Happiness computing based on large-scale online web data and machine learning methods is an emerging research topic that underpins a range of issues, from personal growth to social stability. Many advanced Machine Learning (ML) models with explanations are used to compute the happiness online assessment while maintaining high accuracy of results. However, domain knowledge constraints, such as the primary and secondary relations of happiness factors, are absent from these models, which limits the association between computing results and the right reasons for why they occurred. This article attempts to provide new insights into the explanation consistency from an empirical study perspective. Then we study how to represent and introduce domain knowledge constraints to make ML models more trustworthy. We achieve this through: (1) proving that multiple prediction models with additive factor attributions will have the desirable property of primary and secondary relations consistency, and (2) showing that factor relations with quantity can be represented as an importance distribution for encoding domain knowledge. Factor explanation difference is penalized by the Kullback-Leibler divergence-based loss among computing models. Experimental results using two online web datasets show that domain knowledge of stable factor relations exists. Using this knowledge not only improves happiness computing accuracy but also reveals more significative happiness factors for assisting decisions well.

Deep learning still has drawbacks in terms of trustworthiness, which describes a comprehensible, fair, safe, and reliable method. To mitigate the potential risk of AI, clear obligations associated to trustworthiness have been proposed via regulatory guidelines, e.g., in the European AI Act. Therefore, a central question is to what extent trustworthy deep learning can be realized. Establishing the described properties constituting trustworthiness requires that the factors influencing an algorithmic computation can be retraced, i.e., the algorithmic implementation is transparent. Motivated by the observation that the current evolution of deep learning models necessitates a change in computing technology, we derive a mathematical framework which enables us to analyze whether a transparent implementation in a computing model is feasible. We exemplarily apply our trustworthiness framework to analyze deep learning approaches for inverse problems in digital and analog computing models represented by Turing and Blum-Shub-Smale Machines, respectively. Based on previous results, we find that Blum-Shub-Smale Machines have the potential to establish trustworthy solvers for inverse problems under fairly general conditions, whereas Turing machines cannot guarantee trustworthiness to the same degree.

Is generative AI a degenerative AI? Creativity, exploratory, transformational, or combinational, could be an attribute of reality of LLMs. Machine creativity as computational creativity, artificial creativity, mechanical creativity, creative computing or creative computation is to complement human creativity. With powerful language machines, we have two types of creativity: Stochastic Creativity or Imitative Originality; Spontaneous Creativity or Real Originality; The first one is typical for narrow/weak AI models, combining the data points (tokens) probabilistically, manipulating petabytes of language data of various modalities. Creativity is stochastic if there is uncertainty or randomness involved in the outcomes. Stochastic is a synonym for random and probabilistic, although is different from non-deterministic.

Planting vegetation is one of the practical solutions for reducing sediment transfer rates. Increasing vegetation cover decreases environmental pollution and sediment transport rate (STR). Since sediments and vegetation interact complexly, predicting sediment transport rates is challenging. This study aims to predict sediment transport rate under vegetation cover using new and optimized versions of the group method of data handling (GMDH). Additionally, this study introduces a new ensemble model for predicting sediment transport rates. Model inputs include wave height, wave velocity, density cover, wave force, D50, the height of vegetation cover, and cover stem diameter. A standalone GMDH model and optimized GMDH models, including GMDH honey badger algorithm (HBA) GMDH rat swarm algorithm (RSOA)vGMDH sine cosine algorithm (SCA), and GMDH particle swarm optimization (GMDH-PSO), were used to predict sediment transport rates. As the next step, the outputs of standalone and optimized GMDH were used to construct an ensemble model. The MAE of the ensemble model was 0.145 m3/s, while the MAEs of GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GMDH in the testing level were 0.176 m3/s, 0.312 m3/s, 0.367 m3/s, 0.498 m3/s, and 0.612 m3/s, respectively. The Nash Sutcliffe coefficient (NSE) of ensemble model, GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GHMDH were 0.95 0.93, 0.89, 0.86, 0.82, and 0.76, respectively. Additionally, this study demonstrated that vegetation cover decreased sediment transport rate by 90 percent. The results indicated that the ensemble and GMDH-HBA models could accurately predict sediment transport rates. Based on the results of this study, sediment transport rate can be monitored using the IMM and GMDH-HBA. These results are useful for managing and planning water resources in large basins.

Billions of birds die annually from collisions with windows, communication towers, wind turbines, and other human-made objects. One reason is that birds see a reflection of the sky in the object and think they're flying into an unobstructed path. This is even a problem for solar panel facilities, which see up to 138,000 bird deaths per year in the US from collisions with equipment. Though damage to the solar panels is minimal, officials worry about the impact these structures have on local wildlife. To combat the problem, the Department of Energy (DOE) has awarded Argonne National Laboratory $1.3 million to develop a system that can automatically monitor bird activity.

Artificial intelligence (AI) has been the most far-flung goal of mankind since the birth of the computer. However, we can certainly say that we are closer to that goal than ever with the advent of new cognitive computing models. In a layman's terms, cognitive computing is a mashup of cognitive science and computing science, where cognitive science studies the human brain and how it works and computing science deals with the innovative ways of using computers for the betterment of the community. Cognitive computing systems are used to find solutions to complex situations where answers are uncertain or ambiguous, using computerized models that simulate the human cognition process. Although the term is often used alongside AI, it is closely related to Watson, IBM's cognitive computer system.

I recently attended the 45th ACM/IEEE International Symposium on Computer Architecture (ISCA) in Los Angeles, and was struck by the atmosphere of dramatic change in the field. First, a bit of perspective: For the last 15 years, computer architecture, and as a consequence computing as a whole, has been disrupted from below with the end of Dennard Scaling,1 producing a dramatic slowing in the growth of clock rate and single-thread performance, a shift to multicore, and the rise of throughput engines such GPUs. In addition, and in particular on mobile platforms, we have seen the rise of heavily customized architectures in mobile and embedded devices. These changes, precipitated by device and circuit-level effects, have rippled through the software stack compelling large-scale software rewrites, new compiler techniques and programming approaches, and the pervasive adoption of parallelism as a fundamental basis of performance. In the past three years, we have seen the effective end of Moore's Law scaling, with per transistor prices flat or increasing with recent technology nodes,2 and the slowing rate of advance to new process nodes at Intel (and across the industry).3

Researchers have developed an AI-based computing model which can count cells from histopathological cancer tumour images. A team from the University of Jyväskylä state that they have taken the first step towards developing a digital service centre based on artificial intelligence where doctors and pathologists analyse tumour tissue samples visually with the help of software. The computing model is able to determine the T-cell count in cancer tissue based on nothing but a digital image and with an error margin of a few percent, according to tests. The researchers tested the model against five 523 images of intestinal cancer tumours where previously, the T-cell count of each image was determined by histopathologists. The team found that the model was successful in 90% of cases.

Deep learning algorithms have recently gained attention due to their inherent capabilities and the application opportunities that they provide. Two of the main reasons for the success of deep learning methods are the availability of processing power and big data. Both of these two are expensive and rare commodities that present limitations to the usage and implementation of deep learning. Decentralization of the processing and data is one of the most prevalent solutions for these issues. This paper proposes a cooperative decentralized deep learning architecture. The contributors can train deep learning models with private data and share them to the cooperative data-driven applications initiated elsewhere. Shared models are fused together to obtain a better model. In this work, the contributors can both design their own models or train the models provided by the initiator. In order to utilize an efficient decentralized learning algorithm, blockchain technology is incorporated as a method of creating an incentive-compatible market. In the proposed method, Ethereum blockchain's scripting capabilities are employed to devise a decentralized deep learning mechanism, which provides much higher, collective processing power and grants access to large amounts of data, which would be otherwise inaccessible. The technical description of the mechanism is described and the simulation results are presented.

Intel realizes there will be a post-Moore's Law era and is already investing in technologies to drive computing beyond today's PCs and servers. The chipmaker is "investing heavily" in quantum and neuromorphic computing, said Brian Krzanich, CEO of Intel, during a question-and-answer session at the company's investor day on Thursday. "We are investing in those edge type things that are way out there," Krzanich said. To give an idea of how far out these technologies are, Krzanich said his daughter would perhaps be running the company by then. Researching in these technologies, which are still in their infancy, is something Intel has to do to survive for many more decades.