The Organisation for Economic Co-operation and Development unveiled the first intergovernmental standard for artificial intelligence policies Wednesday--and the organization's 36 member countries including America have initially signed on along with Argentina, Brazil, Colombia, Costa Rica, Peru and Romania. OECD, an international forum that unites stakeholders from many nations to work together to address challenges of globalization, released "Recommendations of the Council on Artificial Intelligence" to help foster a global policy ecosystem that leverages the evolving technology's benefits, while also protecting human rights and democratic values. OECD's Director of the Science, Technology and Innovation Directorate Andrew Wyckoff told reporters that the principles' creators hope they'll help shape a stable regulatory environment that promotes the tech's positive uses, while withstanding unethical abuses. "AI is what we would call a'general purpose technology.' It's going to change the way we do things in nearly every single sector of the economy--that's part of the reason we give so much importance to its development," he said.

"AI is being fed directly into the bloodstream of society, and in many cases without sufficient checks and balances," says Kate Crawford, a professor and cofounder of New York University's AI Now, the world's first academic research institute dedicated to the social impact of artificial intelligence. Last year, Crawford partnered with data-viz guru Vladan Joler to create "Anatomy of an AI System," a map and research paper demonstrating the real-world consequences of developing and manufacturing the Amazon Echo. The paper highlights the radical differences in income distribution between Amazon executives and the workers who enable its vast infrastructure, as well as its devastating environmental impacts. The project has been exhibited at museums around the world, and Crawford has presented it to leaders in France, Germany, Spain, and Argentina.

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According to OECD's Director of the Science, Technology and Innovation Directorate Andrew Wyckoff, the released document, titled "Recommendations of the Council on Artificial Intelligence," will hopefully establish a regulatory environment to promote AI technology in an ethical manner. "AI is what we would call a'general purpose technology.' It's going to change the way we do things in nearly every single sector of the economy -- that's part of the reason we give so much importance to its development," he told reporters Wednesday, according to Defense One. "Some have termed it as'the invention of a method of inventions,' and in fact we can see it already affecting the process of scientific discovery and science itself." The principles outlined in the document have been signed by the OECD's 36 member countries, as well as by the US, Argentina, Brazil, Colombia, Costa Rica, Peru and Romania.

In Multi-Agent Systems (MAS) there are two main models of interaction: among agents, and between agents and the environment. Although there are studies considering these models, there is no practical tool to afford the interaction with external entities with both models. This paper presents a proposal for such a tool based on the Apache Camel framework by designing two new components, namely camel-jason and camel-artifact. By means of these components, an external entity is modelled according to its nature, i.e., whether it is autonomous or non-autonomous, interacting with the MAS respectively as an agent or an artifact. It models coherently external entities whereas Camel provides interoperability with several communication protocols.

We explore self-supervised models that can be potentially deployed on mobile devices to learn general purpose audio representations. Specifically, we propose methods that exploit the temporal context in the spectrogram domain. One method estimates the temporal gap between two short audio segments extracted at random from the same audio clip. The other methods are inspired by Word2Vec, a popular technique used to learn word embeddings, and aim at reconstructing a temporal spectrogram slice from past and future slices or, alternatively, at reconstructing the context of surrounding slices from the current slice. We focus our evaluation on small encoder architectures, which can be potentially run on mobile devices during both inference (re-using a common learned representation across multiple downstream tasks) and training (capturing the true data distribution without compromising users' privacy when combined with federated learning). We evaluate the quality of the embeddings produced by the self-supervised learning models, and show that they can be re-used for a variety of downstream tasks, and for some tasks even approach the performance of fully supervised models of similar size.

We consider the problem of domain generalization, namely, how to learn representations given data from a set of domains that generalize to data from a previously unseen domain. We propose the Domain Invariant Variational Autoencoder (DIVA), a generative model that tackles this problem by learning three independent latent subspaces, one for the domain, one for the class, and one for any residual variations. We highlight that due to the generative nature of our model we can also incorporate unlabeled data from known or previously unseen domains. To the best of our knowledge this has not been done before in a domain generalization setting. This property is highly desirable in fields like medical imaging where labeled data is scarce. We experimentally evaluate our model on the rotated MNIST benchmark and a malaria cell images dataset where we show that (i) the learned subspaces are indeed complementary to each other, (ii) we improve upon recent works on this task and (iii) incorporating unlabelled data can boost the performance even further.

As a novel similarity measure that is defined as the expectation of a kernel function between two random variables, correntropy has been successfully applied in robust machine learning and signal processing to combat large outliers. The kernel function in correntropy is usually a zero-mean Gaussian kernel. In a recent work, the concept of mixture correntropy (MC) was proposed to improve the learning performance, where the kernel function is a mixture Gaussian kernel, namely a linear combination of several zero-mean Gaussian kernels with different widths. In both correntropy and mixture correntropy, the center of the kernel function is, however, always located at zero. In the present work, to further improve the learning performance, we propose the concept of multi-kernel correntropy (MKC), in which each component of the mixture Gaussian kernel can be centered at a different location. The properties of the MKC are investigated and an efficient approach is proposed to determine the free parameters in MKC. Experimental results show that the learning algorithms under the maximum multi-kernel correntropy criterion (MMKCC) can outperform those under the original maximum correntropy criterion (MCC) and the maximum mixture correntropy criterion (MMCC).

Today global history was made, as the first intergovernmental standard on artificial intelligence (AI) was adopted by the OECD--a geopolitical milestone achievement. There is a worldwide investment rush underway in artificial intelligence (AI) technology. Both public and private investment funding are pouring into AI, as nations and corporations seek to gain economic benefits and competitive advantages through automation. IDC estimates the global spending on cognitive and AI systems to reach $57.6 billion by 2021. Last year the UK government announced plans to invest £300 million in AI.

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