Hartmut Neven (born 1964) is a German American scientist working in quantum computing, computer vision, robotics and computational neuroscience. He is best known for his work in face and object recognition and his contributions to quantum machine learning. He is currently Vice President of Engineering at Google where he leads the Quantum Artificial Intelligence Lab, which he founded in 2012. == Education == Hartmut Neven studied Physics and Economics in Brazil, Köln, Paris, Tübingen and Jerusalem. He wrote his Master thesis on a neuronal model of object recognition at the Max Planck Institute for Biological Cybernetics under Valentino Braitenberg. In 1996 he received his Ph.D. in Physics from the Institute for Neuroinformatics at the Ruhr University in Bochum, Germany, for a thesis on "Dynamics for vision-guided autonomous mobile robots" written under the tutelage of Christoph von der Malsburg. He received a scholarship from the Studienstiftung des Deutschen Volkes, Germany's most prestigious scholarship foundation. == Work == In 1998 Neven became research professor of computer science at the University of Southern California at the Laboratory for Biological and Computational Vision. In 2003 he returned as the head of the Laboratory for Human-Machine Interfaces at USC's Information Sciences Institute. === Face recognition, avatars and face filters === Neven co-founded two companies, Eyematic for which he served as CTO and Neven Vision which he initially led as CEO. At Eyematic he developed face recognition technology and real-time facial feature analysis for avatar animation. Teams led by Neven have repeatedly won top scores in government sponsored tests designed to determine the most accurate face recognition software. Face filters, now ubiquitous on mobile phones, were launched for the first time by Neven Vision on the networks of NTT DoCoMo and Vodafone Japan in 2003. Neven Vision also pioneered mobile visual search for camera phones. Neven Vision was acquired by Google in 2006. === Object recognition and adversarial images === At Google he managed teams responsible for advancing Google's visual search technologies. His team launched Google Goggles now Google Lens. The concept of adversarial patterns originated in his group when he tasked Christian Szegedy with a project to modify the pixel inputs of a deep neural network to lower the activity of select output nodes. The motivation was to use this technique for object localization which did not work out. But the idea gave rise to the fields of adversarial learning and DeepDream art. In 2013 his optical character recognition team won the ICDAR Robust Reading Competition by a wide margin and in 2014 the object recognition team won the ImageNet challenge. === Google Glass === Neven was a co-founder of the Google Glass project. His team completed the first prototype, codenamed Ant, in 2011. === Quantum Artificial Intelligence === In 2006 Neven started to explore the application of quantum computing to hard combinatorial problems arising in machine learning. In collaboration with D-Wave Systems he developed the first image recognition system based on quantum algorithms. It was demonstrated at SuperComputing07. At NIPS 2009 his team demonstrated the first binary classifier trained on a quantum processor. In 2012 together with Pete Worden at NASA Ames he founded the Quantum Artificial Intelligence Laboratory. In 2014 he invited John M. Martinis and his group at UC Santa Barbara to join the lab to start a fabrication facility for superconducting quantum processors. The Quantum Artificial Intelligence team performed the first experimental demonstration of a scalable simulation of a molecule. In 2016 the team formulated an experiment to demonstrate quantum supremacy. Quantum supremacy was then declared by Google in October 2019. In 2023 Quantum AI researchers demonstrated that quantum error correction works in practice by showing for the first time that the error of a logical qubit decreases when increasing the number of physical qubits it is composed of. Google's quantum processors have been used to study the physics of quantum many body states that otherwise are challenging to prepare in a laboratory such as time crystals, traversable wormholes and non-Abelian anyons. ==== Neven's law ==== Neven's law states that the performance of quantum computers improves at a doubly exponential rate.
PressWise
PressWise was digital imposition software to quickly and easily impose most any variety of flat and folding layouts. It was acquired by the Aldus Prepress Group affectionately known in the print and publishing industry as the Aldus WiseGuys in August 1991 from Emulation Technologies Inc. of Cleveland, Ohio. It was further developed by the Aldus Press Group and launched as the first of many Aldus prepress products in 1993. It was subsequently owned by Adobe Systems, then Luminous Corporation (Seattle), then Imation, and finally ScenicSoft. PressWise was discontinued by ScenicSoft in 1999 ultimately. == History == In February 2009, the PressWise copyright was acquired by Aethos Technologies and a new print automation product was launched by its creator, Eric Wold of Santa Rosa, California. This new product has no relationship to the old imposition software of the same name. It's notable that Larry Letteney, former President of Creo Americas was a board member and shareholder of Aethos Technologies during its early phase. Datatech SmartSoft acquired exclusive distribution rights to the software in September 2009. In September 2010 Datatech SmartSoft completed the acquisition of the PressWise brand and product.
Syman
SYMAN is an artificial intelligence technology that uses data from social media profiles to identify trends in the job market. SYMAN is designed to organize actionable data for products and services including recruiting, human capital management, CRM, and marketing. SYMAN was developed with a $21 million series B financing round secured by Identified, which was led by VantagePoint Capital Partners and Capricorn Investment Group.
Cognitive robotics
Cognitive robotics or cognitive technology is a subfield of robotics concerned with endowing a robot with intelligent behavior by providing it with a processing architecture that will allow it to learn and reason about how to behave in response to complex goals in a complex world. Cognitive robotics may be considered the engineering branch of embodied cognitive science and embodied embedded cognition, consisting of robotic process automation, artificial intelligence, machine learning, deep learning, optical character recognition, image processing, process mining, analytics, software development and system integration. == Core issues == While traditional cognitive modeling approaches have assumed symbolic coding schemes as a means for depicting the world, translating the world into these kinds of symbolic representations has proven to be problematic if not untenable. Perception and action and the notion of symbolic representation are therefore core issues to be addressed in cognitive robotics. == Starting point == Cognitive robotics views human or animal cognition as a starting point for the development of robotic information processing, as opposed to more traditional artificial intelligence techniques. Target robotic cognitive capabilities include perception processing, attention allocation, anticipation, planning, complex motor coordination, reasoning about other agents and perhaps even about their own mental states. Robotic cognition embodies the behavior of intelligent agents in the physical world (or a virtual world, in the case of simulated cognitive robotics). Ultimately, the robot must be able to act in the real world. == Learning techniques == === Motor Babble === A preliminary robot learning technique called motor babbling involves correlating pseudo-random complex motor movements by the robot with resulting visual and/or auditory feedback such that the robot may begin to expect a pattern of sensory feedback given a pattern of motor output. Desired sensory feedback may then be used to inform a motor control signal. This is thought to be analogous to how a baby learns to reach for objects or learns to produce speech sounds. For simpler robot systems, where, for instance, inverse kinematics may feasibly be used to transform anticipated feedback (desired motor result) into motor output, this step may be skipped. === Imitation === Once a robot can coordinate its motors to produce a desired result, the technique of learning by imitation may be used. The robot monitors the performance of another agent and then the robot tries to imitate that agent. It is often a challenge to transform imitation information from a complex scene into a desired motor result for the robot. Note that imitation is a high-level form of cognitive behavior and imitation is not necessarily required in a basic model of embodied animal cognition. === Knowledge acquisition === A more complex learning approach is "autonomous knowledge acquisition": the robot is left to explore the environment on its own. A system of goals and beliefs is typically assumed. A somewhat more directed mode of exploration can be achieved by "curiosity" algorithms, such as Intelligent Adaptive Curiosity or Category-Based Intrinsic Motivation. These algorithms generally involve breaking sensory input into a finite number of categories and assigning some sort of prediction system (such as an artificial neural network) to each. The prediction system keeps track of the error in its predictions over time. Reduction in prediction error is considered learning. The robot then preferentially explores categories in which it is learning (or reducing prediction error) the fastest. == Other architectures == Some researchers in cognitive robotics have tried using architectures such as (ACT-R and Soar (cognitive architecture)) as a basis of their cognitive robotics programs. These highly modular symbol-processing architectures have been used to simulate operator performance and human performance when modeling simplistic and symbolized laboratory data. The idea is to extend these architectures to handle real-world sensory input as that input continuously unfolds through time. What is needed is a way to somehow translate the world into a set of symbols and their relationships. == Questions == Some of the fundamental questions to be answered in cognitive robotics are: How much human programming should or can be involved to support the learning processes? How can one quantify progress? Some of the adopted ways are reward and punishment. But what kind of reward and what kind of punishment? In humans, when teaching a child, for example, the reward would be candy or some encouragement, and the punishment can take many forms. But what is an effective way with robots?
Sparkles emoji
The Sparkles emoji (U+2728 ✨ SPARKLES) is an emoji that has one large star surrounded by smaller stars. Originating from Japan to represent sparkles used in anime and manga, the sparkles are often used as emphasis in text by surrounding words or phrases with it. It is the third most-used emoji in the world on Twitter as of 2021. Since the early 2020s it has been used by major software companies to represent artificial intelligence, marketing the technology as "like magic". == Development == According to Emojipedia, the Sparkles emoji was first used by Japanese mobile operators SoftBank, Docomo and au in the late 1990s. The emoji was added to Unicode 6.0 in 2010 and Emoji 1.0 in 2015. On some platforms the Sparkles emoji has been multicoloured whilst on other platforms it has been one colour. Twitter and Microsoft's Sparkles have changed from being multicoloured to being a single colour. Samsung's version of the emoji previously had a night sky in the background. == Usage == === Interpersonal communication === The Sparkles emoji was originally meant to represent the usage of sparkles in Japanese anime and manga, where the sparkles are used to represent beauty, happiness or awe. The emoji has several meanings and depends upon context. Starting in the late 2010s, the emoji started being used to surround words or phrases to be used as emphasis, an example from the book Because Internet being "I would simply ✨pass away✨". It can also be used as sarcasm, irony or as a way to mock people. Without emoji this could be represented with tildes or asterisks, for example, "~tildes~" or "~asterisk plus tilde~" or "~~true sparkle exuberance~~". The sparkles emoji can be used to represent stars in text, be used to represent cleanliness or can be used to mean "orgasm" whilst sexting. In September 2021 the Sparkles emoji overtook the Pleading Face (🥺) emoji to become the third most-used emoji in the world according to Emojipedia, with approximately 1 per cent of all tweets containing the Sparkles emoji. === Artificial intelligence === In the early 2020s, the Sparkles emoji started being used as an icon to represent artificial intelligence (AI). Companies who use the emoji this way include Google, OpenAI, Samsung, Microsoft, Adobe, Spotify and Zoom. As of August 2024, seven of the top 10 software companies by market capitalisation use the Sparkles emojis with AI. OpenAI has different versions of the Sparkles for different versions of the models that ChatGPT uses. One explanation is that Sparkles is being used by these companies as a way to market AI as "magic". Marketing technology as "magic" has been used before AI, particularly by Apple. Another explanation given by designers and marketers choosing to use Sparkles to signify AI is simply that other platforms are doing it, making it familiar to users. Around 2024, some of these companies started removing two of the smaller stars from the emoji in their AI services and have kept the one large star, an example being Google's Gemini chatbot. In early 2024, the Nielsen Norman Group provided test subjects with the star in isolation and found that people did not associate the symbol with AI, but instead mostly with "optimisation" or "favourite or save an item".
Harmony (software)
Harmony is a Java-based software for creating high-definition music videos with 2D and 3D animations. The application was developed by Digital Chaotics, a company based in San Jose, California and established in 2010 by Ken and Leanna Scott. == History == During a March 1, 2011 interview published by The LIST magazine, Ken explained how he initially got into music and digital entertainment. According to Scott: “I came at it from both the art and the technology side. … I built one of the first digital audio synthesizers as an undergrad project back in 1979. It was a short jump from there to creating visuals with computers, too.” Taking inspiration from Fantasia – which Scott calls, “The greatest music video of all time” – he began writing software code for Harmony in late 2009, finishing the project in mid-2010. However, Scott has also said that the idea for Harmony began much earlier: I read a book in 1978 called Digital Harmony, by John H Whitney, Sr. (Interestingly, he was the father of the president of Digital Productions.) He said that there was a kind of visual art based on motion, and proposed theories about the underlying mathematical structure of visual harmony. So there's the book, combined with my desire to create art with computers-add a taste or two of things commonly used by college students during the 70's - and lots of Pink Floyd. Add it all up, and the seeds for Harmony were planted. My friends in school and at Floating Point Systems listened to me ranting about "making music videos with computers" incessantly. I'm sure it was both maddening and fascinating to see. == Features == Harmony runs on Windows 7 and Windows Vista. Currently, Digital Chaotics does not offer a macOS or Linux platform for the software. However, Harmony can be run on these platforms by running it on Windows in a virtual machine. == Harmony 2 == On November 1, 2011, Digital Chaotics released the 2.0 version of the Harmony software. Unlike the original version, the second release featured three product levels: Harmony 2 Express, Harmony 2 Pro, and Harmony 2 Extreme. The "Express" version was positioned as an entry-level, free release to allow users a chance to "test-drive" the software. The "Pro" version currently retails at $197, while the "Extreme" is priced at $397. These two versions, aimed more towards VJ and Fulldome theater usage, featured additional software capability and features such as higher resolution, more video formatting options, and more camera angles.
World model (artificial intelligence)
A world model in artificial intelligence is a machine learning system that builds an internal representation of an environment. The model predicts how that environment changes over time in response to actions. Researchers design world models to help agents plan, reason, and act without constant real-world trial and error. World models differ from systems that merely classify or generate outputs. They simulate dynamics such as physics, object interactions, and causality. Early ideas date to the 1990s. Modern versions power robots, autonomous driving, and interactive video generation. == History == Jürgen Schmidhuber introduced the term world model in machine learning in 1990. He proposed recurrent neural networks that predict future states from observations and use those predictions to train agents. David Ha and Schmidhuber revived the concept in a 2018 paper. Their agents learned to drive virtual cars and play video games inside self-generated simulations. Yann LeCun advanced the idea in a 2022 position paper titled "A Path Towards Autonomous Machine Intelligence". He argued that intelligence requires predictive models of the world rather than pure pattern matching. LeCun proposed the joint embedding predictive architecture (JEPA) as a practical foundation. LeCun and collaborators developed several JEPA variants. V-JEPA 2 reached state-of-the-art performance on video understanding and physical reasoning at the time. It supports zero-shot robot control in unfamiliar environments. Introduced in March 2026, LeWorldModel trains stably end-to-end from raw pixels and uses two loss terms and avoids hand-crafted heuristics. LeCun founded Advanced Machine Intelligence Labs in 2026 to further develop world models. Google DeepMind introduced Genie in 2024. The model learned interactive environments from unlabeled internet videos. Genie 2 followed in late 2024 and added three-dimensional generation. The Genie series set benchmarks for general-purpose simulation. Genie 3 was introduced in August 2025. It produces photorealistic, real-time interactive worlds from text prompts which are displayed at 24 frames per second and explored in real time with text or image prompts. The model supports persistent three-dimensional worlds and real-time interaction. Waymo adopted Genie 3 in February 2026 and used it to create a specialized world model for autonomous driving simulation, called the Waymo World Model. It produces synchronized camera and lidar outputs and creates edge cases that real robotaxis rarely encounter. The edge cases were reported to be unusual by PCMag. General Intuition announced a $133.7 million seed round. World Labs raised $1 billion. AMI raised $1.03 billion. In April 2026, Alibaba announced Happy Oyster, its world model designed for real-time and “flowy” world model. It includes a directing mode for world building based on text and image prompts and a wandering mode for exploring the resulting world. It can generate 3-minute in-world video clips. Also in April, World Labs, co-founded by Li Fei Fei, unveiled Spark 2.0, an open-source 3D Gaussian splatting rendering engine that targets smartphone-class devices. In June 2026, Nvidia released Cosmos 3, a family of open-weight models. It combines previously independent physical reasoning, world simulation, and action generation. Cosmos 3 integrates can process and generate text, image, video, audio, and action sequences. The model employs a Mixture-of-Transformers" (MoT) approach. An autoregressive (AR) transformer handles reasoning and next-token prediction, while a diffusion transformer (DT) does multimodal generation. Encoders (ViT for vision, VAE for visual/audio, and domain-specific for actions) and generate a shared representation space using 3D multi-dimensional rotary position embedding (mRoPE) for spatial and temporal information. The family includes Cosmos3-Nano (16B parameters) for workstations; Cosmos3-Super (64B parameters) for research. == Architecture == World models process raw sensory data such as video frames or lidar scans. They compress this input into compact latent representations. The system then predicts future representations rather than pixel-by-pixel reconstructions. Many modern world models use joint embedding predictive architecture (JEPA). An encoder turns observations into embeddings. A predictor estimates one or a suite of embeddings from the current one and an action. In some cases a critic chooses one embedding as the best result. A regularizer keeps embeddings well-behaved. The model trains by minimizing prediction error in embedding space. This approach avoids the high cost of generating every detail. Some architectures add explicit components. A fast reactive path handles immediate responses. A slower deliberative path performs longer-horizon planning. Video prediction accuracy or robot success rates are key metrics, but do not always predict real-world performance. Generative world models such as Genie 3 combine these with a simulator. They accept text prompts or layouts and output consistent video, lidar, or three-dimensional scenes. World models often train with self-supervised learning. They use large unlabeled datasets of video or robot interactions. Self-supervised learning can speed learning. Reinforcement learning can fine-tune a model for specific tasks. == Applications == World models support robot learning. Agents train inside simulations and transfer skills to the physical world. This reduces the need for dangerous or expensive real-world trials. Autonomous vehicles use world models to test rare events. Waymo's system simulates tornadoes or unusual pedestrian behavior. Companies train planners without putting vehicles on public roads. Interactive entertainment benefits from world models. Genie 3 lets users generate playable environments from simple descriptions. Game studios prototype levels faster. Scientific simulation gains from these models. Researchers model physical systems or biological processes at scale. Planners in logistics or urban design test strategies inside accurate digital twins. == Comparison with large language models == Both world models and large language models (LLMs) use inferencing on their inputs to make predictions. LLMs operate on textual inputs. They predict the next token in text sequences. They excel at language-oriented tasks such as translation or summarization. However, they lack understanding of physics. World models operate on sensor inputs such as pixels. They predict state changes in that data in latent space. This design supports planning and causal reasoning. LLMs generate fluent text but often fail at consistent physical predictions. Their architecture employs transformers with refinements such as mixture of experts. World models divide an inferencing task into work performed by encoders, predictors, simulators, and other pieces. They typically handle multimodal inputs such as video, lidar, radar, and audio, guided by textual prompting. LLMs power chatbots and code assistants. World models drive embodied agents that act in dynamic environments, such as autonomous driving. The two may be combined in hybrid systems. For example, a LLM handles instructions, while a world model manages low-level control. World model proponents such as LeCun claim that because LLMs are trained only on text, they have no ability to predict anything beyond text, such as real-world events. == Benchmarks == World model benchmarks test physical understanding, long-term consistency, planning, and generalization from sensor data. Meta introduced three benchmarks for V-JEPA 2. IntPhys 2 measures a model's ability to detect physics violations. It presents pairs of videos that diverge when one breaks physical rules. Humans score near 100% accuracy. V-JEPA 2 achieves little better than random chance on many conditions. Minimal Video Pairs (MVPBench) tests physical understanding through multiple-choice questions based on short video clips. It probes object interactions and causality. Something-Something tests action recognition. Epic-Kitchens-100 tests human action anticipation. DeepMind benchmark: Interactive evaluation measures consistency over minutes of interaction, memory of off-screen objects, and response to user actions or text prompts. Waymo benchmark: Output generation quality: Metrics include realism, controllability (via text prompts), and usefulness for training planners in simulated worlds. However, pixel reconstruction error rate with episodic rewards often fails. Other: Epic-Kitchens-100 (often measured with Recall@5) Ego4D 50 Salads, Breakfast, etc. Potential benchmarks: Zero-shot transfer to robots Long-horizon planning Implausible prediction rate