering (VQA), and many more. Unfortunately, many memes get uploaded each day on social media platforms that need automatic censoring to curb misinformation and hate. Recently, this issue has attracted the attention of researchers and practitioners. State-of-the-art methods that performed significantly on other VL dataset, tends to fail on memes classification. In this context, this work aims to conduct a comprehensive study on memes classification, generally on the VL multimodal problems and cutting edge solutions. We propose a generalized framework for VL problems. We cover the early and next-generation works on VL problems. Finally, we identify and articulate several open research issues and challenges. This is the first study that presents the generalized view of the advanced classification techniques concerning memes classification to the best of our knowledge. We believe this study presents a clear road-map for the Machine Learning (ML) research community to implement and enhance memes classification techniques.

Alexa just read me Monday's baseball scores. It was great for two reasons. First, the A's shut out the Mariners 9-0 in the second game of a doubleheader. And second, she did so in Samuel L. Jackson's voice. With Amazon based in Seattle, I assume they're happy I've chosen to focus on the latter fact for the rest of this post.

Thanks to Covid-19, the mantra for 2020 has got to be "quarantine and chill." Good thing Netflix is here to "entertain people all over the world," as the company's cofounder Reed Hastings explained at this year's WIRED25. Sating the global entertainment palate, though, requires an undying spirit of invention as well as narratives that span both the US and abroad. Netflix's secret, according to Hasting's new book No Rules Rules, is that it values its workers over its work process. It's this employee-centric attitude that allows a startup to maintain a culture of innovation as it grows from, say, a 30-person rent-by-mail DVD provider into the world's largest streaming service, with a film production arm that rivals Hollywood's Big Six.

American Girl is bringing back the '80s. The Mattel-owned brand has added a new doll to its collection, and she's devoted to the decade of big hair and new wave rock, the brand announced Tuesday. In honor of the new American Girl Doll, the Mattel-owned company is matching customer donations to Girls Who Code. Her name is Courtney Moore, the first addition to the brand's historical collection -- aimed at educating and empowering young women about history and pop culture through stories -- in three years. Courtney, in all of her big hair and scrunchie-wearing glory, wears an '80s-inspired, high-waisted, acid-wash jean skirt, hot pink tights and faux leather boots.

In director Sergio Leone's classic film "The Good, The Bad and The Ugly," it was clear who filled each role. The star Clint Eastwood was "good," Lee Van Cleef was born to be "bad" and the cunning and "ugly" bandit was Eli Wallach. Today, the lines that separate good, bad and ugly have blurred; it's not easy to discern what's true and what's not. And for those of us in technology, the demands on sorting that out are critical and ongoing. Solving this problem is one of the drivers behind an important new feature in Genesys Dialog Engine.

Data Science and analytics are transforming businesses. It has penetrated into all departments be it Finance, Marketing, Operations, HR, Designing, etc. It is becoming increasingly important for B-school students to have analytical skills and be well versed with Machine Learning and Statistics. Data is being called the new gold. The fastest growing companies in the coming period will be the ones who can make the most sense of data they collect. As through the power of Data a business can do targeted marketing, transforming the way they convert sales and satisfy demand.