Is it sensical to ascribe psychological predicates to AI systems like chatbots based on large language models (LLMs)? People have intuitively started ascribing emotions or consciousness to social AI ('affective artificial agents'), with consequences that range from love to suicide. The philosophical question of whether such ascriptions are warranted is thus very relevant. This paper advances the argument that LLMs instantiate language users in Ludwig Wittgenstein's sense but that ascribing psychological predicates to these systems remains a functionalist temptation. Social AIs are not full-blown language users, but rather more like Italo Calvino's literature machines. The ideas of LLMs as Wittgensteinian language users and Calvino's literature-producing writing machine are combined. This sheds light on the misguided functionalist temptation inherent in moving from equating the two to the ascription of psychological predicates to social AI. Finally, the framework of mortal computation is used to show that social AIs lack the basic autopoiesis needed for narrative fa\c{c}ons de parler and their role in the sensemaking of human (inter)action. Such psychological predicate ascriptions could make sense: the transition 'from quantity to quality' can take place, but its route lies somewhere between life and death, not between affective artifacts and emotion approximation by literature machines.

The rise of large-scale socio-technical systems in which humans interact with artificial intelligence (AI) systems (including assistants and recommenders, in short AIs) multiplies the opportunity for the emergence of collective phenomena and tipping points, with unexpected, possibly unintended, consequences. For example, navigation systems' suggestions may create chaos if too many drivers are directed on the same route, and personalised recommendations on social media may amplify polarisation, filter bubbles, and radicalisation. On the other hand, we may learn how to foster the "wisdom of crowds" and collective action effects to face social and environmental challenges. In order to understand the impact of AI on socio-technical systems and design next-generation AIs that team with humans to help overcome societal problems rather than exacerbate them, we propose to build the foundations of Social AI at the intersection of Complex Systems, Network Science and AI. In this perspective paper, we discuss the main open questions in Social AI, outlining possible technical and scientific challenges and suggesting research avenues.

From Star Trek's Data and 2001's HAL to Columbus Day's Skippy the Magnificent, pop culture is chock full of fully conscious AI who, in many cases, are more human than the humans they serve alongside. But is all that self-actualization really necessary for these synthetic life forms to carry out their essential duties? In his new book, How to Grow a Robot: Developing Human-Friendly, Social AI, author Mark H. Lee examines the social shortcomings of the today's AI and delves into the promises and potential pitfalls surrounding deep learning techniques, currently believed to be our most effective tool at building robots capable of doing more than a handful of specialized tasks. In the excerpt below, Lee argues that the robots of tomorrow don't necessarily need -- nor should they particularly seek out -- the feelings and experiences that make up the human condition. Although I argue for self-awareness, I do not believe that we need to worry about consciousness.

The world is going bonkers over artificial intelligence and for all the right reasons. AI is almost at the helm of ruling the realm of marketing technology and it is here to stay for a long time to come. At the same time, B2B marketers are exploring and harnessing every possible tool, platforms, and techniques to tap to the most imperative business aspect, that is, customer experience and engagement. Here, social media plays a meaty role! The article will focus on probing the'ifs and buts' of combining social media marketing with artificial intelligence, popularly known as "social artificial intelligence," and will shed some light on how to add intuition to this union without being intrusive.

Are you just a computer made of meat? Are all your thoughts, feelings and experiences nothing more than circuits made from neurons in your head? If you're like a lot of people, your answer to this question will be a definitive "No!" From science to philosophy, there are lots of good reasons to hold that human beings are more than just computing machines. Unfortunately, many of the technologists bringing versions of artificial intelligence to the market are already sure they know that we are. Suddenly we might be leaving our grandmas and maybe even our kids with emotional robots because, oh well, everybody's doing it. For them people are, indeed, just biological computers.

There are two fundamental aspects which define the relationship and overlap between AI and the Internet of Things, and they're quite different. The first is reasonably obvious. The Internet of Things enables the smart automation of many objects. With IoT, you are giving devices, vehicles, buildings the ability to host algorithms and perform functions which can only be driven by software. When you examine the tasks that these objects need to perform, the tasks frequently involve processes that are similar to the cognitive process.

The ultimate role of AI in marketing is to add value and purpose to both brands and customers. AI is a great tool that can augment the idea and capabilities of human marketers. According to the 2017 State of Inbound Report by HubSpot, generating traffic and leads and proving RoI, and lack of budget for marketing activities are the leading challenges that marketers face even today. Data overload from multiple sources, limited resources and time to implement activities, a crowded marketplace and increasing customer expectations make the job all the more difficult. Traditional marketing campaigns are far less efficient in gaining and retaining a customer.

SEOUL--People don't want robots just to work for them. They want robots to talk and play with them, too. Look at the recent Winter Olympics in PyeongChang, South Korea, for an example of this work-and-play dynamic. More than 30 robots from a startup called FutureRobot were on hand at the games to answer visitors' questions in Korean, Chinese, English, or Japanese. With a friendly avatar face and a large touchscreen on a cylindrical body, the robots offered directions to different sporting venues, provided schedules for every event, and more.

Have you ever wanted to have a real conversation with a chatbot and not just shout basic commands at it? Well now you can with Hugging Face, a social AI chatbot that has meaningful conversation with users. The concept was developed by focusing on how social and entertainment use cases can be applied to machine learning. As computers are getting smarter and more teenagers spend time online, social AI is gaining serious attention. The potential use cases of social AI are endless- not just for entertainment – but also for business.

Artificial Intelligence's impact on marketing continues to gain speed. In this article, Shelly Kramer looks at the early steps of Social AI and how it can boost Lead Generation. It is another in our "Great Articles You may have missed" series. Marketers use social in a variety of ways, from searching for social promoters to boost brand awareness to improving and personalizing the customer experience. As investments in Artificial Intelligence (AI) continue to boom, marketers are looking for ways to combine the two powerhouses--AI and social--to optimize their reach and bring more value to consumers.