HALIFAX, NS, Aug. 4, 2020 /CNW/ - Global Spatial Technology Solutions ("GSTS" or "the Company") an Artificial Intelligence (AI) and Maritime Analytics company today announced that it has been selected by the Canadian Space Agency (CSA) to develop space-based AI capability to support enhanced decision-making for a range of space applications focused on tasks using computer vision (such as would be used by exploration landers, rovers, robotics or Earth observation systems). This project is funded under the Space Technology Development Program. "This contribution will enable GSTS to expand our growing AI capabilities into the space sector to support decision making based on the same techniques we utilize in the maritime domain, enabling detection, recognition and prediction," said Richard Kolacz, GSTS CEO. "It is equivalent to placing the brain next to the eyes of any space asset or sensor in order to support decision-making locally, rather than having to relay all the data to Earth for analysis before a decision can be made. It is the first step in the development of truly autonomous space capability." Computer vision involves the automatic extraction, analysis and understanding of information gleaned from digital images. By applying machine learning, which is a type of AI, it can enhance and optimize the production of actionable insights much faster and more accurately than a human can.

The 2.2M parameters in MobileNet are frozen, but there are 1.3K trainable parameters in the dense layers. You need to apply the sigmoid activation function in the final neurons to ouput a probability score for each genre apart. By doing so, you are relying on multiple logistic regressions to train simultaneously inside the same model. Every final neuron will act as a seperate binary classifier for one single class, even though the features extracted are common to all final neurons. When generating predictions with this model, you should expect an independant probability score for each genre and that all probability scores do not necessarily sum up to 1. This is different from using a softmax layer in multi-class classification where the sum of probability scores in the output is equal to 1.

TikTok has become the latest platform to ban deepfakes. Under its new policy, the app says it "prohibits synthetic or manipulated content that misleads users by distorting the truth of events in a way that could cause harm." "Our intent is to protect users from things like shallow or deep fakes, so while this kind of content was broadly covered by our guidelines already, this update makes the policy clearer for our users." TikTok's General Manager Vanessa Pappas wrote in a statement. The deepfake ban is part of a broader set of policy changes meant to fight misinformation and election meddling.

Retailers around the globe are cautiously reopening their doors after facing perhaps the most challenging times in decades, while in some areas of the world the toughest times may still lie ahead. The short-term disruption caused by the COVID-19 pandemic is clear, but its impact on the future of retail operations is yet to be seen. This uncertainty is creating unseen challenges for retailers, who are looking to make smart investments in technology that will not become obsolete once health and safety guidelines and restrictions lift. Many retailers have concentrated on implementing technologies primarily to mitigate risk from the ongoing pandemic. However, investing in a new breed of smart surveillance cameras today enables retailers to design a sustainable system with robust application offerings for multiple types of scenarios, both during the pandemic and as operations begin to normalize.

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From yawning to closing the fridge door, a lot of sounds occur within the home. Such sounds could be useful for home-based artificial intelligence applications, but training that AI requires a robust and diverse set of samples. A video game developed by Carnegie Mellon University researchers leverages live streaming to collect sound donations from players that will populate an open-source database. "The methods for developing machine-learning-based interaction have become so accessible that now it's about collecting the right kind of data to create devices that can do more than just listen to what we say," said Nikolas Martelaro, an assistant professor in the School of Computer Science's Human-Computer Interaction Institute (HCII.) "We want these devices to use all the sounds in our environment to act."

Is a doctor or an AI system better at diagnosing a particular patient? This machine learning tool makes the call.