A significant barrier to progress in data-driven approaches to building dialog systems is the lack of high quality, goal-oriented conversational data. To help satisfy this elementary requirement, we introduce the initial release of the Taskmaster-1 dataset which includes 13,215 task-based dialogs comprising six domains. Two procedures were used to create this collection, each with unique advantages. The first involves a two-person, spoken "Wizard of Oz" (WOz) approach in which trained agents and crowdsourced workers interact to complete the task while the second is "self-dialog" in which crowdsourced workers write the entire dialog themselves. We do not restrict the workers to detailed scripts or to a small knowledge base and hence we observe that our dataset contains more realistic and diverse conversations in comparison to existing datasets. We offer several baseline models including state of the art neural seq2seq architectures with benchmark performance as well as qualitative human evaluations. Dialogs are labeled with API calls and arguments, a simple and cost effective approach which avoids the requirement of complex annotation schema. The layer of abstraction between the dialog model and the service provider API allows for a given model to interact with multiple services that provide similar functionally. Finally, the dataset will evoke interest in written vs. spoken language, discourse patterns, error handling and other linguistic phenomena related to dialog system research, development and design.

Researchers have created a new system that can flag spoilers in online reviews of books and TV shows. The system is based on artificial intelligence (AI) technology. "Spoilers are everywhere on the internet and are very common on social media. As internet users, we understand the pain of spoilers, and how they can ruin one's experience," said Ndapa Nakashole, a professor at the University of California San Diego in the US. Some movie review websites like IMDb allow users to manually flag their posts with tags that serve as'spoiler ahead' warning signs.

Fake news and social media posts are such a threat to U.S. security that the Defense Department is launching a project to repel "large-scale, automated disinformation attacks," as the top Republican in Congress blocks efforts to protect the integrity of elections. The Defense Advanced Research Projects Agency wants custom software that can unearth fakes hidden among more than 500,000 stories, photos, video and audio clips. If successful, the system after four years of trials may expand to detect malicious intent and prevent viral fake news from polarizing society. "A decade ago, today's state-of-the-art would have registered as sci-fi -- that's how fast the improvements have come," said Andrew Grotto at the Center for International Security at Stanford University. "There is no reason to think the pace of innovation will slow any time soon."

As the name implies, machine learning is a form of AI whereby a computer algorithm analyses and stores data over time, then uses this data to make decisions and predict future outcomes. Deep learning is the next evolution of this: instead of requiring human'supervision', algorithms can autonomously use'neural networks' analogous to the human brain. Put simply, lines of computer code can now, to some extent, be programmed to learn for themselves, then use those learnings to perform complex operations on a scale that far surpasses human abilities. Considered the single biggest advancement in software development over the past few years, this technology is possible thanks to revolutionary advancements in computing power and data storage, and is now an integral part of day-to-day life, like how Siri or Alexa intelligently store data to predict future actions. Ever wondered why Facebook's'People You May Know' and those pesky suggested ads on social media are always so accurate?

"The growth of data center workloads and applications continues to scale at a rapid pace, as machine learning and data inference platforms create …

Last Thursday two lowly masters grad students, Aaron Gokaslan and Vanya Cohen managed to replicate the secretive OpenAI model and cheekily named their version OpenGPT-2. The code can be downloaded from this Google Colab page and apparently no prior experience in language modeling is required to use it. More useful might be the skills required to persuade Google to part with $50,000 worth of free cloud compute time for the training! Research firm OpenAI released a new, ever more powerful, version of their GPT language model with 1.5 billion parameters, trained on a data-set of 8 million web pages and although it's most entertaining use is to produce gibberish, it will inevitably also be able to produce coherent text sometime very soon. For us mere mortals, there's a cut down version of the model hosted in the cloud and a webpage that we can visit, type in a short phrase to prompt the system, and print out a few paragraphs of fake news.

We have no chance of escaping the coming robot revolution, nor should we want to. Our modern lives are already full of robots -- they're in our phones, our cars, hospitals and boardrooms, assisting everyone from factory workers to astrophysicists. They make our lives overwhelmingly better -- that is, until one gets between a hungover human and their morning jolt of java. In Talking to Robots, journalist and author David Ewing Duncan -- with help from some of today's leading scientific researchers -- presents 24 visions of the future and what our personal and professional interactions might look like once robots finish taking over. Need my hit of caffeine.

Until recently, artificial intelligence was a thing for science fiction movies and books. However, we are now in the midst of an ever-changing tech world where we are advancing faster than ever before. The future of AI is unknown, but that doesn't stop people from contemplating. Here are some of the best quotes about the future of AI. "Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks."

I expect to suffer some degree of writer's block pretty much every day for the rest of my life. I'm a journalist and a novelist; it comes with the territory. But I have a feeling I'm going to suffer less from now on, thanks to my new best friend, GPT-2. Let me back up a bit: Six months ago, the research lab OpenAI created an AI system that generates text -- from fake news to poetry -- that in some cases actually sounds like it's written by a human being. The OpenAI team has been rolling it out in stages, each time giving us a more powerful version of the language model they dubbed GPT-2, and carefully watching to see how we use it.