Stockholm - Nasdaq has deployed machine learning technology across its entire Nasdaq Nordic markets-Stockholm, Copenhagen, Helsinki, and Iceland to bolster its market surveillance efforts. Nasdaq's SMARTS, in collaboration with the Nasdaq Nordic Market Surveillance team, has implemented machine learning within its surveillance technology to analyze abnormal market events and their subsequent categorization by surveillance analysts across the Nordic markets. The aim of these algorithms is to predict which actions analysts are likely to take based upon their handling of historical activity as well as discover new relationships within the data-thereby strengthening Nasdaq Nordic's surveillance mechanism to detect market abuse. The next stage will be to integrate machine learning technology into the SMARTS offering for exchange and regulator clients worldwide. The machine learning capabilities will initially be used to prioritize the surveillance workflow.
More than 80 Amazon scientists and engineers will attend this year's International Conference on Machine Learning (ICML) in Stockholm, Sweden, with 11 papers co-authored by Amazonians being presented. "ICML is one of the leading outlets for machine learning research," says Neil Lawrence, director of machine learning for Amazon's Supply Chain Optimization Technologies program. "It's a great opportunity to find out what other researchers have been up to and share some of our own learnings." At ICML, members of Lawrence's team will present a paper titled "Structured Variationally Auto-encoded Optimization," which describes a machine-learning approach to optimization, or choosing the values for variables in some process that maximize a particular outcome. The first author on the paper is Xiaoyu Lu, a graduate student at the University of Oxford who worked on the project as an intern at Amazon last summer, then returned in January to do some follow-up work.
A study by researchers at Sweden's prestigious Stockholm School of Economics (SSE), which looks ahead at the likely impact of artificial intelligence (AI), machine learning and robotics on people's lives, should calm the nerves of economic planners and private citizens. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered. You have exceeded the maximum character limit.
H. Schwarze Dept. of Theoretical Physics Lund University Solvegatan 14A 223 62 Lund Sweden J.Hertz Nordita Blegdamsvej 17 2100 Copenhagen 0 Denmark Abstract The problem of learning from examples in multilayer networks is studied within the framework of statistical mechanics. Using the replica formalism we calculate the average generalization error of a fully connected committee machine in the limit of a large number of hidden units. If the number of training examples is proportional to the number of inputs in the network, the generalization error as a function of the training set size approaches a finite value. If the number of training examples is proportional to the number of weights in the network we find first-order phase transitions with a discontinuous drop in the generalization error for both binary and continuous weights. 1 INTRODUCTION Feedforward neural networks are widely used as nonlinear, parametric models for the solution of classification tasks and function approximation. Trained from examples of a given task, they are able to generalize, i.e. to compute the correct output for new, unknown inputs.
The three countries are leading an artificial intelligence (AI) revolution, Malcolm Frank, head of strategy at leading outsourcing firm Cognizant, told CNNMoney in an interview. Frank is the co-author of a recent book entitled "What to Do When Machines Do Everything," on the impact artificial intelligence will have on the global economy in the coming years. "I think it's three horses in the race, and that's probably the wrong metaphor because they are all going to win," he said. "They are just going to win differently." While AI is progressing quickly elsewhere too, Frank said the other development hotspots are mainly city hubs such as London and Stockholm, or far smaller economies such as Estonia.