BISHKEK – I was recently offered the presidency of a university in Kazakhstan that focuses primarily on business, economics, and law, and that teaches these subjects in a narrow, albeit intellectually rigorous, way. I am considering the job, but I have a few conditions. What I have proposed is to transform the university into an institution where students continue to concentrate in these three disciplines, but must also complete a rigorous "core curriculum" in the humanities, social sciences, and natural sciences – including computer science and statistics. Students would also need to choose a minor in one of the humanities or social sciences. There are many reasons for insisting on this transformation, but the most compelling one, from my perspective, is the need to prepare future graduates for a world in which artificial intelligence and AI-assisted technology plays an increasingly dominant role. To succeed in the workplace of tomorrow, students will need new skills.

There is increasing pressure in such fields as manufacturing, energy and transportation to adopt AI and machine learning to help improve efficiencies in operations, optimize workflows, enhance business decisions through analytics and reduce costs in logistics. We have talked about how industries like telecommunications and transportation are looking at recurrent neural networks for helping to better forecast resource demand in supply chains. However, adopting AI and machine learning comes with its share of challenges. Companies whose datacenters are crowded with traditional systems powered by CPUs now have to consider buying and bringing in GPU-based hardware that is better situated to handle machine learning inference work, and they have to find new employees in a relatively shallow pool of available AI talent. None of this is easy, but the trend is irreversibly toward AI, machine learning and deep learning, so decisions need to be made, according to Karim Beguir.

In this DZone Refcard, learn about TensorFlow, an open-source library with a rich set of application programming interfaces for most major languages and environments needed for deep learning programs like sentiment analysis and object detection.

The UK's population is ageing, and one of the outcomes of this is that more men are developing prostate cancer. In fact the number of deaths it causes among men has overtaken the number of deaths caused among women by breast cancer. And the latest available figures, from 2015, show that, overall, it killed 11,819 people - almost 400 more than breast cancer. However, the mortality rates for both diseases have fallen. But Angela Culhane, chief executive of the charity Prostate Cancer UK, says research on prostate cancer gets half the funding of that for breast cancer.

Why you should listen Noriko Arai is the program director of an AI challenge, Todai Robot Project, which asks the question: Can AI get into the University of Tokyo? The project aims to visualize both the possibilities and the limitation of current AI by setting a concrete goal: a software system that can pass university entrance exams. In 2015 and 2016, Todai Robot achieved top 20 percent in the exams, and passed more than 70 percent of the universities in Japan. The inventor of Reading Skill Test, in 2017 Arai conducted a large-scale survey on reading skills of high and junior high school students with Japan's Ministry of Education. The results revealed that more than half of junior high school students fail to comprehend sentences sampled from their textbooks.

Finally, a comprehensive hands-on machine learning course with specific focus on regression based models for the investment community and any passionate investors. In the past few years, there has been a massive adoption and growth in the use of data science, artificial intelligence and machine learning to find alpha. However, information on and application of machine learning to investment are scarce. This course has been designed to address that. It is meant to spark your creative juices.

Some big trends have emerged this week in red hot areas like blockchain, artificial intelligence, and edtech. The bottom line seems to be that China is investing in a big way to ensure it remains on the cutting edge when it comes to the technology of tomorrow, whether it's investing billions into AI or leveraging the country's most important social media platform to embrace blockchain. Here are the some of the most interesting tech stories out of China you might not have heard about. Everyone is adopting blockchain technologies, but it's hard for big companies to move at the speed of technological innovation. This is not the case for Tencent in China, makers of WeChat, a social media messaging app with 1 billion active users.

Hoy traemos a este espacio a Make Music and Art Using Machine Learning, que nos presentan así; About Magenta Magenta is a Google Brain project to ask and answer the questions, "Can we use machine learning to create compelling art and music? Our work is done in TensorFlow, and we regularly release our models and tools in open source. These are accompanied by demos, tutorial blog postings and technical papers. To follow our progress, watch our GitHub and join our discussion group. It's first a research project to advance the state-of-the art in music, video, image and text generation. So much has been done with machine learning to understand content--for example speech recognition and translation; in this project we explore content generation and creativity. Second, Magenta is building a community of artists, coders, and machine learning researchers. To facilitate that, the core Magenta team is building open-source infrastructure around TensorFlow for making art and music. This already includes tools for working with data formats like MIDI, and is expanding to platforms that help artists connect with machine learning models Douglas Eck Education Innovation Human-Computer Interaction and Visualization Information Retrieval and the Web Machine Intelligence Natural Language Processing Co-Authors I'm a research scientist working on Magenta, an effort to generate music, video, images and text using machine intelligence. Magenta is part of the Google Brain team and is using TensorFlow (www.tensorflow.org), The question Magenta asks is, "Can machines make music and art?

Amazon's Alexa is now your personal butler at the Wynn hotel in Las Vegas. Self-learning software developed by Google defeated the world's best player of the highly complex Chinese strategy game Go. IBM's Watson saved the life of a woman in Japan by correctly diagnosing her with a rare form of cancer that doctors missed. We are rapidly approaching an inflection point in human history where artificial intelligence will exceed human intelligence, and debates about humans vs. machines have become part of our common vernacular. How can we prepare our next generation of students to compete?

In recent months, Microsoft, Google, Apple, Facebook, and other entities have declared that we no longer live in a mobile-first world. Instead, it's an artificial intelligence-first world where digital assistants and other services will be your primary source of information and getting tasks done. Your typical smartphone or PC are now your secondary go-getters. Backing this new frontier are two terms you'll likely hear often: machine learning and deep learning. These are two methods in "teaching" artificial intelligence to perform tasks, but their uses goes way beyond creating smart assistants.