The ISO/IEC JTC 1/SC 42 Artificial Intelligence community met in #Google in #SiliconValley #Sunnyvale this week 8th - 12th October 2018 under the Chairmanship of Wael William Diab . The Irish National Delegation was well represented 3 Academics from the Science Foundation Ireland funded Adapt Research Centre, SC42 Convenor Dr. David Filip, SC42 Editor Ray Walshe and SC42 Expert Prof Dave Lewis. The other 3 delegates were SC42 Secretariat Barry Smith (NSAI), JTC1 PAS Brian McAuliffe (HP) and NSAI Board Member Terry Landers (Microsoft). The team were actively involved in multiple SC42 areas. After a long week of discussion in National Body mode and AI Expert Mode has progressed the development of the study group reports and established new projects and working groups in Big Data, Trustworthiness and AI Use Cases.

For good reason, the company has chosen to publish their ethical principles on to their website. They predict that this technology will be widespread in just a few years and they admit there is a risk to misuse of such technology. They have chosen to educate people about the technology and to do, this they released audio samples of digitally created voices of Donald Trump and Barack Obama. An option would have been to build audio avatars in silence and wait someone to use this technology to fool the public audience. Now, thanks to this openness, at least some people know to be suspicious if they hear Obama giving an odd speech.

It can identify objects in a fraction of a second, imitate the human voice and recommend new music, but most machine "intelligence" lacks the most basic understanding of everyday objects and actions -- in other words, common sense. DARPA is teaming up with the Seattle-based Allen Institute for Artificial Intelligence to see about changing that. The Machine Common Sense program aims to both define the problem and engender progress on it, though no one is expecting this to be "solved" in a year or two. But if AI is to escape the prison of the hyper-specific niches where it works well, it's going to need to grow a brain that does more than execute a classification task at great speed. "The absence of common sense prevents an intelligent system from understanding its world, communicating naturally with people, behaving reasonably in unforeseen situations, and learning from new experiences. This absence is perhaps the most significant barrier between the narrowly focused AI applications we have today and the more general AI applications we would like to create in the future," explained DARPA's Dave Gunning in a press release.

On Sep. 25 at the United Nations General Assembly, the United Nations (UN) Secretary General António Guterres spotlighted, "Rapidly developing fields such as artificial intelligence, blockchain and biotechnology have the potential to turbocharge progress towards the Sustainable Development Goals. Artificial Intelligence is connecting people across languages, and supporting doctors in making better diagnoses. Driverless vehicles will revolutionize transportation. But there are also risks and serious dangers." Let's explore them in more detail.

Global defence giant Thales is set to create up to 100 jobs at a new £1million base in Plymouth where it will test robotic boats for dealing with sea-borne mines. The French multinational has opened a trials and training centre at Turnchapel Wharf, creating 20 high-skilled jobs, but with an ambition to grow, possibly to as many as 100. The investment is part of a "major commitment" to developing autonomous and unmanned technology for use in the air and on sea – in other words, robotic vessels. Thales, which reported global sales of €14 billion in 2015, is creating two new UK centres, the other being in Wales. The company said that only through experimentation with new and disruptive technologies will the UK military be able to stay ahead and maintain an advantage over other forces.

"But there are also unknown unknowns – the ones we don't know we don't know." An artificial intelligence strategy is the corporate equivalent of your spleen: everyone has one, but not everyone understands quite what it will accomplish. There are bold plans afoot everywhere in the world of AI to be sure, but its reality is still distant from the vision of artificial general intelligence (AGI) – i.e., machines displaying intelligence equivalent to the natural intelligence of humans – of popular imagination. Investors in particular need a sober and realistic view of what's achievable in the field of machine learning-driven AI today, versus what promises nothing more than a waste of time and money. There are many business problems that map to the attributes above. The key to success in AI is to focus on these classes of practical problems and solutions.

Understanding dynamic fracture propagation is essential to predicting how brittle materials fail. Various mathematical models and computational applications have been developed to predict fracture evolution and coalescence, including finite-discrete element methods such as the Hybrid Optimization Software Suite (HOSS). While such methods achieve high fidelity results, they can be computationally prohibitive: a single simulation takes hours to run, and thousands of simulations are required for a statistically meaningful ensemble. We propose a machine learning approach that, once trained on data from HOSS simulations, can predict fracture growth statistics within seconds. Our method uses deep learning, exploiting the capabilities of a graph convolutional network to recognize features of the fracturing material, along with a recurrent neural network to model the evolution of these features. In this way, we simultaneously generate predictions for qualitatively distinct material properties. Our prediction for total damage in a coalesced fracture, at the final simulation time step, is within 3% of its actual value, and our prediction for total length of a coalesced fracture is within 2%. We also develop a novel form of data augmentation that compensates for the modest size of our training data, and an ensemble learning approach that enables us to predict when the material fails, with a mean absolute error of approximately 15%.

Proceedings of the AAAI Fall Symposium on Artificial Intelligence in Government and Public Sector, Arlington, Virginia, USA, October 18-20, 2018

Adversarial robustness has become an important research topic given empirical demonstrations on the lack of robustness of deep neural networks. Unfortunately, recent theoretical results suggest that adversarial training induces a strict tradeoff between classification accuracy and adversarial robustness. In this paper, we propose and then study a new regularization for any margin classifier or deep neural network. We motivate this regularization by a novel generalization bound that shows a tradeoff in classifier accuracy between maximizing its margin and average margin. We thus call our approach an average margin (AM) regularization, and it consists of a linear term added to the objective. We theoretically show that for certain distributions AM regularization can both improve classifier accuracy and robustness to adversarial attacks. We conclude by using both synthetic and real data to empirically show that AM regularization can strictly improve both accuracy and robustness for support vector machine's (SVM's) and deep neural networks, relative to unregularized classifiers and adversarially trained classifiers.

Police have yet to completely wrap their heads around modern iPhones like the X and XS, and that's clearer than ever thanks to a leak. Motherboard has obtained a presentation slide from forensics company Elcomsoft telling law enforcement to avoid looking at iPhones with Face ID. If they gaze at it too many times (five), the company said, they risk being locked out much like Apple's Craig Federighi was during the iPhone X launch event. They'd then have to enter a passcode that they likely can't obtain under the US Constitution's Fifth Amendment, which protects suspects from having to provide self-incriminating testimony. There are ways around this system, whether or not they're ethically sound -- the FBI recently forced a suspect to unlock his iPhone X using Face ID.