Front office jobs at investment banks are increasingly being taken over by intelligent machines. Many current front office employees are worried about being displaced by artificial intelligence, and their fears are not unfounded. Huy Nguyen Trieu, former head of macro structuring at Citigroup, has a positive message for traders who risk being replaced by automation: become a data facilitator. After 13 years in financial engineering at SocGen and RBS, before becoming an Managing Director and head of macro structuring at Citi, he has shifted his focus to acting as a thought leader in the fintech space. Currently a fintech fellow at London's Imperial College and mentor at fintech accelerator, Level 39, Nguyen Trieu is both fintech guru and entrepreneur.

The use of computers and robots is becoming more prevalent in societies worldwide. More schools are integrating basic robotics and programming concepts in their lessons and curricula. Such initiative is true not only in advanced countries but also in third world countries like the Philippines. "Robotics must be integrated in the schools. It is one of the skills 21st century learners need in order to succeed in life," De La Salle Santiago Zobel School International Robotics Coordinator Genevieve Pillar told Philippine News Agency (PNA).

The race to become the global leader in artificial intelligence (AI) has officially begun. In the past fifteen months, Canada, Japan, Singapore, China, the UAE, Finland, Denmark, France, the UK, the EU Commission, South Korea, and India have all released strategies to promote the use and development of AI. No two strategies are alike, with each focusing on different aspects of AI policy: scientific research, talent development, skills and education, public and private sector adoption, ethics and inclusion, standards and regulations, and data and digital infrastructure. It also highlights relevant policies and initiatives that the countries have announced since the release of their initial strategies. I plan to continuously update this article as new strategies and initiatives are announced. If a country or policy is missing (or if something in the summary is incorrect), please leave a comment and I will update the article as soon as possible.

Today's software industry requires individuals who are proficient in as many programming languages as possible. Structured query language (SQL), as an adopted standard, is no exception, as it is the most widely used query language to retrieve and manipulate data. However, the process of learning SQL turns out to be challenging. The need for a computer-aided solution to help users learn SQL and improve their proficiency is vital. In this study, we present a new approach to help users conceptualize basic building blocks of the language faster and more efficiently. The adaptive design of the proposed approach aids users in learning SQL by supporting their own path to the solution and employing successful previous attempts, while not enforcing the ideal solution provided by the instructor. Furthermore, we perform an empirical evaluation with 93 participants and demonstrate that the employment of hints is successful, being especially beneficial for users with lower prior knowledge.

The multi-path scattering of light within a pixel [1], bidirectional reflectance distribution [2], and the heterogeneity of sub-pixel constituents [3] are the major concerns in the hyperspectral (HS) data classification. These nonlinearity properties naturally place the HS data on a non-euclidean space. Handling these high dimensional redundant data in a non-euclidean space is one of the major bottlenecks in HS data analysis. Typically, HS classification consists of dimensionality reduction (DR) and subsequent classification operation. The popular DR methods such as principal component analysis (PCA) [4] and linear discriminant analysis (LDA) [5] are linear and operate on Euclidean structures. These linear DR methods skip the curved nonlinear structures of the HS data. On the other hand, manifold learning helps in recovering compact, meaningful low dimensional structures from those complex high dimensional data from a non-euclidean space. The manifold learning methods consider the real world high dimensional data to be generated with a few degrees of freedom [6]. This leads to the projection of the data into lower dimensional space while preserving their underlying geometrical structure [7].

It feels like artificial intelligence crept into our lives almost without us knowing, helping us pick movies on Netflix, our favourite tunes on Spotify and buy things on Amazon. As it gets older and smarter, AI's reach will be staggering, with experts at the 2018 Davos World Economic Forum predicting there's a 50-per-cent chance artificial intelligence will outperform humans in all tasks in 45 years. Consider the ways it's already at work in our lives. There is face recognition to unlock our phones; fraud detection on credit cards; smart homes that call Uber, dim lights and lower the heat; fridges that give us recipes when we pull something out for dinner, and stoves that begin to preheat (because they talk to the fridge). All possible because AI – or "deep learning" technology – sorts and identifies huge swaths of data and connects the dots (or thinks) for us. In Davos, the big thinkers believe that in the next five to 25 years, AI will help teach kids in the classroom (there are already AI teaching assistants at some universities), write a Top 40 pop song and pen a New York Times bestseller.

The rise of artificial intelligence will have huge economic implications, disrupting every industry and every member of the workforce. It will create new jobs (2.3 million by 2020, according to research company Gartner) and countless opportunities, but it also has the potential to widen the divide between the haves and have-nots. How can we embrace this technology while creating inclusive opportunities for everybody in the age of Man Machine? The answer is simple: everyone needs to step up. Combating growing social, economic, and financial disparities will require every individual, business, educational institution and government to play a role in preparing the global workforce to thrive alongside intelligent machines.

This paper presents a multidisciplinary task approach for assessing the impact of artificial intelligence on the future of work. We provide definitions of a task from two main perspectives: socio-economic and computational. We propose to explore ways in which we can integrate or map these perspectives, and link them with the skills or capabilities required by them, for humans and AI systems. Finally, we argue that in order to understand the dynamics of tasks, we have to explore the relevance of autonomy and generality of AI systems for the automation or alteration of the workplace.

YouTube presents an unprecedented opportunity to explore how machine learning methods can improve healthcare information dissemination. We propose an interdisciplinary lens that synthesizes machine learning methods with healthcare informatics themes to address the critical issue of developing a scalable algorithmic solution to evaluate videos from a health literacy and patient education perspective. We develop a deep learning method to understand the level of medical knowledge encoded in YouTube videos. Preliminary results suggest that we can extract medical knowledge from YouTube videos and classify videos according to the embedded knowledge with satisfying performance. Deep learning methods show great promise in knowledge extraction, natural language understanding, and image classification, especially in an era of patient-centric care and precision medicine.

Just a quick video to let you know that Japan has just announced that it is going to make everyone, even primary school students, AI-literate within the next few years. The Japanese government wants primary school children to be as familiar with AI as they are with the 3Rs. Facing a severe skills shortage of technicians, Japan is having to rethink its entire tertiary system and government governance of technical infrastructure, which includes a massive reskilling operation. AI is deemed to be a key plank of Japan's economy in the near future. But skilled AI technicians are in short supply and Japan's immigration policies are being looked at with a view to opening the country up to foreign workers.