With a growing demand for customer engagement, conversational AI improves user experience and business communications as well as provides opportunities for more conversion rates, better revenues, and customer satisfaction. Creative Virtual’s Founder and CEO, Chris Ezekiel, recently talked with Sadika Sayyad and shared his views on the topic “Enhance User Experience with Conversational AI” Sadika: Brief us about your company and offerings. Chris Ezekiel: Creative Virtual is a world leader in conversational AI for customer and employee engagement. As a pioneer in the industry, we have played a pivotal role in the evolution of this technology and the ways it...

HR organizations are faced with a widening skills gap, economic headwinds and changing expectations around work. It's no surprise, then, that burnout and exhaustion are widespread in HR, with one survey finding that 42% of teams are struggling under the weight of too many projects and responsibilities. Change starts with personnel and management, some might argue. While there's a fair amount of dissatisfaction with HR tech vendors (at least according to some data), to be fair to the tech-positive crowd, many companies see real value in HR tech. According to a recent Sapient report, over half of businesses with more than 500 employees plan to increase HR tech spending by an average of 21% into the coming year.

Automated rule checking (ARC), which is expected to promote the efficiency of the compliance checking process in the architecture, engineering, and construction (AEC) industry, is gaining increasing attention. Throwing light on the ARC application hotspots and forecasting its trends are useful to the related research and drive innovations. Therefore, this study takes the patents from the database of the Derwent Innovations Index database (DII) and China national knowledge infrastructure (CNKI) as data sources and then carried out a three-step analysis including (1) quantitative characteristics (i.e., annual distribution analysis) of patents, (2) identification of ARC topics using a latent Dirichlet allocation (LDA) and, (3) SNA-based co-occurrence analysis of ARC topics. The results show that the research hotspots and trends of Chinese and English patents are different. The contributions of this study have three aspects: (1) an approach to a comprehensive analysis of patents by integrating multiple text mining methods (i.e., SNA and LDA) is introduced ; (2) the application hotspots and development trends of ARC are reviewed based on patent analysis; and (3) a signpost for technological development and innovation of ARC is provided.

Artificial intelligence is taking over almost every industry. The investment and finance industry is no exception. In Deloitte's 2019 report, the firm reveals that AI is transforming the financial ecosystem to reduce costs and make operations more efficient by providing automated insights and alternative data, analysis and risk management. Technology such as AI has digitized the finance sector, ranging from payments and remittances to lending. However, asset management is still in the nascent stage of digitization, according to the chief strategy officer and co-founder of Akros Technologies, Jin Chung.

Regie.ai, a startup using OpenAI's GPT-3 text-generating system to create sales and marketing content for brands, today announced that it raised $10 million in Series A funding led by Scale Venture Partners with participation from Foundation Capital, South Park Commons, Day One Ventures and prominent angel investors. The fresh investment comes as VCs see a growing opportunity in AI-powered, copy-generating adtech companies, whose tech promise to save time while potentially increasing personalization. Previously a software engineer at Google and Meta, Sridhar is a data scientist by trade, having developed enterprise-scale AI systems that detect duplicate images and rank search results. Millen formerly was a VP at T-Mobile, leading the national sales teams (e.g., strategic accounts and public sector). With Regie, Sridhar says he and Millen aimed to create a way for companies to communicate with their customers via channels like email, social media, text, podcasts, online advertising and more.

AI needs data to work. And with most of the data produced at the edge, this is where more and more AI applications must run. Simply package your app as containers, drop it in the app store and leverage the eco-system in your go-to-market.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. As organizations continue to migrate their workloads and shift to hybrid or remote work, cloud computing is growing at a rapid rate. Last week at the AWS Summit, according to Swami Sivasubramanian, the vice president of data, analytics and machine learning (ML) services at AWS, analysts project that between 5-15% of IT spend has moved to the cloud -- suggesting that organizations will continue to migrate even more of their workloads to the cloud in the future. The enterprise ecosystem is experiencing a disruption that's largely a result of more cloud-native applications coming to the scene. More companies are embracing a mix of both corporate devices and bring-your-own-device strategies.

In a recently published article, Team topology for machine learning, I suggested that organizations in their Machine Learning (ML) journey should adopt a team topology consisting of four types of teams as illustrated in Figure 1. The team types are Stream-aligned ML, ML enabling, Data/Infrastructure Subsystem, and ML platform teams. To get an overview of these teams, please check out the article. In this article, we do a deep dive into ML platform teams. In particular, we explain the following points in more detail.

In a recently published article, Team topology for machine learning, I suggested that organizations in their Machine Learning (ML) journey should adopt a team topology consisting of four types of teams as illustrated in Figure 1. The team types are Stream-aligned ML, ML enabling, Data/Infrastructure Subsystem, and ML platform teams. To get an overview of these teams, please check out the article. In this article, we do a deep dive into ML platform teams. In particular, we explain the following points in more detail.

AI is at the heart of digital disruption and on its way to becoming one of the biggest game-changers in the next few years. Early adopters of AI are reaping significant benefits and have differentiated themselves from the rest. As a result, the AI sector is garnering the attention of numerous investors globally, increasing the number of AI unicorns in just a few years. In India itself, as many as 11 startups earned unicorn tags during the black swan year 2020. This article lists all the AI companies that have reached a valuation of $1 billion or more. A technology platform company, Argo AI, is creating integrated self-driving systems. These are manufactured at scale for safe and reliable deployment in ride-sharing and goods delivery services. Along with Ford and Lyft, Argo AI is planning to launch a self-driving ride-hailing service in the US.