Visualisation makes the system easier to use, and easier to improve. Whether it's an app, a consumer service or part of an internal process, the end goal is to use AI technology to power a product. One of the biggest challenges is understanding and addressing the system's error profile. Your system is almost certainly going to make mistakes. When it does, you want to fail gracefully.

Modern deep learning libraries such as Keras allow you to define and start fitting a wide range of neural network models in minutes with just a few lines of code. Nevertheless, it is still challenging to configure a neural network to get good performance on a new predictive modeling problem. The challenge of getting good performance can be broken down into three main areas: problems with learning, problems with generalization, and problems with predictions. Once you have diagnosed the specific type of problem that you are having with a network, a suite of classical and modern techniques can then be selected to address the issue and improve performance. In this post, you will discover a framework for diagnosing performance problems with deep learning models and techniques that you can use to target and improve each specific performance problem.

Businesses understand the importance of providing seamless customer journeys, but we've seen a growing spate of digital service outages and software performance problems in recent months. There have been online banking outages that have left customers unable to pay bills on time, while problems with major payment systems have left shoppers unable to use their bank cards at the checkouts. These problems seriously disrupt peoples' ability to live their day-to-day lives, so they're becoming a growing concern for businesses and consumers alike. So, if businesses understand the importance of providing seamless customer journeys, why are outages happening more often? The soaring complexity of technology ecosystems is the biggest contributor to the rise in software performance problems. Modern digital services reside in complex hybrid multi-cloud environments, spanning multiple platforms and technologies.

Differential performance debugging is a technique to find performance problems. It applies in situations where the performance of a program is (unexpectedly) different for different classes of inputs. The task is to explain the differences in asymptotic performance among various input classes in terms of program internals. We propose a data-driven technique based on discriminant regression tree (DRT) learning problem where the goal is to discriminate among different classes of inputs. We propose a new algorithm for DRT learning that first clusters the data into functional clusters, capturing different asymptotic performance classes, and then invokes off-the-shelf decision tree learning algorithms to explain these clusters. We focus on linear functional clusters and adapt classical clustering algorithms (K-means and spectral) to produce them. For the K-means algorithm, we generalize the notion of the cluster centroid from a point to a linear function. We adapt spectral clustering by defining a novel kernel function to capture the notion of linear similarity between two data points. We evaluate our approach on benchmarks consisting of Java programs where we are interested in debugging performance. We show that our algorithm significantly outperforms other well-known regression tree learning algorithms in terms of running time and accuracy of classification.

Global technology consultancy DataArt expects to see a handful of financial technology (fintech) trends that emerged in 2017 to strengthen significantly in 2018. In particular, the growth of Artificial Intelligence (AI) in the industry will be exponential for three main reasons; hugely increased opportunities for improved customer centricity; ability to ease the regularity reporting burden through AI enabled'RegTech'; and massively improved cyber-security and data protection. DataArt also expects to see the same growth in blockchain as firms wake up to the huge cost savings and security benefits from distributed ledger technology, and performance problems associated with original Blockchain technology start to be solved. Here are DataArt's technology predictions in full: Artificial Intelligence (AI) will be the industry game changer, but it will not come without problems as the current industry wide skills gap turns into a'war for talent'. We are seeing that war already in one of the biggest users of AI: cyber-security.

Visualisation makes the system easier to use, and easier to improve. Whether it's an app, a consumer service or part of an internal process, the end goal is to use AI technology to power a product. One of the biggest challenges is understanding and addressing the system's error profile. Your system is almost certainly going to make mistakes. When it does, you want to fail gracefully.

Classifier systems are now viewed disappointing because of their problems suchas the rule strength vs rule set performance problem and the credit assignment problem. In order to solve the problems, we have developed ahybrid classifier system: GLS (Generalization Learning System). In designing GLS, we view CSs as model free learning in POMDPs and take a hybrid approach to finding the best generalization, given the total number of rules. GLS uses the policy improvement procedure by Jaakkola et al. for an locally optimal stochastic policy when a set of rule conditions is given. GLS uses GA to search for the best set of rule conditions. 1 INTRODUCTION Classifier systems (CSs) (Holland 1986) have been among the most used in reinforcement learning.

Classifier systems are now viewed disappointing because of their problems such as the rule strength vs rule set performance problem and the credit assignment problem. In order to solve the problems, we have developed a hybrid classifier system: GLS (Generalization Learning System). In designing GLS, we view CSs as model free learning in POMDPs and take a hybrid approach to finding the best generalization, given the total number of rules. GLS uses the policy improvement procedure by Jaakkola et al. for an locally optimal stochastic policy when a set of rule conditions is given. GLS uses GA to search for the best set of rule conditions. 1 INTRODUCTION Classifier systems (CSs) (Holland 1986) have been among the most used in reinforcement learning.

Classifier systems are now viewed disappointing because of their problems such as the rule strength vs rule set performance problem and the credit assignment problem. In order to solve the problems, we have developed a hybrid classifier system: GLS (Generalization Learning System). In designing GLS, we view CSs as model free learning in POMDPs and take a hybrid approach to finding the best generalization, given the total number of rules. GLS uses the policy improvement procedure by Jaakkola et al. for an locally optimal stochastic policy when a set of rule conditions is given. GLS uses GA to search for the best set of rule conditions. 1 INTRODUCTION Classifier systems (CSs) (Holland 1986) have been among the most used in reinforcement learning.