Indoor navigation is challenging due to the absence of satellite positioning. This challenge is manifold greater for Visually Impaired People (VIPs) who lack the ability to get information from wayfinding signage. Other sensor signals (e.g., Bluetooth and LiDAR) can be used to create turn-by-turn navigation solutions with position updates for users. Unfortunately, these solutions require tags to be installed all around the environment or the use of fairly expensive hardware. Moreover, these solutions require a high degree of manual involvement that raises costs, thus hampering scalability. We propose an image dataset and associated image-centric solution called NaVIP towards visual intelligence that is infrastructure-free and task-scalable, and can assist VIPs in understanding their surroundings. Specifically, we start by curating large-scale phone camera data in a four-floor research building, with 300K images, to lay the foundation for creating an image-centric indoor navigation and exploration solution for inclusiveness. Every image is labelled with precise 6DoF camera poses, details of indoor PoIs, and descriptive captions to assist VIPs.

The current paper presents the development and validation of SelfScore, a novel benchmark designed to assess the performance of automated Large Language Model (LLM) agents on help desk and professional consultation tasks. Given the increasing integration of AI in industries, particularly within customer service, SelfScore fills a crucial gap by enabling the comparison of automated agents and human workers. The benchmark evaluates agents on problem complexity and response helpfulness, ensuring transparency and simplicity in its scoring system. The study also develops automated LLM agents to assess SelfScore and explores the benefits of Retrieval-Augmented Generation (RAG) for domain-specific tasks, demonstrating that automated LLM agents incorporating RAG outperform those without. All automated LLM agents were observed to perform better than the human control group. Given these results, the study raises concerns about the potential displacement of human workers, especially in areas where AI technologies excel. Ultimately, SelfScore provides a foundational tool for understanding the impact of AI in help desk environments while advocating for ethical considerations in the ongoing transition towards automation.

This paper proposes TaDaa: Ticket Assignment Deep learning Auto Advisor, which leverages the latest Transformers models and machine learning techniques quickly assign issues within an organization, like customer support, help desk and alike issue ticketing systems. The project provides functionality to 1) assign an issue to the correct group, 2) assign an issue to the best resolver, and 3) provide the most relevant previously solved tickets to resolvers. We leverage one ticketing system sample dataset, with over 3k+ groups and over 10k+ resolvers to obtain a 95.2% top 3 accuracy on group suggestions and a 79.0% top 5 accuracy on resolver suggestions. We hope this research will greatly improve average issue resolution time on customer support, help desk, and issue ticketing systems.

To get the most out of AI and ML to better meet changing customer needs, enterprises need to start integrating their business units, operations and datasets into a more consolidated entity. This is according to Archana Arakkal, Machine Learning Engineer at Synthesis, who was speaking ahead of a webinar on the Customer Service of the Future, to be hosted by Synthesis, AWS and Salesforce next month. Every engagement between the customer and the brand is part of the overall customer experience, and customers expect a great deal more of this experience than they did 10 years ago, says Arakkal. "For example, traditional marketing and advertising has had to evolve beyond the old'spray and pray' approach, since customers now expect hyper-personalisation," she says. "Marketing is getting smarter about targeting customers based on their personal needs, their digital footprint, what platforms they use at what time of day. Customers aren't blind – they know brands have customer data on what they have bought before, their location and interests, so there is an inherent expectation that when they are targeted with a product, it will be a product that makes sense to them." While marketing and advertising are becoming better at personalising offers, there often remains a gap in their understanding of the customer – in the silos of data within the various brand business units, Arakkal says.

Pat Calhoun, a visionary leader focused on UX and adoption, is the CEO and Founder of Espressive, transforming enterprise self-help with AI. One of the most dramatic workplace shifts caused by the pandemic is the escalation of digital transformation initiatives. The numbers say it all. According to research by Twilio, 79% of digital transformation budgets grew in response to the pandemic -- and 26% grew "dramatically." Gartner, Inc. also found that over 80% of CEOs have a digital transformation program underway, and 69% are using Covid-19 as a catalyst to focus on resigning their businesses.

If you work for a big company, you know how frustrating it can be to keep track of all the different systems and applications you need to do your job. Where do you go for your expense reports, your performance review, your GDPR compliance training, your open enrollment? What if you haven't been to that particular application in several months and don't remember your password? Do you have to go to a different application or maybe open a service ticket with IT to reset your password for the first application? How many hours have you wasted navigating to the right places and figuring out again how they work?

Artificial intelligence (AI) technologies are changing the way business is done. From improving predictive analytics across marketing, cybersecurity and risk management to powering a wide range of business decisions and actions to reduce costs and grow revenue, AI is a major engine for transformation within enterprises. IDC predicts that spending on AI systems and solutions will experience a booming cumulative annual growth rate (CAGR) of 20.1% over a four-year period from 2019-2024. AI is expected to drive tremendous growth in global productivity. Given these high expectations, companies are asking themselves how they should bring AI into their enterprise.

Artificial intelligence is one of the biggest markets for growth within the field of technology today. In fact, AI is rapidly empowering us to make major changes to various fields within the realm of technology. Help desk is no stranger to the idea that there is room for improvement within this niche of technology. Businesses use help desk software to manage a variety of different types of information. From customers' questions and concerns to employee computer repair requests, help desk is a solution for organizing, responding to, and gathering results from each of those individual tickets that are completed. If you utilize a help desk for your own business, then you may have wondered how help desk could be changing in the near future.

Artificial intelligence is one of the biggest markets for growth within the field of technology today. In fact, AI is rapidly empowering us to make major changes to various fields within the realm of technology. Help desk is no stranger to the idea that there is room for improvement within this niche of technology. Businesses use help desk software to manage a variety of different types of information. From customers' questions and concerns to employee computer repair requests, help desk is a solution for organizing, responding to, and gathering results from each of those individual tickets that are completed. If you utilize a help desk for your own business, then you may have wondered how help desk could be changing in the near future.

A popular KPI for IT services is the mean time to recovery (MTTR) -- the time it takes to resolve an incident. It is one of the most critical help desk metrics, as the longer an issue takes to resolve, the more frustrated -- and less productive -- an end user will be. Another machine learning use case in IT operations is reduced MTTR. For instance, an end user calls the help desk and complains about receiving a blue screen of death. A machine learning model evaluates that user's device data and finds the likely cause is associated with a recent Windows update. This helps the technician get to the root of the issue, and therefore solve the user's issue more quickly.