Long gone are the days of swapping tapes with friends after school, reading about the latest bands in your weekly magazine or tuning into Top of the Pops at the end of the week for the chart countdown. The excitement of discovering new music has definitely declined as the digitalisation of music has taken over the industry. Spotify is the most popular music streaming service out there and as such, harnesses the most valuable asset a company like this can have – data. Used by over 100 million people, with 30million of those paid subscribers and 55% of those linking their accounts to social media. Around 5million playlists are created or edited daily and in 2015 Spotify users streamed over 20bn hours of music.

OpenML stands for Open Machine Learning and is an online platform, which aims at supporting collaborative machine learning online. It is an Open Science project that allows its users to share data, code and machine learning experiments. At the time of writing this blog I am in Eindoven at an OpenML workshop, where developers and scientists meet to work on improving the project. Some of these people are R users and they (we) are developing an R package that communicates with the OpenML platform. The OpenML R package can list and download data sets and machine learning tasks (prediction challenges).

A few years after the company's launch, some fundraising and proof-of-concept pilots with insurers, Lumiata launched its Risk Matrix platform aimed at health plans and the health systems they work with to assess the future health of individuals with or at risk for chronic diseases. The product enlists artificial intelligence to help physicians make better use of the data they are inundated with and meet some of the goals of value-based care. The health IT business stated in a news release that the product is designed to offer predictions for 20 chronic conditions, such as diabetes, chronic kidney disease and congestive heart failure. The idea is to help insurers allocate resources more effectively. For providers it could mean more efficient patient triage and discharge, and better prescriptive information around symptoms, medications, risk factors and diagnoses.

Tesla announced the latest software update for its self-driving cars on Sunday, revealing that the vehicles will be relying more heavily on radar to make decisions. The announcement comes several months after it was revealed that a driver using autopilot was killed when his Tesla failed to notice a truck stretching across the road in front of him. All Tesla cars manufactured after October 2014 had already been using radar for autonomous driving, but relied more heavily on the car's optical camera. The update to Version 8.0 significantly increases the role radar will play. Tesla's announcement came with some interesting insight into how its vehicles will avoid accidents in the future--and apparently make the driving experience a smoother one.

Summary: What are the earliest seeds of artificial intelligence? To whom do we owe thanks for starting us down this path? Many modern researchers to be sure, but the earliest is Leonardo Torres of Spain, in about 1914. As data scientists it's very cool to be at the forefront in this age of techno-optimism. Since the awakening of the digital age calculated by economic researchers to have begun about 1994, a wave of increased productivity has been credited to computer-related innovations resulting in higher standards of living for many if not most.

Economist Milton Friedman was in favor of replacing the Federal Reserve with a computer. Maybe he was on to something. To the contrary, as a monetarist he wanted the Fed to increase the stock of money at a constant, 3%-5% rate a year, a task he thought a computer quite capable of performing. The Fed's lagged response to the 2008 financial crisis and an anemic recovery since then have given a new sense of urgency to the quest for a monetary rule or rules. For economists, the main benefit of a rule is to make monetary policy transparent and predictable. Everyone knows where the ship is headed and can plan accordingly.

Pete Skomoroch and Jon Bruner will be hosting O'Reilly Bot Day October 19, 2016, in San Francisco. Subscribe to the O'Reilly Bots Podcast to learn about advances in conversational user interfaces, artificial intelligence, and messaging that are revolutionizing the way we interact with software. Find us on Stitcher, iTunes, SoundCloud and RSS. In episode five of the O'Reilly Bots podcast, Pete Skomoroch and I speak with Joshua Browder, the 19-year old founder and CEO of DoNotPay, a series of bots that help people with legal issues, including challenging parking tickets, challenging bank charges, and claiming government assistance for homelessness. Dubbed "the world's first robot lawyer," his bots have attracted 260,000 users and provided 175,000 successful parking-ticket appeals.

The role of technology-fueled algorithms in shaping our society, and how to use them responsibly, is one of the topics I'll be exploring at the Next:Economy Summit in San Francisco, October 10-11. Some years ago, John Mattison, the chief medical information officer of Kaiser Permanente, the large integrated health provider, said to me, "The great question of the 21st century is going to be'Whose black box do you trust?'" Mattison was talking about the growing importance of algorithms in medicine, but his point, more broadly, was that we increasingly place our trust in systems whose methods for making decisions we do not understand. A lot of attention has been paid to the role of algorithms in shaping the experience of consumers. Much less attention has been paid to the role of algorithms in shaping the incentives for business decision-making. For example, there has been hand-wringing for years about how algorithms shape the news we see from Google or Facebook.

When you add something to your cart in Amazon and see a list of other recommended products that you might also like--that's an example of machine learning. This article explains the various types of machine learning and provides additional resources to get you started. In this video, successful Twitch* streamers share their tips and tricks to help you build a presence as a game developer on this channel. Learn about the IoT ecosystem, which consists of a broad set of technologies with a common thread of manageability and security. New non-uniform memory access (NUMA) technologies are spreading across processors in the modern computing world.