For people with a regular drinking establishment of choice, the person behind the bar can even become a friend and confidant. Or, as renowned Canadian economist, Harry Gordon Johnson once said: "The greatest accomplishment of a bartender lies in his ability to exactly suit his customer."

The exponential development and application of artificial intelligence triggered an unprecedented global concern for potential social and ethical issues. Stakeholders from different industries, international foundations, governmental organisations and standards institutions quickly improvised and created various codes of ethics attempting to regulate AI. A major concern is the large homogeneity and presumed consensualism around these principles. While it is true that some ethical doctrines, such as the famous Kantian deontology, aspire to universalism, they are however not universal in practice. In fact, ethical pluralism is more about differences in which relevant questions to ask rather than different answers to a common question. When people abide by different moral doctrines, they tend to disagree on the very approach to an issue. Even when people from different cultures happen to agree on a set of common principles, it does not necessarily mean that they share the same understanding of these concepts and what they entail. In order to better understand the philosophical roots and cultural context underlying ethical principles in AI, we propose to analyse and compare the ethical principles endorsed by the Chinese National New Generation Artificial Intelligence Governance Professional Committee (CNNGAIGPC) and those elaborated by the European High-level Expert Group on AI (HLEGAI). China and the EU have very different political systems and diverge in their cultural heritages. In our analysis, we wish to highlight that principles that seem similar a priori may actually have different meanings, derived from different approaches and reflect distinct goals.

The weaponisation of deepfakes against politicians or nation states has become something we're simply going to have to live with,

It is commonly defined as the ability of machines to self-learn, … In the years since, AI and its sub-category, machine learning, …

A draft EU regulation on artificial intelligence risks exclusion of the social partners and lack of compliance with data-protection requirements.

The researchers used a machine-learning algorithm to screen through hundreds of these proteins to identify likely candidates involved in biofilm …

The dream – or nightmare – for AI is that it will one day be able to perform like the human brain. That concept of general AI (broader intelligence beyond a narrow area) has remained tantalisingly out of reach – or safely so, depending on what science-fiction films you watch. Like the human brain, AI research comes in two halves: symbolic and transformer-based models. Chris Edwards explains how these two halves are now coming together in an awkward but more effective whole and what that means for the quest for general AI. Meanwhile, narrow AI is getting everywhere. This year's AI market of around $90bn is forecast to multiply by ten times within the next seven years.

Creativity is a trait that is often associated with people who are considered to be geniuses. It seems like a quality that we admire and seek in others, but we rarely take the time to think about what it means to be creative. The word "creative" can refer to anything from designing, composing, cooking delicious food, painting, or writing poems. All these things are manifested by a person who does unlimited and revealing work. When someone creates something on their initiative, it feels as if they have created something new and original out of nothing -- something perhaps even better than what was there before.

Picture a machine learning system that relies on crowdsourced data labelers to help rank music recommendations. Labellers are all different and this difference may manifest in their labels. The answer depends on many things, but one of them is who you are asking. Bias means different things to different people. The other day I watched a very interesting discussion along these lines between a lawyer (Jake Goldenfein) and a data scientist (Danula Hettiachchii). It seemed like my colleagues had fundamentally different ideas about bias.

Imagine a world in which smart packaging for supermarket ready meals updates you in real-time to tell you about carbon footprints, gives live warnings on product recalls, and instant safety alerts because allergens were detected unexpectedly in the factory. But how much extra energy would be used powering such a system? And what if an accidental alert meant you were told to throw away your food for no reason? These are some of the questions asked by team of researchers, including a Lancaster University Lecturer in Design Policy and Futures Thinking, who by creating objects from a'smart' imaginary new world are looking at the ethical implications of using Artificial Intelligence in the food sector. Their article, Considering the ethical implications of digital collaboration in the Food Sector, is published today in the November issue of the data science solutions journal'Patterns'.