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Machine Learning Team Lead


Elastic is a search company with a simple goal: to solve the world's data problems with products that delight and inspire. As the creators of the Elastic Stack, we help thousands of organizations including Cisco, eBay, Grab, Goldman Sachs, ING, Microsoft, NASA, The New York Times, Wikipedia, and many more use Elastic to power mission-critical systems. From stock quotes to Twitter streams, Apache logs to WordPress blogs, our products are extending what's possible with data, delivering on the promise that good things come from connecting the dots. We have a distributed team of Elasticians across 30 countries (and counting), and our diverse open source community spans over 100 countries. We are looking for a Senior Engineering Manager to join the machine learning team.

US health insurance firm Cigna is using AI to check if patients are taking their medications

Daily Mail - Science & tech

A US health insurance giant is using an AI system to monitor whether patients with chronic diseases are skipping their medication. Cigna's technology, Health Connect 360, will be rolled out to millions of Americans next month. But experts fear the technology will be used to cancel policies or avoid paying up if patients are found to be missing or incorrectly taking prescriptions. Doctors and nurses will be able to constantly keep an eye on patients' health and step in when they have cause for concern. For example, an alert may be triggered if patients forget to pick up their prescription or miss an appointment.

Lex Ex Machina: A conference on law's computability


Advances in Artificial Intelligence (AI), Machine Learning (ML) and data science are rekindling interest in applying computation to more aspects of legal process and decision-making. This is particularly evident through the development of various AI-leveraging LegalTech applications to assist with legal practice and business, law enforcement, and the prediction of case outcomes, among other things. The use of algorithmic decision-making (ADM) systems to replicate, and in some cases: replace, human judges and other decision-makers has, however, preoccupied the attention of the public, media, and scholars. Powles and Nissenbaum suggest that the'seductive diversion' of solving the'bias problem' makes the totalisation of AI in society contingent on solving narrow computational puzzles and'ethics washing' away hard questions, bad business practices and worse ideas. Not more fundamental questions about the compatibility of autonomous systems with the rule of law, deliberative democracy, and ultimately: should we be building them at all?



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Exciting use cases of Artificial Intelligence in four industry sectors -


As the digital transformation of businesses and services continues with full force, artificial intelligence (AI) has become somewhat of a buzzword in the technology sector. While it's true that we haven't quite reached the level of technology sophistication often shown off in Hollywood blockbusters, there already are a variety of use cases where machine learning algorithms are being deployed to improve different aspects of our daily lives. Below, we look at four industries that are reaping the rewards of using AI and what this might mean for the future. Healthcare is one of the most promising areas likely to be transformed significantly by AI and machine learning. This is because this technology can quickly go through large amounts of data and find patterns that humans might miss.

r/artificial - For Neuralink run tests to see if you can put thoughts into someone or something's head


Easy enough to abstract information from someone's mind, but you'll know you're getting somewhere when you put information "in." Like maybe if you can get a monkey to "get the red ball" and they routinely do after having the thought put in their mind. Or for human trials have then be given a question they could know the answer to if the thought insertion worked. You shouldn't be trying to get a brain and a computer to work directly in tandem. Not at all compatible, but you can translate thoughts into computer code, have the computer do the processing and then insert the thought back.

r/MachineLearning - [D] NeurIPS 2019 Bengio Schmidhuber Meta-Learning Fiasco


This is getting really crazy... I wonder if a discussion about this topic with both of them is possible. Something where all the evidence is presented and discussed. While I feel like there is a lot of damning evidence I feel like we mostly hear about the Schmidhuber side of things on this subreddit. I would like to hear what Bengio et al. have to say for themselves.

r/MachineLearning - [N] AI index 2019 report


The AI Index Report tracks, collates, distills, and visualizes data relating to artificial intelligence. Its mission is to provide unbiased, rigorously-vetted data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. Expanding annually, the Report endeavors to include data on AI development from communities around the globe.

r/MachineLearning - [R] ProteinNet: a standardized data set for machine learning of protein structure (call for contributions, June 2019)


I called for "call for contributions" recently, but it didn't end well. People were too obsessed with keeping their secrets and know little outside of ML. So I searched myself for challenging problems in science, with high meaningful impact, potential for ML to make breakthrough, ready dataset and benchmark, and I found this ProteinNet for protein folding. These scientists seem to think for the sake of science as a whole, and want to see how ML can help advance their field. You are welcome to use it for your side project if you are already tired of old time CV or NLP tutorials.