A subsidiary of Google has entered a two-year partnership with Fudan University, the leading university in China's eastern Shanghai municipality, with a focus on emerging technologies such as artificial intelligence. Google China's Education Cooperation Division support Fudan's curriculum related to emerging science and technology, online news outlet The Paper reported, adding that the pair will jointly build a laboratory as well as an exchange center to boost interaction between students in China and the US. Google's China-based education unit has been working with schools in the country since 2006, covering undergraduate, higher vocational education and secondary schools. The projects supported include joint scientific research, curriculum construction, teacher training and information technology education for middle school students. AI is a key focus of Google's development in China.

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models.

We unwittingly encounter AI in our daily life, in simple form observed in spelling correction to more complex form as in personalized content consumption. Our world as we know it, is running on artificial intelligence. Siri manages our calendars, Facebook suggests our friends, Computers trade our stocks. We have cars that park themselves, and air traffic control is almost fully automated. So, virtually every field has benefited from advances in artificial intelligence.

I want to offer a few considerations about what I consider the inevitable transformation of education by artificial intelligence, but to do so, I'm going to first invite you into my childhood and early college years for a moment. It might not seem related to AI, but if you bear with me, I promise to offer you a few important and incredibly relevant considerations, as well as an important challenge and invitation. Mr. Bently was an extraordinary teacher. Many others faced far greater challenges to be sure, but suffice it to say that when I went to school, it was not easy to set aside worries and concerns from outside of school enough to get the most out of what happened in most of my classes. Nonetheless, when I walked up to the room to enter Mr. Bentley's class, he consistently greeted me and every other student at the door. As he wished us each a good morning, he also paid attention to the little things and deliberately said something that made each of us keenly aware that he cared about us and noticed us.

Whereas artificial intelligence is being used in some countries to mark multiple choice questions, China is experimenting with machine intelligence to mark essays. According to the South China Morning Post, technology has been developed to interpret the general logic and meaning of the text. The platform can then undertake human-like judgment into an essay's overall quality. The platform can then assign a grade to the essay and also provide recommended for improvement, selecting categories such as writing style, sentence structure and overall theme. At present the application of artificial intelligence in Chinese schools is assisting an assessment by a teacher and not removing the teacher from the equation.

Online learning algorithms, widely used to power search and content optimization on the web, must balance exploration and exploitation, potentially sacrificing the experience of current users for information that will lead to better decisions in the future. Recently, concerns have been raised about whether the process of exploration could be viewed as unfair, placing too much burden on certain individuals or groups. Motivated by these concerns, we initiate the study of the externalities of exploration - the undesirable side effects that the presence of one party may impose on another - under the linear contextual bandits model. We introduce the notion of a group externality, measuring the extent to which the presence of one population of users impacts the rewards of another. We show that this impact can in some cases be negative, and that, in a certain sense, no algorithm can avoid it. We then study externalities at the individual level, interpreting the act of exploration as an externality imposed on the current user of a system by future users. This drives us to ask under what conditions inherent diversity in the data makes explicit exploration unnecessary. We build on a recent line of work on the smoothed analysis of the greedy algorithm that always chooses the action that currently looks optimal, improving on prior results to show that a greedy approach almost matches the best possible Bayesian regret rate of any other algorithm on the same problem instance whenever the diversity conditions hold, and that this regret is at most $\tilde{O}(T^{1/3})$. Returning to group-level effects, we show that under the same conditions, negative group externalities essentially vanish under the greedy algorithm. Together, our results uncover a sharp contrast between the high externalities that exist in the worst case, and the ability to remove all externalities if the data is sufficiently diverse.

Traditional exploration methods in RL require agents to perform random actions to find rewards. But these approaches struggle on sparse-reward domains like Montezuma's Revenge where the probability that any random action sequence leads to reward is extremely low. Recent algorithms have performed well on such tasks by encouraging agents to visit new states or perform new actions in relation to all prior training episodes (which we call across-training novelty). But such algorithms do not consider whether an agent exhibits intra-life novelty: doing something new within the current episode, regardless of whether those behaviors have been performed in previous episodes. We hypothesize that across-training novelty might discourage agents from revisiting initially non-rewarding states that could become important stepping stones later in training. We introduce Deep Curiosity Search (DeepCS), which encourages intra-life exploration by rewarding agents for visiting as many different states as possible within each episode, and show that DeepCS matches the performance of current state-of-the-art methods on Montezuma's Revenge. We further show that DeepCS improves exploration on Gravitar (another difficult, sparse-reward game) and performs well on the dense-reward game Amidar. Surprisingly, DeepCS doubles A2C performance on Seaquest, a game we would not have expected to benefit from intra-life exploration because the arena is small and already easily navigated by naive exploration techniques. In one run, DeepCS achieves a maximum training score of 80,000 points on Seaquest, higher than any methods other than Ape-X. The strong performance of DeepCS on these sparse- and dense-reward tasks suggests that encouraging intra-life novelty is an interesting, new approach for improving performance in Deep RL and motivates further research into hybridizing across-training and intra-life exploration methods.

The recent work of Clark et al. (2018) introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into an Easy Set and a Challenge Set. That paper includes an analysis of 100 questions with respect to the types of knowledge and reasoning required to answer them; however, it does not include clear definitions of these types, nor does it offer information about the quality of the labels. We propose a comprehensive set of definitions of knowledge and reasoning types necessary for answering the questions in the ARC dataset. Using ten annotators and a sophisticated annotation interface, we analyze the distribution of labels across the Challenge Set and statistics related to them. Additionally, we demonstrate that although naive information retrieval methods return sentences that are irrelevant to answering the query, sufficient supporting text is often present in the (ARC) corpus. Evaluating with human-selected relevant sentences improves the performance of a neural machine comprehension model by 42 points.

In end-to-end learning of visuomotor behaviors, algorithms such as imitation learning, reinforcement learning (RL), or a combination of both, have achieved remarkable successes in video games [27], board games [36, 37], and robot manipulation tasks [23, 29]. One major issue of using RL alone is its sample efficiency, hence in practice human demonstration can be used to speedup learning [36, 5, 14]. Imitation learning, or learning from demonstration, follows a student-teacher paradigm, in which a learning agent learns from the demonstration of human teachers [1]. A popular approach is behavior cloning, i.e., training an agent to predict (imitate) demonstrated behaviors with supervised learning methods. Imitation learning research mainly focuses on the student-advancing our understanding of the learning agent-while very little effort is made to understand the human teacher. In this work, we argue that understanding and modeling the human teacher is also an important research issue in this paradigm. Specifically, in visuomotor learning tasks, a key component of human intelligence-the visual attention mechanism-encodes a wealth of information that can be exploited by a learning algorithm. Modeling human visual attention and guiding the learning agent with a learned attention model could lead to significant improvement in task performance. We propose the Attention Guided Imitation Learning (AGIL) framework, in which a learning agent first learns a visual attention model from human gaze data, then learns how to perform the visuomotor task from human decision data.

Data living on manifolds commonly appear in many applications. We show that under certain conditions, it is possible to construct an intrinsic and isometric data representation, which respects an underlying latent intrinsic manifold geometry. Namely, instead of learning the structure of the observed manifold, we view the observed data only as a proxy and learn the structure of a latent unobserved intrinsic manifold. For this purpose, we build a new metric and propose a method for robust estimation by assuming mild statistical priors and by using artificial neural networks as a mechanism for metric regularization and parameterization. We show successful application to unsupervised indoor localization in ad-hoc sensor networks. Specifically, we show that our proposed method facilitates accurate localization of a moving agent from imaging data it collects. Importantly, our method is applied in the same way to two different imaging modalities, thereby demonstrating its intrinsic capabilities.