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Problem Statement: AI is hard to consume for non-experts, no formal way to trust the quality, and no formal way to encode and get paid for micro-contributions. SingularityNET is a decentralized marketplace for artificial intelligence. We aim to create the world's global brain with a full-stack AI solution powered by a decentralized protocol. We gathered the leading minds in machine learning and blockchain to democratize access to AI technology. Now anyone can take advantage of a global network of AI algorithms, services, and agents.

If you happened to be around the coast of Dubrovnik, Croatia in September 2021, you might have spotted two robots scouring the seafloor for debris. The robots were embarking on their inaugural mission and being tested in a real-world environment for the first time, to gauge their ability to perform certain tasks such as recognising garbage and manoeuvring underwater. 'We think that our project is the first one that will collect underwater litter in an automatic way with robots,' said Dr Bart De Schutter, a professor at Delft University of Technology in the Netherlands and coordinator of the SeaClear project. The robots are an example of new innovations being developed to clean up underwater litter. Oceans are thought to contain between 22 and 66 million tonnes of waste, which can differ in type from area to area, where about 94% of it is located on the seafloor.

AI in the mining industry can help increase productivity, reduce industrial waste, and enhance machine reliability!

Problem Statement: There is no existing Music platform combining AI and the Metaverse worlds powered by SingularityNET and Cardano blockchains. Solution: We are building a decentralised Music platform on Cardano by using the interactive power of the Metaverse integrated on AI & SingularityNet. SingularityNET is a decentralized marketplace for artificial intelligence. We aim to create the world's global brain with a full-stack AI solution powered by a decentralized protocol. We gathered the leading minds in machine learning and blockchain to democratize access to AI technology.

In the last decade, several studies have proposed the use of automated techniques to estimate the effort of agile software development. In this paper we perform a close replication and extension of a seminal work proposing the use of Deep Learning for agile effort estimation (namely Deep-SE), which has set the state-of-the-art since. Specifically, we replicate three of the original research questions aiming at investigating the effectiveness of Deep-SE for both within-project and cross-project effort estimation. We benchmark Deep-SE against three baseline techniques (i.e., Random, Mean and Median effort prediction) and a previously proposed method to estimate agile software project development effort (dubbed TF/IDF-SE), as done in the original study. To this end, we use both the data from the original study and a new larger dataset of 31,960 issues, which we mined from 29 open-source projects. Using more data allows us to strengthen our confidence in the results and further mitigate the threat to the external validity of the study. We also extend the original study by investigating two additional research questions. One evaluates the accuracy of Deep-SE when the training set is augmented with issues from all other projects available in the repository at the time of estimation, and the other examines whether an expensive pre-training step used by the original Deep-SE, has any beneficial effect on its accuracy and convergence speed. The results of our replication show that Deep-SE outperforms the Median baseline estimator and TF/IDF-SE in only very few cases with statistical significance (8/42 and 9/32 cases, respectively), thus confounding previous findings on the efficacy of Deep-SE. The two additional RQs revealed that neither augmenting the training set nor pre-training Deep-SE play a role in improving its accuracy and convergence speed. ...

Swimming microrobots are increasingly developed with complex materials and dynamic shapes and are expected to operate in complex environments in which the system dynamics are difficult to model and positional control of the microrobot is not straightforward to achieve. Deep reinforcement learning is a promising method of autonomously developing robust controllers for creating smart microrobots, which can adapt their behavior to operate in uncharacterized environments without the need to model the system dynamics. Here, we report the development of a smart helical magnetic hydrogel microrobot that used the soft actor critic reinforcement learning algorithm to autonomously derive a control policy which allowed the microrobot to swim through an uncharacterized biomimetic fluidic environment under control of a time varying magnetic field generated from a three-axis array of electromagnets. The reinforcement learning agent learned successful control policies with fewer than 100,000 training steps, demonstrating sample efficiency for fast learning. We also demonstrate that we can fine tune the control policies learned by the reinforcement learning agent by fitting mathematical functions to the learned policy's action distribution via regression. Deep reinforcement learning applied to microrobot control is likely to significantly expand the capabilities of the next generation of microrobots.

The tools and technology that are currently used to analyze chemical compound structures that identify polymer types in microplastics are not well-calibrated for environmentally weathered microplastics. Microplastics that have been degraded by environmental weathering factors can offer less analytic certainty than samples of microplastics that have not been exposed to weathering processes. Machine learning tools and techniques allow us to better calibrate the research tools for certainty in microplastics analysis. In this paper, we investigate whether the signatures (Raman shift values) are distinct enough such that well studied machine learning (ML) algorithms can learn to identify polymer types using a relatively small amount of labeled input data when the samples have not been impacted by environmental degradation. Several ML models were trained on a well-known repository, Spectral Libraries of Plastic Particles (SLOPP), that contain Raman shift and intensity results for a range of plastic particles, then tested on environmentally aged plastic particles (SloPP-E) consisting of 22 polymer types. After extensive preprocessing and augmentation, the trained random forest model was then tested on the SloPP-E dataset resulting in an improvement in classification accuracy of 93.81% from 89%.

The increasing automation in many areas of the Industry expressly demands to design efficient machine-learning solutions for the detection of abnormal events. With the ubiquitous deployment of sensors monitoring nearly continuously the health of complex infrastructures, anomaly detection can now rely on measurements sampled at a very high frequency, providing a very rich representation of the phenomenon under surveillance. In order to exploit fully the information thus collected, the observations cannot be treated as multivariate data anymore and a functional analysis approach is required. It is the purpose of this paper to investigate the performance of recent techniques for anomaly detection in the functional setup on real datasets. After an overview of the state-of-the-art and a visual-descriptive study, a variety of anomaly detection methods are compared. While taxonomies of abnormalities (e.g. shape, location) in the functional setup are documented in the literature, assigning a specific type to the identified anomalies appears to be a challenging task. Thus, strengths and weaknesses of the existing approaches are benchmarked in view of these highlighted types in a simulation study. Anomaly detection methods are next evaluated on two datasets, related to the monitoring of helicopters in flight and to the spectrometry of construction materials namely. The benchmark analysis is concluded by recommendation guidance for practitioners.

Process mining has evolved into a mainstream approach to discover and improve business processes, and its market is projected to grow by 40-50% by passing $1 billion in 2022. It is being applied to numerous sectors and departments, ranging from healthcare to logistics. Case studies have shown that retailers, telco, and finance companies were some of the top beneficiaries of process mining. In this article, we explore experts' opinions, and we leverage our own research to predict process mining trends in 2022, and how businesses can benefit from these trends. Wil van der Aalst, the founder of process mining, states that he observes a shift towards more integrated tools and capabilities in the process mining market.