Slate Plus members, stick around after the show to listen to producer Jayson De Leon chat with Steve Vladeck, a professor of law at the University of Texas and co-–ditor-in-chief of the Just Security blog, about Robert Mueller's latest move: impaneling a grand jury in the Russia investigation.

Systems that provide automated investment advice from financial firms have been referred to as robo-advisers. While no one in the industry is particularly fond of the term, it has caught on nonetheless. However, the enhanced human advising process -- augmented by machine learning -- that was recently announced by Morgan Stanley goes well beyond the robo label, and may help to finally kill off the term. New York–based Morgan Stanley, in business since 1935, has been known as one of the more human-centric firms in the retail investing industry. It has 16,000 financial advisors (FAs), who historically have maintained strong relationships with their investor clients through such traditional channels as face-to-face meetings and phone calls.

Monday September 4, join us for a panel with chatbot experts Thomas Schranz, Barbara Ondrisek & Anna Berger at sektor5. Find out about the most interesting use cases for chatbots and what the biggest take-aways are, developing conversations between ANI (Artificial Narrow Intelligence) and humans. Thomas Schranz is the Founder and CEO of Blossom, project tracking for distributed companies, and organizer of Lemmings I/O, a program for everyone who wants to get into AI and building bots. Lemmings started its 3rd batch this Summer. Dr. Barbara Ondrisek, aka "Bot Mother", is a software developer with 15 years of experience.

The advertising industry faces major challenges. One of them is the industry's widespread difficulty targeting ads effectively. Too many ads are seen or heard by people who are not interested in the products or services being advertised. Frequently, advertisers don't know whom the correct people are to target or how to reach them. A second challenge is how to deliver ads that engage consumers and convey to them the experience of a product or service.

If you're following industrial cyber security trends you know that industrial networks, endpoints and control systems typically have inherent weaknesses that make them insecure and vulnerable to compromise through digital methods. With 5.6 million newly connected devices added per day in 2016 and an estimated 21 billion online by 2020, these current growth trends only increase the potential attack surface within industrial automation and process control environments. So, it shouldn't come as a big surprise to learn that the May 2017 report, Rogue Robots: Testing the Limits of an Industrial Robot's Security, tells us that industrial robots are insecure too, and for many of the same reasons. However, what might surprise industrial firms with robotic applications is how much cyber risk exists within their robotic ecosystem. This report was produced from research by students at Politecnico di Milano in Italy and researchers from Japanese, multi-national antivirus and security vendor Trend Micro.

Convolutional Neural Network (CNNs) are typically associated with Computer Vision. CNNs are responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today. More recently CNNs have been applied to problems in Natural Language Processing and gotten some interesting results. In this paper, we will try to explain the basics of CNNs, its different variations and how they have been applied to NLP. This is a more concise survey than the paper below, and does a good job at 1/5 the length.

A computationally simple genome-wide association study (GWAS) algorithm for estimating the main and epistatic effects of markers or single nucleotide polymorphisms (SNPs) is proposed. It is based on the intuitive assumption that changes of alleles corresponding to important SNPs in a pair of individuals lead to large difference of phenotype values of these individuals. The algorithm is based on considering pairs of individuals instead of SNPs or pairs of SNPs. The main advantage of the algorithm is that it weakly depends on the number of SNPs in a genotype matrix. It mainly depends on the number of individuals, which is typically very small in comparison with the number of SNPs. Numerical experiments with real data sets illustrate the proposed algorithm.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. It's not always clear whether Boston Dynamics' robots are operating autonomously or being controlled by a human, but it's definitely clear in this video of a presentation and demo by Marc Raibert: At the end, the human driving Atlas accidentally walks the robot off the stage. We developed a mobile robot that rolls by active deformation of the soft outer shell.

The topic of machine learning-enabled artificial intelligence (AI) is gaining increasing visibility in the world of investment management. Of particular interest is the application of AI to the development of smarter robo-advisers that some hope, while others fear, will yield'intelligent' and cost-effective investment management advice. This topic was raised by investment professionals during the recent CFA Institute travelling conference that went to Central and Eastern Europe and the Middle East. AI has also been the subject of a recent European Commission (EC) consultation document, to which CFA Institute submitted a response. For the uninitiated, some quick defining of terms could be helpful.

Alphabet-owned Artificial Intelligence laboratory DeepMind is bankrolling NHS research, Business Insider has revealed. The London-based company has provided Moorfields Eye Hospital's trust with £110,000 in funding since July 2016-- when the two organisations kick-started a partnership to test DeepMind's new technology to diagnose eye diseases. The collaboration had already sparked controversy, as over one million patient data were processed by DeepMind's algorithm: this raised questions on patients' consent to data treatment and the opportunity of sharing clinical data with a private technology corporation. Business Insider, which obtained the information under an FOI request, says that DeepMind's payment went to cover "the costs incurred by the [Moorfields Eye Hospital's] Trust", rather than being a fee to access patient data. The hospital did not pay any money to DeepMind.