About this course: This 1-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. At the end of this course, participants will be able to: • Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform • Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform • Employ BigQuery and Cloud Datalab to carry out interactive data analysis • Choose between Cloud SQL, BigTable and Datastore • Train and use a neural network using TensorFlow • Choose between different data processing products on the Google Cloud Platform Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following: • A common query language such as SQL • Extract, transform, load activities • Data modeling • Machine learning and/or statistics • Programming in Python Google Account Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google services are currently unavailable in China).

The Open Philanthropy Project is proud to announce our first class of AI Fellows – seven very promising machine learning researchers to whom we're collectively recommending a total of about $1.1 million in PhD fellowship support over the next five years. These fellows were selected from more than 180 applicants for their academic excellence, technical knowledge, careful reasoning, and interest in making the long-term, large-scale impacts of AI a central focus of their research. We believe that progress in artificial intelligence may eventually lead to changes in human civilization that are as large as the agricultural or industrial revolutions; while we think it's most likely that this would lead to significant improvements in human well-being, we also see significant risks. The AI Fellows have a broad mandate to think through which kinds of AI and ML research are likely to be most valuable, to share ideas and form a community with like-minded students and professors, and ultimately to act in the way that they think is most likely to improve outcomes from progress in AI. For more on the Open Philanthropy Project's views about the potential impacts of AI, see our previous blog posts.

Delane Parnell is the cofounder and CEO of PlayVS. If there's ever a constant in the flourishing world of esports, it's that enthusiasm often outpaces the necessary infrastructure to match it. In particular, high school students and teachers who hope to participate in competitive gaming must self-organize without the structure of an official body. Delane Parnell's high school science teacher was someone who took it upon themselves to organize a gaming club for students. He provided the equipment, he kept track of stats and even awarded trophies for the myriad of games they played.

University of Toronto graduate student Avishek "Joey" Bose, under the supervision of associate professor Parham Aarabi in the school's department of electrical and computer engineering, has created an algorithm that dynamically disrupts facial recognition systems. The project has privacy-related and even safety-related implications for systems that use so-called machine learning -- and for all of us whose data may be used in ways we don't realize. Major companies such as Amazon, Google, Facebook and Netflix are today leveraging machine learning. Financial trading firms and health care companies are using it, too -- as are smart car manufacturers. What is machine learning, anyway?

For many AI services, it is critical to be able to comprehend human language and even converse in it with human users. So far, advances in natural language processing (NLP) powered with "sub-symbolic" machine learning based on deep neural networks allows us to solve multiple tasks like machine translation, classification, and emotion recognition. However, using these approaches requires enormous amount of training. Additionally, there are increasing legal restrictions in particular applications due to recent regulations, making current solutions unviable. The ultimate goal for these industry initiatives is to allow humans and AI to interact fluently in a common language.

LAHORE - An exhibition titled "2nd Machine Learning Projects" featuring projects of International Technology University students on Artificial Intelligence opened at the Punjab Signal Processing and Information Decoding Research Laboratory on Sunday. The exhibition opened after four-month training of MS and PhD students of ITU who presented their Machine Learning course projects, geared towards solving interesting and locally relevant problems. The projects included a project on grocery stores who always find difficult to forecast sales and purchase of items. The project Walmart Data is aimed at predicting unit sales quantities of sales items across 54 grocery stores using the technique of rolling means and LSTM neural. This project will help managers in warehouse management, manpower estimation and effective sales promotions.

Anthony Seldon is one of Britain's leading educationalists and social commentators. He has served as a close adviser to former leaders, including Tony Blair and David Cameron. In recent years, he has turned his attention to the ongoing impact of new technologies, in particular, artificial intelligence (AI), on education and on society, writes Kyran Fitzgerald. With Oladimeji Abidoye, Mr Seldon recently published The Fourth Education Revolution: Will Artificial intelligence Liberate or Infantilise Humanity? He does not pull his punches, warning that we may be "sleepwalking into the biggest potential disaster of modern times". Despite these tides of change, the education sector has been slow to respond.

Reinforcement learning is a powerful technique to train an agent to perform a task. However, an agent that is trained using reinforcement learning is only capable of achieving the single task that is specified via its reward function. Such an approach does not scale well to settings in which an agent needs to perform a diverse set of tasks, such as navigating to varying positions in a room or moving objects to varying locations. Instead, we propose a method that allows an agent to automatically discover the range of tasks that it is capable of performing. We use a generator network to propose tasks for the agent to try to achieve, specified as goal states. The generator network is optimized using adversarial training to produce tasks that are always at the appropriate level of difficulty for the agent. Our method thus automatically produces a curriculum of tasks for the agent to learn. We show that, by using this framework, an agent can efficiently and automatically learn to perform a wide set of tasks without requiring any prior knowledge of its environment. Our method can also learn to achieve tasks with sparse rewards, which traditionally pose significant challenges.

A reciprocal recommendation problem is one where the goal of learning is not just to predict a user's preference towards a passive item (e.g., a book), but to recommend the targeted user on one side another user from the other side such that a mutual interest between the two exists. The problem thus is sharply different from the more traditional items-to-users recommendation, since a good match requires meeting the preferences of both users. We initiate a rigorous theoretical investigation of the reciprocal recommendation task in a specific framework of sequential learning. We point out general limitations, formulate reasonable assumptions enabling effective learning and, under these assumptions, we design and analyze a computationally efficient algorithm that uncovers mutual likes at a pace comparable to those achieved by a clearvoyant algorithm knowing all user preferences in advance. Finally, we validate our algorithm against synthetic and real-world datasets, showing improved empirical performance over simple baselines.

We consider a network scenario in which agents can evaluate each other according to a score graph that models some interactions. The goal is to design a distributed protocol, run by the agents, that allows them to learn their unknown state among a finite set of possible values. We propose a Bayesian framework in which scores and states are associated to probabilistic events with unknown parameters and hyperparameters, respectively. We show that each agent can learn its state by means of a local Bayesian classifier and a (centralized) Maximum-Likelihood (ML) estimator of parameter-hyperparameter that combines plain ML and Empirical Bayes approaches. By using tools from graphical models, which allow us to gain insight on conditional dependencies of scores and states, we provide a relaxed probabilistic model that ultimately leads to a parameter-hyperparameter estimator amenable to distributed computation. To highlight the appropriateness of the proposed relaxation, we demonstrate the distributed estimators on a social interaction setup for user profiling. A common feature of online social networks (OSNs) is the possibility of individuals to continuously interact among themselves, by sharing contents and expressing opinions or ratings on different topics [1], [2].