Lifelong Person Re-Identification (LReID) aims to continuously learn from successive data streams, matching individuals across multiple cameras. The key challenge for LReID is how to effectively preserve old knowledge while incrementally learning new information, which is caused by task-level domain gaps and limited old task datasets. Existing methods based on CNN backbone are insufficient to explore the representation of each instance from different perspectives, limiting model performance on limited old task datasets and new task datasets. Unlike these methods, we propose a Diverse Representations Embedding (DRE) framework that first explores a pure transformer for LReID. The proposed DRE preserves old knowledge while adapting to new information based on instance-level and task-level layout. Concretely, an Adaptive Constraint Module (ACM) is proposed to implement integration and push away operations between multiple overlapping representations generated by transformer-based backbone, obtaining rich and discriminative representations for each instance to improve adaptive ability of LReID. Based on the processed diverse representations, we propose Knowledge Update (KU) and Knowledge Preservation (KP) strategies at the task-level layout by introducing the adjustment model and the learner model. KU strategy enhances the adaptive learning ability of learner models for new information under the adjustment model prior, and KP strategy preserves old knowledge operated by representation-level alignment and logit-level supervision in limited old task datasets while guaranteeing the adaptive learning information capacity of the LReID model. Compared to state-of-the-art methods, our method achieves significantly improved performance in holistic, large-scale, and occluded datasets.

Marco Iansiti and Karin Lakhani write in their book, Competing in the Age of AI, "the learning algorithms at the heart of new digital systems can be misused to tailor, optimize and amplify inaccurate and harmful information from targeting and shaping misleading ads to creating highly realistic fake social personas that are used to extract personal information from users." The question is what should CIOs and other data leaders do to protect enterprises and their key stakeholders? CIO Anthony McMahon of Target State Consulting suggests in a recent #CIOChat on Twitter that ethical and privacy issues are not unique to artificial intelligence (AI). "Every decision on how to use data, either in platform or offline, has an ethical consideration," he said. Other data leaders claim there are all sorts of unique ethical and privacy issues to AI. Privacy is particularly important when a piece of data becomes connected in a new and novel way.

More and more tech companies have initiatives in place to support Diversity, Equity & Inclusion (DEI) work. But even as Chief Diversity Officers get hired and diversity statements make their way onto company websites, diverse representation in tech is still lagging. This representation deficit, particularly in product and engineering departments, has huge implications. With the current population of software engineers comprising 25% women, 7.3% Latinos and 4.7% Black people, the teams building technology are not adequately representing the people using it. Artificial Intelligence (AI) is an area of computer science that focuses on enabling computers to perform tasks that have traditionally required human intelligence.