The continuous expansion of the urban traffic sensing infrastructure has led to a surge in the volume of widely available road related data. Consequently, increasing effort is being dedicated to the creation of intelligent transportation systems, where decisions on issues ranging from city-wide road maintenance planning to improving the commuting experience are informed by computational models of urban traffic instead of being left entirely to humans. The automation of traffic management has received substantial attention from the research community, however, most approaches target highways, produce predictions valid for a limited time window or require expensive retraining of available models in order to accurately forecast traffic at a new location. In this article, we propose a novel and accurate traffic flow prediction method based on symbolic regression enhanced with a lag operator. Our approach produces robust models suitable for the intricacies of urban roads, much more difficult to predict than highways. Additionally, there is no need to retrain the model for a period of up to 9 weeks. Furthermore, the proposed method generates models that are transferable to other segments of the road network, similar to, yet geographically distinct from the ones they were initially trained on. We demonstrate the achievement of these claims by conducting extensive experiments on data collected from the Darmstadt urban infrastructure.

Solving long-horizon sequential decision making tasks in environments with sparse rewards is a longstanding problem in reinforcement learning (RL) research. Hierarchical Reinforcement Learning (HRL) has held the promise to enhance the capabilities of RL agents via operation on different levels of temporal abstraction. Despite the success of recent works in dealing with inherent nonstationarity and sample complexity, it remains difficult to generalize to unseen environments and to transfer different layers of the policy to other agents. In this paper, we propose a novel HRL architecture, Hierarchical Decompositional Reinforcement Learning (HiDe), which allows decomposition of the hierarchical layers into independent subtasks, yet allows for joint training of all layers in end-to-end manner. The main insight is to combine a control policy on a lower level with an image-based planning policy on a higher level. We evaluate our method on various complex continuous control tasks, demonstrating that generalization across environments and transfer of higher level policies, such as from a simple ball to a complex humanoid, can be achieved. See videos https://sites.google.com/view/hide-rl.

I am about to graduate in a few months and I am looking for Ph.D. positions with a deadline till July so that I can bookmark them. I am specifically looking for Ph.D. in computer vision/ deep learning/ self-driving cars (but, broadly speaking, computer vision deep learning). I simply go on Google and type: Ph.D. position in XYZ (Ph.D. position in Germany etc.) and I don't find many. Are there any good websites where I can look for Ph.D. positions specifically in Europe?

This folder contains validation results for the models in this collection having pretrained weights. Since the focus for this repository is currently ImageNet-1k classification, all of the results are based on datasets compatible with ImageNet-1k classes. There are currently results for the ImageNet validation set and 3 additional test sets. The standard 50,000 image ImageNet-1k validation set. Model selection during training utilizes this validation set, so it is not a true test set.

But research in 3D deep learning has been limited because of the lack of sufficient tools and resources to support the complexities of using neural networks with 3D data and the fact that many traditional graphic operators are not differentiable. Facebook AI has built and is now releasing PyTorch3D, a highly modular and optimized library with unique capabilities designed to make 3D deep learning easier with PyTorch. PyTorch3D provides a set of frequently used 3D operators and loss functions for 3D data that are fast and differentiable, as well as a modular differentiable rendering API -- enabling researchers to import these functions into current state-of-the-art deep learning systems right away. PyTorch3D was recently a catalyst in Facebook AI's work to build Mesh R-CNN, which achieved full 3D object reconstruction from images of complex interior spaces. We fused PyTorch3D with our highly optimized 2D recognition library, Detectron2, to successfully push object understanding to the third dimension.

In the past, we've highlighted some West Coast AI research labs that we think are doing some really incredible work. Now, in an attempt to look past the dominating presence of Silicon Valley, we're turning our focus overseas and taking a closer look at some of the cutting-edge Europe AI research labs. Founded in 2015, the Alan Turing Institute is a fairly new research lab with a unique structure. Located within the British Library in London, it's a national institute comprised of 13 universities and the UK Engineering and Physical Science Research Council. This structure creates an environment that promotes collaborate across disciplines.

For the first time, MIT researchers have enabled a soft robotic arm to understand its configuration in 3D space, by leveraging only motion and position data from its own "sensorized" skin. Soft robots constructed from highly compliant materials, similar to those found in living organisms, are being championed as safer, and more adaptable, resilient, and bioinspired alternatives to traditional rigid robots. But giving autonomous control to these deformable robots is a monumental task because they can move in a virtually infinite number of directions at any given moment. That makes it difficult to train planning and control models that drive automation. Traditional methods to achieve autonomous control use large systems of multiple motion-capture cameras that provide the robots feedback about 3D movement and positions.

GlobalData research has found the top artificial intelligence influencers based on their performance and engagement online. Using research from GlobalData's Influencer platform, Verdict has named ten of the most influential people in artificial intelligence on Twitter during Q4 2019. Ronald van Loon is a recognised thought leader and one of the leading influencers of technologies including AI, IoT, big data, and data science. As director of Advertisement, now acquired by Digital Power, the influencer provides insights and secures analytics data quality, among other responsibilities. The influencer states that enhanced AI capabilities will allow drones to be controlled with the mind, along with robots eventually overtaking humans.

Nowadays, artificial intelligence is present in almost every part of our lives. Smartphones, social media feeds, recommendation engines, online ad networks, and navigation tools are some examples of AI-based applications that already affect us every day. Deep learning in areas such as speech recognition, autonomous driving, machine translation, and visual object recognition has been systematically improving the state of the art for a while now. However, the reasons that make deep neural networks (DNN) so powerful are only heuristically understood, i.e. we know only from experience that we can achieve excellent results by using large datasets and following specific training protocols. Recently, one possible explanation was proposed, based on a remarkable analogy between a physics-based conceptual framework called renormalization group (RG) and a type of neural network known as a restricted Boltzmann machine (RBM).

That's troubling not only because these fakes might be used to sway opinions during an election or implicate a person in a crime, but because they've already been abused to generate pornographic material of actors and defraud a major energy producer. In anticipation of this new reality, a coalition of academic institutions, tech firms, and nonprofits are developing ways to spot misleading AI-generated media. Their work suggests that detection tools are a viable short-term solution but that the deepfake arms race is just beginning. The best AI-produced prose used to be closer to Mad Libs than The Grapes of Wrath, but cutting-edge language models can now write with humanlike pith and cogency. San Francisco research firm OpenAI's GPT-2 takes seconds to craft passages in the style of a New Yorker article or brainstorm game scenarios.