Squid Game is not a subtle show. It is impossible to misinterpret its very obvious message that the games are bad, and people should NOT be driven to such desperation by a merciless capitalist system that they will murder each other for rich people's entertainment. I would not be the first to point out that there is some conflict in the fact that we, the viewers, are watching all these competitors get killed for our entertainment, but still: despite the violence, despite the shock value, there is no ambiguity around the narrative intention. In this spin-off video game from Netflix, by contrast, the games are not bad. They are supposed to be fun.

TL;DR: With VideoProc, AI is your video editor, and it's only 29.97 for a lifetime license when you use promo code PROCLIFETIME through February 2. It's so annoying when you see something amazing and actually manage to catch it on video, only to find out your video is blurry, shaky, and totally unwatchable. Good thing there's an AI tool that can fix that. VideoProc Converter AI is a tool that lets you enhance your videos in a matter of moments, and you don't need to be an AI expert to use it. The video of baby's first steps turned out blurry, but don't worry. VideoProc lets you upscale videos to 4K and preserve every precious detail.

Jon Voight spoke to Fox News Digital while promoting his film "Reagan" and weighed in on the family drama between his daughter, Angelina Jolie, and her ex, Brad Pitt. A happily-ever-after with whom a woman assumed to be Hollywood hunk Brad Pitt quickly turned into a living nightmare. On Jan. 12, the French television channel TF1 aired an episode of its show "Sept à Huit," which told the story of a 53-year-old interior designer named Anne who revealed that she had lost 830,000 euros (approximately 850,000) in personal funds because she thought she was sending money to a cancer-ridden Pitt. Through falsified documents and images as well as artificial intelligence, Anne believed she was speaking to, and eventually in a relationship with, the 61-year-old actor. WHAT IS ARTIFICIAL INTELLIGENCE (AI)? A woman was conned into believing she was in a relationship with Brad Pitt after being contacted by someone claiming to be the actor on Instagram.

Keir Starmer this week announced a 50-point plan that aims to give the UK world leader status in artificial intelligence and grow the economy by as much as 47bn a year over a decade. The multibillion-pound investment, which seeks to create a 20-fold increase in the amount of AI computing power under public control by 2030, has been framed as a gamechanger for businesses and public organisations. The reaction to the announcement has been mixed, given it is far from clear that the much-hyped potential of AI will result in the level of economic benefit forecast. Many are concerned that the technology could lead to widespread job cuts, while others fear a destruction in the value and growth of the creative industries after learning of proposals to make it easier for AI companies to mine artistic works for data, for no cost. Despite such concerns, for many in the world of business the AI revolution is already here and transforming their industries.

Deploying deep convolutional neural networks on Internet-of-Things (IoT) devices is challenging due to the limited computational resources, such as limited SRAM memory and Flash storage. Previous works re-design a small network for IoT devices, and then compress the network size by mixed-precision quantization. In this work, we propose a one-stage solution that optimizes both jointly and automatically. The key idea of our approach is to cast the joint architecture design and quantization as an Entropy Maximization process. Particularly, our algorithm automatically designs a tiny deep model such that: 1) Its representation capacity measured by entropy is maximized under the given computational budget; 2) Each layer is assigned with a proper quantization precision; 3) The overall design loop can be done on CPU, and no GPU is required.

Trajectory prediction for scenes with multiple agents and entities is a challenging problem in numerous domains such as traffic prediction, pedestrian tracking and path planning. We present a general architecture to address this challenge which models the crucial inductive biases of motion, namely, inertia, relative motion, intents and interactions. Specifically, we propose a relational model to flexibly model interactions between agents in diverse environments. Since it is well-known that human decision making is fuzzy by nature, at the core of our model lies a novel attention mechanism which models interactions by making continuous-valued (fuzzy) decisions and learning the corresponding responses. Our architecture demonstrates significant performance gains over existing state-of-the-art predictive models in diverse domains such as human crowd trajectories, US freeway traffic, NBA sports data and physics datasets.

In this paper, we answer the question of when inserting label noise (less informative labels) can instead return us more accurate and fair models. We are primarily inspired by three observations: 1) In contrast to reducing label noise rates, increasing the noise rates is easy to implement; 2) Increasing a certain class of instances' label noise to balance the noise rates (increasing-to-balancing) results in an easier learning problem; 3) Increasing-to-balancing improves fairness guarantees against label bias. In this paper, we first quantify the trade-offs introduced by increasing a certain group of instances' label noise rate w.r.t. the loss of label informativeness and the lowered learning difficulties. We analytically demonstrate when such an increase is beneficial, in terms of either improved generalization power or the fairness guarantees. Then we present a method to insert label noise properly for the task of learning with noisy labels, either without or with a fairness constraint.

This leads to inaccurate results when the true depth or disparity does not match any of these values. The fact that this distribution is usually learned indirectly through a regression loss causes further problems in ambiguous regions around object boundaries. We address these issues using a new neural network architecture that is capable of outputting arbitrary depth values, and a new loss function that is derived from the Wasserstein distance between the true and the predicted distributions. We validate our approach on a variety of tasks, including stereo disparity and depth estimation, and the downstream 3D object detection. Our approach drastically reduces the error in ambiguous regions, especially around object boundaries that greatly affect the localization of objects in 3D, achieving the state-of-the-art in 3D object detection for autonomous driving.

The purpose of multi-task reinforcement learning (MTRL) is to train a single policy that can be applied to a set of different tasks. Sharing parameters allows us to take advantage of the similarities among tasks. However, the gaps between contents and difficulties of different tasks bring us challenges on both which tasks should share the parameters and what parameters should be shared, as well as the optimization challenges due to parameter sharing. In this work, we introduce a parameter-compositional approach (PaCo) as an attempt to address these challenges. In this framework, a policy subspace represented by a set of parameters is learned. Policies for all the single tasks lie in this subspace and can be composed by interpolating with the learned set.

We propose a general class of sample based explanations of machine learning models, which we term generalized representers. To measure the effect of a training sample on a model's test prediction, generalized representers use two components: a global sample importance that quantifies the importance of the training point to the model and is invariant to test samples, and a local sample importance that measures similarity between the training sample and the test point with a kernel. A key contribution of the paper is to show that generalized representers are the only class of sample based explanations satisfying a natural set of axiomatic properties. We discuss approaches to extract global importances given a kernel, and also natural choices of kernels given modern non-linear models. As we show, many popular existing sample based explanations could be cast as generalized representers with particular choices of kernels and approaches to extract global importances.