Mo Gawdat is the chief business officer at Google X – the "moonshot factory" responsible for some of the company's more audacious projects, such as self-driving cars and a balloon-powered global internet. Before he joined Google, while working as stock trader and tech executive in Dubai and in response to a period of depression, he used his engineer's mindset to create an "equation for happiness". The equation says that happiness is greater than, or equal to, your perception of the events in your life minus your expectation of how life should be. When his 21-year-old son Ali died during a routine operation, Gawdat turned to the equation, which they had worked on together, in an attempt to come to terms with his tragic loss. Gawdat's book, Solve for Happy, explains the theories underpinning the equation and how it helped him sustain his life after Ali's death.

Evolutions in machine learning will require a shake-up of higher education to ensure people have the right skills to get the most out of a world gradually succumbing to automation. That's according to a report by The Royal Society into the various impact machine learning will have on society and business; unsurprisingly, like many reports and hot takes from various bodies and industry, The Royal Society found that the rise of machine learning can bring a host of benefits. But amid the potential to yield smart applications, better services, and extract value from big data being harvested by Internet of Things (IoT) networks, The Royal Society highlighted that smart machines and systems will need new skills to not only keep them up and running but also ensure that robots do not replace human workers completely. "Machine learning will increasingly feature in both our work and personal lives. While not necessarily replacing jobs or functions outright, machine learning will force us to think about our occupations, and the skills necessary to function in a world where these systems are ubiquitous," the report explained.

The world of startups is constantly moving and evolving. With the exponential growth of deep learning research and technologies in recent years, innovative new companies are often funded, acquired and transformed from startups to industry leaders extremely quickly. MedicSen is a startup focused on developing non-invasive treatments for diabetes, utilising connected devices, machine learning algorithms and a cloud platform to revolutionise diabetic patient's lives. By applying artificial intelligence and sensor tech, the algorithm can predict future glucose levels and risky events and, according to that, give the patient medical advice and instructions for the amount of insulin they need and at what time. Since we first met the MedicSen team at the 2016 Deep Learning in Healthcare Summit in London, their team has grown and their mission has evolved.

As the emergence and the thriving development of social networks, a huge number of short texts are accumulated and need to be processed. Inferring latent topics of collected short texts is useful for understanding its hidden structure and predicting new contents. Unlike conventional topic models such as latent Dirichlet allocation (LDA), a biterm topic model (BTM) was recently proposed for short texts to overcome the sparseness of document-level word co-occurrences by directly modeling the generation process of word pairs. Stochastic inference algorithms based on collapsed Gibbs sampling (CGS) and collapsed variational inference have been proposed for BTM. However, they either require large computational complexity, or rely on very crude estimation. In this work, we develop a stochastic divergence minimization inference algorithm for BTM to estimate latent topics more accurately in a scalable way. Experiments demonstrate the superiority of our proposed algorithm compared with existing inference algorithms.

We consider forecasting a single time series using high-dimensional predictors in the presence of a possible nonlinear forecast function. The sufficient forecasting (Fan et al., 2016) used sliced inverse regression to estimate lower-dimensional sufficient indices for nonparametric forecasting using factor models. However, Fan et al. (2016) is fundamentally limited to the inverse first-moment method, by assuming the restricted fixed number of factors, linearity condition for factors, and monotone effect of factors on the response. In this work, we study the inverse second-moment method using directional regression and the inverse third-moment method to extend the methodology and applicability of the sufficient forecasting. As the number of factors diverges with the dimension of predictors, the proposed method relaxes the distributional assumption of the predictor and enhances the capability of capturing the non-monotone effect of factors on the response. We not only provide a high-dimensional analysis of inverse moment methods such as exhaustiveness and rate of convergence, but also prove their model selection consistency. The power of our proposed methods is demonstrated in both simulation studies and an empirical study of forecasting monthly macroeconomic data from Q1 1959 to Q1 2016. During our theoretical development, we prove an invariance result for inverse moment methods, which make a separate contribution to the sufficient dimension reduction.

Matrix completion is a problem that arises in many data-analysis settings where the input consists of a partially-observed matrix (e.g., recommender systems, traffic matrix analysis etc.). Classical approaches to matrix completion assume that the input partially-observed matrix is low rank. The success of these methods depends on the number of observed entries and the rank of the matrix; the larger the rank, the more entries need to be observed in order to accurately complete the matrix. In this paper, we deal with matrices that are not necessarily low rank themselves, but rather they contain low-rank submatrices. We propose Targeted, which is a general framework for completing such matrices. In this framework, we first extract the low-rank submatrices and then apply a matrix-completion algorithm to these low-rank submatrices as well as the remainder matrix separately. Although for the completion itself we use state-of-the-art completion methods, our results demonstrate that Targeted achieves significantly smaller reconstruction errors than other classical matrix-completion methods. One of the key technical contributions of the paper lies in the identification of the low-rank submatrices from the input partially-observed matrices.

The ubiquitous presence of sequence data across fields such as the web, healthcare, bioinformatics, and text mining has made sequence mining a vital research area. However, sequence mining is particularly challenging because of difficulty in finding (dis)similarity/distance between sequences. This is because a distance measure between sequences is not obvious due to their unstructuredness---arbitrary strings of arbitrary length. Feature representations, such as n-grams, are often used but they either compromise on extracting both short- and long-term sequence patterns or have a high computation. We propose a new function, Sequence Graph Transform (SGT), that extracts the short- and long-term sequence features and embeds them in a finite-dimensional feature space. Importantly, SGT has low computation and can extract any amount of short- to long-term patterns without any increase in the computation, also proved theoretically in this paper. Due to this, SGT yields superior result with significantly higher accuracy and lower computation compared to the existing methods. We show it via several experimentation and SGT's real world application for clustering, classification, search and visualization as examples.

Nutrient-based meal recommendations have the potential to help individuals prevent or manage conditions such as diabetes and obesity. However, learning people's food preferences and making recommendations that simultaneously appeal to their palate and satisfy nutritional expectations are challenging. Existing approaches either only learn high-level preferences or require a prolonged learning period. We propose Yum-me, a personalized nutrient-based meal recommender system designed to meet individuals' nutritional expectations, dietary restrictions, and fine-grained food preferences. Yum-me enables a simple and accurate food preference profiling procedure via a visual quiz-based user interface, and projects the learned profile into the domain of nutritionally appropriate food options to find ones that will appeal to the user. We present the design and implementation of Yum-me, and further describe and evaluate two innovative contributions. The first contriution is an open source state-of-the-art food image analysis model, named FoodDist. We demonstrate FoodDist's superior performance through careful benchmarking and discuss its applicability across a wide array of dietary applications. The second contribution is a novel online learning framework that learns food preference from item-wise and pairwise image comparisons. We evaluate the framework in a field study of 227 anonymous users and demonstrate that it outperforms other baselines by a significant margin. We further conducted an end-to-end validation of the feasibility and effectiveness of Yum-me through a 60-person user study, in which Yum-me improves the recommendation acceptance rate by 42.63%.

Igor Kononenko studied computer science at the University of Ljubliana, Slovenia, receiving his BSc in 1982, MSc in 1985 and PhD in 1990. He is now professor at the Faculty of Computer and Information Science there, teaching courses in Programming Languages, Algorithms and Data Structures; Introduction to Algorithms and Data Structures; Knowledge Engineering, Machine Learning and Knowledge Discovery in Databases. He is the head of the Laboratory for Cognitive Modelling and a member of the Artificial Intelligence Department at the same faculty. His research interests include artificial intelligence, machine learning, neural networks and cognitive modelling. He is the (co) author of 170 scientific papers in these fields and 10 textbooks.

Previously, I had started this conversation with the saying "I am not a Geek, but I need a job too…". Here is why: Technological anxiety (oh yes, it is a thing). I don't want to be a victim of the inevitable wave of "robots taking over our jobs" which is a simplistic explanation for the impact of advancements in technology in the workplace. The idea that half of today's jobs may vanish has changed my view of my children's future. Quincy Larson, Teacher at FreeCodeCamp (an open-source community that helps you learn to code, build pro bono projects for nonprofits, and get a job as a developer) has not stopped in his attempt to get more people coding.