Deep-learning applications typically rely on a trained neural net to accomplish their goal (e.g. photo recognition, automatic translation, or playing go). That neural net uses what is essentially a large collection of weighting numbers that have been empirically determined as part of its training (which generally uses a huge set of training data). A free-software application could use those weights, but there are a number of barriers for users who might want to tweak them for various reasons. A discussion on the debian-devel mailing list recently looked at whether these deep-learning applications can ever truly be considered "free" (as in freedom) because of these pre-computed weights—and the difficulties inherent in changing them.

The conversation was started by Zhou Mo ("Lumin"); he is concerned that, even if deep-learning application projects release the weights under a free license, there are questions about how much freedom that really provides. In particular, he noted that training these networks is done using NVIDIA's proprietary cuDNN library that only runs on NVIDIA hardware.

There are many different things that individuals might consider to be a life threatening event and then there are extinction level events, for example an asteroid hitting Earth.

While the idea of an asteroid hitting Earth and ending all life is the stuff of Hollywood movie like Armageddon, it's an actual, though remote, possibility that NASA is investigating, with the help of Docker containers.

NASA is currently developing a mission known as DART - the Double Asteroid Redirection Test, which is a spacecraft that will deploy a kinetic impact technique to deflect an asteroid. Christopher Heistand, DART Flight Software Lead, at the The Johns Hopkins University Applied Physics Laboratory (APL) that is helping to build the DART ship, detailed how his group is using Docker.

• How open-source computing is making AI affordable

  computing and the cloud have brought many previously unaffordable IT options to small and medium-sized enterprises (SMEs). The same is becoming true of artificial intelligence (AI), although it brings new challenges to all sizes of company.

  Even though many of the commercial, high-profile products are aimed at Global 2000 companies, and others marketed at SMEs are perhaps heavier on hype than intelligence, the smarter smaller organisations can learn, build on and use AI techniques right now, with those same open-source and
  .

• Free Ebook Offers Insight on 16 Open Source AI Projects

  Open source AI is flourishing, with companies developing and open sourcing new AI and machine learning tools at a rapid pace. To help you keep up with the changes and stay informed about the latest projects, The Linux Foundation has published a free ebook by Ibrahim Haddad examining popular open source AI projects, including Acumos AI, Apache Spark, Caffe, TensorFlow, and others.

  “It is increasingly common to see AI as open source projects,” Haddad said. And, “as with any technology where talent premiums are high, the network effects of open source are very strong.”

In this AI based Science article, we explore How Netflix adopted an Open Source Model to improve their Entertainment Recommender Systems.