To alleviate the shortage of computing power faced by clients in training deep neural networks (DNNs) using federated learning (FL), we leverage the edge computing and split learning to propose a model-splitting allowed FL (SFL) framework, with the aim to minimize the training latency without loss of test accuracy. Under the synchronized global update setting, the latency to complete a round of global training is determined by the maximum latency for the clients to complete a local training session. Therefore, the training latency minimization problem (TLMP) is modelled as a minimizing-maximum problem. To solve this mixed integer nonlinear programming problem, we first propose a regression method to fit the quantitative-relationship between the cut-layer and other parameters of an AI-model, and thus, transform the TLMP into a continuous problem. Considering that the two subproblems involved in the TLMP, namely, the cut-layer selection problem for the clients and the computing resource allocation problem for the parameter-server are relative independence, an alternate-optimization-based algorithm with polynomial time complexity is developed to obtain a high-quality solution to the TLMP. Extensive experiments are performed on a popular DNN-model EfficientNetV2 using dataset MNIST, and the results verify the validity and improved performance of the proposed SFL framework.

The Stout AgTech autonomous cultivator uses machine vision and AI to identify weeds. The smart implement then uses articulated "blades" to cut the undesired plants from their root systems. Today, farmers have a number of options for cultivating their crops. Herbicides and genetically modified crops are one cultivation method that has increasingly gone out of fashion due primarily to market pressure from consumers for organically grown crops. For organic-certified farms, the only option for cultivation is to use a mechanical means of removing weeds.

Artificial Intelligence will significantly impact the work environment of radiologists. I suggest that up to 50% of a radiologists work in 2021 will be performed by AI-models in 2025. However, it won't increase beyond that 50% level, as radiologists remain key for human-centered aspects of their job. I project that few to no radiologists will be laid off in China due to the existing supply shortage of radiology services in 2021. The application of AI in radiology could contribute 1.7 billion USD to China's GDP in 2025. It will further allow radiologists to start productive work up to four years earlier. AI in radiology will positively impact the health of patients and radiologists themselves.

By means of a local surrogate approach, an analytical method to yield explanations of AI-predictions in the framework of regression models is defined. In the case of the AI-model producing additive corrections to the predictions of a base model, the explanations are delivered in the form of a shift of its interpretable parameters as long as the AI- predictions are small in a rigorously defined sense. Criteria are formulated giving a precise relation between lost accuracy and lacking model fidelity. Two applications show how physical or econometric parameters may be used to interpret the action of neural network and random forest models in the sense of the underlying base model. This is an extended version of our paper presented at the ISM 2020 conference, where we first introduced our new approach BAPC.

In the past twenty years, the healthcare sector has is revolutionised with technology. MRI, CT scan and X-ray are some of the excellent techniques that are employed for quick diagnosis of diseases. In fact if we have to discern the present scenario, an ideal way to detect the COVID 19 symptoms would be with the help of chest X-rays. Though these methods entail an enormous amount of investment, they serve the purpose. However, one of the drawbacks while using this technique is that they are time-consuming.

The way humans interact, communicate and share, are transforming rapidly. The pace at which AI is replacing the way humans work forecast the future to be fully automated even it seems to extinct the jobs. From homes to offices, Artificial intelligence will be penetrating such that the ratio of automation will increase and ultimately affect human jobs. Industries like banking, e-commerce, social media, estate brokers, gaming and insurance companies, all are taking advantage of AI-based solutions that conclude to be less expensive as compared to physical efforts. Artificial intelligence is used with a blend of many other strong and innovative technologies and algorithms and gives shape to state of the art solutions that no business can deny. Solving real-world problems – the way human does, they think.