Until January few had heard of Clearview AI, a company that has scraped billions of publicly available images from millions of websites in order to build a facial image search engine app. Clearview claims that more than six hundred law enforcement agencies have used its technology in the last year. News that police officers can search against a plethora of images uploaded to the most popular social media platforms has prompted outcry from officials, activists, and civil libertarians. Clearview's technology should concern everyone who values privacy and security. Clearview CEO Hoan Ton-That has been on the defensive since a New York Times report raised the company's profile from relative obscurity to the topic of a nationwide privacy discussion.

Much has been written about the coronavirus since it was first identified in China in January and much more will undoubtedly be written before the subsequently alarming spread abates and medical science comes up with an effective cure. And while news of the steady increase in reported numbers of people infected by and dying from COVID-19, as it is now known, has been dire, the good news is that we are getting much better at predicting and tracking the spread of infectious diseases. Three out of four infectious diseases originate in other species but their rapid spread in humans is facilitated by our ever-increasing mobility. International travel is now such that a disease that might once have stayed relatively contained can now spread across the world in mere weeks. We saw this with the Severe Acute Respiratory Syndrome (SARS) virus in 2003 and we see it again today.

Much has been written about the coronavirus since it was first identified in China in January and much more will undoubtedly be written before the subsequently alarming spread abates and medical science comes up with an effective cure. And while news of the steady increase in reported numbers of people infected by and dying from COVID-19, as it is now known, has been dire, the good news is that we are getting much better at predicting and tracking the spread of infectious diseases. Three out of four infectious diseases originate in other species but their rapid spread in humans is facilitated by our ever-increasing mobility. International travel is now such that a disease that might once have stayed relatively contained can now spread across the world in mere weeks. We saw this with the Severe Acute Respiratory Syndrome (SARS) virus in 2003 and we see it again today.

An AI technique called group analysis, used to detect e-commerce fraud, holds great promise for catching fraud rings sooner rather than later. Insurance fraud scams seem to make the news at least every month, as organized criminals seek to exploit the way insurers reimburse clinics, pharmacies and other providers for their services. What's often shocking is how much money fraudsters can steal from insurers before they're caught. Recently, in a single month, two separate alleged fraud rings based in California were busted for scams that investigators say netted $20 million or more. Clearly, there's a need for fraud detection tools that can spot these frauds in their early stages.

Getting deep convolutional neural networks to perform well requires a large amount of training data. When the available labelled data is small, it is often beneficial to use transfer learning to leverage a related larger dataset (source) in order to improve the performance on the small dataset (target). Among the transfer learning approaches, domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. In this paper, we consider the domain adaptation problem from the perspective of dimensionality reduction and propose a generic framework based on graph embedding. Instead of solving the generalised eigenvalue problem, we formulate the graph-preserving criterion as a loss in the neural network and learn a domain-invariant feature transformation in an end-to-end fashion. We show that the proposed approach leads to a powerful Domain Adaptation framework; a simple LDA-inspired instantiation of the framework leads to state-of-the-art performance on two of the most widely used Domain Adaptation benchmarks, Office31 and MNIST to USPS datasets.

An adversarial attack paradigm explores various scenarios for vulnerability of machine and especially deep learning models: we can apply minor changes to the model input to force a classifier's failure for a particular example. Most of the state of the art frameworks focus on adversarial attacks for images and other structured model inputs. The adversarial attacks for categorical sequences can also be harmful if they are successful. However, successful attacks for inputs based on categorical sequences should address the following challenges: (1) non-differentiability of the target function, (2) constraints on transformations of initial sequences, and (3) diversity of possible problems. We handle these challenges using two approaches. The first approach adopts Monte-Carlo methods and allows usage in any scenario, the second approach uses a continuous relaxation of models and target metrics, and thus allows using general state of the art methods on adversarial attacks with little additional effort. Results for money transactions, medical fraud, and NLP datasets suggest the proposed methods generate reasonable adversarial sequences that are close to original ones, but fool machine learning models even for blackbox adversarial attacks.

Currently, we serve pre-trained embeddings for four knowledge graphs. We introduce the service and its usage, and we show further that the trained models have semantic value by evaluating them on multiple semantic benchmarks. The evaluation also reveals that the combination of multiple models can lead to a better outcome than the best individual model.

Artificial intelligence (AI) has the potential to solve many routine business challenges -- from quickly spotting a few questionable charges in thousands of invoices to predicting consumers' needs and wants. But there may be a flipside to these advances. Privacy concerns are cropping up as companies feed more and more consumer and vendor data into advanced, AI-fuelled algorithms to create new bits of sensitive information, unbeknownst to affected consumers and employees. This means that AI may create personal data. When it does, "it's data that has not been provided with [an individual's] consent or even with knowledge", said Chantal Bernier, assistant and interim privacy commissioner in the Office of the Privacy Commissioner of Canada from 2008 until 2014 who now consults in the privacy and cybersecurity practice of global law firm Dentons.

Artificial intelligence (AI) has the potential to solve many routine business challenges -- from quickly spotting a few questionable charges in thousands of invoices to predicting consumers' needs and wants. But there may be a flipside to these advances. Privacy concerns are cropping up as companies feed more and more consumer and vendor data into advanced, AI-fuelled algorithms to create new bits of sensitive information, unbeknownst to affected consumers and employees. This means that AI may create personal data. When it does, "it's data that has not been provided with [an individual's] consent or even with knowledge", said Chantal Bernier, assistant and interim privacy commissioner in the Office of the Privacy Commissioner of Canada from 2008 until 2014 who now consults in the privacy and cybersecurity practice of global law firm Dentons.

In a whitepaper released last month, the European Commission (EC) unveiled its strategy to promote the development of artificial intelligence (AI) in Europe whilst ensuring the respect of fundamental rights. The whitepaper outlines the institution's plan to develop what it calls an "ecosystem of excellence" and an "ecosystem of trust." The idea here is to create a legal framework that would address the risks for fundamental rights and safety related to AI, all the while introducing initiatives to support and facilitate the adoption of the technology. "AI offers important efficiency and productivity gains that can strengthen the competitiveness of European industry and improve the wellbeing of citizens," the paper says, adding that AI can also contribute to finding solutions to urging societal challenges related to sustainability, demographic changes, democracy and crime. At the same time, AI entails a number of potential risks such as opaque decision-making, discrimination, intrusion in citizens' private lives or can be being used for criminal purposes.