A panel of executives spoke at the TIME100 AI Leadership Forum on Wednesday night in New York City about the ways artificial intelligence is reshaping the business landscape, and how they're shepherding their companies into a technologically capricious future. Included on the panel at the TIME forum, which spotlighted AI-driven business leadership, were Nigel Vaz, the chief executive officer of Publicis Sapient, a tech-consulting firm that uses AI to help modernize business and a sponsor of Wednesday's event; Deepa Soni, the executive vice president and chief information officer of New York Life Insurance Company; and Ravi Radhakrishnan, the executive vice president and chief information officer of American Express. Vaz began the conversation discussing the "exponential" capability of AI to transform and enhance companies' abilities to problem solve and become more efficient. For his company, AI is a tool used to extract value and optimize performance for clients by reducing time and cost. Many of them, he notes, must bridge the gap between their relatively outdated technology and increasingly more useful AI tools--what he referred to as their "tech debt."

Cross-modal person re-identification (Re-ID) is critical for modern video surveillance systems. The key challenge is to align cross-modality representations induced by the semantic information present for a person and ignore background information. This work presents a novel convolutional neural network (CNN) based architecture designed to learn semantically aligned cross-modal visual and textual representations. The underlying building block, named AXM-Block, is a unified multi-layer network that dynamically exploits the multi-scale knowledge from both modalities and re-calibrates each modality according to shared semantics. To complement the convolutional design, contextual attention is applied in the text branch to manipulate long-term dependencies. Moreover, we propose a unique design to enhance visual part-based feature coherence and locality information. Our framework is novel in its ability to implicitly learn aligned semantics between modalities during the feature learning stage. The unified feature learning effectively utilizes textual data as a super-annotation signal for visual representation learning and automatically rejects irrelevant information. The entire AXM-Net is trained end-to-end on CUHK-PEDES data. We report results on two tasks, person search and cross-modal Re-ID. The AXM-Net outperforms the current state-of-the-art (SOTA) methods and achieves 64.44\% Rank@1 on the CUHK-PEDES test set. It also outperforms its competitors by $>$10\% in cross-viewpoint text-to-image Re-ID scenarios on CrossRe-ID and CUHK-SYSU datasets.

On the first day of the Association of Data Scientist's (ADaSci) Deep Learning DevCon 2021 (DLDC), Radhakrishnan G, Head- Global Commercial and Merchant Risk Decision Science at American Express (Amex), spoke about how his company helps small businesses with real-time credit decisioning using machine learning and artificial intelligence. Radhakrishnan is an alumnus of Management Development Institute, Gurugram. Throughout his almost two-decade-long ongoing stint at American Express, Radhakrishnan has been associated with risk management. His current role as the Head of Global Commercial and Merchant Risk Data Science and Risk Models across customer life cycle for card and non-card portfolios involves leading a team of more than 80 data and decision scientists across the globe. Radhakrishnan began his talk by introducing the audience by providing insights into the financial services company American Express.

American Express (Amex) is a globally integrated payments company, providing customers with access to products, insights and experiences that enrich lives and build business success. And inside the company, the Amex Credit Fraud Risk business unit's mission is all about minimising credit and fraud losses while promoting business growth and delivering superior customer service. Nothing about this will surprise you so far, we're presuming. What may: while the financial services industry uses digital for just about every process imaginable, there's one surprising remaining exception-the commercial card underwriting process, which to you and me is'Are you going to lend my small business any money?' In a lot of Europe, this process is still completely manual and takes an underwriter a good chunk of time to complete.

As it is, the world is unfair. The question now is, do we want automated tech to be unfair too? As we build more and more AI-dependent smart digital infrastructure in our cities and beyond, we have pretty much overlooked the emerging character of artificial intelligence that would have a profound bearing on our nature and future. Are we happy with algorithms making decisions for us? Naturally, one would expect the algorithm to possess discretion.