In the 1.4 release of Seldon we have added an alpha release of gRPC endpoints to complement our REST and Javascript endpoints. Remote Procedure Calls (RPC) and Google's version of this (gRPC) provides several advantages over REST. However, gRPC may be unfamiliar to many and requires a certain expertise for the developer building a gRPC client or server. For the 1.4 release of Seldon we have added gRPC as an external prediction endpoint as well as allowing prediction microservices to be deployed as gRPC servers internal to Seldon. The stages to deploy a gRPC service are discussed in detail in our docs and our summarized below.

As we kick off what will surely be another very exciting year of progress in artificial intelligence, machine learning and data science, we start with a quick recap of our "Top 10" most popular posts (based on aggregate readership) from the year just concluded. We announced a major set of updates to the Data Science VM (DSVM) in September last year. DSVM gives you a comprehensive set of tools for data movement, storage, exploration/visualization, modeling with ML/AI algorithms, and operationalization – and using multiple languages in either Linux or Windows environments. Just last month, we announced our latest and most powerful version of Microsoft R Server. Supporting popular operating systems and a variety of data sources, MRS 9.0 helps you create and deploy sophisticated analytics models for real world problems, efficiently and at scale.

Snapchat debuted its camera-equipped Spectacles this fall. Snapchat is adding ways to optimize campaign performance with the help of machine learning. Earlier this month, Snap Inc. began rolling out what it calls Goal-Based Bidding (GBB). The option, available to marketers buying ads through Snapchat's API, uses machine learning to know which users are most likely to swipe a certain type of ad. Here's how it works: With goal-based bidding, advertisers can inform Snapchat of when their main goal is increasing swipe-ups--perhaps for app installs, web views or movie trailers--instead of focusing solely on impressions.

I wanted to try and detect glioblastoma in diseased brain MRI from healthy ones, but I changed it to tiles because I guess for medical imaging it was a nightmare trying to scour the web and collect images to train on.

Next year, IoT-related conversations will everything from augmented reality to machine learning to voice recognition, according to three industry experts we spoke with. While the field of IoT companies ballooned in 2016, a handful of winners will likely become evident in 2017. Here's their advice on how to stay competitive in a maturing marketplace. THINKstrategies believes organizations can jumpstart their corporate IoT initiatives in 2017 by turning around the "long-tail" idea. "For many organizations, the IoT idea is too big to gain momentum until corporate decision-makers are more confident with their IoT readiness," says THINKstrategies' managing director, Jeff Kaplan.

The pervasiveness of and increasing authority vested in artificial intelligence and autonomous systems has created tremendous anxiety amongst the public. This has led to industry- and academic-based initiatives to address ethics in AI/AS through research and public engagement. The IEEE Global Initiative for Ethical Considerations in the Design of Artificial Intelligence and Autonomous Systems is one such initiative designed to support engineers and serve as a springboard for developing operational IEEE Standards in AI ethics. Last week the IEEE Global Initiative released version one of its working reference, entitled "Ethically Aligned Design: A Vision for Prioritizing Human Wellbeing with Artificial Intelligence and Autonomous Systems." I had the honor of working with experts in information privacy and contributing to the Personal Data and Individual Access Control Committee.

The "willing suspension of disbelief" is the idea that the audience (readers, viewers, content consumers) is willing to suspend judgment about the implausibility of the narrative for the quality of the audience's own enjoyment. We do it all the time. Two-dimensional video on our screens is smaller than life and flat and not in real time, but we ignore those facts and immerse ourselves in the stories as if they were real. We have also learned the "conventions" of each medium. While we watch a movie or a video, we don't yell to the characters on the screen "Duck!" or "Look out!" when something is about to happen to them.

It is hardly surprising Artificial Intelligence is the number one ICT trend for 2017. We already have been warned about the rise of A.I. by Elon Musk, Stephen Hawking, Bill Gates and others. Their message is that our society can potentially benefit from Artificial Intelligence, but researchers must not create something that cannot be controlled. Coming years we will see in which direction this is heading. Artificial Intelligence is not something new.

This post highlights the various value-additions that machine learning can provide to actuaries in their analytical work for insurance companies. As such, a key problem of swapping specific risk for systematic risk in general insurance ratemaking is highlighted along with key solutions and applications of machine learning algorithms to various insurance analytical problems. 'In pricing, are we swapping specific risk for systematic risk?'[1] The hypothesis is that in normal market conditions, premiums are kept at low levels to increase revenues and market share. The traditional approach requires precise figures (point estimates) and so leads to understatement of uncertainty.

Two former allies of New Jersey Governor Chris Christie plan to appeal their conviction for intentionally causing traffic gridlock in Fort Lee during morning rush hour for a week in September 2013. Over the past five years, mobile tech has allowed companies like Uber, Lyft, and Juno to disrupt traditional travel with a new ride-hail industry worth billions. According to recent figures from the Massechusetts Institute of Technology (MIT), a mere 3000 ride-pools could even handle the business of New York City's entire taxi fleet with hardly any delay--provided, of course, that riders are willing to share. A study by MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) revealed this week that ride-sharing platforms similar to UberPOOL and Lyft Line could handle the passenger traffic of NYC's 14,000 taxis with just a few thousand vehicles. In addition, the team says, such programs could help reduce congestion on city streets (if not its' sidewalks) by an impressive 300%.