What makes a work of art ‘difficult’, and what makes it difficult to engage with as a viewer? Why are some works considered scandalous while similar works are accepted without a murmur? Art that deals with emotionally charged subjects, such as racism, abortion or conflict can have both a strong physical and emotional effect. Equally, not everyone is comfortable viewing intimate, personal works, like those exploring illness or grief. For this event we will talk about emotionally difficult artwork in relation to both the creator and the audience. Can emotion be embodied within an artwork? How do we use art to navigate a difficult experience? This evening we aim to answer these questions by taking a range of perspectives, considering the issues from the positions of artist or writer, critic and curator.Click here for more info.
Cambridge Spark: Big Data Analytics
24/25 June 2017 (all day)
Our partner Cambridge Spark is running a two-day course on big data analytics.What you will learn:You will learn about Big data, the Hadoop ecosystem and Spark in practice. After taking this class you will be able to:
Understand the challenges in the Big Data ecosystem
Describe the fundamentals of the Hadoop ecosystem
Use the core Spark APIs to express data processing queries
Whilst neuroscience can point to measurable evidence for the neural basis of a body-mind connection, a holistic understanding of that connection is yet to be revealed. Philosophy and art play an important role in the search for a wider comprehension of body and mind. For this event, we will engage in a search to sense the invisible, exploring the conversation between body and mind, or body-mind, through the lens of philosophy and dance. Expect a dance performance, followed by an open roundtable discussion with academic experts – ranging from philosophy to neuroscience to dance – examining ideas around body-mind through both, theory and in practice.Click here for more info.
With the advent of machinic decision-making in the world and consequently our lives, whether it’s through self-driving cars, biometrics, artificial intelligence, it is important to investigate the connotations of such. The phrase or statement ‘truth as data’ is to be explored – it connotes a machinic worldview that is free from subjectivity and human error and is presented as the ultimate form or empiricism or objectivity. But how valid is this claim? Do biases exist within algorithms? Deep learning technologies through artificial neural networks suggest a certain opacity at this moment in time as it is not known how most advanced algorithms work. At the core of this issue, lies the question – how do machines make decisions, and what are their consequences? The roundtable discussion will explore these questions. Click here for more info.