In this paper, we study a transfer reinforcement learning problem where the state transitions and rewards are affected by the environmental context. Specifically, we consider a demonstrator agent that has access to a context-aware policy and can generate transition and reward data based on that policy. These data constitute the experience of the demonstrator. Then, the goal is to transfer this experience, excluding the underlying contextual information, to a learner agent that does not have access to the environmental context, so that they can learn a control policy using fewer samples. It is well known that, disregarding the causal effect of the contextual information, can introduce bias in the transition and reward models estimated by the learner, resulting in a learned suboptimal policy. To address this challenge, in this paper, we develop a method to obtain causal bounds on the transition and reward functions using the demonstrator's data, which we then use to obtain causal bounds on the value functions. Using these value function bounds, we propose new Q learning and UCB-Q learning algorithms that converge to the true value function without bias. We provide numerical experiments for robot motion planning problems that validate the proposed value function bounds and demonstrate that the proposed algorithms can effectively make use of the data from the demonstrator to accelerate the learning process of the learner.

Reinforcement Learning (RL) in various decision-making tasks of machine learning provides effective results with an agent learning from a stand-alone reward function. However, it presents unique challenges with large amounts of environment states and action spaces, as well as in the determination of rewards. This complexity, coming from high dimensionality and continuousness of the environments considered herein, calls for a large number of learning trials to learn about the environment through Reinforcement Learning. Imitation Learning (IL) offers a promising solution for those challenges using a teacher. In IL, the learning process can take advantage of human-sourced assistance and/or control over the agent and environment. A human teacher and an agent learner are considered in this study. The teacher takes part in the agent training towards dealing with the environment, tackling a specific objective, and achieving a predefined goal. Within that paradigm, however, existing IL approaches have the drawback of expecting extensive demonstration information in long-horizon problems. This paper proposes a novel approach combining IL with different types of RL methods, namely state action reward state action (SARSA) and asynchronous advantage actor-critic (A3C) agents, to overcome the problems of both stand-alone systems. It is addressed how to effectively leverage the teacher feedback, be it direct binary or indirect detailed for the agent learner to learn sequential decision-making policies. The results of this study on various OpenAI Gym environments show that this algorithmic method can be incorporated with different combinations, significantly decreases both human endeavor and tedious exploration process.

A seminar on Artificial Intelligence (AI) was held at Santa Clara University (Silicon Valley, California) from April 3-5, 2019, sponsored by the China Forum for Civilizational Dialogue (an institution born from the joint commitment of La Civiltà Cattolica and Georgetown University) and the Pontifical Council for Culture. The event was hosted by the Tech & the Human Spirit Initiative at Santa Clara. The meeting brought together, in addition to the two authors of these reflections, another 11 participants, scholars from China, the United States and Europe, to examine how the great changes underway are posing challenges to the Christian and Confucian traditions, as well as to other religious and secular traditions.[1 The enormous progress made in the last 10 years in the field of AI marks a historical discontinuity. China and the West have just begun to address the implications. In the long term, the AI revolution could redefine several fundamental philosophical questions: If machines surpass humans in intelligence, what will become of human uniqueness, dignity and freedom? Will computers become "aware" and "creative"?

The Mohamed bin Zayed University of Artificial Intelligence, MBZUAI, the first graduate-level, research-based AI university in the world, held its first official Advisory Board meeting to review ongoing activities as the University gears up to welcome its first class of graduate students in September 2020. In a press statement today MBZUAI said applications into its MSc and PhD programmes for 2020 that include Machine Learning, Natural Language Processing and Computer Vision, are going through the vetting process "with over 1,000 applications reviewed and in the final stage," before acceptance is finalised. During the meeting, Advisory Board members discussed the importance of ensuring that the University exerts every effort towards producing practical, tangible results that can directly and positively impact economies and industries. They also emphasised the need to directly engage with industries to ensure the proper integration between the output of the University and the needs of the marketplace. This would include making sure that students are aware of the full ecosystem required for a successful market deployment including knowledge of patent laws and access to startup financial advice.

The use of artificial intelligence is growing in many areas: hiring, healthcare, travel, household functions. These AI examples rely heavily on deep learning and natural language processing, but its use has sparked a debate about bias and fairness. Human decision making can be shaped by unconscious individual and societal biases. Will AI's decisions be less biased than human ones? How are companies addressing this potential AI bias with regards to hiring, data crunching, and other critical business functions?

For those who want to understand how Artificial Intelligence is transforming financial services i.e. AI in Finance, learn from those who are building the future of finance in the biggest banks, tech companies and fast-growing startups: http://www.cfte.education/aifinance It is designed around 18 modules of video lectures, reading assignments and assessment quizzes. Learners can interact with other participants through an online forum, and receive weekly emails with additional content. Once enrolled in the course, participants join a global community of finance professionals, technologists and entrepreneurs interested in AI.

Duolingo AI Research is one of Duolingo's fastest-growing teams. We use real-world data to develop new hypotheses about language and learning, test them empirically, and ship products based on our research. Duolingo has revolutionized language learning for more than 300 million people around the world, and we're looking for out-of-the-box thinkers who bring creative, interdisciplinary ideas on how to deliver a high-quality education to anyone, anywhere, through AI.

Data is key to any data science and machine learning task. Data comes in different flavors such as numerical data, categorical data, text data, image data, sound data, and video data. The predictive power of a model depends on the quality of data used in building the model. Whatever the source of your data, it's important that you understand how the data was collected. For example, data collected from surveys may contain lots of missing data, and false information.

The future of extreme weather prediction may lie in modernizing a piece of technology from the past. Researchers recently developed a new technique to augment an old-fashioned weather forecasting method with the power of deep learning, a subset of artificial intelligence (AI). Once the deep learning system is fully trained, it is able to predict extreme weather events like heat waves and cold spells with 80% accuracy up to 5 days beforehand. "This is a very inexpensive way of predicting extreme events at least a few days ahead of time," said Ashesh Chattopadhyay, a mechanical engineering graduate student at Rice University in Houston and lead author on the project. The project began when Pedram Hassanzadeh, an assistant professor of mechanical engineering at Rice, realized that extreme weather events like heat waves and cold spells usually arise from very unusual atmospheric circulation patterns that could potentially be taught to a pattern recognition computer program.

The future of extreme weather prediction may lie in modernizing a piece of technology from the past. Researchers recently developed a new technique to augment an old-fashioned weather forecasting method with the power of deep learning, a subset of artificial intelligence (AI). Once the deep learning system is fully trained, it is able to predict extreme weather events like heat waves and cold spells with 80% accuracy up to 5 days beforehand. "This is a very inexpensive way of predicting extreme events at least a few days ahead of time," said Ashesh Chattopadhyay, a mechanical engineering graduate student at Rice University in Houston and lead author on the project. The project began when Pedram Hassanzadeh, an assistant professor of mechanical engineering at Rice, realized that extreme weather events like heat waves and cold spells usually arise from very unusual atmospheric circulation patterns that could potentially be taught to a pattern recognition computer program.