In February, the outreach director for an organization called Communities Against Rider Surveillance wrote to Evan Greer. CARS wanted to know if Fight for the Future, a nonprofit digital-rights advocacy group where Greer is the deputy director, would join, and allow itself to be listed as a member of the newly formed coalition. "CARS is a new coalition working to raise awareness of a dangerous technology called Mobility Data Specification," the email from outreach director Rich Dunn read. "In the wrong hands, the information collected by MDS poses grave privacy and safety risks." MDS is a technical specification created by Los Angeles' Department of Transportation, now managed by a third-party foundation.

With enterprises that produce large quantities of data there is a growing need for better enterprise search solutions. With the availability of Lucene, Solr and Elasticsearch over the last 10 years, dealing with the challenges of finding content these solutions help in more ways than you realize. Whether your company needs a solution for sentiment analysis, text analytics or advanced faceted search technologies, Solr and Elasticsearch provide a great solution to meet multiple requirements. Enterprise Content Understanding how important text mining/analytics and search technologies are for current enterprise-level businesses, you only need to look at the volume of data that is created across the multitude of various content creation platforms. Most businesses employ many different internal and external software solutions for everything from accounting to social media marketing and industry specific examples such as autocad for digital drawings and engineering.

Everyone knows that Artificial Intelligence (AI) is a big thing and almost every single tech company in the world seems to be riding that hype wave. However, we feel like it's our purpose and responsibility to inform you that the claims of almost every tech company using AI are absolutely false, and here's why. Such hype wave comes with a whole new world of marketers and scammers using the buzz word with fake AI to trick their potential users into thinking they're using real autonomous AI. Our job and responsibility is to inform you on the business aspects of truly "Useful Autonomous AI vs Useless AI"; because there's no point in supposedly using AI if it's not going to outperform humans. Useful and autonomous AI implies using a combination of neural networks, machine, transfer, reinforcement and deep learning in production models that are actually taking action on the predictions of the simulations, not just making recommendations and providing insights of what to do (sorry "AI" analytics companies, but if some human needs to actually do what your platform recommends, then that's not a truly useful, independent and autonomous AI).

We are excited to share several research papers on the Julia and Flux machine learning ecosystem, to be presented at the NIPS Systems for ML Workshop. Since initially proposing the need for a first-class language and ecosystem for machine learning (ML), we have made considerable progress, including the ability to take gradients of arbitrary computations by leveraging Julia's compiler, and compiling the resulting programs to specialized hardware such as Google's Tensor Processing Units. Here we talk about these papers and the projects that have brought these to life, namely: Flux.jl [paper], Zygote.jl Flux.jl is a library that gives a fresh take on machine learning as it exposes powerful tools to the user in a non-intrusive manner while remaining completely hackable, right to its core. "Careful design of the underlying automatic differentiation allows freely mixing mathematical expressions, built-in and custom layers and algorithms with control flow in one model. This makes Flux unusually easy to extend to new problems."

Many health organizations lack the capabilities needed to ensure that their artificial intelligence (AI) and Internet of Things (IoT) systems act accurately, responsibly, and transparently, according to a new study. Accenture's Digital Health Technology Vision 2018 report identified a range of issues related to the aggressive adoption of AI and the greater role it plays in healthcare decision making, and counseled the need for organizations to instill trust and transparency into the design of their technology systems. The study also explored five trends facing healthcare over the next three years as technology becomes an intrinsic part of care delivery. "We see these technology trends in two categories--'enablers' and'consequences' (the first three being'enablers' and the last two'consequences')--as emerging technologies enable the system to help people in new ways but also introduce new issues as technology becomes deeply intertwined in our lives and in our care," says Kaveh Safavi, MD, JD, head of Accenture's global health practice. Some of the report's most interesting findings focus on the consequences of the greater role that technology--particularly intelligent technology such as AI--plays in healthcare decision making.

Academic Torrents is a platform for researchers to share data. It consists of two pieces: a site where users can search for datasets, and a BitTorrent backbone which makes sharing data scalable and fast. The goal is to facilitate the sharing of datasets amongst researchers. It was created by the Institute for Reproducible Research (a U.S. 501(c)3 non-profit). The site provides access to over 15TB of data including popular machine learning datasets such as all of UCI, Imagenet, and Wikipedia.

Last Friday, at NYU's Skirball Center, the White House hosted a symposium on Artificial Intelligence, ethics, health, and machine learning. Led by Kate Crawford, a prinicipal researcher at Microsoft Research, and Meredith Whittaker, lead for Google Open Source Research Group. The day time events (invitation only) consisted of lightening talks from researchers at IBM Watson, Microsoft, policy makers, lawyers, artists and data visualizers such as Jer Thorp (blprnt). It was an incredibly diverse crowd, from careers to gender to race, and was something that the organizers had intended and carefully curated for the event itself. To create and germinate better discussions around AI, and to make better artificial intelligence, the group better be diverse, and AINow beyond succeeded with that.