Bias in AI refers to the presence of unfair or unjustifiable assumptions or preferences in the decision-making processes of an AI system. These biases can arise from various sources, including the data used to train the AI, the algorithms used to process that data, or even the biases of the individuals who design and develop the AI system. One of the primary ways in which bias can arise in AI is through the data used to train the system. If the data used to train an AI system is biased towards one particular group of people, it may lead to the system making biased decisions that disadvantage or discriminate against other groups. For example, if an AI system is trained on data that predominantly represents one particular group of people, it may make biased decisions that disadvantage or discriminate against other groups.

Abstract: A well formed query is defined as a query which is formulated in the manner of an inquiry, and with correct interrogatives, spelling and grammar. While identifying well formed queries is an important task, few works have attempted to address it. In this paper we propose transformer based language model -- Bidirectional Encoder Representations from Transformers (BERT) to this task. We further imbibe BERT with parts-of-speech information inspired from earlier works. Furthermore, we also train the model in multiple curriculum settings for improvement in performance.

AI was used for the detection and quantification of COVID-19 cases from chest x-ray and CT scan images . Researchers have developed a deep learning model called COVID-19 detection neural network (COVNet), for differentiating between COVID-19 and community-acquired pneumonia based on visual 2D and 3D features extracted from volumetric chest CT scan Singh et al. developed a novel deep learning model using MultiObjective Differential Evolution and convolutional neural networks for COVID-19 diagnosis using a chest CT Unprecedented pace of efforts to address the COVID-19 pandemic situation is leveraged by big data and artificial intelligence (AI). Various offshoots of AI have been used in several disease outbreaks earlier. AI can play a vital role in the fight against COVID-19. AI is being successfully used in the identification of disease clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, diagnosis of COVID-19, disease management by resource allocation, facilitating training, record maintenance and pattern recognition for studying the disease trend.

When we talk about artificial intelligence (AI) most people still imagine robots who can talk, act, and behave (to a certain extent) like a human being -- like a C-3PO (Star Wars), sans the metallic look. Or maybe, a supercomputer that can read human behavior so well that it interacts seamlessly with us, while controlling the system -- like Hal 9000 (2001: A Space Odyssey) or Auto (Wall-E). While, arguably, we may not be there yet in terms of our command of AI, we are not that far. AI is definitely the direction tech development is taking, as evidenced by most recent trends, including the formation of a partnership by tech giants to push the frontier of AI. While we may not be nearing the Singularity, AI has taken leaps and bounds of improvement over the past few years alone.