Collaborating Authors


Controversial call center analytics firm Loris raises $12M – TechCrunch


While some surveys show that people prefer to talk to a human as opposed to a chatbot, whether they're shopping online or dealing with a customer service issue, that hasn't dissuaded companies from adopting them. A 2019 Salesforce report found that 53% of service organizations expected to use chatbots within 18 months. According to Statista, the size of the global chatbot market could surpass $1.25 billion by 2025, a steep climb from $190 million in 2016. A customer's satisfaction -- or lack thereof -- with a chatbot ultimately depends on the scenario and the capabilities of the chatbot in question. Obviously, a chatbot that fails to answer basic questions will lead to frustration.

Explainable AI - AI Summary


More than two dozen artificial intelligence experts from business and academia, including Texas McCombs, explored the importance of understanding how machine learning systems arrive at their conclusions so humans can trust those results. Although AI is more than 50 years old, "deep learning has been a mini-scientific revolution" since the 2010s, said one keynote speaker, Charles Elkan, a professor of computer science at the University of California, San Diego. Alice Xiang, a lawyer and a senior research scientist for Sony Group, said, "I see explainability as an important part of providing transparency and, in turn, enabling accountability." She noted the challenge of black boxes, citing as examples drug-sniffing dogs, whose abilities are mysterious but highly accurate, and the horse Clever Hans, who appeared to understand math but was really following cues from its owner. In a panel discussion called "Adopting AI," James Guszcza, a behavioral research affiliate at Stanford University and chief data scientist on leave from Deloitte LLP, said: "I think one of the previous speakers said we need to be interdisciplinary; I take it a little bit further and say we need to be transdisciplinary."

Engineering the Future of Artificial Intelligence


To connect our people with the latest ideas, a vibrant firmwide network links Booz Allen with outside research leaders from across the global academic community. We actively collaborate with computer science labs and math departments at Harvard University, Syracuse University, the Montreal-based Mila institute, and other organizations. And our university-wide master collaboration agreement with the University of Maryland Baltimore County lets Booz Allen practitioners work on interdisciplinary projects with any professor from any department. Open access to academic environments enables our people to hone their expertise, often at the Ph.D. level, while continuing to build careers in industry. We support emerging researchers by providing them with mentoring and ongoing opportunities to explore transformational AI concepts.

Director, Data Science


The world's largest and fastest-growing companies such as Accenture, Adobe, DocuSign, and Salesforce rely on Demandbase to drive their Account-Based Marketing strategy and maximize their B2B marketing performance. We pioneered the ABM category nearly a decade ago, and today we lead the category as an indispensable part of the B2B MarTech stack. Our achievements and innovation would not be possible without the driven and collaborative teams here at Demandbase. As a company, we're as committed to growing careers as we are to building world-class technology. We invest heavily in people, our culture, and the community around us, and have continuously been recognized as one of the best places to work in the Bay Area.

AI Adoption Skyrocketed Over the Last 18 Months


When it comes to digital transformation, the Covid crisis has provided important lessons for business leaders. Among the most compelling lessons is the potential data analytics and artificial intelligence brings to the table. "Launching a direct-to-consumer business was always on our roadmap, but we certainly hadn't planned on launching it in 30 days in the middle of a pandemic," says Michael Lindsey, chief growth officer at Frito-Lay. "The pandemic inspired our teams to move faster that we would have dreamed possible." The crisis accelerated the adoption of analytics and AI, and this momentum will continue into the 2020s, surveys show. Fifty-two percent of companies accelerated their AI adoption plans because of the Covid crisis, a study by PwC finds.

The exciting possibilities of boring AI


We all know about the paradigm-changing use of AI for Netflix recommendations, chatbots that impersonate customer service agents online, and the dynamic pricing of hotel rooms. Such efforts are the value creation engines of countless large, successful companies. But organisations can also adopt a decidedly less splashy and, at face value, more pedestrian use of AI--to process documents faster and simplify operational procedures. Although this use is aimed at reducing costs rather than transforming industries, 'boring AI' is actually quite exciting--because it confronts issues that all companies wrestle with, and because the gains in productivity are real. Recent research by PwC on automating analytics found that even the most rudimentary AI-based extraction techniques can save businesses 30–40% of the hours typically spent on such processes.

Deloitte: The top business use cases for AI in 6 consumer industries


The biggest challenge implementing artificial intelligence is moving from concept to scale. A new report from Deloitte finds that in consumer-related businesses, the challenge is especially difficult because many have large legacy data and analytics platforms, and decentralized data and analytics operations. Another common obstacle is achieving alignment and integration across business units and among IT stakeholders. These consumer businesses include consumer products, retail, automotive, lodging, restaurants, travel and transportation. Yet, "consumer-related businesses are actively exploring ways to harness the power of AI, and many valuable use cases are emerging,'' according to the report, The AI Dossier. However, AI adoption and maturity levels vary widely for reasons including scalability due to data quality and complexity, organizational constructs and talent scarcity, and lack of trust, the report noted. For each industry, the report highlighted the most valuable, business-ready use cases for AI-related technologies and examined the key business issues and opportunities, how AI can help and the benefits that are likely to be achieved. The report also highlighted the top emerging AI use cases that are expected to have a major impact on the industry's future. For example, in customer service, one of the largest segments of customer relationship management, it is now possible to personalize the customer experience across all channels, using machine learning, conversational AI and natural language processing through the customer journey and lifecycle, the report said. SEE: Digital transformation: A CXO's guide (free PDF) (TechRepublic) AI can help by automating customer interactions through the use of chatbots. It can also be used in tandem with Internet of Things devices to sense the sentiments and needs of connected customers and to personalize the customer experience, the report said. Consumer demand planning, forecasting and marketing will also be enhanced through AI, the report said. "As the number of sales channels used by consumers continues to grow, retailers should continue to improve how they plan across multiple sales channels--and how they handle disruptions.

5 things to know about AI


Artificial intelligence (AI) is a constellation of technologies harmoniously enabling machines to act, learn and understand with human-like levels of reasoning. Maybe that's why everyone's definition of AI is different: It's so much more than just one thing. Machine learning and natural language processing are at the heart of AI. When paired with analytics and automation, these evolving innovations help companies improve customer service, optimize supply chains and achieve a seemingly endless number of business goals. And, fun fact, it can even help restore coral reefs.

The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.

Named Entity Normalization Model Using Edge Weight Updating Neural Network: Assimilation Between Knowledge-Driven Graph and Data-Driven Graph Artificial Intelligence

The text mining technology is undergoing a rapid evolution thanks to the exponential growth in the number of text-rich documents available online, and as a result, it is being widely applied in a range of domains such as finance and bioinformatics. Text mining aims to extract the information from documents to derive valuable insights. Documents subject to analysis contain many named entities, which are proper names that denote unique objects such as organizations, products, persons, and locations. The technique used to extract named entities from documents is called named entity recognition (NER, henceforth). Furthermore, named entity normalization (NEN, henceforth) involves matching extracted named entities with homogeneous identity and is pivotal for text mining tasks. More specifically, in the biomedical domain, disease names and chemicals in drugs often have different surface forms while sharing the same concept. Types of named entities with different surface forms that share same concept can be divided into following categories: (1) synonyms, (2) abbreviations, (3) acronyms, (4) different combinations of punctuations and alphabets, (5) descriptive phrases, and (6) possible NER parsing errors.