Companies looking to protect their innovations in artificial intelligence and machine learning face a dilemma. Data and algorithms are essential components to developing products based on these technologies, but it is notoriously difficult to protect them from an intellectual property perspective. There are two possible approaches here. One is to apply for a patent. Another is to seek trade secret protection.

This paper explores the important role of critical science, and in particular of post-colonial and decolonial theories, in understanding and shaping the ongoing advances in artificial intelligence. Artificial Intelligence (AI) is viewed as amongst the technological advances that will reshape modern societies and their relations. Whilst the design and deployment of systems that continually adapt holds the promise of far-reaching positive change, they simultaneously pose significant risks, especially to already vulnerable peoples. Values and power are central to this discussion. Decolonial theories use historical hindsight to explain patterns of power that shape our intellectual, political, economic, and social world. By embedding a decolonial critical approach within its technical practice, AI communities can develop foresight and tactics that can better align research and technology development with established ethical principles, centring vulnerable peoples who continue to bear the brunt of negative impacts of innovation and scientific progress. We highlight problematic applications that are instances of coloniality, and using a decolonial lens, submit three tactics that can form a decolonial field of artificial intelligence: creating a critical technical practice of AI, seeking reverse tutelage and reverse pedagogies, and the renewal of affective and political communities. The years ahead will usher in a wave of new scientific breakthroughs and technologies driven by AI research, making it incumbent upon AI communities to strengthen the social contract through ethical foresight and the multiplicity of intellectual perspectives available to us; ultimately supporting future technologies that enable greater well-being, with the goal of beneficence and justice for all.

The threat status and criminal collaborations of potential terrorists are hidden but give rise to observable behaviours and communications. Terrorists, when acting in concert, need to communicate to organise their plots. The authorities utilise such observable behaviour and communication data to inform their investigations and policing. We present a dynamic latent network model that integrates real-time communications data with prior knowledge on individuals. This model estimates and predicts the latent strength of criminal collaboration between individuals to assist in the identification of potential cells and the measurement of their threat levels. We demonstrate how, by assuming certain plausible conditional independences across the measurements associated with this population, the network model can be combined with models of individual suspects to provide fast transparent algorithms to predict group attacks. The methods are illustrated using a simulated example involving the threat posed by a cell suspected of plotting an attack.

Neural network models using predictive coding are interesting from the viewpoint of computational modelling of human language acquisition, where the objective is to understand how linguistic units could be learned from speech without any labels. Even though several promising predictive coding -based learning algorithms have been proposed in the literature, it is currently unclear how well they generalise to different languages and training dataset sizes. In addition, despite that such models have shown to be effective phonemic feature learners, it is unclear whether minimisation of the predictive loss functions of these models also leads to optimal phoneme-like representations. The present study investigates the behaviour of two predictive coding models, Autoregressive Predictive Coding and Contrastive Predictive Coding, in a phoneme discrimination task (ABX task) for two languages with different dataset sizes. Our experiments show a strong correlation between the autoregressive loss and the phoneme discrimination scores with the two datasets. However, to our surprise, the CPC model shows rapid convergence already after one pass over the training data, and, on average, its representations outperform those of APC on both languages.

Extreme waves in the Arctic typically occur every 20 years, but as climate change continues to plague the region these events could happen every two to five years, a new study reveals. Much of this area is frozen for a majority of the year, but rising temperatures have increased periods of open water that could result in catastrophic waves. Using computer models, researchers found the area hit the hardest was in the Greenland Sea, which could experience maximum annual wave heights of more than 19 feet. The team also warns coastal flooding might increase by a factor of four to 10 by the end of this century. Extreme waves in the Arctic typically occur every 20 years, but as climate change continues to plague the region these events could happen every two to five years, a new study reveals.

The US drone strike that killed Iran's top general Qassem Soleimani was "unlawful", the United Nations expert on extrajudicial killings concluded in a report on Tuesday. US President Donald Trump ordered the killing in a January 3 drone strike near Baghdad international airport. Soleimani was "the world's top terrorist" and "should have been terminated long ago", Trump said at the time. Iraqi commander Abu Mahdi al-Muhandis was also killed in the attack. Callamard concluded that it was an "arbitrary killing" that violated the UN charter.

Clearview AI will no longer sell its facial recognition software in Canada, according to government privacy officials investigating the company. The end of Clearview AI operations in Canada will also mean the end of the company's contract with the Royal Canadian Mounted Police, according to an announcement released today by the Office of the Privacy Commissioner of Canada. Canadian privacy officials started investigating Clearview AI in February following media reports about the company's practice of scraping billions of images from social media and the web without consent from the people in photos in order to create its facial recognition system. Critics say Clearview's approach could mean the end of privacy. Government officials from Quebec, British Columbia, and Alberta provinces continue to investigate Clearview AI and Royal Canadian Mounted Police use of its facial recognition software despite Clearview's exit.

Decentralised Machine Learning Special Report with Fetch.ai The advances in Artificial Intelligence over the past decade have been driven by the revolution in "machine learning" – the ability of computers to improve in the delivery of a process or task, using algorithms to "learn" based on large datasets. This has driven huge improvements in how businesses can operate at scale, and at lower cost, as well as opening new areas of the economy. However, this revolution has come at a cost, huge data aggregators now control large portions of our online lives, presenting ethical and legal challenges. Furthermore this data often lives in standalone "silos" that organisations struggle to benefit from since advanced machine learning algorithms require data to be trained on.

When it comes to AI ethics around the use of facial recognition, China does not have a good record. As India has banned Chinese apps including TikTok, one that went viral in 2019 and 2020 that uses AI to recommend micro videos, Australia and the U.S. are likely to be next. Kevin Mayer left Disney recently to join ByteDance, as CEO of TikTok, but you cannot separate TikTok, from its parent company with an HQ located in Beijing. If this company isn't helping export China's police surveillance capitalism play, I don't know what is. It's the greatest PR stunt by ByteDance I've seen yet.

Click here to read the full article. THE WORLD first took notice of Beijing's prowess in artificial intelligence (AI) in late 2017, when BBC reporter John Sudworth, hiding in a remote southwestern city, was located by China's CCTV system in just seven minutes. At the time, it was a shocking demonstration of power. Today, companies like YITU Technology and Megvii, leaders in facial recognition technology, have compressed those seven minutes into mere seconds. What makes those companies so advanced, and what powers not only China's surveillance state but also its broader economic development, is not simply its AI capability, but rather the math power underlying it.