Self-supervised depth estimators have recently shown results comparable to the supervised methods on the challenging single image depth estimation (SIDE) task, by exploiting the geometrical relations between target and reference views in the training data. However, previous methods usually learn forward or backward image synthesis, but not depth estimation, as they cannot effectively neglect occlusions between the target and the reference images. Previous works rely on rigid photometric assumptions or on the SIDE network to infer depth and occlusions, resulting in limited performance. On the other hand, we propose a method to Forget About the LiDAR (FAL), with Mirrored Exponential Disparity (MED) probability volumes for the training of monocular depth estimators from stereo images. Our MED representation allows us to obtain geometrically inspired occlusion maps with our novel Mirrored Occlusion Module (MOM), which does not impose a learning burden on our FAL-net.

We've been hearing a lot lately about the metaverse. Though the concept is far from new and the name itself is 30 years old, the hype cycle is in full swing, with headlines like "Everyone wants to own the metaverse, including Facebook and Microsoft. But what exactly is it?" While people may not fully agree on what it is and when it will get here, one thing is clear: The metaverse is all about virtual avatars in a virtual world doing virtual things. Unless we all end up hooked to machines that keep us alive while we gallivant in a virtual world a la The Matrix, we are still stuck with the real world, climate change and all.

An effective artificial intelligence strategy uses the right tools to solve the right problems, an analyst says. Dean Lacheca, Senior Director Analyst for Gartner, told delegates at the Gartner IT Symposium/Xpo in the Gold Coast on Monday that they need to stop seeing AI as a futuristic piece of technology. "AI, in reality, is more than just a tool. It's a whole range of tools with different variations in complexities, costs of ownerships, consequences and opportunities," he said. A good strategy allows organisations to manage risks, address and mitigate concerns and accelerate the role of AI within an organisation, Mr Lacheca says.

Deep learning is changing the way we use and think about machines. Current incarnations are better than humans at all kinds of tasks, from chess and Go to face recognition and object recognition. In particular, humans have the extraordinary ability to constantly update their memories with the most important knowledge while overwriting information that is no longer useful. The world provides a never-ending source of data, much of which is irrelevant to the tricky business of survival, and most of which is impossible to store in a limited memory. So humans and other creatures have evolved ways to retain important skills while forgetting irrelevant ones.

Deep learning is changing the way we use and think about machines. Current incarnations are better than humans at all kinds of tasks, from chess and Go to face recognition and object recognition. In particular, humans have the extraordinary ability to constantly update their memories with the most important knowledge while overwriting information that is no longer useful. The world provides a never-ending source of data, much of which is irrelevant to the tricky business of survival, and most of which is impossible to store in a limited memory. So humans and other creatures have evolved ways to retain important skills while forgetting irrelevant ones.

The ability of discarding or hiding irrelevant information has been recognized as an important feature for knowledge based systems, including answer set programming. The notion of strong equivalence in answer set programming plays an important role for different problems as it gives rise to a substitution principle and amounts to knowledge equivalence of logic programs. In this paper, we uniformly propose a semantic knowledge forgetting, called HT- and FLP-forgetting, for logic programs under stable model and FLP-stable model semantics, respectively. Our proposed knowledge forgetting discards exactly the knowledge of a logic program which is relevant to forgotten variables. Thus it preserves strong equivalence in the sense that strongly equivalent logic programs will remain strongly equivalent after forgetting the same variables. We show that this semantic forgetting result is always expressible; and we prove a representation theorem stating that the HT- and FLP-forgetting can be precisely characterized by Zhang-Zhou's four forgetting postulates under the HT- and FLP-model semantics, respectively. We also reveal underlying connections between the proposed forgetting and the forgetting of propositional logic, and provide complexity results for decision problems in relation to the forgetting. An application of the proposed forgetting is also considered in a conflict solving scenario.