Local arts group Therefore already incorporates elements of theater, performance art and music into their work. Now creative director Dean Terry is adding artificial intelligence to the mix. For their latest show, the group will have scripted live interactions with home assistants such as Amazon's Alexa.

On this week's Tech Tent we hear why China's determination to be a leading player in artificial intelligence could lead to tensions with the United States. We have two other reports on this week's programme.

Artificial Intelligence and machine learning have entered the spotlight more and more in recent years, as major tech companies have each made innovating around AI a focus.

Machines are beginning to outperform humans at highly-specific tasks that often take humans years to master.

A lot of press about artificial intelligence (AI) focuses on its scary side. Many paint the vision of robots, smart houses (think back to the 1999 Disney movie "Smart House" of a cyborg maid that takes over) and artificial beings crafting our experiences. This fear is not all hype, as Microsoft's AI-powered …

November 15, 2017 Written by: Kareem Yusuf, Ph.D As a huge Star Wars fan, I have always wanted to have a set of supernatural powers, such as the ability to sense impending attacks; influence the thoughts of others, known as the "Jedi mind trick;" and even see the future. In essence, the Force – meaning the wisdom, expertise and ability to know exactly what will happen next and what I should do in the moment. Of course, there is only one problem with all this--our current reality. Wilson predicted, the world will be run by synthesizers, people able to put together the right information at the right time and think critically about it to make important choices wisely. And, that is exactly what IBM Watson is doing today.

The Boston public access station WGBH has partnered with PBS for another short series in its long-running Nova family of programs. Nova Wonder will follow three researchers exploring big scientific mysteries. Each episode tackles a different complex question: Do animals have a secret language? Which AI technologies could surpass human abilities? How ethical is it to grow life in a lab?

We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural language generation and retrieval models, including neural network and template-based models. By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble. The system has been evaluated through A/B testing with real-world users, where it performed significantly better than other systems. The results highlight the potential of coupling ensemble systems with deep reinforcement learning as a fruitful path for developing real-world, open-domain conversational agents.

Human susceptibility to epidemics of misinformation and disease has grown manyfold as the world we inhabit keeps getting smaller due to increased access to online information and soaring global mobility. Social media platforms have changed the way we consume information [Schmidt et al., 2017], and more and more people find their news through social media [Newman et al., 2015]. Following the 2016 presidential election in the United States, there have been investigations into the spread of false stories, or "fake news" on social media, and based on web browsing data, archives of fact-checking websites, and results from an online survey, a recent study found that social media were an important source of election news [Allcott and Gentzkow, 2017]. While ascertainment of social network structures is generally difficult using traditional survey-based approaches, such as name generators, which are survey questions designed to solicit information about friends and acquaintances of a subject, online platforms readily capture the structure of large-scale social networks, therefore making them well suited to study spread of information whether accurate or not. Further, although the transmission mechanisms are very different, the spread of information in online systems has many similarities to the spread of infectious diseases among hosts in a population. From a mathematical and statistical point of view, one can therefore investigate the spread of pathogens and the spread of information in the same framework as long as the network structure accurately captures the transmission pathways and the spreading process is parametrized appropriately. In this paper, we consider a simple susceptible-infected (SI) process and a more complex spreading process on a fixed and known network structure. This spreading process may be conceptualized as propagating either a pathogen or a piece of information. We focus on addressing two distinct questions that are relevant in both settings: (1) How to infer the unknown parameters associated with the spreading process?