When you are angry, do you scowl, cry or even laugh? To what extent do your facial movements depend on the situation you are in -- whether you are in a formal meeting, say, or at home with your family? And do other people around the world express anger in such situations in the same way? These questions are at the centre of a contentious scientific debate about the nature of emotion that has raged for more than a century. Writing in Nature, Cowen et al.1 enter the fray.

Understanding how Google's search works is a key to figuring out why so many companies find it nearly impossible to compete and, in fact, go out of their way to cater to its needs. Every search request provides Google with more data to make its search algorithm smarter. Google has performed so many more searches than any other search engine that it has established a huge advantage over rivals in understanding what consumers are looking for. That lead only continues to widen, since Google has a market share of about 90 percent. Google directs billions of users to locations across the internet, and websites, hungry for that traffic, create a different set of rules for the company.

As we make tremendous advances in machine learning and artificial intelligence technosciences, there is a renewed understanding in the AI community that we must ensure that humans being are at the center of our deliberations so that we don't end in technology-induced dystopias. As strongly argued by Green in his book Smart Enough City, the incorporation of technology in city environs does not automatically translate into prosperity, wellbeing, urban livability, or social justice. There is a great need to deliberate on the future of the cities worth living and designing. There are philosophical and ethical questions involved along with various challenges that relate to the security, safety, and interpretability of AI algorithms that will form the technological bedrock of future cities. Several research institutes on human centered AI have been established at top international universities. Globally there are calls for technology to be made more humane and human-compatible. For example, Stuart Russell has a book called Human Compatible AI. The Center for Humane Technology advocates for regulators and technology companies to avoid business models and product features that contribute to social problems such as extremism, polarization, misinformation, and Internet addiction. In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical challenges to a successful deployment of AI or ML in human-centric applications, with a particular emphasis on the convergence of these challenges. We provide a detailed review of existing literature on these key challenges and analyze how one of these challenges may lead to others or help in solving other challenges. The paper also advises on the current limitations, pitfalls, and future directions of research in these domains, and how it can fill the current gaps and lead to better solutions.

Like humans, pigs have idiosyncratic faces, and new players in the Chinese pork market are taking notice, experimenting with increasingly sophisticated versions of facial recognition software for pigs. China is the world's largest exporter of pork, and is set to increase production next year by 9%. As the nation's pork farms grow in scale, more farmers are turning to AI systems like facial recognition technology – known as FRT – to continuously monitor, identify, and even feed their herds. This automated style of farming has the potential to be safer, cheaper and generally more effective: In 2018, pig farmers in China's Guangxi province trialling FRT found that it slashed costs, cut down on breeding time, and improved welfare outcomes for the pigs themselves. But it also has the potential to leave behind independent, small-scale farmers, who cannot afford to introduce this kind of technology to their operations.

It is notoriously difficult to control the behavior of artificial neural networks such as generative neural language models. We recast the problem of controlling natural language generation as that of learning to interface with a pretrained language model, just as Application Programming Interfaces (APIs) control the behavior of programs by altering hyperparameters. In this new paradigm, a specialized neural network (called a Neural Programming Interface or NPI) learns to interface with a pretrained language model by manipulating the hidden activations of the pretrained model to produce desired outputs. Importantly, no permanent changes are made to the weights of the original model, allowing us to re-purpose pretrained models for new tasks without overwriting any aspect of the language model. We also contribute a new data set construction algorithm and GAN-inspired loss function that allows us to train NPI models to control outputs of autoregressive transformers. In experiments against other state-of-the-art approaches, we demonstrate the efficacy of our methods using OpenAI's GPT-2 model, successfully controlling noun selection, topic aversion, offensive speech filtering, and other aspects of language while largely maintaining the controlled model's fluency under deterministic settings.

Robots can pick themselves up after a fall, even in an unfamiliar environment, thanks to an artificially intelligent controller that can adapt to new scenarios. It could make four-legged robots more useful in responding to natural disasters, such as earthquakes. Zhibin (Alex) Li at the University of Edinburgh, UK and his colleagues used an AI technique called deep reinforcement learning to teach four-legged robots a set of basic skills, such as trotting, steering and fall recovery. This involves the robots experimenting with different ways of moving and being rewarded with a numerical score for achieving a certain goal, such as standing up after a fall, and penalised for failing. This lets the AI recognise which actions are desired and repeat them in the similar situations in the future.

Joy Buolamwini from the MIT Media Lab says facial-recognition software has the highest error rates for darker-skinned females. New applications powered by artificial intelligence (AI) are being embraced by the public and private sectors. Their early uses hint at what's to come. In June 2020, IBM, Amazon and Microsoft announced that they were stepping back from facial-recognition software development amid concerns that it reinforces racial and gender bias. Amazon and Microsoft said they would stop selling facial-recognition software to police until new laws are passed in the United States to address potential human-rights abuses.

In the last years, AI safety gained international recognition in the light of heterogeneous safety-critical and ethical issues that risk overshadowing the broad beneficial impacts of AI. In this context, the implementation of AI observatory endeavors represents one key research direction. This paper motivates the need for an inherently transdisciplinary AI observatory approach integrating diverse retrospective and counterfactual views. We delineate aims and limitations while providing hands-on-advice utilizing concrete practical examples. Distinguishing between unintentionally and intentionally triggered AI risks with diverse socio-psycho-technological impacts, we exemplify a retrospective descriptive analysis followed by a retrospective counterfactual risk analysis. Building on these AI observatory tools, we present near-term transdisciplinary guidelines for AI safety. As further contribution, we discuss differentiated and tailored long-term directions through the lens of two disparate modern AI safety paradigms. For simplicity, we refer to these two different paradigms with the terms artificial stupidity (AS) and eternal creativity (EC) respectively. While both AS and EC acknowledge the need for a hybrid cognitive-affective approach to AI safety and overlap with regard to many short-term considerations, they differ fundamentally in the nature of multiple envisaged long-term solution patterns. By compiling relevant underlying contradistinctions, we aim to provide future-oriented incentives for constructive dialectics in practical and theoretical AI safety research.

Many Google employees and others in the tech and academic communities are furious over the sudden exit from Google of a pioneer in the study of ethics in artificial intelligence--a departure they see as a failure by an industry titan to foster an environment supportive of diversity. Timnit Gebru is known for her research into bias and inequality in AI, and in particular for a 2018 paper she coauthored with Joy Buolamwini that highlighted how poorly commercial facial-recognition software fared when attempting to classify women and people of color. Their work sparked widespread awareness of issues common in AI today, particularly when the technology is tasked with identifying anything about human beings. At Google, Gebru was the co-leader of the company's ethical AI team, and one of very few Black employees at the company overall (3.7% of Google's employees are Black according to the company's 2020 annual diversity report)-- let alone in its AI division. The research scientist is also cofounder of the group Black in AI.On Wednesday night, Gebru tweeted that she had been "immediately fired" for an email she recently sent to Google's Brain Women and Allies internal mailing list.

Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Therefore, through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence, which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, natural language processing tasks and other aspects derived from the published studies. Such multi-source information fusion plays a fundamental role in mining of people's social opinions from social media platforms. These can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. Future research directions are presented, whereas further research and development has the potential of leaving a wider academic and societal impact.