The development of Digital Twins (DTs) represents a transformative advance for simulating and optimizing complex systems in a controlled digital space. Despite their potential, the challenge of constructing DTs that accurately replicate and predict the dynamics of real-world systems remains substantial. This paper introduces an intelligent framework for the construction and evaluation of DTs, specifically designed to enhance the accuracy and utility of DTs in testing algorithmic performance. We propose a novel construction methodology that integrates deep learning-based policy gradient techniques to dynamically tune the DT parameters, ensuring high fidelity in the digital replication of physical systems. Moreover, the Mean STate Error (MSTE) is proposed as a robust metric for evaluating the performance of algorithms within these digital space. The efficacy of our framework is demonstrated through extensive simulations that show our DT not only accurately mirrors the physical reality but also provides a reliable platform for algorithm evaluation. This work lays a foundation for future research into DT technologies, highlighting pathways for both theoretical enhancements and practical implementations in various industries.

The rapid development of information technology, especially the Internet, has facilitated users with a quick and easy way to seek information. With these convenience offered by internet services, many individuals who initially invested in gold and precious metals are now shifting into digital investments in form of cryptocurrencies. However, investments in crypto coins are filled with uncertainties and fluctuation in daily basis. This risk posed as significant challenges for coin investors that could result in substantial investment losses. The uncertainty of the value of these crypto coins is a critical issue in the field of coin investment. Forecasting, is one of the methods used to predict the future value of these crypto coins. By utilizing the models of Long Short Term Memory, Support Vector Machine, and Polynomial Regression algorithm for forecasting, a performance comparison is conducted to determine which algorithm model is most suitable for predicting crypto currency prices. The mean square error is employed as a benchmark for the comparison. By applying those three constructed algorithm models, the Support Vector Machine uses a linear kernel to produce the smallest mean square error compared to the Long Short Term Memory and Polynomial Regression algorithm models, with a mean square error value of 0.02. Keywords: Cryptocurrency, Forecasting, Long Short Term Memory, Mean Square Error, Polynomial Regression, Support Vector Machine

Data-driven intelligent computational design (DICD) is a research hotspot emerged under the context of fast-developing artificial intelligence. It emphasizes on utilizing deep learning algorithms to extract and represent the design features hidden in historical or fabricated design process data, and then learn the combination and mapping patterns of these design features for the purposes of design solution retrieval, generation, optimization, evaluation, etc. Due to its capability of automatically and efficiently generating design solutions and thus supporting human-in-the-loop intelligent and innovative design activities, DICD has drawn the attentions from both academic and industrial fields. However, as an emerging research subject, there are still many unexplored issues that limit the development and application of DICD, such as specific dataset building, engineering design related feature engineering, systematic methods and techniques for DICD implementation in the entire product design process, etc. In this regard, a systematic and operable road map for DICD implementation from full-process perspective is established, including a general workflow for DICD project planning, an overall framework for DICD project implementation, the computing mechanisms for DICD implementation, key enabling technologies for detailed DICD implementation, and three application scenarios of DICD. The road map reveals the common mechanisms and calculation principles of existing DICD researches, and thus it can provide systematic guidance for the possible DICD applications that have not been explored.

Today is the 28th of June. As usual we are having this June AMA with our CEO Mr. Owen Tao. And just for your information, Owen has just been released from a 17 days quarantine because he has been identified as a cross contact, and as a result of visiting a shopping mall. So why don't you, Owen, share with us what's the life like being quarantined in a hotel, is it a hotel? So the environment is good, yeah?

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner How Important is Data, for #AutonomousDriving Technology? The data output of intelligent driving sensors is generally divided into the following three types, The first is obstacle detection, obstacle tracking, and multi-sensor fusion. In addition to the basic obstacle labeling capabilities of monocular and binocular cameras, fisheye cameras, and surround-view cameras, lidar point cloud data with different beams ranging from 4 lines to 128 lines can be annotated. In terms of multi-sensor detection (including lidar, camera fusion, and sensors such as millimeter-wave radar), multi-sensor annotation is feasible as well. The second type is the environment perception outside the car and lane information.

If you're an AI professional or aspire to be one, one thing you must be aware of is: machine learning algorithms are your closest aid and ally. These algorithms can also be annoying. Given that there is a multitude of algorithms. The knowledge of algorithms is essential to be an effective AI engineer, data scientist, and machine learning engineer. To give you a gist of how these algorithms work, let's get down to know these algorithms.

Artificial intelligence will play a pivotal role in the future of information security. By combining big data, deep learning, and machine learning, AI give machines life; they can imitate human learning, replicate work behaviors, and bring new ways to operate businesses. However, AI assets are very valuable, making them the target of hackers. Once a hacker has an opportunity to discern how the AI model is trained and operated, the model can be easily manipulated. For instance, hackers can destroy the data in the training model, causing major disruption in both the supply and demand side of the entire AI system.