Large language models are increasingly being integrated into applications that shape the production and discovery of societal knowledge such as search, online education, and travel planning. As a result, language models will shape how people learn about, perceive and interact with global cultures making it important to consider whose knowledge systems and perspectives are represented in models. Recognizing this importance, increasingly work in Machine Learning and NLP has focused on evaluating gaps in global cultural representational distribution within outputs. However, more work is needed on developing benchmarks for cross-cultural impacts of language models that stem from a nuanced sociologically-aware conceptualization of cultural impact or harm. We join this line of work arguing for the need of metricizable evaluations of language technologies that interrogate and account for historical power inequities and differential impacts of representation on global cultures, particularly for cultures already under-represented in the digital corpora. We look at two concepts of erasure: omission: where cultures are not represented at all and simplification i.e. when cultural complexity is erased by presenting one-dimensional views of a rich culture. The former focuses on whether something is represented, and the latter on how it is represented. We focus our analysis on two task contexts with the potential to influence global cultural production. First, we probe representations that a language model produces about different places around the world when asked to describe these contexts. Second, we analyze the cultures represented in the travel recommendations produced by a set of language model applications. Our study shows ways in which the NLP community and application developers can begin to operationalize complex socio-cultural considerations into standard evaluations and benchmarks.

While a large body of work inspects language models for biases concerning gender, race, occupation and religion, biases of geographical nature are relatively less explored. Some recent studies benchmark the degree to which large language models encode geospatial knowledge. However, the impact of the encoded geographical knowledge (or lack thereof) on real-world applications has not been documented. In this work, we examine large language models for two common scenarios that require geographical knowledge: (a) travel recommendations and (b) geo-anchored story generation. Specifically, we study four popular language models, and across about $100$K travel requests, and $200$K story generations, we observe that travel recommendations corresponding to poorer countries are less unique with fewer location references, and stories from these regions more often convey emotions of hardship and sadness compared to those from wealthier nations.

Consumers are the most positive and excited for AI technologies that benefit their lives outside of work, research from O'Reilly shows. The survey, which delves into the opinions of consumers and compares them to that of AI-creators – those working to develop AI driven solutions including CTOs, data scientists, software engineers, solutions architects and IT Directors – reveals a wider indifference to the potential of AI in a work setting. The results suggest that while AI may be inserting itself into our lives in more ways than we recognise, to encourage adoption, developers should focus their efforts on leveraging AI to make consumers lives easier, augmenting existing experiences to make them more seamless. Adoption and acceptance outside the office, will ultimately lead to the same in a work setting, alleviating fears of job loss and instead focusing on job enhancement. Rachel Roumeliotis, Vice President of Data and AI at O'Reilly said: "Consumer conceptions of AI are still very much influenced by popular culture, science fiction and the virtual assistants they use every day. However, there are strong areas of overlap between AI developers and AI users. Both groups appreciate the success of smart home technology and are watching the development of autonomous vehicles very closely. It's up to these sectors to capitalise on the hype, but the results are also a call for the creators of work-focused AI to make solutions that capture the imagination and generate excitement."

Over the years, the influence of artificial intelligence (AI) has spread to almost every aspect of the travel and the hospitality industry. Thirty percent of hospitality businesses use AI to augment at least one of their primary sales processes, and most customer personalisation is done using AI. The proliferation of AI in the travel and hospitality industry can be credited to the humongous amount of data being generated today. AI helps analyse data from obvious sources, brings value in assimilating patterns in image, voice, video, and text, and turns it into meaningful and actionable insights for decision making. Trends, outliers, and patterns are figured out using machine learning-based algorithms that help in guiding a travel or hospitality company to make informed decisions.

The acquisition will enhance trivago's product with personalization technology which uses both Big Data and a customer-centric approach. Founded in 2015, tripl has developed an algorithm to give tailored travel recommendations by identifying trends in users' social media activities and comparing it with in-app data of like-minded users. The AI-driven product imitates the way a travel agent would recommend hotel experiences relevant to the customer, and combines it with the ease of online services. Following the acquisition, the former CTO and creator of the tripl algorithm, Hendrik Kleinwächter, will join trivago's development team to continue breaking new ground in personalization. Founded in 2005 and headquartered in Düsseldorf, Germany, trivago is a global hotel search platform, focused on reshaping the way travelers search for and compare hotels.

Chat Bots are already declared as the'new apps'. Early months of 2016 Zuckerburg introduced the Facebook Messenger Platform. The platform allows developers to build AI chatbots for the messenger application. When we say'Humech', we mean the bond that'Humans and technology share today. The first ever encounter as far as I can remember was somewhere in the 1960s with Eliza; a chatbot created by Professor Joseph Weizenbaum from Massachusetts Institute of Technology.