According to Algorithmic Information Theory (AIT) -- Intelligent representations compress data into the shortest possible program that can reconstruct its content, exhibiting low Kolmogorov Complexity (KC). In contrast, most visual representation learning systems use fixed-length representations for all inputs, ignoring variations in complexity or familiarity. Recent adaptive tokenization methods address this by allocating variable-length representations but typically require test-time search over multiple encodings to find the most predictive one. Inspired by Kolmogorov Complexity principles, we propose a single-pass adaptive tokenizer, KARL, which predicts the appropriate number of tokens for an image in a single forward pass, halting once its approximate KC is reached. The token count serves as a proxy for the minimum description length. KARL's training procedure closely resembles the Upside-Down Reinforcement Learning paradigm, as it learns to conditionally predict token halting based on a desired reconstruction quality. KARL matches the performance of recent adaptive tokenizers while operating in a single pass. We present scaling laws for KARL, analyzing the role of encoder/decoder size, continuous vs. discrete tokenization and more. Additionally, we offer a conceptual study drawing an analogy between Adaptive Image Tokenization and Algorithmic Information Theory, examining the predicted image complexity (KC) across axes such as structure vs. noise and in- vs. out-of-distribution familiarity -- revealing alignment with human intuition.

-- We present Kalman-filter Assisted Reinforcement Learner (KARL) for dynamic object tracking and grasping over eye-on-hand (EoH) systems, significantly expanding such systems' capabilities in challenging, realistic environments. In comparison to the previous state-of-the-art, KARL (1) incorporates a novel six-stage RL curriculum that doubles the system's motion range, thereby greatly enhancing the system's grasping performance, (2) integrates a robust Kalman filter layer between the perception and reinforcement learning (RL) control modules, enabling the system to maintain an uncertain but continuous 6D pose estimate even when the target object temporarily exits the camera's field-of-view or undergoes rapid, unpredictable motion, and (3) introduces mechanisms to allow retries to gracefully recover from unavoidable policy execution failures. Extensive evaluations conducted in both simulation and real-world experiments qualitatively and quantitatively corroborate KARL's advantage over earlier systems, achieving higher grasp success rates and faster robot execution speed. Source code and supplementary materials for KARL will be made available at: https://github.com/arc-l/karl . Humans, and animals in general, interact with the physical world through observing and handling everyday objects [1], which makes object tracking and manipulation arguably the most fundamental skill for physical intelligence. In robotics, autonomous grasping in stationary settings has been extensively studied [2], [3], typically using decoupled vision and manipulation sub-systems where the camera does not move with the manipulator. While effective for static tasks, this approach struggles in dynamic scenarios where objects move or become occluded. Real-world interactions, such as handovers, require continuous tracking and adaptive grasping, highlighting the need for more integrated solutions.

Friston and co-workers have applied his ideas to modeling attention. For example, in the following paper they state: "We have suggested recently that perception is the inference about causes of sensory inputs and attention is the inference about the uncertainty (precision) of those causes (Friston, 2009).

Graphic footage: Fox News host Tucker Carlson weighs in on issues facing Americans ahead of the midterm elections on "Tucker Carlson Tonight." A Michigan man pleaded guilty last week to murdering, dismembering and eating the body parts of another man he met on a dating app. Mark David Latunski, 53, of Shiawassee County, Michigan, admitted in court last Thursday that he killed 25-year-old hairdresser Kevin Bacon after luring the University of Michigan-Flint student to his home in December 2019, according to local outlet Mlive.com. Latunski pleaded guilty as charged to mutilation of a body and to open murder, which encompasses murder in the first and second degree. Latunski acknowledged stabbing Bacon in the back and taking parts of his dead body to the kitchen, where he ate them, after meeting the young man on Grindr, which is a hookup app for gay, bisexual and transgender men.

The three of us were in a 1957 de Havilland Beaver, floating in the middle of a crater lake in the southwest quadrant of Alaska. The pilot was recounting the toll that the Vietnam War had taken on him, while, over in the right seat, my boyfriend, Karl, listened. Thanks to proximity, I was listening as well, though chances are they'd forgotten I was there. Outside, water sloshed against the pontoons, rocking the plane gently from side to side. No one had asked this man to tell his story in a long time, but Karl had asked, and so the pilot put the plane down on the lake, turned off the ignition, and began.

Karl has been Purch's infographics specialist across all editorial properties since 2010. Before joining Purch, Karl spent 11 years at the New York headquarters of The Associated Press, creating news graphics for use around the world in newspapers and on the web. He has a degree in graphic design from Louisiana State University.

There will be many people who will say it does exist and has working technologies, hardware and software. It is an interesting error in thinking to focus on closed system devices/products as to what Ubiquity (IoT3) is. Devices are used to get across the point of various types of connections and networks being accessed. But more importantly in a full implementation of the concept of Ubiquity (often described as the IoT) devices may not even be owned anymore. The ownership of devices ceases to be important if you can own your digital identity, can verify it and establish your own ecosystem of assets in Blockchain.