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'Assassin's Creed: Origins' Update Comes With Annoying Stuttering Issue
"Assassin's Creed: Origins" on PC received a new update on Wednesday. And while fans at first were joyful at the thought of a new patch, they soon found out that the update actually has a big problem. Apparently, the update is causing the game on quad-core CPUs to stutter. The stuttering issue is so bad that some players thought Ubisoft downgraded the game's performance on PC with the new update. One player with the handle stormesp took to Reddit to claim that the update caused Draw Distance and LOD (level of detail) to drop to the ground in all settings.
[P] Adversarial examples on MNIST (PyTorch) • r/MachineLearning
For a Computer Science project, I decided to work on adversarial attacks. The purpose of this project is to see how to train road sign slassifiers, then how to deceive them, then how to protect them from such attacks. I started by working on MNIST, and everything works now. The repo contains modules to easily train models and run adversarial attacks against them. I hope you will enjoy this project, and I await your criticism and advice!
Today: Time for a Gratitude Adjustment
Here are the stories you shouldn't miss today: What are you doing today, other than turning up the air conditioning if you live in Southern California? Spending it with family and friends? Watching TV? (Check out these tips.) Wondering how you are going to cook that turkey? Feeling remorse about the bird that gave its life? However you spend the day, columnist Bill Plaschke wants you to remember the Thanksgiving spirit.
Prince Harry and robot to edit Radio 4's Today Programme
Prince Harry and a robot have been announced as two guest editors on Radio 4's Today Programme. Their fellow editors will be Baroness Trumpington, Tamara Rojo and Ben Okri. This is the 14th year control has been handed over to public figures between Christmas and New Year. Kensington Palace said Prince Harry would use the opportunity to "shine a spotlight on issues that are close to his heart". The palace added: "He is working closely with Today's team to produce segments on a range of topics, including youth violence, conservation and mental health."
How-Artificial-Intelligence-Will-Drive-B2B-Commerce-120554.htm
Artificial Intelligence (AI) has quickly become a driving force in retail, with Forrester Research predicting earlier this year that investments into AI would triple before 2018. These trends in consumer marketing have primed the pump for widespread use of AI and insight-based marketing in B2B relationships. In fact, recent studies of business buyers show that almost two-thirds of them fully expect AI to anticipate their needs in the near future. One of the most recognizable examples of B2C AI-driven marketing is preemptive marketing, which includes things like the movies Netflix recommends to you based on your viewing history and ratings, and the products companies like Amazon suggest based on your past purchases. Another example most people are familiar with is targeted advertising.
Six gadgets that work with your smart speaker to automate your home
Dozens of gizmos will work with one or both of these speakers, and third-party manufacturers continue to bring out additional ones. If you'd like to see all of your options, Google has made a list of Home-compatible devices and Amazon has collected the Echo-compatible ones. With so much smart tech out there, it can be hard to figure out which device to buy first. So we collected six of our favorite gadgets for smartening up your home. Google's dinky streaming dongle works like a charm with Google Home.
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems
Lipton, Zachary C., Li, Xiujun, Gao, Jianfeng, Li, Lihong, Ahmed, Faisal, Deng, Li
We present a new algorithm that significantly improves the efficiency of exploration for deep Q-learning agents in dialogue systems. Our agents explore via Thompson sampling, drawing Monte Carlo samples from a Bayes-by-Backprop neural network. Our algorithm learns much faster than common exploration strategies such as $\epsilon$-greedy, Boltzmann, bootstrapping, and intrinsic-reward-based ones. Additionally, we show that spiking the replay buffer with experiences from just a few successful episodes can make Q-learning feasible when it might otherwise fail.
[R] [1711.07971] Non-local Neural Networks • r/MachineLearning
So if I'm understanding this paper correctly, the primary way that these non-local blocks differ from a fully connected net, is that when calculating y_i, you are also able to take into account the input at x_i (not just x_j). Thus, you're able to capture relationships in your image between non-local x_i and x_j, instead of merely local relationships through conv filters. For example, if you ignore x_i in your function and set f(x_i, x_j) W_ij * x_j, then this non-local block essentially approximates a fully connected layer. One thing that confuses me is that it seems like you're throwing away your relative positional data. Is the idea that this relative positional data is already captured through conv nets?