A survey of UK decision makers has found the interest and support for the use of robotic technology in the workplace has seen a notable increase since 2018, suggesting the use of artificial intelligence (AI) in office is set to grow. Conducted by software provider Advanced and detailed in its Annual Trends Survey for 2019-20, the survey focuses on perceptions around workplace robotic technology, including AI and robotic process automation (RPA). It found that more decision makers now see robotic technology as a benefit to the workplace, with 77% of the 1,000 decision makers surveyed now saying they would be happy to work alongside such technologies if it meant that manual processes were reduced. This is a significant jump from last year's survey, where the number stood at just 65%. AI is now also the technology that the most people would like to see in their daily lives, with 38% placing it as the top priority.

Mobile is now the first and primary channel for consumers to interact with various products and services. Gradual and consistent advancement in the technology industry has created a necessity for enterprises and businesses to incorporate various features that supports and enhances the usability for the next breed of mobile users. Next Generation applications may require a new, improved and innovative approach to development that also helps in the rapid and exponential growth of businesses. The latest technological trends like Artificial Intelligence, Internet of Things, AR / VR, and cloud-driven mobile app development have gained significant popularity in recent years. Because of that developers are more focused on leveraging these cutting-edge technologies by offering more robust and scalable next-generation mobile apps that exceed the standards of what businesses and customers expect.

Time series classification problems have drawn increasing attention in the machine learning and statistical community. Closely related is the field of functional data analysis (FDA): it refers to the range of problems that deal with the analysis of data that is continuously indexed over some domain. While often employing different methods, both fields strive to answer similar questions, a common example being classification or regression problems with functional covariates. We study methods from functional data analysis, such as functional generalized additive models, as well as functionality to concatenate (functional-) feature extraction or basis representations with traditional machine learning algorithms like support vector machines or classification trees. In order to assess the methods and implementations, we run a benchmark on a wide variety of representative (time series) data sets, with in-depth analysis of empirical results, and strive to provide a reference ranking for which method(s) to use for non-expert practitioners. Additionally, we provide a software framework in R for functional data analysis for supervised learning, including machine learning and more linear approaches from statistics. This allows convenient access, and in connection with the machine-learning toolbox mlr, those methods can now also be tuned and benchmarked.

--Fair classification has become an important topic in machine learning research. While most bias mitigation strategies focus on neural networks, we noticed a lack of work on fair classifiers based on decision trees even though they have proven very efficient. In an up-to-date comparison of state-of- the-art classification algorithms in tabular data, tree boosting outperforms deep learning [1]. For this reason, we have developed a novel approach of adversarial gradient tree boosting. The objective of the algorithm is to predict the output Y with gradient tree boosting while minimizing the ability of an adversarial neural network to predict the sensitive attribute S . The approach incorporates at each iteration the gradient of the neural network directly in the gradient tree boosting. We empirically assess our approach on 4 popular data sets and compare against state-of- the-art algorithms. The results show that our algorithm achieves a higher accuracy while obtaining the same level of fairness, as measured using a set of different common fairness definitions. I NTRODUCTION Machine learning models are increasingly used in decision making processes. In many fields of application, they generally deliver superior performance compared with conventional, deterministic algorithms. However, those models are mostly black boxes which are hard, if not impossible, to interpret.

As deep learning models become more and more popular in real-world business applications and training datasets grow very large, machine learning (ML) infrastructure is becoming a critical issue in many companies. To help you stay aware of the latest research advances in ML infrastructure, we've summarized some of the most important research papers recently introduced in this area. As you read these summaries, you will be able to learn from the experience of the leading tech companies, including Google, Microsoft, and LinkedIn. The papers we've selected cover data labeling and data validation frameworks, different approaches to distributed training of ML models, a novel approach to tracking ML model performance in production, and more. If you'd like to skip around, here are the papers we've summarized: If these accessible AI research analyses & summaries are useful for you, you can subscribe to receive our regular industry updates below.

As deep learning models become more and more popular in real-world business applications and training datasets grow very large, machine learning (ML) infrastructure is becoming a critical issue in many companies. To help you stay aware of the latest research advances in ML infrastructure, we've summarized some of the most important research papers recently introduced in this area. As you read these summaries, you will be able to learn from the experience of the leading tech companies, including Google, Microsoft, and LinkedIn. The papers we've selected cover data labeling and data validation frameworks, different approaches to distributed training of ML models, a novel approach to tracking ML model performance in production, and more. If you'd like to skip around, here are the papers we've summarized: If these accessible AI research analyses & summaries are useful for you, you can subscribe to receive our regular industry updates below.

Artificial Intelligence is boldly walking across the corridors of eCommerce and steadily taking over the world. Don't you agree with this fact? Some people say, Artificial Intelligence is replacing human beings and will eat up their jobs. Furthermore, they can do the jobs that you could have ever imagined that robots will do one day in this real-world. Can we call AI, a real game-changer in the eCommerce Industry?

Feature selection is a crucial preprocessing step in data analytics and machine learning. Classical feature selection algorithms select features based on the correlations between predictive features and the class variable and do not attempt to capture causal relationships between them. It has been shown that the knowledge about the causal relationships between features and the class variable has potential benefits for building interpretable and robust prediction models, since causal relationships imply the underlying mechanism of a system. Consequently, causality-based feature selection has gradually attracted greater attentions and many algorithms have been proposed. In this paper, we present a comprehensive review of recent advances in causality-based feature selection. To facilitate the development of new algorithms in the research area and make it easy for the comparisons between new methods and existing ones, we develop the first open-source package, called CausalFS, which consists of most of the representative causality-based feature selection algorithms (available at https://github.com/kuiy/CausalFS). Using CausalFS, we conduct extensive experiments to compare the representative algorithms with both synthetic and real-world data sets. Finally, we discuss some challenging problems to be tackled in future causality-based feature selection research.

Do you default to primary research to gather qualitative insights? That may not always be necessary. Increasingly, cutting edge machine learning algorithms mine existing data for rich qualitative insights that can be used to inform new product development and improve marketing messaging. This webinar will provide an overview of how machine learning can be used to uncover actionable insights quickly and cost-effectively.

From Equifax to Yahoo, and Facebook to Marriott, large-scale data breaches impacting hundreds of millions of consumers have received their fair share of media attention in recent years. All this ink hasn't been spilled (or pixels displayed) in vain: there's growing awareness among business leaders of the security and privacy risks their organizations face, and increasing concern that their preparedness may be inadequate. In a recent PwC survey, for example, 72% of CEOs worldwide listed cybercriminal activity as a significant threat to their businesses, yet only 35% were comfortable with their organization's digital resilience and readiness to face such threats. Especially among small and mid-sized enterprises, the growth in awareness of the severity and urgency of cybersecurity risks is driving demand for managed security services. Organizations are increasingly turning to external vendors to help them build, maintain, and monitor their security operations programs and the technologies that comprise them.