It's a custom mixtape of 30 songs they've never listened to before but will probably love. It's called Discover Weekly, and it's pretty much magic. It makes me feel seen. It knows my musical tastes better than any person in my life ever has, and I am consistently delighted by how it satisfies me just right every week, with tracks I myself would never have found or known I would like. As it turns out, I'm not alone in my obsession with Discover Weekly--the user base went crazy for it, which has driven Spotify to completely rethink its focus, investing more resources into algorithm-based playlists.

Today's content strategy blog post is a tale of two new cameras. First of all, Google Clips, a tiny'always on' device that uses machine learning and artificial intelligence to take photos automatically throughout the day, serving up the best images to your mobile device. In the opposite corner, the Nikon D850, a digital single lens reflex (DSLR) that sets a new standard for professional cameras. Let's get price out of the way first. Google's latest will retail for $250, while the Nikon will set you back $3,300 (body only).

Deep inside the Googleplex, a small group of writers is huddling around a whiteboard that is plastered with ideas. These read like notes-to-self that Jack Skellington might've made: "Halloween survival kit," "How to defeat monsters." "People did not like'smell my feet' last year," he says, laughing. "It was trick or treat, and one response was'smell my feet.' Germick has spent the afternoon bouncing between brainstorming meetings like this one, in which Googlers debate life's big questions, like whether the sound of a bubbling cauldron or distant howling is spookier. All of which is part of his job as principal personality designer for Google Assistant, the company's voice-activated helper found on a wide range of smartphones and its Home smart speaker, which first went on sale last fall. It's August, but Germick's team is grappling with what users might ask Google on Halloween and why. Or will they want to hear a seasonally appropriate joke?

This 2 hour long talk clarifies and goes over in detail many of the details people were interested in from his original talk. Questions in order; what is the difference between the noise in SGD and the typical Langevin dynamics, how does the theory deal with saturated gradients, is it a reasonable strategy to perform early stopping specifically before the compression phase, have you tried the framework on ResNets, what is the message for practitioners, how does the performance of dropout/regularizers relate to the theory, have you considered the connection to a fermi gas equilibrium.

The new report from MIT Technology Review is sponsored by Genesys and called Getting to Iconic. It reveals iconic firms are more likely to recognize that automated AI tools are most effective when they supplement and extend the capabilities of their customer support team, rather than replace human investment. As so, 60 percent of survey respondents felt they had the right mix of "live" and automated customer communication channels, compared to only 26 percent of the poor performers and 40 percent overall. Furthermore, the report concludes that iconic companies are using AI for more than just chatbots. Most respondents indicate AI is guiding their customer analytic capabilities.

Blade Runner 2049 is a miracle. It's a sequel that nobody really wanted -- certainly not fans of the seminal 1982 original by Ridley Scott. But it turns out that Blade Runner 2049 -- directed by Denis Villeneuve -- is actually an ideal sequel. It builds on its incredibly influential predecessor by asking deeper questions about AI. As the lines between humans and replicants blur, the idea of being "more human than human" seems truer than ever.

PornHub has announced an AI for tagging adult content, but I can't help but feel it demonstrates promising technology with better use elsewhere. The company's AI is virtually perving on PornHub's entire online catalogue, frame-by-frame, and tagging it with relevant tags for real users to discover content faster. It has been fed with thousands of images of models and specific acts to generate a database of names, faces, and positions. PornHub shared clips of the AI in action with Engadget who claim it was able to identify both the names of the performers in a scene, and what they were doing. "Tags such as'blowjob', 'doggy', 'cowgirl', and'missionary' floated on screen with the corresponding action," wrote Engadget.

This week, the critics discuss the new film Blade Runner 2049, directed by Denis Villeneuve. It's been 35 years since the original Blade Runner. How does the sequel stack up against the revered sci-fi classic? Then the gabbers turn their attention to the latest season of Amazon's Transparent, which follows the Pfefferman family to Israel. They explore how the series has evolved over the past four years and talk about the political discourse that Transparent has embraced.

If you want drama, Wednesday's premieres are perfect whether you're in the mood for catfights, hackers saving the world or teens in a creepy town. The "Riverdale" Season 2 premiere picks up shortly after last season's cliffhanger. Fred (Luke Perry) might not survive the shooting at Pop's diner, and Archie (KJ Apa) will have trouble coping. Jughead (Cole Sprouse) and Betty (Lili Reinhart) will play detective as they try to learn the gunman's true motives. Veronica (Camila Mendes) has her own daddy drama to deal with, though.

I've been trying for some time to replicate the results of the Bayesian Recurrent Neural Networks paper from Deepmind (https://arxiv.org/abs/1704.02798, the simple model without posterior sharpening), however my perplexity never reaches the result they have in the paper - 78.8 on validation, 75.5 on test set. I wanted to ask if anyone could spare a few minutes to take a look at my implementation and point out if anything is wrong. I've done an extensive hyperparameter search over various initialization schemes, learning rate, etc but haven't gotten close to their results. I'm using \pi 0.25, log \sigma_1 -1.0, log \sigma_2 -8.0 for the prior, and everything else should be like the paper states. Full implementation is here, and the exact file with the model is here.