In this very personal workography, I relate my 40-year experiences as a researcher and educator in and around Artificial Intelligence (AI), more specifically Natural Language Processing. I describe how curiosity, and the circumstances of the day, led me to work in both industry and academia, and in various countries, including The Netherlands (Amsterdam, Eindhoven, and Utrecht), the USA (Stanford), England (Brighton), Scotland (Aberdeen), and China (Beijing and Harbin). People and anecdotes play a large role in my story; the history of AI forms its backdrop. I focus on things that might be of interest to (even) younger colleagues, given the choices they face in their own work and life at a time when AI is finally emerging from the shadows.

Maria Elena Giannaccini has a degree in Biomedical Engineering from the Università di Pisa in Italy. She conducted her Master's thesis at Scuola Superiore Sant'Anna as part of the EU-funded OCTOPUS project. In 2015, she obtained her PhD in Robotics at the Bristol Robotics, where she focussed on developing safe, variable stiffness robotic devices. She worked at the University of Bristol on the soft, bioinspired Tactip sensor and a soft robotics artificial larynx. In 2019, Elena was appointed as a Lecturer in Robotics at the University of Aberdeen where she pioneered research in soft robotics.

Sword is a leader in data insights, digital transformation, and technology services with a substantial reputation in software development, complex business IT projects and mission critical operations. With over 2,500 Technology, Digital & Software specialists working globally, we unlock solutions to the most critical business technology challenges. Having the right people is essential for us to deliver the exceptional levels of service that our clients deserve. Our skilled team are experts in their field with market-leading experience and accreditation levels. Sword partner with private and public sector organisations across Finance, Manufacturing, Media, Energy and Government who count on the Sword team to deliver innovative Information Technology and Information Management solutions and services.

It is almost 40 years since a full-body magnetic resonance imaging (MRI) machine was used for the first time to scan a patient and generate diagnostic-quality images. The scanner and signal processing methods needed to produce an image were devised by a team of medical physicists including John Mallard, Jim Hutchinson, Bill Edelstein and Tom Redpath at the University of Aberdeen, leading to the widespread use of the MRI scanner, now a ubiquitous tool in radiology departments across the world. MRI was a game-changer in medical diagnostics because it didn't require exposure to ionising radiation (such as X-rays), and could generate images on multiple cross-sections of the body with superb definition of soft tissues. This allowed, for example, the direct visualisation of the spinal cord for the first time. Most people today will have undergone an MRI or know somebody who has.

Artificial intelligence, energy diversification and the transformation of the workforce will be amongst the major talking points at SPE Offshore Europe 2019. Senior international industry figures will co-chair the keynote sessions which also includes late life and decommissioning, underwater innovation, transformative technologies to lower the carbon footprint, digital security, integrated technologies, digitalisation, standardisation and finance. The event will take place from 3-6 September at the new £333million The Event Complex Aberdeen (TECA), under the theme: 'Breakthrough to Excellence – Our license to operate'. Michael Borrell, SPE Offshore Europe 2019 Conference Chair & Senior Vice President, North Sea and Russia at Total said: "Our committee is full of international oil and gas industry leaders and they have developed an excellent programme which gets to the heart of the main opportunities and challenges facing the region. "Offshore Europe 2019 is a great opportunity for us to challenge ourselves in the North Sea basin.

In the first post of this three-part series, we noted that now is the time of the year CX leaders are evaluating their 2018 activities and planning for 2019. Furthermore, we cautioned that it is also the time of the year where there's an abundance of hype fueled by self-proclaimed CX experts. In order to avoid falling for false pretenses, we highly recommend that you follow the best practices that deliver actual results. Aberdeen's recent survey of CX leaders helped us uncover the top technologies companies plan to incorporate in their CX technology toolbox in 2019. These are not based on hype, but rather self-reported insights shared by worldwide CX leaders in companies of all sizes and across all industries.

Machine learning will never replace organic human creativity (unless a miracle of AI produces Ultron, Skynet, Chappie, or Johnny-5). In other words, creative marketers and charismatic sales people need not worry; machine learning algorithms aren't going to put you out of a job any time soon. Machine learning, as the common definition states, is the subfield of computer science that "gives computers the ability to learn without being explicitly programmed" (Arthur Samuel, 1959). In essence, machine learning makes it possible for an imperfect program to perfect itself. Imagine, for example, a very simple program that used a quiz to help you figure out what dog is best for you. The basic version would offer a finite number of results based on pre-established connections to the questions.

This project is jointly funded by the Data Lab and MASTS Industrial Doctorate program and by the University of Strathclyde. The successful candidate will be based at the University of Strathclyde in the Physics Department but will work with a range of experts in machine learning (Dr Jinchang Ren, EEE, Strathclyde), remote sensing (Dr Jacqueline Tweddle, University of Aberdeen) and with Scottish Government scientists (Drs Alejandro Gallego, Matthew Gubbins and Eileen Bresnan, Marine Scotland, Aberdeen). The PhD is open to EU nationals and is fully funded for a total of 3.5 years, with preferred start date of 1st Oct 2018. FTE Category A staff submitted: 27.00

The Ninth International Conference on Machine Learning was held in Aberdeen, Scotland, from 1-3 July 1992, with 198 participants in attendance. The conference covered a broad range of topics drawn from the general area of machine learning, including concept-learning algorithms, clustering, speedup learning, formal analysis of learning systems, neural networks, genetic algorithms, and applications of machine learning. This article briefly touches on six selected talks that were of exceptional interest. Conference organizers were Derek Sleeman (conference chair) and Peter Edwards (local arrangements chair), both of the University of Aberdeen. Since the first machine-learning workshop was held at Carnegie-Mellon University (CMU) in July 1980, meetings have been held regularly, alternating between a more formal conference format and a more informal workshop format.

We also require a generic model of provenance The Web has evolved from a collection of hyperlinked documents in order to support the diverse ecosystem of sensor to a complex ecosystem of interconnected documents, platforms and data. We have investigated a number of existing services and devices. Due to the inherent open nature of the models for representing provenance information but Web, data can be published by anyone or any'thing'. As a found many of these to be tailored to specific domains result of this, there is enormous variation in the quality of (e.g.