China has recently published its first artificial intelligence (AI) textbook for high school students, following a plan by central government last year to include AI courses in primary and secondary school. Under the joint efforts by the research center for MOOC at East China Normal University and AI startup SenseTime Group, the nine-chapter textbook, named Fundamentals of Artificial Intelligence, was written by eminent scholars from well-known schools nationwide, Xinhua reported on Sunday. It includes the history of AI and how the technology can be applied in areas such as facial recognition, auto driving and public security. "The textbook focuses not only on basics of AI, also on practical use of AI in daily life," said Chen Yukun, a professor at East China Normal University, who is also a contributor to the book. At present, about 40 high schools across the country have joined the first batch of AI high education pilot program, by introducing the textbook in curriculum.

He said the company had made big strides in the country in terms of enterprises adopting its technologies such as cloud services, security, artificial intelligence and machine learning. How are Indian enterprises adopting your technologies, especially cloud and artificial intelligence? How large is the opportunity? Globally... only about 5%-10% of all workloads in IT run on the cloud. I think the estimates are quite conservative.

Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.

In this study, we present a machine learning approach to infer the worker and student mobility flows on daily basis from static censuses. The rapid urbanization has made the estimation of the human mobility flows a critical task for transportation and urban planners. The primary objective of this paper is to complete individuals' census data with working and studying trips, allowing its merging with other mobility data to better estimate the complete origin-destination matrices. Worker and student mobility flows are among the most weekly regular displacements and consequently generate road congestion problems. Estimating their round-trips eases the decision-making processes for local authorities. Worker and student censuses often contain home location, work places and educational institutions. We thus propose a neural network model that learns the temporal distribution of displacements from other mobility sources and tries to predict them on new censuses data. The inclusion of multi-task learning in our neural network results in a significant error rate control in comparison to single task learning.

Most Reading Comprehension methods limit themselves to queries which can be answered using a single sentence, paragraph, or document. Enabling models to combine disjoint pieces of textual evidence would extend the scope of machine comprehension methods, but currently there exist no resources to train and test this capability. We propose a novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods. In our task, a model learns to seek and combine evidence - effectively performing multi-hop (alias multi-step) inference. We devise a methodology to produce datasets for this task, given a collection of query-answer pairs and thematically linked documents. Two datasets from different domains are induced, and we identify potential pitfalls and devise circumvention strategies. We evaluate two previously proposed competitive models and find that one can integrate information across documents. However, both models struggle to select relevant information, as providing documents guaranteed to be relevant greatly improves their performance. While the models outperform several strong baselines, their best accuracy reaches 42.9% compared to human performance at 74.0% - leaving ample room for improvement.

My guest on the podcast this week is Ryan Falkenberg, Co-founder, and Co-CEO of CLEVVA, a South African Augmented AI company that leverages digital intelligence to empower, not simply replace people. Ryan has always been fascinated by what makes people tick, and what makes them perform optimally. He's been frustrated at the slow pace of change when it comes to education and learning. To address that he created a learning consultancy, Hi-Performance Learning, that aimed to push the boundaries of organizational learning through e-learning, gamification and expert systems. To then remove the constraints by tech.

About this course: The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance. A learner with some or no previous knowledge of Machine Learning (ML) will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance. Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy.

At the 2017 Web Summit in Lisbon, when Sophia, a humanoid robot powered by artificial intelligence (AI), joked "We will take your jobs," behind the nervous laughter of the 60,000 attendees was a realisation this could be a reality sooner than they think. The rapid pace of developments in AI has already begun to disrupt entire industries. While technology is helping replace antiquated systems with agile, innovative tools, it is also set to impact jobs and millions of people. While popular opinion from technophobes suggests machines will entirely take over our jobs, the truth about the future of jobs is a bit complex. What will future jobs be like?

The practice of Business Analysis revolves around the formation, transformation and finalisation of requirements to recommend suitable solutions to support enterprise change programmes. Practitioners working in the field of business analysis apply a wide range of modelling tools to capture the various perspectives of the enterprise, for example, business process perspective, data flow perspective, functional perspective, static structure perspective, and more. These tools aid in decision support and are especially useful in the effort towards the transformation of a business into the "intelligent enterprise", in other words, one which is to some extent "self-describing" and able to adapt to organisational change. However, a fundamental piece remains missing from the puzzle. Achieving this capability requires us to think beyond the idea of simply using the current mainstream modelling tools.

Enterprises worldwide are engaged in digital transformation and leaders of the future will have mastered the new forms of organisation design in a digital world. As enterprises embrace digital technologies and redefine the value they could offer to their customers, leaders need to be mindful of the significance of the enormous volumes of data that would be used and get generated through the multiple touch points created by digital systems. Successful business models will be centered around data, harnessed through cognitive tools leading to insights and a combination of insights with new engagement mechanisms leading to new knowledge. The ability to tap and apply the knowledge through such efforts wisely will create future winners in the marketplace. The traditional models focused on codifying knowledge nuggets derived from processes and projects.