In many ways, artificial intelligence is the face of the technological future. And yet, there is still so much untapped potential. One area where the AI has been able to run a bit freer, though, is in the fanciful realm of cinema. Movies have been busily employing elaborate machine learning, robotics, and neural networks in various forms of fiction for decades now. Nowhere has this been more heavily on display than in superhero movies -- especially Marvel's ever-expanding cinematic universe.

This was a banner week for AI (Artificial Intelligence). Well, C3.ai came public and soared …

Pick up Data Science by actually getting your hands dirty! Wondering whether you should go through all the trouble? Take this Bootcamp if ... Year after year, the one profession that consistently ranks on top amongst emerging jobs is Data Science. Growing exponentially at more than 25% every year, Data Science finds increasing use in more and more industries by the day, with exciting applications such as self-driving cars, intelligent automation, and dynamic business decision support systems to show for. This is a growing profession whose demand in every industry and sector is increasing by the day.

At the present scenario, the AI and machine learning scene are at a relatively early stage in India, where most of the AI and machine learning roles tend …

But manufacturing a reliable structure in an innovative manner is not enough--first-time certification needs to be realised as well.

Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains. However, most of the existing NER benchmarks lack domain-specialized entity types or do not focus on a certain domain, leading to a less effective cross-domain evaluation. To address these obstacles, we introduce a cross-domain NER dataset (CrossNER), a fully-labeled collection of NER data spanning over five diverse domains with specialized entity categories for different domains. Additionally, we also provide a domain-related corpus since using it to continue pre-training language models (domain-adaptive pre-training) is effective for the domain adaptation. We then conduct comprehensive experiments to explore the effectiveness of leveraging different levels of the domain corpus and pre-training strategies to do domain-adaptive pre-training for the cross-domain task. Results show that focusing on the fractional corpus containing domain-specialized entities and utilizing a more challenging pre-training strategy in domain-adaptive pre-training are beneficial for the NER domain adaptation, and our proposed method can consistently outperform existing cross-domain NER baselines. Nevertheless, experiments also illustrate the challenge of this cross-domain NER task. We hope that our dataset and baselines will catalyze research in the NER domain adaptation area. The code and data are available at https://github.com/zliucr/CrossNER.

Depending on how you look at ongoing AI news, artificial intelligence (AI) either promises unprecedented, sweeping changes in societies for good or threatens to supplant humanity in every imaginable way. We choose to focus on the first option, albeit with all the precautions about all the AI trends emerging from out of just about any lab furiously working to bring us the best artificial intelligence software. Thus, we present all the crucial artificial intelligence trends that you should know. If you're in business, these should give you ideas on how to navigate your own markets. If you're a casual observer, the list should tell you how AI should figure in your personal and social spheres in the near future. There is not any country that is not already touched by AI in any form.

If Netflix's decisions on marketing its hundreds of original shows seem highly calculated... that's probably because they were. Netflix has outlined how it uses AI to market shows and predict their success in ways that conventional box office numbers and Nielsen ratings likely couldn't match. Effectively, it comes down to finding connections and determining the likely audience sizes. The method relies on transfer learning, where the the parameters learned from a "source task" improve the performance of a "target task." In this case, the source tasks are simple: what titles are comparable to a Netflix original, and what kind of viewership can the service expect?

Sometimes, like a total eclipse, my two great passions--technology and reality TV--become one. It happened in November 2017, when, on the Wednesday before Thanksgiving, I cajoled then-Future Tense contributor Jacob Brogan into writing about the previous night's Teen Mom 2 episode. It grappled with a critical question: Is it OK to shoot down a drone flying above private property? The segment in question begins as (former) teen mom Jenelle prepares for her wedding to David, a very cool and normal dude who we will return to in a moment. David, stalking the property like an ornery bison, calls Jenelle, informing her that "some girls" were attempting to take pictures of the event before it began.

This isn't the first time that we must question whether "seeing" is in fact "believing" Earlier this week the YouTube channel Shamook highlighted the latest Deepfake technology in which scenes involving two notable characters in Rogue One: A Star Wars Story were greatly enhanced. This was the latest such use of Deepfakes in pop culture. What was impressive about this most recent use of the technology is that it made the character of Governor Tarkin seem more lifelike and more closely resembled Peter Cushing, the late actor who played the role originally in the 1977 film Star Wars. The same technology was previously employed earlier this year on the YouTube channel to "de-age" Robert De Niro and the results were far than what was actually seen in the Netflix original film The Irishman. Both of the "original" versions seem far more cartoony and fall into that "uncanny valley" of not being quite real.