This paper reports applications of Difference of Convex functions (DC) programming to Learning from Demonstrations (LfD) and Reinforcement Learning (RL) with expert data. This is made possible because the norm of the Optimal Bellman Residual (OBR), which is at the heart of many RL and LfD algorithms, is DC. Improvement in performance is demonstrated on two specific algorithms, namely Reward-regularized Classification for Apprenticeship Learning (RCAL) and Reinforcement Learning with Expert Demonstrations (RLED), through experiments on generic Markov Decision Processes (MDP), called Garnets.

We address the task of simultaneous feature fusion and modeling of discrete ordinal outputs. We propose a novel Gaussian process(GP) auto-encoder modeling approach. In particular, we introduce GP encoders to project multiple observed features onto a latent space, while GP decoders are responsible for reconstructing the original features. Inference is performed in a novel variational framework, where the recovered latent representations are further constrained by the ordinal output labels. In this way, we seamlessly integrate the ordinal structure in the learned manifold, while attaining robust fusion of the input features. We demonstrate the representation abilities of our model on benchmark datasets from machine learning and affect analysis. We further evaluate the model on the tasks of feature fusion and joint ordinal prediction of facial action units. Our experiments demonstrate the benefits of the proposed approach compared to the state of the art.

If you answered in the affirmative to both of these questions, you might want to consider putting your little gray cells to good use by signing up to the newly opened IBM Watson AI Xprize. The AI and cognitive computing competition challenges teams from around the world to come up with ways in which humans and AI can team up to solve the world's biggest conundrums. "We see tremendous opportunity in the emerging generation of problem solvers to use AI to solve humanity's grandest challenges," Amir Banifatemi, prize lead of the IBM Watson AI Xprize, told Digital Trends. "The'open' nature of the competition will allow teams for the first time to define their own challenges and demonstrate their solutions utilizing any AI technology, allowing for myriad problem-solving approaches." Yep, that's right: the contest doesn't specify which of humanity's biggest dilemmas you need to approach -- instead encouraging people to pick their own topic.

The XGBoost library for gradient boosting uses is designed for efficient multi-core parallel processing. This allows it to efficiently use all of the CPU cores in your system when training. In this post you will discover the parallel processing capabilities of the XGBoost in Python. How to Best Tune Multithreading Support for XGBoost in Python Photo by Nicholas A. Tonelli, some rights reserved. XGBoost is the high performance implementation of gradient boosting that you can now access directly in Python.

For the sake of reproducibility, I'm giving you access to personalized Docker image for provisioning the environment. You should be able to run it on your operating system. If you don't want to (or can't) you will have to install all the required libraries manually. You should also have Git installed to download necessary course materials. The course starts now and never ends!

Say what you will, but IBM Watson is one resourceful supercomputer. We've previously seen the AI describe the contents of photos, predict the most popular toys during Christmas season and gauge your emotional state – all of that with an exceptional accuracy. Now IBM Watson has added yet another skill to its arsenal as it just learned how to make movie trailers. Earlier this week, 20th Century Fox trusted the supercomputer with the task to create the trailer for its upcoming sci-fi drama Morgan. Our new event for New York is focused on quality, not quantity.

Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

The discussion on artificial intelligence has been flooded with concerns of "singularity" and futuristic robot takeovers. But how will AI impact our lives five years from now, compared to 50? The study, which is led by a panel of 17 tech leaders, aims to predict the impact of AI on day-to-day life – everywhere from the home to the workplace. "We felt it was important not to have those single-focus isolated topics, but rather to situate them in the world because that's where you really see the impact happening," Barbara Grosz, an AI expert at Harvard and chair of the committee, said in a statement. Researchers kicked off the study with a report titled "Artificial Intelligence and Life in 2030," which considers how advances like delivery drones and autonomous vehicles might integrate into American society.

Rapid eye movement sleep (REM sleep, REMS) is a unique phase of mammalian sleep characterized by random movement of the eyes, low muscle tone throughout the body, and the propensity of the sleeper to dream vividly. This phase is also known as paradoxical sleep (PS) and sometimes desynchronized sleep because of physiological similarities to waking states, including rapid, low-voltage desynchronized brain waves. Electrical and chemical activity regulating this phase seems to originate in the brain stem and is characterized most notably by an abundance of the neurotransmitter acetylcholine, combined with a nearly complete absence of monoamine neurotransmitters histamine, serotonin, and norepinepherine.[1] The cortical and thalamic neurons of the waking or paradoxically sleeping brain are more depolarized--i.e., can "fire" more readily--than in the deeply sleeping brain.[2] The right and left hemispheres of the brain are more coherent in REM sleep, especially during lucid dreams.[3] REM sleep is punctuated and immediately preceded by PGO (ponto-geniculo-occipital) waves, bursts of electrical activity originating in the brain stem.[4] These waves occur in clusters about every 6 seconds for 1–2 minutes during the transition from deep to paradoxical sleep.[5] They exhibit their highest amplitude upon moving into the visual cortex and are a cause of the "rapid eye movements" in paradoxical sleep.[6][7] Brain energy use in REM sleep, as measured by oxygen and glucose metabolism, equals or exceeds energy use in waking. The rate in non-REM sleep is 11–40% lower.[8]

A panel of academic and industrial thinkers has looked ahead to 2030 to forecast how advances in artificial intelligence (AI) might affect life in a typical North American city - in areas as diverse as transportation, health care and education--and to spur discussion about how to ensure the safe, fair and beneficial development of these rapidly emerging technologies. Titled "Artificial Intelligence and Life in 2030," this year-long investigation is the first product of the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted by Stanford to inform societal deliberation and provide guidance on the ethical development of smart software, sensors and machines. "We believe specialized AI applications will become both increasingly common and more useful by 2030, improving our economy and quality of life," said Peter Stone, a computer scientist at the University of Texas at Austin and chair of the 17-member panel of international experts. "But this technology will also create profound challenges, affecting jobs and incomes and other issues that we should begin addressing now to ensure that the benefits of AI are broadly shared." The new report traces its roots to a 2009 study that brought AI scientists together in a process of introspection that became ongoing in 2014, when Eric and Mary Horvitz created the AI100 endowment through Stanford.