We describe the Customer LifeTime Value (CLTV) prediction system deployed at ASOS.com, a global online fashion retailer. CLTV prediction is an important problem in e-commerce where an accurate estimate of future value allows retailers to effectively allocate marketing spend, identify and nurture high value customers and mitigate exposure to losses. The system at ASOS provides daily estimates of the future value of every customer and is one of the cornerstones of the personalised shopping experience. The state of the art in this domain uses large numbers of handcrafted features and ensemble regressors to forecast value, predict churn and evaluate customer loyalty. Recently, domains including language, vision and speech have shown dramatic advances by replacing handcrafted features with features that are learned automatically from data. We detail the system deployed at ASOS and show that learning feature representations is a promising extension to the state of the art in CLTV modelling. We propose a novel way to generate embeddings of customers, which addresses the issue of the ever changing product catalogue and obtain a significant improvement over an exhaustive set of handcrafted features.

High-speed, low-latency obstacle avoidance that is insensitive to sensor noise is essential for enabling multiple decentralized robots to function reliably in cluttered and dynamic environments. While other distributed multi-agent collision avoidance systems exist, these systems require online geometric optimization where tedious parameter tuning and perfect sensing are necessary. We present a novel end-to-end framework to generate reactive collision avoidance policy for efficient distributed multi-agent navigation. Our method formulates an agent's navigation strategy as a deep neural network mapping from the observed noisy sensor measurements to the agent's steering commands in terms of movement velocity. We train the network on a large number of frames of collision avoidance data collected by repeatedly running a multi-agent simulator with different parameter settings. We validate the learned deep neural network policy in a set of simulated and real scenarios with noisy measurements and demonstrate that our method is able to generate a robust navigation strategy that is insensitive to imperfect sensing and works reliably in all situations. We also show that our method can be well generalized to scenarios that do not appear in our training data, including scenes with static obstacles and agents with different sizes. Videos are available at https://sites.google.com/view/deepmaca.

Subtle facial cues could allow others to determine whether you're rich or poor upon first impression. A new study has found that neutral facial expressions are a reliable indicator of a person's economic standing, and could even act as a'self-fulfilling prophecy' that influences social interactions and success in the job market. On the other hand, the researchers say smiling and other emotionally-charged expressions are less likely to give away your social class, as they mask the'relics' of emotions and life experiences that become etched onto the face over the years. The researchers say the phenomenon occurred regardless of race and gender, or even how much time the participants were given to look at the photos. The best'rich' composites (C) and the best'poor' composites (D) are shown above The researchers found that participants were able to sort the faces among socio-economic groups with roughly 53 percent accuracy.

The issue of Artificial Intelligence (AI), which will replace human employees, has become one of the hottest topics in the news, and studies continue to praise the growth and wealth potential of the AI due to increased productivity. At the same time, other reports warn of threats to employment and worsening wage inequality between skilled and less skilled workers. Despite these alerts, nothing seems to stop technological progress. According to the latest study published by the prestigious Yale and Oxford universities, AI should surpass humans in all areas in less than 50 years. To arrive at this prediction, The two universities turned to specialists in artificial intelligence from around the world.

The Decision Tree is one of the most popular classification algorithms in current use in Data Mining and Machine Learning. This tutorial can be used as a self-contained introduction to the flavor and terminology of data mining without needing to review many statistical or probabilistic pre-requisites. If you're new to data mining you'll enjoy it, but your eyebrows will raise at how simple it all is! After having defined the job of classification, we explain how information gain (next Andrew Tutorial) can be used to find predictive input attributes. We show how applying this procedure recursively allows us to build a decision tree to predict future events.

In my earlier post, 'The Era of Cognitive Systems', I discussed how the understanding of data determines their value and the way data are experienced. In this article, I will elaborate on a specific use of data that the pharma industry is grappling with and how cognitive computing could come to the rescue. Randomized clinical trials (RCTs) are the "Gold Standard" for testing new therapeutics for safety and efficacy in human subjects. However, their success rates range between 40–80% across phases. 1 Significant failure rates can be attributed to patient recruitment, which is influenced by a number of factors.2 About 90% of successful trials are delayed by at least six weeks due to the failure of meeting enrollment timelines.1 At today's high cost of conducting RCTs, extending a study timeline by as little as a month can result in significant budget overruns, not to mention the potential revenue and opportunity loss from delayed drug commercialization.

STAGE 1: Phone Interview STAGE 2: In-person Interview at Idealab (we cover travel expenses for the day) STAGE 3: We require a sample project submission and a candidate proposal submission(To know more about what an ObEN candidate proposal is, click here) STAGE 4: Spend a day at our office and participate in all team activities.

Is AI on your software development roadmap? With technologies like advanced machine learning, deep learning, natural language processing, and business rules, AI is poised to disrupt both how developers build applications and the nature of those applications. The risks--unrealistic expectations, integration with traditional applications, and more--can't be ignored as your organization strives for the rewards of an accelerated development cycle and a new generation of self-learning applications. Uncover this shifting digital landscape and how your business can take advantage of it in the Forrester Research report, "How AI Will Change Software Development And Applications." Fill out the form at right to read the free report.

There is a lot of hype and plenty of fear around artificial intelligence and its impact on the future of healthcare. There are many signs pointing towards the fact that AI will completely move the world of medicine. As deep learning algorithms and narrow AI started to buzz especially around the field of medical imaging, many radiologists went into panic mode. In his presentation at the GPU Tech Conference in San Jose in May 2017, Curtis Langlotz, Professor of Radiology and Biomedical Informatics at Stanford University, mentioned how he received an e-mail from one of his students saying he was thinking about going into radiology but does not know whether it is a viable profession anymore. But the assumption that the radiologist profession is dying, is just plain wrong.

Over the course of the last few years, eCommerce has sprung up and put the brick-and-mortar side of the retail industry on notice. A growing number of eCommerce-focused merchants like Amazon have gradually increased their footprint in the physical store space. With the opening of the Amazon Go grocery store and bookstore in Seattle, Amazon is breathing innovation into the stodgy brick-and-mortar experience. It is also changing the retail game by eliminating the need for checkout stations through its artificial intelligence (AI) infused checkout system in its Amazon Go grocery store. In combination with its recent $13.7 billion acquisition of Whole Foods, it may be safe to assume that merchants in the retail space will follow Amazon's integration of more technology into the in-store experience.