This study introduces a simple yet effective method for identifying similar data points across non-free text domains, such as tabular and image data, using Large Language Models (LLMs). Our two-step approach involves data point summarization and hidden state extraction. Initially, data is condensed via summarization using an LLM, reducing complexity and highlighting essential information in sentences. Subsequently, the summarization sentences are fed through another LLM to extract hidden states, serving as compact, feature-rich representations. This approach leverages the advanced comprehension and generative capabilities of LLMs, offering a scalable and efficient strategy for similarity identification across diverse datasets. We demonstrate the effectiveness of our method in identifying similar data points on multiple datasets. Additionally, our approach enables non-technical domain experts, such as fraud investigators or marketing operators, to quickly identify similar data points tailored to specific scenarios, demonstrating its utility in practical applications. In general, our results open new avenues for leveraging LLMs in data analysis across various domains.

Machine learning is transforming advertising and marketing by segmenting and targeting specific audiences with relevant messages. Publishers as varied as the Washington Post & Reuters and even smaller publishers regularly use machine learning tools to produce copy around financial and sports results, leaving journalists more time to get on with analysis that really adds value to their consumers. Advertisers and marketers can use machine learning to create more personalized experiences, target the right audience, reduce costs, and make faster decisions. Knowing your target audience and understanding their needs, interests, and preferences is essential for the success of your digital marketing efforts. Having an accurately defined ideal customer profile and buyer personas lets you segment your email and content marketing and personalise your marketing messaging and content.

Intelligent automation drives the best ROI for repetitive, standardized, and attention-demanding workflows. Claims management is a great example of such. Largely paper-based and rarely end-to-end digitized, the claims management process can eat up to 50%-80% of premiums' revenues. Being primarily manual, claims processing is also prone to errors and inefficiencies, which further drive up the insurers' operating costs. As McKinsey stated at the beginning of 2019, larger insurance carriers haven't quite addressed the costs of services delivery: In particular, the increase in connectivity--telematics and onboard computers in cars, smart home assistants, fitness trackers, healthcare wearables, and other types of IoT devices--now allows insurers to automatically collect more comprehensive data from customers.

If you can afford for your team to spend more time on critical tasks, you will always be a step ahead of the competitors that can't automate simple processes. This is why marketing automation is one of the most important aspects when it comes to saving money and time. It is important to stand on top of marketing automation trends if you want to get the most out of your marketing campaigns. Bear in mind that you have to put in at least some effort to take your marketing campaign to the next level. Use the right automation tool, do your research, talk with our employees, and follow the trends mentioned below!

Explainable AI can help decision-makers in insurance understand the rationale and logic behind AI and machine learning results. "Most businesses believe that machine learning models are opaque and non-intuitive and no information is provided regarding their decision-making and predictions," -- Swathi Young, host at Women in AI. Explainable AI is evolving to give meaning to artificial intelligence and machine learning in insurance. The XAI (explainable AI) model has the key factors, which are explained in the passed and not passed cases. The features that are extracted from the insurance customer profile and the accident image are highlighted in the XAI model.

There are only so many insights businesses can gather by mining their sales data. In the best-case scenario, marketing-wise, data mining tools make it easier to understand which products and services customer groups want to purchase the most. But all of the data mining tools in the world put together are still too clunky and time-consuming to personalize the customer experience and convert more customers. Digital marketers can, fortunately, convert their sales data into a retention and acquisition strategy that uses artificial intelligence (AI)-driven personalization to better connect with customers. With AI, businesses can take advantage of the more than two hours that social media users spend daily on social channels to personalize their real-time customer interactions.

Perhaps one of the most sophisticated and valuable elements of AI in call centers is something called predictive behavioral routing (PBR). PBR first came about in 2014 and is designed to connect consumers with agents most equipped to handle certain personality types. The technology listens to a customer's words and tone. It creates a customer profile, which then allows it to route the call to a specific agent rather than a random one, which ultimately leads to a better customer experience. The more times PBR is used, the more customer profiles it's able to create, thus allowing businesses to match customer profiles with the right employee. This creates positive, natural, and tailored interactions to a customer's personality, so they're more likely to feel helped.

Over the years, AI or artificial intelligence has transformed the way people market their products and services. It has helped businesses improve their customer experience, optimize the speed of various marketing tasks, and increase conversion rates. However, a lot of marketers still don't understand the benefits that artificial intelligence can provide. Some are afraid it may steal their jobs and replace them in the future. But the truth is that this new technology enables marketers to provide a more personalized consumer interaction. To give you a better understanding of what we are talking about, here are 5 ways intelligent marketers use artificial intelligence.

The very mention of Artificial Intelligence reminds most people of movies like the Terminator but in actuality, AI is already very present in our daily lives making things much easier for us in a multitude of fields. For example, according to a Harvard Business Review study, companies that were using AI for sales managed to bring in 50% more leads and reduce their costs by 40%-60%. AI applications are not necessarily actual robots walking about the office. In most cases, it means the introduction of software and tools that make conducting business easier, more affordable, and faster by automating as much as possible. Mathematician Alan Turing was the first to really ask the question'Can machines think?'.

As organizations worldwide continue to respond and react to a new business climate, we have seen more clearly than ever that the traditional approach to business applications is no longer sufficient. Organizations need a new class of business applications that provides the agility to see and respond to emerging trends, new opportunities, and potential risks so that ultimately, they can delight their customers and deliver the business outcomes that truly matter. That's why today we are excited to announce, together with C3.ai and Adobe, the launch of C3 AI CRM powered by Microsoft Dynamics 365. C3 AI CRM leverages Dynamics 365 as the foundation for end-to-end, intelligent customer engagement, with Adobe Experience Cloud providing real-time customer profile and customer journey management, together with C3.ai's industry-specific enterprise AI capabilities. With C3 AI CRM, organizations can unlock the power of AI-driven customer relationship management in a solution purpose-built for specific industries, leveraging data from any source to produce predictive business insights.