There has never been a more significant time to work in data science and AI. There is recognition of the importance of these technologies to our economic and social future: the so-called fourth industrial revolution. The technical challenge of keeping our data secure and private has grown in its urgency and importance. At the same time, voices from academia, industry, and government are coming together to debate how these technologies should be governed and managed. The Alan Turing Institute, as the UK's national institute for data science and artificial intelligence, plays an important part in driving forward advances in these technologies in order to change the world for the better.

LIVERMORE, CALIFORNIA – Today, U.S. Secretary of Energy Rick Perry and Founder of the Weill Family Foundation, Mr. Sandy Weill, signed a Memorandum of Understanding to formally initiate a public-private partnership for artificial intelligence (AI), neurological disorders, and related subjects. The partnership will apply DOE-fueled AI capabilities to advance transformative scientific opportunities in biomedical and public health research. The MOU will foster collaboration to demonstrate AI based research breakthroughs that span from basic science focused on a better understanding of how the brain functions, to clinical and translational research focused on developing novels methods for preventing, treating, and repairing damage caused by diseases and disorders of the brain. "Artificial Intelligence has the power to literally change the world we live in by tackling some of the biggest problems facing humanity – from improving our environment, to advancing our understanding of the cosmos; from increasing cyber security to improving crop production," said U.S. Secretary of Energy Rick Perry. "This Memorandum of Understanding between the Department of Energy and Weill Family Foundation will advance groundbreaking AI research and development in health sciences that will enhance our overall security and improve our quality of life."

Companies are increasingly using algorithms to manage their remote workforces. Called "algorithmic management," this approach has been most widely adopted in gig economy companies. For example, ride-hailing company Uber substantially increases its efficiency by managing some three million workers with an app that instructs drivers which passengers to pick up and which route to take. Being managed in this way offers some benefit to self-employed workers as well: for example, Uber drivers are free to decide when and for how long they would like to work and which area they would like to serve. However, our research reveals that algorithmic management is also frustrating to workers, and their resentment can lead them to behave subversively with the potential to cause real harm to their companies.

RIYADH: The royal decree to establish an artificial intelligence (AI) center will enhance the drive toward innovation and digital transformation in Saudi Arabia, according to Minister of Communications and Information Technology Abdullah Al-Sawaha. King Salman issued the decree on Friday, to establish the National Center for Artificial Intelligence and an organization called the National Data Management Office, which will be linked to the Saudi Data and Artificial Intelligence Authority. The establishment of the center came in line with the objectives of the Kingdom's Vision 2030 program, and will help develop performance efficiency through the applications of AI and big data, Al-Sawaha said. He added that the establishment of the center was a clear indication of the Kingdom's determination to develop its digital capabilities and build a future based on AI and innovation. Al-Sawaha said that AI would enhance productivity, boost decision-making processes across all sectors, render services provided to Saudi citizens more innovative, and open new horizons to stimulate entrepreneurship and support young people.

Bengaluru-based startup Yottaasys is all set to ride the wave of industry 4.0 and address the pain points in factories by making machines talk. Imagine you're running a factory and your machines are facing problems because they are functioning independently, individually out of sync with the larger goals of your production line. Their efficiency can certainly be augmented with some sort of connectivity. One sure-shot solution would be to use the power of AI to change the way the machines work, with industry 4.0 applications. When it comes to AI, in the absence of home-grown technical know-how, most firms approach an established IT service provider – but that would mean large-scale transformation and long-term contracts.

Numerous countries worldwide are currently facing major problems related to waste collection, particularly in urban areas, due to the large amount of waste generated daily by the population. Technology could play a significant role in tackling these issues, for instance, through the development of more effective tools to gather and collect garbage. With this in mind, researchers at Vishwakarma Government Engineering College in India have recently created a cheap and effective system for automatic garbage detection and collection. Their system, presented in a paper pre-published on arXiv, uses artificial intelligence (AI) algorithms to detect and locate waste in its surroundings, then picks it up with a robotic gripper. "Contemporaneous methods find it difficult to manage the volume of solid waste generated by the growing urban population," the researchers wrote in their paper.

Tesla Inc. Chief Executive Officer Elon Musk and Alibaba Group Holding Ltd. Chairman Jack Ma, two of the leading luminaries in the technology world, sparred against each other on Thursday on subjects ranging from the dangers of artificial intelligence, the need to explore Mars and the future of education. Held at the World Artificial Intelligence Conference in Shanghai, the rare debate between China's richest man and one of America's more controversial billionaires began with thoughts on AI -- Ma is betting humans will prevail over machines, while Musk fears doomsday is coming -- but the conversation, which went viral on social media, soon transgressed into areas such as Mars exploration and aliens. Below are some of the more memorable exchanges, which have been edited lightly for clarity. They sort of think like it's a smart human. Ma: I never in my life say human beings will be controlled by machines, it's impossible...Human beings can never create another thing that is smarter than human beings.

Researchers seek to understand our Universe by making model predictions to match observations. Historically, they have been able to model simple or highly simplified physical systems, jokingly dubbed the "spherical cows," with pencils and paper. Later, the arrival of computers enabled them to model complex phenomena with numerical simulations. For example, researchers have programmed supercomputers to simulate the motion of billions of particles through billions of years of cosmic time, a procedure known as the N-body simulations, in order to study how the Universe evolved to what we observe today. "Now with machine learning, we have developed the first neural network model of the Universe, and demonstrated there's a third route to making predictions, one that combines the merits of both analytic calculation and numerical simulation," said Yin Li, a Postdoctoral Researcher at the Kavli Institute for the Physics and Mathematics of the Universe, University of Tokyo, and jointly the University of California, Berkeley.

In recent years the power systems research community has seen an explosion of work applying operations research techniques to challenging power network optimization problems. Regardless of the application under consideration, all of these works rely on power system test cases for evaluation and validation. However, many of the well established power system test cases were developed as far back as the 1960s with the aim of testing AC power flow algorithms. It is unclear if these power flow test cases are suitable for power system optimization studies. This report surveys all of the publicly available AC transmission system test cases, to the best of our knowledge, and assess their suitability for optimization tasks. It finds that many of the traditional test cases are missing key network operation constraints, such as line thermal limits and generator capability curves. To incorporate these missing constraints, data driven models are developed from a variety of publicly available data sources. The resulting extended test cases form a compressive archive, NESTA, for the evaluation and validation of power system optimization algorithms.

A semantic equivalence assessment is defined as a task that assesses semantic equivalence in a sentence pair by binary judgment (i.e., paraphrase identification) or grading (i.e., semantic textual similarity measurement). It constitutes a set of tasks crucial for research on natural language understanding. Recently, BERT realized a breakthrough in sentence representation learning (Devlin et al., 2019), which is broadly transferable to various NLP tasks. While BERT's performance improves by increasing its model size, the required computational power is an obstacle preventing practical applications from adopting the technology. Herein, we propose to inject phrasal paraphrase relations into BERT in order to generate suitable representations for semantic equivalence assessment instead of increasing the model size. Experiments on standard natural language understanding tasks confirm that our method effectively improves a smaller BERT model while maintaining the model size. The generated model exhibits superior performance compared to a larger BERT model on semantic equivalence assessment tasks. Furthermore, it achieves larger performance gains on tasks with limited training datasets for fine-tuning, which is a property desirable for transfer learning.