We have a long history of "moral panics." Things that we fear, whether we should or not. In most cases, these fears aren't entirely irrational, but based on exaggerations or predictions that could, but probably won't, come true or simply are not nearly as horrific as may first appear. Many of us remember the Y2K scare of 1999, when we were told that the power grid, ATMs and our transportation systems could come to a screeching halt at midnight on Jan.1, 2000 because computers weren't programmed to recognize a new century. And, yes, there were a handful of problems, but the world didn't come to an end. There was a panic that our personal privacy was over in 1988 when Kodak introduced the first portable camera.

Promotion was easy in the 1960s. Things have changed a bit since then. Not to mention, technological disruption that (a) erodes, (b) replaces, or (c) reverses most of the current best practices every 6-12 months. That makes a marketer's role… complicated. But you ain't seen nothing yet.

Summary: A new artificial neural network can assess a viewer's reaction to movies based on patterns of facial expressions. With enough information, researchers say the ANN will be able to assess how an audience is reacting to a movie and predict an individual's response based on a few minutes of observation. Software automatically discovers patterns in facial expressions. Engineers have created a new deep-learning software capable of assessing complex audience reactions to movies using the viewer's facial expressions. Developed by Disney Research in collaboration with Yisong Yue of Caltech and colleagues at Simon Fraser University, the software relies on a new algorithm known as factorized variational autoencoders (FVAEs).

Robotic process automation (RPA) is a family of technologies designed to replicate human actions in order to complete a task or series of tasks. Unlike traditional programming constructs, RPA is designed to allow everyday workers to quickly deploy virtual counterparts to learn from and mimic the actions they use to accomplish routine tasks, therefore freeing the worker to handle more complex, and in some cases, more fulfilling work activities. According to a new report from Tractica, the market for RPA is developing rapidly, and the market intelligence firm forecasts that worldwide revenue in the sector will increase from $151 million in 2016 to more than $5.1 billion by 2025. The key industry sectors that are embracing RPA implementations include financial services & banking, utilities & telecommunications, retail & commercial, and healthcare & insurance. The market opportunity for RPA will be largest in Europe during the forecast period, with Asia Pacific and North America not far behind.

Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions. In the present work, we extend the usage of LRP to recurrent neural networks. We propose a specific propagation rule applicable to multiplicative connections as they arise in recurrent network architectures such as LSTMs and GRUs. We apply our technique to a word-based bi-directional LSTM model on a five-class sentiment prediction task, and evaluate the resulting LRP relevances both qualitatively and quantitatively, obtaining better results than a gradient-based related method which was used in previous work.

In one of the many conversations that animate "Columbus," a serenely intelligent first feature from the Korean American writer-director Kogonada, a part-time librarian named Casey (Haley Lu Richardson) and her co-worker Gabriel (Rory Culkin) discuss a tricky double standard. As Gabriel notes, someone who loves video games but finds books boring is criticized for having a short attention span, while someone with the opposite inclination is praised for having a long one. "It's not a matter of attention span, but of interest," he says. "Are we losing interest in things that matter?" Mercifully, he does not go on to extol the importance of gentle, gorgeously contemplative independent films like this one, though by that point "Columbus" has already made the case in much more delicate and persuasive terms.

A bunch of people have been asking for an implementation of Deep Reinforcement Learning from Human Preferences [Christiano et al., 2017] that came out last month. This contains a simplified system designed to be easy to read and understand, plus the webapp that we used for collecting feedback from humans. Happy to answer any questions that you have here!

AI can also greatly improve customer service. Often, the focus on individual customers decreases after a sale has been completed. Follow-ups are overlooked, and customer contact reduces. The task of keeping track of customer satisfaction or product problems is time-consuming for reps--but can be automatically executed by AI technology. AI can gather and organize all the data from new purchases, track customer satisfaction, and check in regularly with customers.

Recently Lofty Labs was engaged by a law firm. Now, that's not the type of business we generally target as clients for a data analysis consultancy. It turned out they had a data problem, though. When two large companies sue each other, a lot of historical communication records get exchanged between the two sides through the facilitation of the court in a process known within the field as "discovery". This is the process you probably think of from the movies, where lawyers can be seen carting dollies full archival boxes overflowing with paper documents.

Aida is the perfect employee: always courteous, always learning and, as she says, "always at work, 24/7, 365 days a year." Aida, of course, is not a person but a virtual customer-service representative that SEB AB, one of Sweden's biggest banks, is rolling out. The goal is to give the actual humans more time to engage in more complex tasks. After blazing a trail in online and digital banking, Sweden's financial industry is now emerging as a pioneer in the use of artificial intelligence (AI). Besides Aida at SEB, there's Nova, which is a chatbot Nordea Bank is introducing at its life and pensions unit in Norway.