Hello, I'm currently writing a series of free articles about Deep Reinforcement Learning, where we'll learn the main algorithms (from Q* learning to PPO), and how to implement them in Tensorflow. I wrote these articles because I wanted to have articles that begin with the big picture (understand the concept in simpler terms), then the mathematical implementation and finally a Tensorflow implementation explained step by step (each part of the code is commented). And too much articles missed the implementation part or just give the code without any comments. Let me see what you think! What architectures you want and any feedback.

When the Arizona Daily Star began experimenting with chatbots in 2016, readers seemed excited…and a little confused. They were fascinated by the new technology, but often responded to the bot in ways that hinted they were unsure what, or who, was on the other end. "A lot of users feel like they're talking to a person," says Daily Star Product Manager Becky Pallack, who helped test one bot targeted at local parents and another for super shoppers. "They'll say thank you and send emojis." Bots are everywhere now, helping people hail Lyfts, order pizza, and choose lipstick--and the experience can range from simple and easy to befuddling and unpleasant.

The help is getting surly. We were in Brooklyn heading to a favorite Mexican dive when a pal, Demetri, asked about a movie we'd seen recently. My wife, Wink, happened to be using the voice recognition system on her phone at the moment, and suddenly came a stern rebuke along these lines: "There's no need to talk to me like that." Demetri, startled, mumbled a good-natured apology, but the voice evidently was too miffed to reply. Why an inquiry about "Three Billboards Outside Ebbing, Missouri" prompted disapproval, we'll never know.

Steven Spielberg's new blockbuster, Ready Player One, is the most significant Hollywood depiction of gamer culture to date. For the first time in mainstream cinema, it presents video games not merely as the cliched subcultural world of geeks and nerds, but as a significant force shaping the future of entertainment, communication, love, and politics. In this way, it does justice to the importance of video games, which have an increasing role in social and cultural life. As such, the celebrated director is showing the worst side of gamer culture. The movie adapts Ernest Cline's 2012 novel, in which "most of the human race spend all of their free time inside a video game" powerful enough to transform "entertainment, social networking and even global politics".

We all know about the wonders of AI and ML, and the disruption it has brought in the entertainment industry . Machine learning algorithms are changing the way we listen to music . Youtube now knows which song to play next based on your playing history, Soundcloud and Spotify expose you to artists you have never heard before . By analysing millions of songs and extracting features that are characteristics of each song and then storing it in a database', each app is able to compare the characteristics of the song being listened to, and compare it to songs in its database until it finds a match. Machine learning and algorithms help us identify songs, and these same technologies are shaping the music we listen to in our spare time. AI is playing a role in driving recommendation engines behind leading music streaming services like Spotify and Pandora.

Technology is an integral part of composer Kate Simko's work. As well as writing for film and television, she founded the London Electronic Orchestra, which combines classical instruments with electronic music. Every conference this year contains a dead human genius reincarnated as software system or a robot. Yes, there is a lot of hype, but there is real worth in AI and Machine Learning. Read our counseling on how to avoid adopting "black box" approach.

Welcome to Invisibilia Season 4! The NPR program and podcast explores the invisible forces that shape human behavior, and we here at Shots are joining in to probe the science of why we act the way we do. On paper, Shon Hopwood's life doesn't make a lot of sense, not even to him. "I don't have a great excuse as to why I did these things. And everybody always wants that," he tells me. "It closes the circle for people. But that's not really how it happened."

An AI startup including original pioneers from DeepMind – the generalised artificial intelligence company acquired by Google for around $500 million – has roared out of stealth with a pledge to revolutionise the digital economy. Its starting premise is that today's digital economy is disconnected, with assets such as hotel rooms and cars drastically under-utilised and systems such as transport and energy networks poorly optimised. The underlying'smart ledger' contains several world first innovations including built-in AI, a new Useful Proof of Work consensus mechanism and a unique data structure that combines blockchain and Directed Acyclic Graph architecture to achieve the performance and scalability necessary to support millions of agents transacting together. CTO Toby Simpson explains: "Autonomous Economic Agents are set to revolutionise commerce. They're digital entities that can transact independently of human intervention and can represent people, machines or themselves. "Imagine a world that connects anything to anything and everything to everything, where data, services and information get up on their own two feet and deliver themselves with incredible precision.

The key to getting better at deep learning (or most fields in life) is practice. Each of these problem has it's own unique nuance and approach. But where can you get this data? A lot of research papers you see these days use proprietary datasets that are usually not released to the general public. This becomes a problem, if you want to learn and apply your newly acquired skills.

I'm trying to train a deep learning model to classify stores, similar to Google's "Ontological Supervision for Fine Grained Classification of Street View Storefronts" paper. That's why I generated a variety of datasets from these raw images: What do you think, I was hoping All POI dataset would achieve higher mAP like .70-.80. Are these values accurate enough?