For this year's report, I took a stab at learning all the CSS/JS that I've always wanted to know, and then went after the task of making a fully responsive report. I've succeeded in doing so, and so this PDF version isn't as good as the real thing. In the next few days I will be sharing a link to the real digital experience. But for now -- enjoy this static version of the report which has a few parts that couldn't render to static form. Because ... this year's report is truly computationally designed and therefore needs to be expressed appropriately (smile). Expect a video version on my new YouTube channel "John Maeda is Learning." What can I do about it? As the marginal return on computing power (a la Moore's law) diminishes and technology is less of a differentiating factor, the value of design has entered the foreground. Five (20%) of the top cumulative-funded VC- backed ventures that have raised additional capital since 2013 are noted to have designer co-founders.

The article is devoted to investigating the application of aggregating algorithms to the problem of the long-term forecasting. We examine the classic aggregating algorithms based on the exponential reweighing. For the general Vovk's aggregating algorithm we provide its generalization for the long-term forecasting. For the special basic case of Vovk's algorithm we provide its two modifications for the long-term forecasting. The first one is theoretically close to an optimal algorithm and is based on replication of independent copies. It provides the time-independent regret bound with respect to the best expert in the pool. The second one is not optimal but is more practical and has O( T) regret bound, where T is the length of the game. Keywords: aggregating algorithm, long-term forecasting, prediction with experts' advice, delayed feedback.

It is common in modern prediction problems for many predictor variables to be counts of rarely occurring events. This leads to design matrices in which many columns are highly sparse. The challenge posed by such "rare features" has received little attention despite its prevalence in diverse areas, ranging from natural language processing (e.g., rare words) to biology (e.g., rare species). We show, both theoretically and empirically, that not explicitly accounting for the rareness of features can greatly reduce the effectiveness of an analysis. We next propose a framework for aggregating rare features into denser features in a flexible manner that creates better predictors of the response. Our strategy leverages side information in the form of a tree that encodes feature similarity. We apply our method to data from TripAdvisor, in which we predict the numerical rating of a hotel based on the text of the associated review. Our method achieves high accuracy by making effective use of rare words; by contrast, the lasso is unable to identify highly predictive words if they are too rare. A companion R package, called rare, implements our new estimator, using the alternating direction method of multipliers.

Lactate threshold is considered an essential parameter when assessing performance of elite and recreational runners and prescribing training intensities in endurance sports. However, the measurement of blood lactate concentration requires expensive equipment and the extraction of blood samples, which are inconvenient for frequent monitoring. Furthermore, most recreational runners do not have access to routine assessment of their physical fitness by the aforementioned equipment so they are not able to calculate the lactate threshold without resorting to an expensive and specialized centre. Therefore, the main objective of this study is to create an intelligent system capable of estimating the lactate threshold of recreational athletes participating in endurance running sports. The solution here proposed is based on a machine learning system which models the lactate evolution using recurrent neural networks and includes the proposal of standardization of the temporal axis as well as a modification of the stratified sampling method. The results show that the proposed system accurately estimates the lactate threshold of 89.52% of the athletes and its correlation with the experimentally measured lactate threshold is very high (R=0,89). Moreover, its behaviour with the test dataset is as good as with the training set, meaning that the generalization power of the model is high. Therefore, in this study a machine learning based system is proposed as alternative to the traditional invasive lactate threshold measurement tests for recreational runners.

The ongoing debate about robots and artificial intelligence eliminating jobs calls for dramatic shifts in the way financial services will be delivered. And yet, the banking profession hasn't really changed much in the last few years, and new technologies haven't caused radical disruption (yet). Nonetheless, I believe change and a high-tech approach are inevitable. Change might just take longer and will happen in rather unexpected ways. There is a lot of hype about robots and artificial intelligence (AI) taking our jobs, and for many observers, especially bankers, Frankenstein's monster is still present in their minds.

Trondheim, 9 March 2018: The new IoT ProtoLab in Trondheim is opening its doors today for entrepreneurs, scientists and students who want to develop new Internet of Things services and products. The new powerhouse created by Telenor Group and Wireless Trondheim aims to increase innovation, new national competencies and promote competitiveness amongst Norwegian entrepreneurs. "Exactly one year ago we launched Telenor-NTNU AI-Lab and it is a pleasure to announce another technology powerhouse in Trondheim," says Sigve Brekke, Telenor Group CEO. "IoT ProtoLab will be an experimental centre for research and innovation within the Internet of Things. IoT means that data on our physical surroundings are made available in large quanta, which in turn fuels fantastic opportunities for research and innovation within artificial intelligence (AI). Our two labs in Trondheim will be strengthened by each other and will contribute to fostering digital innovation in Norway," adds Brekke.

The GraphGrail Ai team has begun development of the blockchain as details of the tagger bot for Telegram were set out. The main task was the adequate integration of the blockchain technology and the AI. At an early stage, there was a need to separate the development blockchain from the development of the AI for placing AI data in a smart contract. The specs were discussed (specification for development with formats and data fields, and functions with their purpose), as well as technical details related to what particular data is written to the blockchain system and, accordingly, how this data is unloaded from the AI. The specs themselves look like functions, where a usage script is prescribed (how a particular function interacts with users) along with a description of data structures (how the function moves data when markup, etc.).

Integrative analysis of disparate data blocks measured on a common set of experimental subjects is a major challenge in modern data analysis. This data structure naturally motivates the simultaneous exploration of the joint and individual variation within each data block resulting in new insights. For instance, there is a strong desire to integrate the multiple genomic data sets in The Cancer Genome Atlas to characterize the common and also the unique aspects of cancer genetics and cell biology for each source. In this paper we introduce Angle-Based Joint and Individual Variation Explained capturing both joint and individual variation within each data block. This is a major improvement over earlier approaches to this challenge in terms of a new conceptual understanding, much better adaption to data heterogeneity and a fast linear algebra computation. Important mathematical contributions are the use of score subspaces as the principal descriptors of variation structure and the use of perturbation theory as the guide for variation segmentation. This leads to an exploratory data analysis method which is insensitive to the heterogeneity among data blocks and does not require separate normalization. An application to cancer data reveals different behaviors of each type of signal in characterizing tumor subtypes. An application to a mortality data set reveals interesting historical lessons. Software and data are available at GitHub .

Robot Wars has been axed by the BBC for a second time. The show featuring duelling robots was rebooted on BBC Two in 2016 and ran for three series. Presented by Dara Ó Briain and Angela Scanlon since its return, it is to be scrapped to "make room for new shows", the BBC said. Soon after the announcement the hashtag #BringBackRobotWars started trending on social media. Sad to confirm the BBC's decision to de-activate our House Robots.

I usually cringe whenever Stephen Hawking's name comes up in a conversation about AI. If the world was divided into critics and believers, Hawking would certainly fall into the AI critic column, but slotting him there ignores a great deal of nuance to his position on artificial intelligence. With his death earlier this week, I fear people will only remember which side he was on and miss his thoughtful perspectives on what specific dangers could lie ahead. To be clear, Hawking was no great fan of general artificial intelligence. He repeatedly said that a superintelligent AI could spell the end of humanity.