Learning to drive faithfully in highly stochastic urban settings remains an open problem. To that end, we propose a Multi-task Learning from Demonstration (MT-LfD) framework which uses supervised auxiliary task prediction to guide the main task of predicting the driving commands. Our framework involves an end-to-end trainable network for imitating the expert demonstrator's driving commands. The network intermediately predicts visual affordances and action primitives through direct supervision which provide the aforementioned auxiliary supervised guidance. We demonstrate that such joint learning and supervised guidance facilitates hierarchical task decomposition, assisting the agent to learn faster, achieve better driving performance and increases transparency of the otherwise black-box end-to-end network. We run our experiments to validate the MT-LfD framework in CARLA, an open-source urban driving simulator. We introduce multiple non-player agents in CARLA and induce temporal noise in them for realistic stochasticity.

Multi-hop reasoning is an effective approach for query answering (QA) over incomplete knowledge graphs (KGs). The problem can be formulated in a reinforcement learning (RL) setup, where a policy-based agent sequentially extends its inference path until it reaches a target. However, in an incomplete KG environment, the agent receives low-quality rewards corrupted by false negatives in the training data, which harms generalization at test time. Furthermore, since no golden action sequence is used for training, the agent can be misled by spurious search trajectories that incidentally lead to the correct answer. We propose two modeling advances to address both issues: (1) we reduce the impact of false negative supervision by adopting a pretrained one-hop embedding model to estimate the reward of unobserved facts; (2) we counter the sensitivity to spurious paths of on-policy RL by forcing the agent to explore a diverse set of paths using randomly generated edge masks. Our approach significantly improves over existing path-based KGQA models on several benchmark datasets and is comparable or better than embedding-based models.

We introduce a new virtual environment for simulating a card game known as "Big 2". This is a four-player game of imperfect information with a relatively complicated action space (being allowed to play 1,2,3,4 or 5 card combinations from an initial starting hand of 13 cards). As such it poses a challenge for many current reinforcement learning methods. We then use the recently proposed "Proximal Policy Optimization" algorithm to train a deep neural network to play the game, purely learning via self-play, and find that it is able to reach a level which outperforms amateur human players after only a relatively short amount of training time and without needing to search a tree of future game states.

The current state of the art in playing many important perfect information games, including Chess and Go, combines planning and deep reinforcement learning with self-play. We extend this approach to imperfect information games and present ExIt-OOS, a novel approach to playing imperfect information games within the Expert Iteration framework and inspired by AlphaZero. We use Online Outcome Sampling, an online search algorithm for imperfect information games in place of MCTS. While training online, our neural strategy is used to improve the accuracy of playouts in OOS, allowing a learning and planning feedback loop for imperfect information games.

Level assessment for foreign language students is necessary for putting them in the right level group, furthermore, interviewing students is a very time-consuming task, so we propose to automate the evaluation of speaker fluency level by implementing machine learning techniques. This work presents an audio processing system capable of classifying the level of fluency of non-native English speakers using five different machine learning models. As a first step, we have built our own dataset, which consists of labeled audio conversations in English between people ranging in different fluency domains/classes (low, intermediate, high). We segment the audio conversations into 5s non-overlapped audio clips to perform feature extraction on them. We start by extracting Mel cepstral coefficients from the audios, selecting 20 coefficients is an appropriate quantity for our data. We thereafter extracted zero-crossing rate, root mean square energy and spectral flux features, proving that this improves model performance. Out of a total of 1424 audio segments, with 70% training data and 30% test data, one of our trained models (support vector machine) achieved a classification accuracy of 94.39%, whereas the other four models passed an 89% classification accuracy threshold.

With more companies leveraging the cloud in their products and infrastructures, pursuing a career in cloud services can prove to be lucrative. However, aspiring cloud engineers will need a proper understanding of cloud concepts such as neural networks and deep learning to succeed. For $39, the Google Cloud Mastery Bundle offers courses designed to get you up to speed with the cloud and its uses. A simple way to dive into cloud mastery with no experience is by building a basic chatbot, an increasingly popular innovation companies use in their customer support roles. DialogFlow makes building them easy, and you can learn its ins and outs in the Google DialogFlow For Chatbots course.

Artificial intelligence has been the domain of science fiction for decades -- think HAL, the computer in "2001: A Space Odyssey" -- but as many people know, it's actually established well-developed and growing roots in modern-day life. Amazon's Echo, Netflix's recommendation engines, Facebook's facial recognition technology, auto-braking on cars, it's all based on the ability to analyze massive amounts of data in near real-time and being able to mimic human behavior based on the results. AI and its various subsegments -- like machine learning and deep learning -- are also reaching deep into the enterprise, helping to automate many of the tasks that now are done manually, creating greater efficiencies, reducing errors and offering valuable new insights into the massive amounts of data being generated. In a dynamic and fast-changing market like manufacturing, systems that can learn and adapt on their own will be crucial in driving the next-generation flexible environments. According to Accenture, 85 percent of executives plan to invest in AI technologies over the next three years.

Artificial Intelligence has exploded throughout the last year, improving many existing technologies and solving new problems. AI's involvement in the open-source community should be no surprise either, as the research from open-source AI frameworks has led to algorithms and pre-trained machine learning and deep learning models. In the 2018 Guide to Artificial Intelligence: Automating Decision-Making, you'll read about the breadth of uses cases that AI can solve, including how Apache Ignite can combat credit card fraud. You'll also read about the rise of predictive analytics, the revolutionary qualities of Edge AI, and four use cases for predictive analytics.

From smartphone assistants to image recognition and translation, machine learning already helps us in our everyday lives. But it can also help us to tackle some of the world's most challenging physical problems -- such as energy consumption. Large-scale commercial and industrial systems like data centers consume a lot of energy, and while much has been done to stem the growth of energy use, there remains a lot more to do given the world's increasing need for computing power. Google is taking many steps to reduce energy consumptions . Compared to five years ago, Google now get around 3.5 times the computing power out of the same amount of energy.

This robotic hand practiced rotating a block for 100 years inside a 50 hour simulation! Is this the next revolutionary step for neural networks? A.I. Is Monitoring You Right Now and Here's How It's Using Your Data - https://youtu.be/KpybityrXfs Read More: OpenAI: Learning Dexterity https://blog.openai.com/learning-dext... "Our system, called Dactyl, is trained entirely in simulation and transfers its knowledge to reality, adapting to real-world physics using techniques we've been working on for the past year. Dactyl learns from scratch using the same general-purpose reinforcement learning algorithm and code as OpenAI Five. Our resultsshow that it's possible to train agents in simulation and have them solve real-world tasks, without physically-accurate modeling of the world."