In order for an e-commerce platform to maximize its revenue, it must recommend customers items they are most likely to purchase. However, the company often has business constraints on these items, such as the number of each item in stock. In this work, our goal is to recommend items to users as they arrive on a webpage sequentially, in an online manner, in order to maximize reward for a company, but also satisfy budget constraints. We first approach the simpler online problem in which the customers arrive as a stationary Poisson process, and present an integrated algorithm that performs online optimization and online learning together. We then make the model more complicated but more realistic, treating the arrival processes as non-stationary Poisson processes. To deal with heterogeneous customer arrivals, we propose a time segmentation algorithm that converts a non-stationary problem into a series of stationary problems. Experiments conducted on large-scale synthetic data demonstrate the effectiveness and efficiency of our proposed approaches on solving constrained resource allocation problems.

I am working in ML/AI field for 6 years and apart from technical skills that I acquired while working on the projects, I have also discussed various aspects of ML/AI with my non-technical colleagues, who have mostly been senior manager, VPs or CXOs. When I heard about "AI For Everyone" course, I was a bit reluctant in attending it as I thought I know most of the generic stuff that might have been talked in the course. Recently, one of my colleagues discussed with me a few topics covered in this course which intrigued me to get a fresh perspective on these topics. So, I recently attended this course on Coursera. My motivation to write this blog is to make sure that I have understood key aspects of this course and am able to make my non-technical colleagues and project stakeholders understand the benefits & limitations of using AI.

DAKAR (Thomson Reuters Foundation) - Data analyst Fabrice Sonzahi enrolled in a course on artificial intelligence (AI) in Dakar, hoping to help struggling farmers improve crop yields in his home country of Ivory Coast. He is part of an inaugural batch of students at a new AI programming school in Senegal, one of the first in West Africa. Its mission is to train local people in using data to solve pressing issues like the impact of climate change on crops. The Dakar Institute of Technology (DIT), which opened in September, is running its first 10-week boot camp with nine students in partnership with French AI school VIVADATA. "I am convinced that by analyzing data we can give (farmers) better solutions," said Sonzahi, 30.

The MIT Stephen A. Schwarzman College of Computing (SCC) will reorient the Institute to bring the power of computing and artificial intelligence to all fields at MIT, and to allow the future of computing and AI to be shaped by all MIT disciplines. To support ongoing planning for the new college, Dean Melissa Nobles invited faculty from all 14 of MIT's humanistic disciplines in the School of Humanities, Arts, and Social Sciences to respond to two questions: As Nobles says in her foreword to the series, "Together, the following responses to these two questions offer something of a guidebook to the myriad, productive ways that technical, humanistic, and scientific fields can join forces at MIT, and elsewhere, to further human and planetary well-being." The following excerpts highlight faculty responses, with links to full commentaries. The excerpts are sequenced by fields in the following order: the humanities, arts, and social sciences. "The advent of artificial intelligence presents our species with an historic opportunity -- disguised as an existential challenge: Can we stay human in the age of AI? In fact, can we grow in humanity, can we shape a more humane, more just, and sustainable world? With a sense of promise and urgency, we are embarked at MIT on an accelerated effort to more fully integrate the technical and humanistic forms of discovery in our curriculum and research, and in our habits of mind and action."

The MIT Stephen A. Schwarzman College of Computing (SCC) will reorient the Institute to bring the power of computing and artificial intelligence to all fields at MIT, and to allow the future of computing and AI to be shaped by all MIT disciplines. To support ongoing planning for the new college, Dean Melissa Nobles invited faculty from all 14 of MIT's humanistic disciplines in the School of Humanities, Arts, and Social Sciences to respond to two questions: As Nobles says in her foreword to the series, "Together, the following responses to these two questions offer something of a guidebook to the myriad, productive ways that technical, humanistic, and scientific fields can join forces at MIT, and elsewhere, to further human and planetary well-being." The following excerpts highlight faculty responses, with links to full commentaries. The excerpts are sequenced by fields in the following order: the humanities, arts, and social sciences. "The advent of artificial intelligence presents our species with an historic opportunity -- disguised as an existential challenge: Can we stay human in the age of AI? In fact, can we grow in humanity, can we shape a more humane, more just, and sustainable world? With a sense of promise and urgency, we are embarked at MIT on an accelerated effort to more fully integrate the technical and humanistic forms of discovery in our curriculum and research, and in our habits of mind and action."

The inability of Deep Learning to perform compositional learning is one of the main reasons for Deep Learning's most critical limitations, including the need to feed them tons of data. Compositionality is the algebraic capacity to understand and produce novel combinations from known components (Loula 2018). While the human brain can easily learn compositionally, Neural Networks (NNs) are not able to discover and store skills that are common across problems, and to re-combine them in a hierarchical fashion to solve new challenges (Liška 2018). The human language learning enjoys a good kind of combinatorial explosion -- if a person knows the meaning of "to run" and that of "slowly", she can immediately understand what it means "to run slowly", even if she has never uttered or heard this expression before the human language learning enjoys a good kind of combinatorial explosion -- if a person knows the meaning of "to run" and that of "slowly", she can immediately understand what it means "to run slowly", even if she has never uttered or heard this expression before (Loula 2018). This principle helps to explain how, when acquiring a language, we can quickly bootstrap to a potentially infinite number of expressions from very limited training data (Loula 2018).

Considering the high salary and growing interest in this role, specialization as an ML engineer is a great career move. The tech world is certainly where the money is for employees because it is where businesses currently struggle. The biggest companies have already shown us the power of having specialists on hand working to do the impossible. Google's investment in quantum computing has just made a huge leap, for example, and the future of tech and specialization looks bright. Big firms, rather than tech giants, are not scrambling to catch up, and by investing your time and effort to return to higher education and specialize with a Masters of Science or even a Ph.D. will pay off and provide you with a secure, lucrative, and rewarding position in your chosen field. This article does not necessarily reflect the opinions of the editors or management of EconoTimes.

Flinders University has launched a range of new law courses studyng the legal implications of emerging technologies and how they can be used to in legal practice to increase the quality of service. It follows a successful pilot at the uni in which law students designed and built their own apps using Neota Logic's artificial intelligence software to increase their technological literacy. The app building pilot was also billed as a way of increasing people's access to legal services in areas where they might otherwise not be able to - another goal of the new suite of courses. Dean of Law, Associate Professor Tania Leiman said in a statement that the new courses will be available for study from March next year. "On of the core topics is Law in a'Digital Age', which seeks to equip students with the digital skills to assist clients to access justice," Leiman said.

Almost 37% of organizations have invested $5 million or more in cognitive technologies, states a survey by Deloitte. Inside and under every app we use every day there lies the revolution of technology. A revolution that started decades ago is now empowering organizations to deliver better and smarter services. The demand for artificial intelligence professionals has rapidly increased. But since AI adoption is still in its infancy there is a dearth for talent.

New research suggests that 87% of teachers are worried about the potential for artificial intelligence (AI) to have a negative impact on friendships and social skills in the classroom. Only 1% of the more than 5,000 UK teachers surveyed by Bett said they were wholly unconcerned by the possibility. Older teachers were more likely to have significant misgivings, with 45% aged over 50 professing to be "very worried", compared to 33% of counterparts in their 20s. On the subject of plagiarism, results were altogether more mixed, with 27% of respondents predicting that AI will make plagiarism more detectable and 32% worrying that it will be harder to spot.