Registration required: https://skillsmatter.com/meetups/11756-quantum-computing-london

After decades of being always seemingly “20 years away”, quantum computing in various forms is starting to become a reality. Applications are being anticipated in areas as diverse as logistics, defence, transport, and finance, while quantum simulation in chemistry and physics promises to revolutionise materials and drug development.

With their promise of immense power to overcome some of the most intractable problems in computer science, quantum computers are likely to engender social transformations at least as great as those which have followed other major technological advances. This is emerging in a society in which technology already plays an increasing role in our “digitally hybrid” lives. The use of quantum computing is expected to be similarly hybrid, with quantum co-processors or cloud-style servers being invoked as needed and classical computers continue to provide capabilities in the areas in which they excel.

As with all new technologies, the outcomes of innovation are hard to predict, and may entail unwanted and unanticipated side effects and economic disruption, as well as many benefits. The lessons of earlier technologies may provide some pointers to ways in which quantum technologies will interact with, and possibly amplify, existing socio-technical trends. However, quantum technologies have particular characteristics, being on the one hand comparable with other powerful technologies harnessing the “fundamental fabrics of nature” – such as genetic modification, nanotechnology, and synthetic biology - and on the other hand, as “big tech”, currently require expensive resources and highly skilled practitioners. Issues around the “Ethical, Legal and Societal Aspects” of technology have been recognised for probably as long as there has been innovation. Regulation is the traditional way to minimise downsides from technology, but regulation is always backward-looking, and cannot address the uncertain impacts and complex, dynamic nature of contemporary innovation processes.

Responsible Innovation, a relatively new term which nevertheless builds on a rich history of technology assessment, tries to look beyond the balance of immediate threats and challenges of new technologies to consider their wider social implications. Although we cannot predict the future, we can prepare for it by anticipating the outcomes and by asking questions such as “what if ...” and “what else might it do ...”?

This talk will look at the implications of Quantum Machine Learning, from the perspective of Responsible Innovation. Will Quantum Machine Learning be “more of the same”, or will it have unique social and economic implications beyond what we can already see with classical methods? How do quantum methods fit together with Machine Learning, why has this received particular focus from the research community, and are the hopes (and fears) of Machine Learning as quantum's “killer app” justified? How can Responsible Innovation contribute at the conjunction of (at least) two disciplines which, until recently, have been quite separate?

This talk will not have the definitive answers to these questions, but will point to some ways in which we might start to think about them.

Dr Philip Inglesant is a Research Assistant in the Human Centred Computing Group led by Professor Marina Jirotka in the Department of Computer Science at the University of Oxford. He is researching Responsible Innovation (RI) in quantum computing as part of the Networked Quantum Information Technologies (NQIT) Hub, one of four Hubs set up in the first 5-year phase of the UK National Quantum Technologies Strategy. He co-authored the NQIT reports “Thinking Ahead to a World with Quantum Computers: the Landscape of Responsible Research and Innovation in Quantum Computing” and “Responsible Innovation in Quantum Technologies applied to Defence and National Security”.

Details: http://www.cybersecurity.ox.ac.uk/about-us/seminars

Hacking smart machines with smarter ones

Thanks to Machine Learning (ML) techniques, computers learn to recognize profiles and trends, make decisions or react autonomously to dynamic environments. Although ML's algorithms are known, the datasets used to train them are usually not public and, in fact, can be protected by privacy laws, or can be kept as trade secrets.

This presentation focuses on ML classifiers and how sensitive information associated with the training dataset can be reconstructed through legitimate interactions with them. The Generative Adversarial Networks (GANs) model is presented and it illustrates how GAN neural networks can be used to attack other automatic learning systems. In particular, it is shown that it is possible to deduce sensitive information from ML classifiers. This information leakage can be exploited, for example, by a competitor to build more effective classifiers, to acquire sensitive user data, or to acquire trade secrets by interacting with an apparatus, potentially violating its intellectual property rights.

BIO: Luigi V. Mancini is full professor at the University of Rome “La Sapienza” (Italy), and the director (Presidente) of the local Master of Science program (Laurea magistrale) in Cybersecurity.

The current research interests of Luigi V. Mancini include: network and information security, and user privacy. Luigi V. Mancini published more than 110 scientific papers in international conferences and journals. He has served on the program committees of several international conferences, among which: ACM Conference on Computer and Communication Security, ACM Symposium on Access Control Models and Technology, European Symposium on Research in Computer Security, and Financial Cryptography and Data Security Conference.

He is the founder of several Master degree programs in Information and Network Security at the University of Rome "La Sapienza". He participated in numerous national and international research projects in the area of security.

Luigi V. Mancini received the PhD degree in Computer Science from the University of Newcastle, UK, in 1989.

Citations: 6365 (December 2018). H-index: 40, based on Google Scholar

Web: http://www.di.uniroma1.it/mancini