The new open-source IBM Cloud-Native Toolkit is the focus of this week's automation tales. This solution is for individuals who want to integrate and execute AI and machine learning technologies in cloud environments. Codex, a deep learning model that generates software source code, has been revealed by OpenAI. One of the most compelling reasons to adopt a public workspace is to improve developer onboarding by shortening the time to first call (TTFC), the most essential measure for a public API. While Elon Musk's brain-chip company messes around with gaming monkeys, another group of researchers has achieved a major milestone in neuroprosthetics: allowing a man who can't talk to form sentences with his mind.

EU trade policy should carve out space for the regulation of ethical and responsible artificial intelligence (AI) in future trade talks. This is the finding of a new study by researchers from the University of Amsterdam's (UvA) Institute for Information Law. The Dutch Ministry of Foreign Affairs commissioned the study to generate further knowledge about the interface between international trade law and European norms and values when it comes to the use of AI. As AI seeps ever more comprehensively into our daily lives--through our phones, our cars, even in our doctors' offices--the need to ensure responsible use of such technologies becomes ever greater. Responsible use of AI is therefore a top priority for the Dutch government and for the EU as a whole.

Software is becoming the fabric that binds our personal and social lives, embodying a vast part of the technological knowledge that powers our industry and fuels innovation. Software is a pillar of most scientific research activities in all fields, from mathematics to physics, from chemistry to biology, from finance to social sciences. Software is also an essential mediator for accessing any digital information. In short, a rapidly increasing part of our collective knowledge is embodied in, or dependent on, software artifacts. Our ability to design, use, understand, adapt, and evolve systems and devices on which our lives have come to depend relies on our ability to understand, adapt, and evolve the source code of the software that controls them.

Advances in natural language processing have led to a variety of successful tools and techniques for solving problems such as understanding, generating, and translating natural languages. Given the success of these techniques, a natural question is whether they can also be applied to programming languages. However, the initial research has been mixed. Researchers attempting to translate between programming languages by employing statistical machine translation (SMT) found that a large percentage of the translated programs were not syntactically valid. On the other hand, SMT has been successfully employed to recover identifiers in obfuscated JavaScript code. In this paper, we discuss several differences between natural languages and programming languages that can thwart successful application of NLP techniques to program transformation. We also discuss several strategies to cope with these differences in practice, using our own experiences with using SMT to assign meaningful identifier names to variables in decompiled C programs as an example.