Much as the Industrial Revolution enabled the rise of the United States and other advanced economies, so AI and machine learning are poised to reshape the global order. Forecasts suggest that AI will add a massive $15.7 trillion to the global economy by 2030. Prospects for sustaining global competitiveness are now directly tied to the industrialization of AI. The industrial application of AI to a wide array of industries will mean a constant state of innovation. AI is predicted to reshape manufacturing, energy management, urban transportation, agricultural production, labor markets, and financial management.

The Association for Project Management (APM) is an award-winning registered educational charity with over 27,000 individual members and 500 corporate partners, making it the largest professional body of its kind in Europe. Following a landmark two years, the APM is evolving. The receipt of their Royal Charter in 2017 marked a significant step for the organisation's development and 2018 saw them announce their first cohort of Chartered Project Professionals (ChPPs) covering nearly 50 different sectors. These milestones have helped lay the foundations for APM to build on their success and perfectly sets up 2019 to be another transformational year. Leading this exciting transformation, since being appointed as Chief Executive in August 2018, is Debbie Dore and with more than three years' experience at APM, she, more than anyone, recognises the impact the organisation can have for project management.

Amy Webb, futurist, NYU professor, and award-winning author, has spent much of the last decade researching, discussing, and meeting with people and organizations about artificial intelligence. "We've reached a fever pitch in all things AI," she says. Now it's time to step back to see where it's going. This is the task of her new book, The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity, where she takes a bird's-eye view of trends that, she warns, have put the development of technology on a dangerous path. In the US, Google, Microsoft, Amazon, Facebook, IBM, and Apple (the "G-MAFIA") are hamstrung by the relentless short-term demands of a capitalistic market, making long-term, thoughtful planning for AI impossible. In China, Tencent, Alibaba, and Baidu are consolidating and mining massive amounts of data to fuel the government's authoritarian ambitions.

The changes I discuss in the book cannot be solely categorised as'good' or'bad'. They are both heaven and hell, depending on a person or organisation's ability to accept and absorb them, and depending on our political will to govern these vast new powers. Specifically, both AI as well as human genome editing will surely bring tantalizing gains as well as frightening aberrations - depending on how we govern their power. The question is no longer IF or HOW but WHY and WHO. For example, my opinion on AI is that we need to focus on IA (intelligent assistance - the real and tangible promise of cognitive computing, today), be less obsessed with building broader AI, and regulate AGI (artificial general intelligence) just like we regulate nuclear weapons (with NPTs).

Thousands of graduates to become qualified experts in artificial intelligence (AI) as part of a new joint government-industry package to drive up skills in the AI sector, Business Secretary Greg Clark and Digital Secretary Jeremy Wright announced today (21 February 2019). For the first time, the UK will have a nationwide programme of industry-funded AI Masters courses coupled with work-based placements. The new skills and talent package is a major milestone of the modern Industrial Strategy's AI Sector Deal which was launched in April 2018. The announcement comes as new figures prepared for Tech Nation by Dealroom.co The UK has long been a nation of innovators.

This is an age of artificial intelligence (AI) driven automation and autonomous machines. The increasing ubiquity and rapidly expanding potential of self-improving, self-replicating, autonomous intelligent machines has spurred a massive automation driven transformation of human ecosystems in cyberspace, geospace and space (CGS). As seen across nations, there is already a growing trend towards increasingly entrusting complex decision processes to these rapidly evolving AI systems. From granting parole to diagnosing diseases, college admissions to job interviews, managing trades to granting credits, autonomous vehicles to autonomous weapons, the rapidly evolving AI systems are increasingly being adopted by individuals and entities across nations: its government, industries, organizations and academia (NGIOA). Individually and collectively, the promise and perils of these evolving AI systems are raising serious concerns for the accuracy, fairness, transparency, trust, ethics, privacy and security of the future of humanity -- prompting calls for regulation of artificial intelligence design, development and deployment.

Artificial intelligence (AI) is no longer the stuff of science fiction. The technology is already disrupting multiple industries, many of which impact you on a daily basis. Own an iPhone X? Its facial recognition system is powered by AI. Ever been redirected by Google Maps because of an accident or construction ahead? And those are just a couple of small examples.

Tech workers are increasingly uneasy about their employers' work with the US government, especially the military. Some protests have led to real change: Google last year decided not to renew a contract with the Pentagon to apply artificial intelligence to drone footage, after 4,000 employees signed a petition protesting the arrangement. Last week, a group of Microsoft workers published a petition asking executives to terminate a contract to develop augmented reality technology for the US military. "We did not sign-up to develop weapons, and we demand a say in how our work is used," the petition, which was published on Twitter, says. Monday, Microsoft CEO Satya Nadella rebuffed the plea.

In spite of achieving revolutionary successes in machine learning, deep convolutional neural networks have been recently found to be vulnerable to adversarial attacks and difficult to generalize to novel test images with reasonably large geometric transformations. Inspired by a recent neuroscience discovery revealing that primate brain employs disentangled shape and appearance representations for object recognition, we propose a general disentangled deep autoencoding regularization framework that can be easily applied to any deep embedding based classification model for improving the robustness of deep neural networks. Our framework effectively learns disentangled appearance code and geometric code for robust image classification, which is the first disentangling based method defending against adversarial attacks and complementary to standard defense methods. Extensive experiments on several benchmark datasets show that, our proposed regularization framework leveraging disentangled embedding significantly outperforms traditional unregularized convolutional neural networks for image classification on robustness against adversarial attacks and generalization to novel test data.

Verification of neural networks enables us to gauge their robustness against adversarial attacks. Verification algorithms fall into two categories: exact verifiers that run in exponential time and relaxed verifiers that are efficient but incomplete. In this paper, we unify all existing LPrelaxed verifiers, to the best of our knowledge, under a general convex relaxation framework. This framework works for neural networks with diverse architectures and nonlinearities and covers both primal and dual views of neural network verification. We further prove strong duality between the primal and dual problems under very mild conditions. Next, we perform large-scale experiments, amounting to more than 22 CPUyears, to obtain exact solution to the convex-relaxed problem that is optimal within our framework for ReLU networks. We find the exact solution does not significantly improve upon the gap between PGD and existing relaxed verifiers for various networks trained normally or robustly on MNIST and CIFAR datasets. Our results suggest there is an inherent barrier to tight verification for the large class of methods captured by our framework. We discuss possible causes of this barrier and potential future directions for bypassing it.