Appropriate treatment regimens play a vital role in improving patient health status. Although some achievements have been made, few of the recent studies of learning treatment regimens have exploited different kinds of patient information due to the difficulty in adopting heterogeneous data to many data mining methods. Moreover, current studies seem too rigid with fixed intervals of treatment periods corresponding to the varying lengths of hospital stay. To this end, this work proposes a generic data-driven framework which can derive group-treatment regimens from electronic medical records by utilizing a mixed-variate restricted Boltzmann machine and incorporating medical domain knowledge. We conducted experiments on coronary artery disease as a case study. The obtained results show that the framework is promising and capable of assisting physicians in making clinical decisions.

With the latest wave of learning models taking advantage of advances in deep learning [21], [22], [23], Artificial Intelligence (AI) systems are gaining widespread publicity, coupled with a drive from industry to incorporate intelligence into all manner of processes that handle our private and personal data, giving them a central position in our modern-day society. This development has lead to demand for fairer AI, where we wish to establish trust in the automated intelligent systems by ensuring that systems represent us fairly and transparently. However, there has been growing concern about potential biases in learning systems [1], [6] which can be difficult to analyse or query for explanations of their predictions, leading to an increasing number of studies investigating the way blackbox systems represent knowledge and make decisions [7], [9], [11], [19], [20]. Indeed, principled methods are now required that allow us to measure, understand and remove biases in our data in order for these systems to be truly accepted as a prominent part of our lives. In the domain of text, many modern approaches often begin by embedding the input text data into an embedding space that is used as the first layer in a subsequent deep network [4], [14]. These word embeddings have been shown to contain the same biases [3], due to the source data from which they are trained.

There is no free lunch, no single learning algorithm that will outperform other algorithms on all data. In practice different approaches are tried and the best algorithm selected. An alternative solution is to build new algorithms on demand by creating a framework that accommodates many algorithms. The best combination of parameters and procedures is searched here in the space of all possible models belonging to the framework of Similarity-Based Methods (SBMs). Such meta-learning approach gives a chance to find the best method in all cases. Issues related to the meta-learning and first tests of this approach are presented.

At the conference, Inspur showcased the ODCC standard rack scale server, high-end mission critical 8-way server TS860, OpenPower9 systems, storage optimized server 4U106 Bay servers, and other systems designed for cloud. The full-stack AI solution includes the highest density GPU server -- AGX-2 -- with NVIDIA NVLink enabled, management software AIstation, care-MPI framework and the capability for application optimization. As the world's first NVLink enabled supercomputer with 8 NVIDIA V100 within 2U form factor, the AGX-2 improves the computing efficiency to develop high performance to propel AI, deep learning and advanced analytics development. With over 60% market share, Inspur's rack scale servers are widely used by CSPs such as Alibaba, Baidu, Tencent and 12306 (railway ticket online booking platform). It has set and maintains the record of deploying 10,000 nodes per day.

A team of scientists at Universität Bonn in Germany has developed not-at-all-creepy software able to predict the future. However, before heading out for a lottery ticket, potential users should be aware that the software is currently at its best when predicting what a chef might be about to do or need when preparing a salad. The research is concerned with predicting actions, and the self-learning software is pretty good at it, once it's gone through a few hours of training videos. In this case, the software was fed 40 videos of around six minutes each in which different salad dishes were prepared consisting of an average of 20 actions. It also sat through 1,712 videos of 52 different actors making breakfast.

It seems as if everybody is starting to look at artificial intelligence as some sort of make-or-break technology for the human race. Where the fate of the planet is concerned, there is an increasing collision between the nationalistic view that AI's overriding purpose is to help countries hold their own in geopolitical struggles and the humanitarian view that AI should deliver the benefits of material prosperity to all peoples, serving as an activist force in the universal struggle for equality, free expression, personal autonomy and democratic governance. The nationalistic perspective keeps popping out in headlines. For example, there are the sentiments expressed in this recent article by Horacio Rozanski, chief executive of Booz Allen Hamilton Inc. He discusses what he regards as a "close race" between the United States and China in developing and exploiting AI. I've been exploring AI benchmarking initiatives recently, and I take issue with the assumption that we can validly benchmark one nation against another in this regard.

As humans, we've gotten pretty good at shaping the world around us. We can choose the molecular design of our fruits and vegetables, travel faster and further and stave off life threatening diseases with personalized medical care. However, what continues to elude our molding grasp is the airy notion of "time" – how to see further than our present moment, and ultimately how to make the most of it. As it turns out, robots might be the ones who can answer this question. Computer scientists from the University of Bonn in Germany wrote this week that they were able to design a software that could predict a sequence of events up to five minutes in the future with accuracy between 15 and 40 percent.

The application of artificial intelligence algorithms in the justice system - for example to decide which offenders are eligible for alternatives to custodial sentences - will be among the first items on the agenda of a year-long investigation into the impact of technology opened by the Law Society. The Public Policy Technology and Law Commission - Algorithms in the Justice System, will meet in public three times, its chair Christina Blacklaws, who next month assumes the presidency of the Law Society, announced last night. The commmission's formation reflects growing concern about the advent of so-called'Schrodinger's justice' - in which decisions are taken by self-learning systems impervious to examination or challenge. Pressure group Big Brother Watch revealed yesterday that it has instructed human rights firm Leigh Day to take action against the Metropolitan Police over to demand the withdrawal of'dangerously authoritarian' automated technology for recognising faces at public events such as the Notting Hill Carnival. Blacklaws told an event at Chancery Lane last night that facial recognition systems in effect require'a degree of privacy to be surrendered in return for a promise of greater security' - but that the technology had so far failed to work.

With the biggest event in the soccer calendar now underway, fans are speculating on which team might emerge victorious from the 2018 World Cup in Russia – and an artificial intelligence model based on 100,000 simulations has made its prediction too. Using a database of statistics from previous tournaments, and three different AI methods to crunch the numbers, the international team of researchers behind the work thinks Spain is going to emerge victorious... but it's going to be close. At the moment the bookmakers are backing Germany to be World Cup winners, but the AI analysed both the strength of the teams and their route to the final. While Germany would beat Spain in a one-off game, the models showed, the German team is likely to face more difficult opponents through the course of the competition. "By analysing the winning probabilities conditional on reaching the single stages of the tournament, it turns out that the fact that overall Spain is slightly favoured over Germany is mainly due to the fact that Germany has a comparatively high chance to drop out in the round of 16," write the researchers.

White woman cyborg on blurred background scanning human DNA 3D rendering. Artificial Intelligence (AI) has been much talked about in recent times and especially as regards the technology disrupting the jobs market. Some have even suggested it might not be too long before one won't be able to tell the difference at work between colleagues who are human or a digital replication. And, so it was that I went off to New York's financial district in recent days to hear the movers and shakers in the field. The presentations and keynote addresses from academics and industry players around AI as well as the "elephant in the room" - what happens once machines outsmart humans at all tasks - certainly gave much food for thought.