Allowing machines to choose whether to kill humans would be devastating for world peace and security. But how do we equip machines with the ability to learn ethical or even moral choices? Jentzsch et al.(2019) showed that applying machine learning to human texts can extract deontological ethical reasoning about "right" and "wrong" conduct by calculating a moral bias score on a sentence level using sentence embeddings. The machine learned that it is objectionable to kill living beings, but it is fine to kill time; It is essential to eat, yet one might not eat dirt; it is important to spread information, yet one should not spread misinformation. However, the evaluated moral bias was restricted to simple actions -- one verb -- and a ranking of actions with surrounding context. Recently BERT ---and variants such as RoBERTa and SBERT--- has set a new state-of-the-art performance for a wide range of NLP tasks. But has BERT also a better moral compass? In this paper, we discuss and show that this is indeed the case. Thus, recent improvements of language representations also improve the representation of the underlying ethical and moral values of the machine. We argue that through an advanced semantic representation of text, BERT allows one to get better insights of moral and ethical values implicitly represented in text. This enables the Moral Choice Machine (MCM) to extract more accurate imprints of moral choices and ethical values.

Fox News Flash top headlines for Dec. 10 are here. Check out what's clicking on Foxnews.com A Wisconsin state trooper used a drone to find a dog and reunite it with its injured owner following a vehicle crash last week, officials said. Trooper John Jones used his drone to locate River, a 3-year-old Australian Shepherd, in a wooded area in Brown County. The dog and a driver were traveling on State Highway 57 on Friday when a deer darted onto the road, causing the unidentified driver to swerve and crash into a median, the Wisconsin Department of Transportation (DOT) said.

Courts use algorithms to help determine sentencing, but random people get the same results.

Aletheia, a Greek word for the unveiling of truth, has had alternate fortunes in the history of Western thought, becoming appreciated in the philosophical debate of the twentieth century. Today it has again become central to social and political discussion, after the Oxford English Dictionary defined and recognized the term Post-truth as "word of the year" in 2016. Since then, this theme has seen key roles being taken on by fake news, the political debate around the theme of misinformation (which we must not translate with the simple concept of disinformation, whose connotation is much more intelligence-oriented) and finally deep fakes, that is, videos similar to real ones, but elaborately fabricated, thanks to Artificial Intelligence techniques. How greatly these issues have an impact on our daily lives today is demonstrated by the fact that many operators believe they are capable of putting at risk the proper performance of democratic processes. Climate, immigration and the European context are typical scenarios in which false news can influence the perception of social and commercial actions.

Yesterday, the Netflix team announced to open-source Metaflow, a Python library that helps scientists and engineers build and manage real-life data science projects. The Netflix team writes, "Over the past two years, Metaflow has been used internally at Netflix to build and manage hundreds of data-science projects from natural language processing to operations research." Metaflow was developed by Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to deep learning. It provides a unified API to the infrastructure stack required to execute data science projects, from prototype to production. Models are only a small part of an end-to-end data science project.

There have been concerns surrounding the prospective security of human jobs as AI and ML become more advanced, but do experts believe that this kind of tech will overhaul workplaces entirely, and what will the AI evolution in 2020 mean for other developing technologies, like 5G? For some, it's important not to get carried away by the idea that AI will take over all aspects of culture anytime soon, and we should instead prepare for more subtle but nevertheless practical uses of it within our immediate environment. Investment in sustainable technology is imperative if we are to halt and reverse some of the damage we have done to our planet. Data scientist at Hitachi Vantara, Dr Anya Rumyantseva, said: "Artificial intelligence isn't just something debated by techies or sci-fi writers anymore – it's increasingly creeping into our collective cultural consciousness, but there's a lot of emphasis on the negative. "While those big picture questions around ethics cannot and should not be ignored, in the near-term we won't be dealing with the super-AI you see in the movies.

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Ten years ago, I invited the community to envision the future of Data, AI and Analytics (I called it then:"BI 2020"). From the Museum of Information in Paris, I asked: what could the world of AI, Data and Analytics look like by 2020?! Many predicted the advent of natural interfaces like search and voice for analysis. Conversational AI or Big Data weren't common industry terms back then but many saw that such trends would be transformative over the next decade. And we are far from having achieve the ambitious goals of the past decade.

In today's competitive and crowded news market, finding and retaining loyal readers is an essential component of sustainability for news organisations. After all, with so many competing ways to access news, reader loyalty is hard to come by. The data team at the South China Morning Post (SCMP) recently set out to understand how readers develop loyalty to specific news outlets and how to nurture that loyalty. In January 2019, the team began to build an algorithm using machine learning to predict reader loyalty. We've named our predictive engine Bluefin because: While the ability to predict reader loyalty has many applications, we were interested in using the prediction to optimise our marketing campaigns.