An ability to coordinate cross-functional projects and/or prior project management experience. Strong analytical skills and an ability to synthesize information across a broad ecosystem to diagnose problems and devise solutions. Comfortable with ambiguity; able to thrive with minimal oversight and process, while keeping leadership informed on progress against deadlines. A passion to serve the needs of Production within the Netflix organization. Ability to thrive in a fast-paced collaborative environment, possess an abundance of intellectual curiosity, focus on generating results, and exhibit the highest personal and professional standards of integrity.

Reuben Wu, a British photographer and visual artist based in Chicago, was first introduced to National Geographic as most people are: When he was a child, he enjoyed looking at the magazines his father subscribed to for decades. He dreamed of seeing his photographs in the same magazine--and even on the cover. So when National Geographic asked him to photograph an iconic monument he knows well, he was ready to work. Last summer, Wu experienced a stark contrast of modern and prehistoric, as he used drones and artificial light to photograph Stonehenge, one of the best-known prehistoric monuments, while hearing honking cars passing by. The site in Wiltshire, England, is bisected by the A303--a major road that may soon be in a tunnel should a 2020 proposal become reality--which means motorists may have seen Wu's photo shoot and lit-up drones.

Consciousness And Light Are Explored. The Inter Mind Bridges The Gap Between The Physical Mind And The Conscious Mind.

Prototype Generation (PG) methods are typically considered for improving the efficiency of the $k$-Nearest Neighbour ($k$NN) classifier when tackling high-size corpora. Such approaches aim at generating a reduced version of the corpus without decreasing the classification performance when compared to the initial set. Despite their large application in multiclass scenarios, very few works have addressed the proposal of PG methods for the multilabel space. In this regard, this work presents the novel adaptation of four multiclass PG strategies to the multilabel case. These proposals are evaluated with three multilabel $k$NN-based classifiers, 12 corpora comprising a varied range of domains and corpus sizes, and different noise scenarios artificially induced in the data. The results obtained show that the proposed adaptations are capable of significantly improving -- both in terms of efficiency and classification performance -- the only reference multilabel PG work in the literature as well as the case in which no PG method is applied, also presenting a statistically superior robustness in noisy scenarios. Moreover, these novel PG strategies allow prioritising either the efficiency or efficacy criteria through its configuration depending on the target scenario, hence covering a wide area in the solution space not previously filled by other works.

Natural language generation models are computer systems that generate coherent language when prompted with a sequence of words as context. Despite their ubiquity and many beneficial applications, language generation models also have the potential to inflict social harms by generating discriminatory language, hateful speech, profane content, and other harmful material. Ethical assessment of these models is therefore critical. But it is also a challenging task, requiring an expertise in several specialized domains, such as computational linguistics and social justice. While significant strides have been made by the research community in this domain, accessibility of such ethical assessments to the wider population is limited due to the high entry barriers. This article introduces a new tool to democratize and standardize ethical assessment of natural language generation models: Tool for Ethical Assessment of Language generation models (TEAL), a component of Credo AI Lens, an open-source assessment framework.

Pretrained language models have become the standard approach for many NLP tasks due to strong performance, but they are very expensive to train. We propose a simple and efficient learning framework, TLM, that does not rely on large-scale pretraining. Given some labeled task data and a large general corpus, TLM uses task data as queries to retrieve a tiny subset of the general corpus and jointly optimizes the task objective and the language modeling objective from scratch. On eight classification datasets in four domains, TLM achieves results better than or similar to pretrained language models (e.g., RoBERTa-Large) while reducing the training FLOPs by two orders of magnitude. With high accuracy and efficiency, we hope TLM will contribute to democratizing NLP and expediting its development.

In this work, we propose a novel architecture for face age editing that can produce structural modifications while maintaining relevant details present in the original image. We disentangle the style and content of the input image and propose a new decoder network that adopts a style-based strategy to combine the style and content representations of the input image while conditioning the output on the target age. We go beyond existing aging methods allowing users to adjust the degree of structure preservation in the input image during inference. To this purpose, we introduce a masking mechanism, the CUstom Structure Preservation module, that distinguishes relevant regions in the input image from those that should be discarded. CUSP requires no additional supervision. Finally, our quantitative and qualitative analysis which include a user study, show that our method outperforms prior art and demonstrates the effectiveness of our strategy regarding image editing and adjustable structure preservation.

As developments, standards and controversy around Artificial Intelligence (AI) explodes, compelling new groups are emerging that will drive expansion and implementation of AI at a new pace and depth. However, one such exclusive, burgeoning collective entitled #AIShowbiz Executive Roundtable is making particular moves. This Roundtable is one of the first business communities in the country solely dedicated to the intersection of AI and the entertainment industry, and it has powerful plans for 2018. In fact, the #AIShowbiz Executive Roundtable is an ancillary property of the larger #AIShowbiz Summit which actually just completed its second-year of panels and keynotes with various influencers in AI from around the world during a day-long conference in Los Angeles, California. The overall #AIShowbiz organization is founded and helmed by Molly Lavik, creator of MentorInsight, a media and market development company.

As Netflix's profits have begun to wane, some business analysts have argued that, when compared to rivals like HBO Max or Amazon Prime, Netflix has a "quantity over quality" problem with its content. Critics have joined this bandwagon, turning on the streaming service's wide array of original material. This trend manifested itself most recently in the wake of the release of the television series Resident Evil, loosely based on the Capcom survival-horror video game from the 1990s. A week after its July 14th release, the show has been snubbed by critics, earning a 51% on Rotten Tomatoes, as well as absolutely savaged by viewers who rated the show on that website, leaving a bloodbath of one-star reviews and an "Audience Score" of 26%. Given the history of the Resident Evil movie franchise--six schlocky Milla Jovovich vehicles that contained, in total, exactly one memorable scene; one forgettable 2021 prequel--this kind of critical drubbing might be the expected outcome.

Is AI finally achieving selfhood? You'll find lots of opinions about this, many of them based on loose, if-it-walks-like-a-duck parallels. Here's my opinion based on 25 years research attempting to explain what selves are and how they emerged within nothing but physical chemistry. The big difference between selves and non-selves is that we exist by persistence. We're fragile, yet we've somehow managed to survive for an uninterrupted 3.8 billion-year run.