Value alignment is a property of an intelligent agent indicating that it can only pursue goals and activities that are beneficial to humans. Traditional approaches to value alignment use imitation learning or preference learning to infer the values of humans by observing their behavior. We introduce a complementary technique in which a value aligned prior is learned from naturally occurring stories which encode societal norms. Training data is sourced from the childrens educational comic strip, Goofus and Gallant. In this work, we train multiple machine learning models to classify natural language descriptions of situations found in the comic strip as normative or non normative by identifying if they align with the main characters behavior. We also report the models performance when transferring to two unrelated tasks with little to no additional training on the new task.

General-purpose planning algorithms for automated driving combine mission, behavior, and local motion planning. Such planning algorithms map features of the environment and driving kinematics into complex reward functions. To achieve this, planning experts often rely on linear reward functions. The specification and tuning of these reward functions is a tedious process and requires significant experience. Moreover, a manually designed linear reward function does not generalize across different driving situations. In this work, we propose a deep learning approach based on inverse reinforcement learning that generates situation-dependent reward functions. Our neural network provides a mapping between features and actions of sampled driving policies of a model-predictive control-based planner and predicts reward functions for upcoming planning cycles. In our evaluation, we compare the driving style of reward functions predicted by our deep network against clustered and linear reward functions. Our proposed deep learning approach outperforms clustered linear reward functions and is at par with linear reward functions with a-priori knowledge about the situation.

AI is better than humans in a lot of things. What size is the cylinder that is left of the brown metal thing that is left of the big sphere? Any 6-year-old could answer this pretty easily, yet these kinds of questions are just out of the scope of traditional deep learning models. Deep learning models are pretty good at understanding relationships between inputs and outputs, but that's about as far as it goes. Whether it's supervised learning or reinforcement learning, the input and desired output are clearly defined and easy for the model to understand.

This subreddit is dedicated to all information related to compressive sensing (including nonlinear compressive sensing including deep learning algorithms), advanced matrix factorization (codes, webpages, examples of use, etc...) and related Machine Learning techniques...

Most deep-learning algorithms perform well when trained on large sets of labeled data, but their performance tends to decline when processing new data. Researchers worldwide have thus been trying to develop techniques that could improve the ability of these algorithms to generalize well across both new and previously processed data, enabling what is known as lifelong learning. Researchers at the University of Notre Dame and GlobalFoundries Fab1 have recently developed a new method to facilitate lifelong learning in artificial neural networks, which entails the use of a ferroelectric ternary content-addressable memory component. Their study, featured in Nature Electronics, was aimed at replicating the human brain's ability to learn rapidly from only a few examples, adapting to new tasks based on past experiences. "When a trained deep neural network encounters previously unseen classes, it often fails to generalize from its prior knowledge and must re-learn the network parameters to extract relevant information from the given class," Kai Ni, one of the researchers who carried out the study, told TechXplore.

Machine learning is an exciting topic about designing machines that can learn from examples. The course covers the necessary theory, principles and algorithms for machine learning. The methods are based on statistics and probability-- which have now become essential to designing systems exhibiting artificial intelligence. Reference textbooks for different parts of the course are "Pattern Recognition and Machine Learning" by Chris Bishop (Springer 2006) and "Probabilistic Graphical Models" by Daphne Koller and Nir Friedman (MIT Press 2009) and "Deep Learning" by Goodfellow, Bengio and Courville (MIT Press 2016).

The web has transformed from the world's most widely used document platform to its most widely used application platform. In the past few years, we have seen tremendous growth in the field of AI. Web as a platform is making great progress, allowing developers to ship some excellent experiences leveraging AI advancements. Today, we have devices with great processing power and browsers capable of leveraging them to the full extent. Tech giants have invested heavily in making it easier for developers to ship AI features with their web apps.

Understanding the Quality of Experience (QoE) for visual media has been very important for optimal compression and content delivery for diverse video formats, and it has been a hot topic of research for the last decades. Researchers mostly studied this problem from a signal processing perspective using image and video processing tools, while learning-based methods have been increasing momentum recently. The popularisation of deep learning-based methods affected the whole signal and image processing community as a disruptive force, and visual QoE estimation is no different than others. Use of learning-based methods and especially deep learning methods open a new path for understanding the human visual system in the perception process and other QoE parameters. The objectives of this special session are twofold: first, to develop new metrics reaching beyond the performance of the legacy signal processing approaches for visual QoE estimation, and second, to understand the stages of human perception for visual media better utilising the learning-based methods and different analysis methods such as ablation studies.

Synaptic is a powerful cognitive engine that incorporates predictive analytics, machine-learning, control systems and feedback, natural language processing, and other proprietary algorithms to personalize projects, campaigns or pitches at a human and behavioral level. Synaptic is modeled on neuron activity and learning mechanisms occurring in the human brain, exceeding the capabilities of traditional deep learning systems. He interacts with the real world and learns in real-time–reinforcing the intelligence as new concepts and situations arise. This means that Synaptic owns the same limitless understanding of information that humans enjoy. Synaptic allows you to analyze your own unstructured data to unearth meaningful insights hidden within.

When I read Ender's Game, one of the parts that most stuck with me was the delightfully creepy Mind Game–a game designed to probe the player's subconcious. In the book, we learn that the Mind Game isn't actually programmed–it's powered by an AI that makes up the gameplay as it goes, reacting to the player's decisions and getting progressively more surreal. Ender's Game was written in 1985, and since then AI has actually gotten pretty good at creative tasks since then. The real-time graphics of something like the Mind Game are probably still out of reach, but what about something simpler like a text adventure? I decided to try writing a game like this by training GPT-2, a state-of-the-art predictive text model, on some transcripts of classic text advetnures.