The IBM Watson AI XPRIZE is a four-year competition where teams work to improve the world with artificial intelligence. The competition began in 2017 with 148 problem domains in sustainability, artificial general intelligence, education, and a variety of other grand challenge areas. 59 teams advanced to the second year of the competition and ten teams earned special recognition as “milestone nominees.” The properties of the advancing problem domains highlight opportunities and challenges for the “AI for Good” movement. We detail the judging process and highlight preliminary results from cutting the field of competing teams.

This article frames and presents the discussion at an invited panel “AI History: Expert Systems” held at the AAAI-17 conference held in San Francisco. The panel’s purpose was to open up this history of expert systems, its transformational aspects, and its connections to today’s AI awakening.

Huge scale machine learning problems are nowadays tackled by distributed optimization algorithms, i.e. algorithms that leverage the compute power of many devices for training. The communication overhead is a key bottleneck that hinders perfect scalability. Various recent works proposed to use quantization or sparsification techniques to reduce the amount of data that needs to be communicated, for instance by only sending the most significant entries of the stochastic gradient (top-k sparsification). Whilst such schemes showed very promising performance in practice, they have eluded theoretical analysis so far. In this work we analyze Stochastic Gradient Descent (SGD) with k-sparsification or compression (for instance top-k or random-k) and show that this scheme converges at the same rate as vanilla SGD when equipped with error compensation (keeping track of accumulated errors in memory). That is, communication can be reduced by a factor of the dimension of the problem (sometimes even more) whilst still converging at the same rate. We present numerical experiments to illustrate the theoretical findings and the better scalability for distributed applications.

Quizlet is the most popular online learning tool in the United States, and is used by over 2/3 of high school students, and 1/2 of college students. With more than 95% of Quizlet users reporting improved grades as a result, the platform has become the de-facto tool used in millions of classrooms. In this paper, we explore the task of recommending suitable content for a student to study, given their prior interests, as well as what their peers are studying. We propose a novel approach, i.e. Neural Educational Recommendation Engine (NERE), to recommend educational content by leveraging student behaviors rather than ratings. We have found that this approach better captures social factors that are more aligned with learning. NERE is based on a recurrent neural network that includes collaborative and content-based approaches for recommendation, and takes into account any particular student's speed, mastery, and experience to recommend the appropriate task. We train NERE by jointly learning the user embeddings and content embeddings, and attempt to predict the content embedding for the final timestamp. We also develop a confidence estimator for our neural network, which is a crucial requirement for productionizing this model. We apply NERE to Quizlet's proprietary dataset, and present our results. We achieved an R^2 score of 0.81 in the content embedding space, and a recall score of 54% on our 100 nearest neighbors. This vastly exceeds the recall@100 score of 12% that a standard matrix-factorization approach provides. We conclude with a discussion on how NERE will be deployed, and position our work as one of the first educational recommender systems for the K-12 space.

Automated scoring engines are increasingly being used to score the free-form text responses that students give to questions. Such engines are not designed to appropriately deal with responses that a human reader would find alarming such as those that indicate an intention to self-harm or harm others, responses that allude to drug abuse or sexual abuse or any response that would elicit concern for the student writing the response. Our neural network models have been designed to help identify these anomalous responses from a large collection of typical responses that students give. The responses identified by the neural network can be assessed for urgency, severity, and validity more quickly by a team of reviewers than otherwise possible. Given the anomalous nature of these types of responses, our goal is to maximize the chance of flagging these responses for review given the constraint that only a fixed percentage of responses can viably be assessed by a team of reviewers.

Many real-world decision problems are characterized by multiple objectives which must be balanced based on their relative importance. In the dynamic weights setting this relative importance changes over time, as recognized by Natarajan and Tadepalli (2005) who proposed a tabular Reinforcement Learning algorithm to deal with this problem. However, this earlier work is not feasible for reinforcement learning settings in which the input is high-dimensional, necessitating the use of function approximators, such as neural networks. We propose two novel methods for multi-objective RL with dynamic weights, a multi-network approach and a single-network approach that conditions on the weights. Due to the inherent non-stationarity of the dynamic weights setting, standard experience replay techniques are insufficient. We therefore propose diverse experience replay, a framework to maintain a diverse set of experiences in the replay buffer, and show how it can be applied to make experience replay relevant in multi-objective RL. To evaluate the performance of our algorithms we introduce a new benchmark called the Minecart problem. We show empirically that our algorithms outperform more naive approaches. We also show that, while there are significant differences between many small changes in the weights opposed to sparse larger changes, the conditioned network with diverse experience replay consistently outperforms the other algorithms.

The rise of the robots has plagued the imagination of science-fiction since its inception. Whether it involved Asimov's three rules of robotics or the time travelling robotic psychopaths in the Terminator franchise, the world has worried about the role of robotics. The world has prepared itself for the robotic invasion with many low skilled jobs in retail, customer services and industrial industries like factories all replacing humans with robotic counterparts to some extent; many of which improve efficiency, productivity and costs. Shockingly, San Francisco-based company, Atrium, believe that robots have the potential to supersede and complete the job of some high-paid legal service workers. As young as 14 months, Atrium, along with the $65 million investments from adventure capitalists, are set to revolutionise the legal sector by using artificial intelligence to work alongside legal service professionals, and in some cases, replace them.

It's no surprise that Google used its Cloud Next 2018 event in Tokyo today -- one of a number of international Cloud Next events that follow its flagship San Francisco conference -- to announce a couple of new initiatives that specifically focus on the Japanese market. These announcements include a couple of basic updates like translating its Machine Learning with TensorFlow on Google Cloud Platform Coursera specialization, its Associate Cloud Engineer certification and fifty of its hands-on Qwiklabs into Japanese. In addition, Google is also launching an Advanced Solutions Lab in Tokyo as well. Previously Google opened similar labs in Dublin, Ireland, as well as Sunnyvale and New York. These labs offer a wide range of machine learning-centric training options, collaborative workspaces for teams that are part of the company's four-week machine learning training program, and access to Google experts.

SAP has released its guiding principles for artificial intelligence (AI). Recognizing the significant impact of AI on people, our customers, and wider society, SAP designed these guiding principles to steer the development and deployment of our AI software to help the world run better and improve people's lives. For us, these guidelines are a commitment to move beyond what is legally required and to begin a deep and continuous engagement with the wider ethical and socioeconomic challenges of AI. We look forward to expanding our conversations with customers, partners, employees, legislative bodies, and civil society; and to making our guiding principles an evolving reflection on these discussions and the ever-changing technological landscape. We recognize that, like with any technology, there is scope for AI to be used in ways that are not aligned with these guiding principles and the operational guidelines we are developing.

So these are the Best Machine Learning Courses and Best Deep Learning Courses will make you stand out from others and help you earn those extra hundred thousands of dollars. In this era, everybody needs to scale up their skills. And I wish you best of luck on your journey to upgrade your skills set.