Welcome! to Issue One of the Tomorrow's Tech Today newsletter accompanying the podcast series with the full discussion related to this piece available here. The world is looking for organizations who stand for something. Indeed recent research by the Edelman Trust Barometer 2021 shows people now typically trust your business more than their country's government, news media, and even NGOs. So as we reflect on our aspirations and indeed resolutions for 2022 and the'moments which matter' to take forward from last year, the rise in prominence of Sustainability as a core component of business model evolution, alongside – the Diversity, Equality, Inclusion and Belonging (DEIB) – continuum is personally very much front of mind. Especially building on attending events such as COP26, the Social Innovation Forum, Re:Invent and now CES2022 where this has been center stage.

To assess how well a brain MRI lesion segmentation algorithm trained at one institution performed at another institution, and to assess the effect of multi-institutional training datasets for mitigating performance loss. In this retrospective study, a three-dimensional U-Net for brain MRI abnormality segmentation was trained on data from 293 patients from one institution (IN1) (median age, 54 years; 165 women; patients treated between 2008 and 2018) and tested on data from 51 patients from a second institution (IN2) (median age, 46 years; 27 women; patients treated between 2003 and 2019). The model was then trained on additional data from various sources: (a) 285 multi-institution brain tumor segmentations, (b) 198 IN2 brain tumor segmentations, and (c) 34 IN2 lesion segmentations from various brain pathologic conditions. All trained models were tested on IN1 and external IN2 test datasets, assessing segmentation performance using Dice coefficients. Performance was lower when tested at an external institution (median Dice score, 0.70 [IN2] vs 0.76 [IN1]).

Non-fungible token (NFT) sales in 2021 were worth close to $25 billion (roughly Rs. 1,85,722 crore), proving that the sector saw tremendous growth within crypto assets, according to CoinMarketCap. Artificial Intelligence-generated NFTs are expected to be the next big boom within the space. As the name suggests, artificial intelligence Art or AI-generated art refers to any artwork created through the use of artificial intelligence and with companies like OpenAI launching its artificial intelligence programs that creates images from textual descriptions, AI art is more accessible than ever. British auction house Christie's during its fourth annual Art Tech Summit in August 2021 estimated a value of $93 million (roughly Rs. 691 crore) in NFT sales, suggesting that art NFTs have had a huge influence in 2021. Despite their importance, project like CryptoPunks and Bored Ape Yacht Club seem to have grabbed centre stage in the crypto art scene, dominated by cartoons and memes.

NLP can be broadly defined as the automatic manipulation of natural language like text and speech by software. Our expert trainers are always motivated to share the best information followed in the industry and also solve your queries at any given time. Our NLP program online is packed with all the examples and real-time-based scenario projects for practical. Our live instructor-led classes give you the best learning environment with classes being more interactive and engaging. You will learn about logistic regression, dynamic programming, produce insights from text and audio, and many more key concepts.

Digital twins are emerging as powerful tools for supporting innovation as well as optimizing the in-service performance of a broad range of complex physical machines, devices, and components. A digital twin is generally designed to provide accurate in-silico representation of the form (i.e., appearance) and the functional response of a specified (unique) physical twin. This paper offers a new perspective on how the emerging concept of digital twins could be applied to accelerate materials innovation efforts. Specifically, it is argued that the material itself can be considered as a highly complex multiscale physical system whose form (i.e., details of the material structure over a hierarchy of material length and time scales) and function (i.e., response to external stimuli typically characterized through suitably defined material properties) can be captured suitably in a digital twin. This paper establishes the foundational concepts and frameworks needed to formulate and continuously update both the form and function of the digital twin of a selected material physical twin. The form of the proposed material digital twin can be captured effectively using the broadly applicable framework of n-point spatial correlations, while its function at the different length scales can be captured using homogenization and localization process-structure-property (PSP) surrogate models calibrated to collections of available experimental and physics-based simulation data.

With the continuous increase of installed capacity of wind power, the influence of large-scale wind power integration on the power grid is becoming increasingly apparent. Ultra-short-term wind power prediction is conducive to the dispatching management of the power grid, and improves the operating efficiency and economy of the power system. In order to overcome the intermittency and uncertainty of wind power generation, this paper proposes the DE-BP (Dfferential Evolution-Back Propagation) algorithm to predict wind power, and addresses such shortcomings of BP neural network as its falling into local optimality and slow training speed when predicting. In this paper, the differential evolution algorithm is used to find the optimal value of the initial weight and threshold of the BP neural network, and the DE-BP neural network prediction model is obtained. According to the data of a wind farm in Northwest China, the short-term wind power is predicted. Compared with the application of the BP model in wind power prediction, the results show that the accuracy of the DE-BP algorithm is improved by about 5%; Compared with the GA-BP(Genetic Algorithm-Back Propagation) model, the prediction time is shortened by 23.1%.

This paper offers a new hybrid probably approximately correct (PAC) reinforcement learning (RL) algorithm for Markov decision processes (MDPs) that intelligently maintains favorable features of both model-based and model-free methodologies. The designed algorithm, referred to as the Dyna-Delayed Q-learning (DDQ) algorithm, combines model-free Delayed Q-learning and model-based R-max algorithms while outperforming both in most cases. The paper includes a PAC analysis of the DDQ algorithm and a derivation of its sample complexity. Numerical results are provided to support the claim regarding the new algorithm's sample efficiency compared to its parents as well as the best-known PAC model-free and model-based algorithms in application. A real-world experimental implementation of DDQ in the context of pediatric motor rehabilitation facilitated by infant-robot interaction highlights the potential benefits of the reported method.

Tactile sensing endows the robots to perceive certain physical properties (which are not directly viable to visual and acoustic sensors) of the object in contact. Robots with tactile perception are able to identify different textures of the object touched. Interestingly, textures of fine micro-geometry beyond the nominal resolution of the tactile sensors, can also be identified through exploratory robotic movements like sliding and rubbing. To study the problem of fine texture classification via robotic sliding, we design a robotic sliding experiment using daily fabrics (as fabrics are likely to be the most common materials of fine textures). We propose a feature extraction process to encode the acquired tactile signals (in the form of time series) into a low dimensional (<= 7D) feature vector. The vector captures the frequency signature of a fabric texture such that distinctive fabrics can be classified by their correspondent feature vectors. The experiment includes multiple combinations of sliding parameters, i.e., speed and pressure, for the investigation into the correlation between sliding parameters and the generated feature space. Results show that changing the contact pressure can greatly affect the significance of the extracted feature vectors. For our specific sensor used in the experiments, there exists a sweet spot of pressure for the fabric classification task. Adversely, variation of sliding speed shows no apparent impact on the performance of the feature ext...

The world of search and user intent is more than just digital marketing and analytics, it has now evolved into a science. Once you have a solution to a pain point and you solve one minuscule issue from a massive compilation of data, consider yourself a professional who loves solutions. In the year 2022, we'll want to continue to advance and adapt with digital marketing shifts and customer wants and needs. With these two ideas, the world of AI aka artificial intelligence, is born. What we as digital marketers need to figure out is what is the pain point?

It seems like AI is constantly in the news these days. However, it can be challenging for people who are not experts in AI to precisely understand how this technology will change our lives. As an AI art curator, I'm writing this article to connect some dots for readers who don't know much about AI and its impact on the arts.