Don't consider "Jumanji: Welcome to the Jungle" a straight remake of the 1995 Robin Williams board game adventure film. It's more of a spiritual sequel or reimagining, loosely based on the same Chris Van Allsburg book. If it was a jungle in there for Williams, it's a jungle out there for Dwayne Johnson and pals in this video game-inspired romp directed by Jake Kasdan. No longer is Jumanji a simple board game where a roll of the dice can unleash a supernatural jungle explosion in the living room. This time around, "Jumanji" is an old video game console and cartridge dusted off by a mismatched group of high-schoolers stuck with detention one afternoon. The breakfast club fires it up, selecting their avatars: neurotic nerd Spencer (Alex Wolff) chooses Dr. Smolder Bravestone; hulking jock Fridge (Ser'Darius Blain) picks zoologist Moose Finbar; weirdo smartypants Martha (Morgan Turner) is Ruby Roundhouse; while the selfie-obsessed Bethany (Madison Iseman) goes for the "curvy cartographer" Professor Shelly Oberon.

When Amazon unveiled the Echo Show earlier this year, we questioned if we really needed an Echo with a touchscreen. Surprisingly, the display turned out to be quite useful -- it was good for video, making calls or just displaying bite-sized information. But its peculiar mall-kiosk design left a lot to be desired, especially for people thinking of putting it in a central location in their home. A few months later, however, Amazon unveiled the Echo Show's smaller, more adorable sibling: the Echo Spot. It has all the same functionality as the Show, except it's wrapped in a much cuter package.

Amazon Echo Spot with Alexa has a 2.5-inch display. Were Amazon's Echo Show and Echo Dot smart speakers to make a baby, the resulting offspring would likely resemble Echo Spot. From Echo Show, this softball-sized latest speaker inherits a screen. From the Dot, the Spot gets its small size and relative cuteness (sorry, Amazon, but I don't think the Echo Show is very cute.) Of course, the voice-activated cloud-based Alexa digital assistant inside Echo Spot is passed down from both parents.

From incessant breaking news alerts to the collapse of net neutrality to a string of natural disasters, we are all very tired. Though it's worth revisiting why this was a momentous twelve months in science and tech, we'd much rather look ahead to the new year. Over the next two weeks, we'll be looking back on the year that was, and sharing our hopes and predictions for 2018. Join us as we place our bets on AI, algorithms, social media regulations, green tech, streaming services, robotics, self-driving cars and even space taxis. And, of course, since we're Engadget, you can expect to hear about the upcoming products and games we're most excited about.

In our quest for convenience, we are trading away our free choice. This is the free choice that our forebears fought wars of independence for: the right to decide where, how, and with whom we live, and the sacred rights of self-determination, a full life, and the opportunity to reach our full potential. Yet today, AI makes decisions for us in every area of our lives without our conscious involvement. Machines mine our past patterns and those of allegedly similar people across the world, and then decide not just what news articles we see, but with whom we should commune and forge bonds, what goods and services we should purchase, or for whom we should vote in our political processes. This influences our opinions, our relationships, and our social fabric.

Artificial intelligence is all around us. As I shared in Content Creation Robots Are Here, billions of AI-created pieces of content are published yearly. What does this mean for humans who create content? Are you in danger of losing a job? Despite the growth in artificial intelligence capabilities, the human content writer is needed more than ever.

There are many graffiti "pieces" that many people can easily look at, and connect multiple pieces together to "common creator". The process seems to follow " I can't define it, but "I know it when I see it." Taggers are a different story. They discourage each others from using tags already use by others but many tags are illegible and difficult to index them. I've learned recently that some taggers have many tags and don't always stick with the same ones forever like I previously believed.

NIPS has a responsibility to provide an inclusive and welcoming environment for everyone in the fields of AI and machine learning. Unfortunately, several events held at (or in conjunction with) this year's conference fell short of these standards. We are determined to do better in 2018 and beyond. Our immediate actions include: recruiting a Diversity and Inclusion Chair, strengthening our Code of Conduct, and formalizing procedures for reporting and communicating concerns. All chairs, attendees and sponsors will be required to acknowledge and abide by our Code of Conduct.

We propose a recurrent extension of the Ladder networks whose structure is motivated by the inference required in hierarchical latent variable models. We demonstrate that the recurrent Ladder is able to handle a wide variety of complex learning tasks that benefit from iterative inference and temporal modeling. The architecture shows close-to-optimal results on temporal modeling of video data, competitive results on music modeling, and improved perceptual grouping based on higher order abstractions, such as stochastic textures and motion cues. We present results for fully supervised, semi-supervised, and unsupervised tasks. The results suggest that the proposed architecture and principles are powerful tools for learning a hierarchy of abstractions, learning iterative inference and handling temporal information.

We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network computation via a simple, feature-wise affine transformation based on conditioning information. We show that FiLM layers are highly effective for visual reasoning - answering image-related questions which require a multi-step, high-level process - a task which has proven difficult for standard deep learning methods that do not explicitly model reasoning. Specifically, we show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are robust to ablations and architectural modifications, and 4) generalize well to challenging, new data from few examples or even zero-shot.