About this course: If you're a software developer and new to blockchain, this is the course for you. Several experienced IBM blockchain developer advocates will lead you through a series of videos that describe high-level concepts, components, and strategies on building blockchain business networks. You'll also get hands-on experience modeling and building blockchain networks as well as create your first blockchain application. The first part of this course covers basic concepts of blockchain, and no programming skills are required. However, to complete three of the four labs, you must understand basic software object-oriented programming and how to use the command line. It's also helpful, but not required, that you can write code in JavaScript.

Princeton's head of computer science, Jennifer Rexford, explains how artificial intelligence is set to change not only what teachers teach but how they teach. The field of education is searching for ways to respond to the fast-approaching impact of artificial intelligence (AI) across all sectors and fields, including its own. In this conversation with Jennifer Rexford, computer-science chair at Princeton University, she explains how this requires not only focusing on teaching different skills to prepare today's youth for work in an AI world but also employing AI to adopt novel ways of teaching. She also reiterates a common call for including diversity of thought in developing AI to utilize it successfully. An edited transcript of Rexford's remarks follows.

About this course: In this course, we will learn all the core techniques needed to make effective use of H2O. Even if you have no prior experience of machine learning, even if your math is weak, by the end of this course you will be able to make machine learning models using a variety of algorithms. We will be using linear models, random forest, GBMs and of course deep learning, as well as some unsupervised learning algorithms. You will also be able to evaluate your models and choose the best model to suit not just your data but the other business restraints you may be under.

Speaking at TEDx Talks, on how artificial intelligence can transform the education system, Scott Bolland, a cognitive scientist stated that 63 percent of learners are disengaged at school. That is, they are either physically or mentally absent when the teacher is teaching, and that's why only a few grasp the skills they need to make a meaningful life. Nonetheless, hope is coming to pull that number down, that I can bet. We've come from far and are definitely going somewhere. A few years back, you just needed at least a wooden blackboard and a chalk to say the classroom is equipped.

NEW DELHI: Sanskrit is not restricted to spiritualism, philosophy, or literature, President Ram Nath Kovind on Saturday said, stressing that experts believe that the language is most appropriate for writing algorithms besides use in machine learning and artificial intelligence. The president made the remarks during his address at the 17th convocation of the Shri Lal Bahadur Shastri Rashtriya Sanskrit Vidyapeetha here. "The tradition of Sanskrit language, literature and science has been the most effective chapter in the glorious journey of our intellectual growth. "It is said that India's soul is reflected in Sanskrit language, which is the mother of several languages," he said, according to a press release. Kovind said the most important thing is that proliferation of the knowledge available in Sanskrit is very relevant for the welfare of the world. "It is not that the works in Sanskrit are limited to spiritualism, philosophy, devotion, ritualism or literature.

Originally posted on The Horizons Tracker. Machine learning is typified by algorithms that are capable of deriving patterns and'intelligence' from vast quantities of training data. As company's such as DeepMind are beginning to show us however, the real improvements come when the machines are capable of learning independently of data fed it by us. A recent paper from researchers at the University of Toronto highlights some of the progress being made. The team developed an algorithm that attempts to learn from human instructions.

Let's continue learning about misconceptions around artificial neural networks. In Part 1, we discussed the most simple neural network architecture: the multi-layer perceptron. There are many different neural network architectures (far too many to mention here) and the performance of any neural network is a function of its architecture and weights. Many modern-day advances in the field of machine learning do not come from rethinking the way that perceptrons and optimization algorithms work but rather from being creative regarding how these components fit together. Below, I discuss some very interesting and creative neural network architectures that have developed over time.

Reliance is set to pick up a majority 72.69 percent stake in online education platform Embibe, which uses data analytics to deliver personalised learning outcomes to students. Reliance today agreed to invest the rupee equivalent of $180 million into Embibe, the Bengaluru-based AI education platform, over the next three years. A part of this will be towards acquiring a stake of 72.69 percent from Embibe's existing investors. The transaction is subject to customary closing conditions. This is one of the biggest transactions in the Indian education and deep technology space.

EXPERTS warn that "the substitution of machinery for human labour" may "render the population redundant". They worry that "the discovery of this mighty power" has come "before we knew how to employ it rightly". Such fears are expressed today by those who worry that advances in artificial intelligence (AI) could destroy millions of jobs and pose a "Terminator"-style threat to humanity. But these are in fact the words of commentators discussing mechanisation and steam power two centuries ago. Back then the controversy over the dangers posed by machines was known as the "machinery question".

In this paper, we present BigDL, a distributed deep learning framework for Big Data platforms and workflows. It is implemented on top of Apache Spark, and allows users to write their deep learning applications as standard Spark programs (running directly on large-scale big data clusters in a distributed fashion). It provides an expressive, "data-analytics integrated" deep learning programming model, so that users can easily build the end-to-end analytics + AI pipelines under a unified programming paradigm; by implementing an AllReduce like operation using existing primitives in Spark (e.g., shuffle, broadcast, and in-memory data persistence), it also provides a highly efficient "parameter server" style architecture, so as to achieve highly scalable, data-parallel distributed training. Since its initial open source release, BigDL users have built many analytics and deep learning applications (e.g., object detection, sequence-to-sequence generation, visual similarity, neural recommendations, fraud detection, etc.) on Spark.