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.

Inhibition can take place at the level of neurotransmitters in the synaptic cleft, neurons can inhibit each other's fire rate, it can be s h own at a physiological level - for instance by measuring the EEG, and finally it can be investigated on a purely behavioral level. Behavioral inhibition typically means something like'making a content/action less accessible or suppressing it altogether' in order to enhance processing of relevant information . In cognition, thus, the concept of inhibition implies cognitive mechanisms that actively lower currently irrelevant or inter fering information. Psychological theories that posit the existence of inhibitory mechanisms in our mind have elicited much research across diverse fields of C ognitive P sychology like perception, attention, action control, and memory but have also been tra nsferred to other research fields like D evelopmental P sychology as, fo r instance, understanding the aging brain or the developing brain is closely linked to understanding how the brain handles irrelevant or interfering information - that is how or whether the brain can inhibit such information. The two areas in Cognitive Psychology in which inhibition is traditionally investigated to the largest extent are the research fields of attention and memory. In attention research, typically the interference due to distracting stimuli or actions is analyzed in experimental paradigms that try to tap a specific form of cognitive inhibition. For example, in the Negative Priming task (for a review, Frings, Schneider, & Fox, 2015) it is typically analyzed how an irrelevant distractor stimulus is inhibited. In the cuing task that elicits the inhibition of return effect (Posner, Choate, Rafal, & Vaughn, 1985) it is typically analyzed how an irrelevant location is inhibited. In task switchin g (Kiesel et al., 2010) lowering competition by a just previously performed task while currently executing a novel task is achieved by inhibiting that previous task.

Due to the increasing use of machine learning in practice it becomes more and more important to be able to explain the pred iction and behavior of machine learning models. An instance of expl anations are counterfactual explanations which provide an intuitive an d useful explanations of machine learning models. In this survey we review model-specific methods for efficientl y computing counterfactual explanations of many different machine learning models and propose methods for models that have not been considered in l iterature so far.

Recent research has found that many families of machine learning models are vulnerable to adversarial examples: inputs that are specifically designed to cause the target model to produce erroneous outputs. In this survey, we focus on machine learning models in the visual domain, where methods for generating and detecting such examples have been most extensively studied. We explore a variety of adversarial attack methods that apply to image-space content, real world adversarial attacks, adversarial defenses, and the transferability property of adversarial examples. We also discuss strengths and weaknesses of various methods of adversarial attack and defense. Our aim is to provide an extensive coverage of the field, furnishing the reader with an intuitive understanding of the mechanics of adversarial attack and defense mechanisms and enlarging the community of researchers studying this fundamental set of problems.

Artificial intelligence (AI) is a game-changing technology that is affecting all our lives and shaping our future. In the latest ISOfocus issue, we debunk the AI myths, explore the opportunities and explain why globally relevant standards are key. Are killer robots about to take over the world? Mention artificial intelligence to the average person today and this is one of the many scary scenarios that spring to mind. Perhaps this is no surprise when you consider how AI is the technology that enables computers to think and act like human beings.

Many domains of science have developed complex simulations to describe phenomena of interest. While these simulations provide high-fidelity models, they are poorly suited for inference and lead to challenging inverse problems. We review the rapidly developing field of simulation-based inference and identify the forces giving new momentum to the field. Finally, we describe how the frontier is expanding so that a broad audience can appreciate the profound change these developments may have on science.