China's massive investment in AI is changing the entire world at an accelerating rate. Just like climate change is accelerating, so is AI. Recently Elon Musk at a fireside debate with the original founder of Alibaba, and it was amusing. Elon Musk said some of his usual quotes. His warnings about AI are to me salient though, even if sensationalistic.

Red Hat has updated its Process Automation offering, adding predictive decision modeling and support for micro-frontend development. In addition to these new applied artificial intelligence (AI) capabilities, Red Hat has brought in features to improve user experience. Applied AI for automated decisioning enables users to incorporate predictive analytics into decision management applications. The new feature allows users to import and execute predictive models in Predictive Model Markup Language (PMML). This enables businesses to automate rules for specific business outcomes. Furthermore, users are able to incorporate predictive capabilities within a Decision Model and Notation decision model (DMN) to analyse and act on data while also gaining more visibility into the automated system's decision making process.

This fair usage agreement ("Agreement") is between CyberExtruder.com, Inc., a New York corporation with its principal office located at 1401 Valley Road, Wayne, New Jersey, 07470, USA ("Company") and the user of the Data Set, as defined below ("Licensee"). Whereas the Licensee is interested in the fair use of the Data Set for the non-commercial purposes of testing face recognition algorithms, and the Company wants to facilitate Licensee's testing of face recognition algorithms, the parties agree as follows: The Data Set contains 10,205 images of 1000 people collected randomly from the internet and is unrestricted with regard to the subject's pose, environmental lighting conditions, facial expression, subject's race and subject's age and contains images which are artistic impressions, drawings, paintings and other non-photographic representations of faces, and a multitude of facial occlusions like hats, glasses and makeup. All images are sized to 600 x 600 pixels and are stored with jpeg compression. LICENSE GRANT A non-exclusive, nontransferable, royalty-free license is granted to Licensee to use the Data Set on an appropriate computer system located at Licensee's premises. The Company is free, at its sole discretion, to distribute the Data Set to others and to use it for its own purposes.

As the specter of killer warrior robots looms large, the Pentagon has published a set of ethical guidelines for its use of artificial intelligence. It's a document designed to guide the use of AI in both combat and non-combat military scenarios. The document comes from the Defense Innovation Board, a group of 16 heavy hitters chaired by former Alphabet executive chair Eric Schmidt and counting Hayden Planetarium director Neil deGrasse Tyson and LinkedIn cofounder Reid Hoffman among its members. Called AI Principles: Recommendations on the Ethical Use of Artificial Intelligence by the Department of Defense, the missive is an attempt to lay out ground rules for the use of AI early on, giving the military a framework on which to build its AI systems. Frameworks like these are important for the safe rollout of new technologies, it points out, citing civil engineering and nuclear-powered vessels as examples. Now is the time, at this early stage of the resurgence of interest in AI, to hold serious discussions about norms of AI development and use in a military context – long before there has been an incident.

In September 2018, I got the opportunity to attend the Deep Learning Indaba conference that was held in Stellenbosch University, South Africa. Deep Learning Indaba was formed with an aim to strengthen African Machine Learning as well as to increase African participation and contribution to the advances in artificial intelligence and machine learning, and address issues of diversity in these fields of science. One of the lectures that I really enjoyed was on Success Stories of Reinforcement Learning where we got introduced to reinforcement learning as well as how it was used to build some pretty awesome computer programs. This lecture was presented by David Silver. Professor David Silver Leads the reinforcement learning research group at DeepMind which is an AI company based in London that was acquired by Google in 2014.

The Max Planck ETH Center for Learning Systems (CLS) offers a unique fellowship program, where PhD students are co-supervised by one advisor from ETH Zurich and one from the MPI for Intelligent Systems in Tübingen and Stuttgart. PhD students are expected to take advantage of the opportunities offered by both organizations and to actively seek cross-group collaborations. The Center also offers a wide range of activities like retreats, workshops, and summer schools, as well as the possibility to engage in organizing such events. This is an exciting new program and admission is highly competitive. Each PhD fellow will have a primary location (chosen based on interests and match) and spends one year at the other location as well.

A driving instructor has shared the moment one of their learner vehicles encountered a new hazard on the road - a driverless car. While overtaking the slow moving car, a front and rear dash cam on the learner vehicle captured the autonomous car's response. Already travelling at a glacial five miles per hour, the driverless car comes to a standstill in the middle of the road and cannot be seen to set off again. The car was encountered in Woolwich, London, on November 1 and is believed to be owned by Smart Mobility Living London Lab (SMLL). A Nissan e-NV200 model, seen in the video, will be used during public trials later this month, according to an event listing by the company.

Operating from campuses in Lille, Nice, Paris, London and Singapore, EDHEC is one of the world's top 15 business schools. Fully international and directly connected to the business world, EDHEC commands a strong reputation for research excellence and the ability to train entrepreneurs and managers capable of breaking new ground. EDHEC functions as a genuine laboratory of ideas and produces innovative solutions valued by businesses. The School's teaching is inspired by its research work and a focus on "learning by doing", all with the aim of equipping people with the skills to succeed in business.

The confluence of access to large granular data sources ('Big Data') and the rapid advance of modelling techniques like those from machine learning (ML) promises new insights into the economy and a larger information set for policymakers. The Bank of England (BoE) and the Data Analytics for Finance and Macro (DAFM) Research Centre at King's College London have recently initiated a series of annual scientific conferences to discuss these advances. Our events aim both to discuss recent developments and, crucially, focus on particular aspects of Big Data and ML approaches which are of increased interest to applied researchers. Two such aspects form the focus of this two-day conference. The first relates to a commonly cited weakness of ML methods when applied to economic problems and data, which is lack of interpretability of ML model outputs.

Artificial Intelligence (AI) has become part of our everyday lives, from smart personal assistants such as Siri and Alexa, to security surveillance, and automatic parking systems. In education, particularly in higher education, Artificial Intelligence is transforming traditional methods of teaching for both students and teachers, making the whole learning experience more customised, efficient and engaging. The world has changed due to the momentum of technology, and teaching must reflect the fact that information is available at all times and in all places through smartphones and tablets. Today, students do not necessarily need to attend physical classes as long as they have computers and internet connection, and although challenging, AI has and will continue to benefit education in various ways. Firstly, from a Business School perspective, new technology can be used to enhance the efficiency of the school itself.