It would be an understatement to say that artificial intelligence (AI) is much in the news these days. It's widely viewed as likely to usher in the next big step-change in computing, but a recent interesting development in the field has particular implications for open source. It concerns the rise of "ethical" AI.

In October 2016, the White House Office of Science and Technology Policy, the European Parliament's Committee on Legal Affairs and, in the UK, the House of Commons' Science and Technology Committee, all released reports on how to prepare for the future of AI, with ethical issues being an important component of those reports. At the beginning of last year, the Asilomar AI Principles were published, followed by the Montreal Declaration for a Responsible Development of Artificial Intelligence, announced in November 2017.

Abstract discussions of what ethical AI might or should mean became very real in March 2018. It was revealed then that Google had won a share of the contract for the Pentagon's Project Maven, which uses artificial intelligence to interpret huge quantities of video images collected by aerial drones in order to improve the targeting of subsequent drone strikes. When this became known, it caused a firestorm at Google. Thousands of people there signed an internal petition addressed to the company's CEO, Sundar Pichai, asking him to cancel the project. Hundreds of researchers and academics sent an open letter supporting them, and some Google employees resigned in protest.

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
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• 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.”