This report sets out a series of strategic recommendations to the government, based on core pillars including data supply and exchange, skills and education and developing an artificial intelligence infrastructure in the UK, with a view to growing the country's AI sector, something which was also augmented by the recent Budget and government's Industrial Strategy White Paper this week. The professional panel included speakers such as Westminster Senior Lecturer and journalist Dr Mercedes Bunz, Westminster Business School Senior Lecturer Dr Steven Cranfield, and well known journalist and technology author Joanna Goodman, a Visiting Fellow at Westminster Law School's Centre on the Legal Profession. Speakers dissected the report and its implications for the future and were then questioned by the audience on the matter for nearly one and a half hours. Convener and Westminster Senior Lecturer in Law, as well as artificial intelligence, robotics and the law researcher, Dr Paresh Kathrani, who chaired the event, said: "2017 was undoubtedly an important year for artificial intelligence in the United Kingdom, not least with the House of Lords Select Committee on Artificial Intelligence's work on AI, this report and the recent Industrial Strategy White Paper. The University of Westminster and Westminster Law School will continue putting on LawTech and AI events in 2018 looking at these vital developments."

There's no doubt about it - Data Science is big news right now. We see it on the news every day, the increasing number of news stories about Big Data, the Internet of Things, Deep Learning, Artificial Intelligence, smart cars, smart cities, smart politicians. OK, maybe I went a bit too far with that last one... There's also a great appetite for learning about Data Science too. Every month I get an email from Udemy telling me which courses are their best sellers. The list isn't about Data Science, but there are always plenty of Data Science courses right up there at the top of the list.

We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative-filtering methods to make unfair predictions for users from minority groups. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fairness objectives effectively help reduce unfairness.

Learning to Optimize is a recently proposed framework for learning optimization algorithms using reinforcement learning. In this paper, we explore learning an optimization algorithm for training shallow neural nets. Such high-dimensional stochastic optimization problems present interesting challenges for existing reinforcement learning algorithms. We develop an extension that is suited to learning optimization algorithms in this setting and demonstrate that the learned optimization algorithm consistently outperforms other known optimization algorithms even on unseen tasks and is robust to changes in stochasticity of gradients and the neural net architecture. More specifically, we show that an optimization algorithm trained with the proposed method on the problem of training a neural net on MNIST generalizes to the problems of training neural nets on the Toronto Faces Dataset, CIFAR-10 and CIFAR-100.

In a major step toward creating artificial life, U.S. researchers have developed a living organism that incorporates both natural and artificial DNA and is capable of creating entirely new, synthetic proteins. The work, published in the journal Nature, brings scientists closer to the development of designer proteins made to order in a laboratory. However, the team say their work is safe and say the semi-synthetic organisms cannot live outside of a laboratory. This undated photo provided by The Scripps Research Institute shows a semi-synthetic strain of E. coli bacteria that can churn out novel proteins. Scientists reported on Wednesday, Nov. 29, 2017, that they have expanded the genetic code of life and used man-made DNA to create this strain of bacteria.

We all have used for-loops for majority of the tasks which needs an iteration over a long list of elements. I am sure almost everybody, who is reading this article, wrote their first code for matrix or vector multiplication using a for-loop back in high-school or college. For-loop has served programming community long and steady. However, it comes with some baggage and is often slow in execution when it comes to processing large data sets (many millions of records as in this age of Big Data). This is particularly true for interpreted language like Python, where, if the body of your loop is simple, the interpreter overhead of the loop itself can be a substantial amount of the overhead.

Organizations are transforming their sales functions with artificial intelligence to stay ahead of the game. If you have not yet embraced the trend, you are missing a crucial competitive edge. The emergence of vast amounts of data from multiple sources and platforms, generating new information every minute, has gifted companies with more consumer information than they've ever had before. Technology is getting smarter as it continues learning and optimizing recommendations. A study published in MIT Sloan Management Review reveals that "76% of early adopters are targeting higher sales growth with machine learning."

A professional highlight of 2017 has been teaching A Glance at Reinforcement Learning – an introductory course I've developed. You can find the course materials on GitHub. This one day course is aimed at data scientists with a grasp of supervised machine learning but no prior understanding of reinforcement learning. Course scope – introduction to the fundamental concepts of reinforcement learning – value function methods dynamic programming, Monte Carlo, temporal difference, Q-Learning, DQN – policy gradient methods score function, REINFORCE, advantage actor-critic, AC3 – AlphaGo – practical concerns reward scaling, mistakes I've made, advice from Vlad Mnih & John Schulman – literature highlights distributional perspective, auxiliary loss functions, inverse RL I've given this course to three batches at Data Science Retreat in Berlin and once to a group of startups from Entrepreneur First in London. Each time I've had great questions, kind feedback and improved my own understanding.

As the world accelerates, fewer people have a clear view of what the future has in store. 'Advisory Board as a Service' platform, powered by AI, is revolutionizing qualitative research, demand generation, and professional learning by allowing clients and partners to engage directly with custom panels of experts at a speed, and specificity never achieved before.

The world is commonly divided into industrialized and emerging economies. A new study of how technology will transform demand for workers suggests we might talk of the automated and automating worlds instead. Economic think tank McKinsey Global Institute forecast changes in demand for different kinds of labor across 45 countries as technologies improve to perform physical or office tasks. One key result: Robots pose a more immediate and disruptive threat to the US middle class than they do to middle-income workers in less developed countries like India. The report warns that in the US technology will crimp demand for many types of work, such as office administration and operating construction equipment.