The objective of legal text entailment is to ascertain whether the assertions in a legal query logically follow from the information provided in one or multiple legal articles. ChatGPT, a large language model, is robust in many natural language processing tasks, including legal text entailment: when we set the temperature = 0 (the ChatGPT answers are deterministic) and prompt the model, it achieves 70.64% accuracy on COLIEE 2022 dataset, which outperforms the previous SOTA of 67.89%. On the other hand, if the temperature is larger than zero, ChatGPT answers are not deterministic, leading to inconsistent answers and fluctuating results. We propose to leverage label models (a fundamental component of weak supervision techniques) to integrate the provisional answers by ChatGPT into consolidated labels. By that way, we treat ChatGPT provisional answers as noisy predictions which can be consolidated by label models. The experimental results demonstrate that this approach can attain an accuracy of 76.15%, marking a significant improvement of 8.26% over the prior state-of-the-art benchmark. Additionally, we perform an analysis of the instances where ChatGPT produces incorrect answers, then we classify the errors, offering insights that could guide potential enhancements for future research endeavors.

ChatGPT hit internet browsers on Nov. 30, 2022. Within five days of release, it reached 1 million users -- something it took Netflix 3.5 years to achieve. Is ChatGPT the revolutionary AI we've all been waiting for? Or is it just another fad that's raced to the finish line before technology could keep up? ChatGPT is an artificial intelligence (AI) model trained on a large fraction of text from the internet.

The rapid pace of AI adoption in business could be heading for some major speed bumps. According to Gartner experts presenting at the Gartner CFO & Finance Executive Conference in June 2022, half of all AI deployments are expected to be postponed between now and 2024, as companies face barriers to upscaling AI in-house. AI governance and how enterprises are going to monitor and control the use of data in their AI platforms are emerging as significant snags. AI governance is a relatively new concept, as AI itself is still only in the early stages of development, but there are already complications emerging. For some companies, the governance of AI applications is included in data or model governance structures.

Today's organizations need to pursue digital transformation to survive in the future, consulting firm Baker Tilley noted in a blog post. "Digital transformation is critical to preparing yourself for what's next." Technology leaders had approximately five times the revenue growth of the laggards, displaying the rising importance of adopting technology in the post-pandemic economic climate, Baker Tilley added. "But digital transformation goes beyond just implementing new tools and technology," the blog post stated. "By conducting a financial assessment, creating a business case and building a road map, your organization can make the financial case for your digital transformation."

On April 21, the EU officially proposed the Artificial Intelligence Act, outlining the ability to monitor, regulate and ban uses of machine learning technology. The goal, according to officials, is to invest in and accelerate the use of AI in the EU, bolstering the economy while also ensuring consistency, addressing global challenges and establishing trust with human users. AI use cases with unacceptable risk will be banned outright. High-risk applications, similarly, pose a high risk to health, safety and fundamental rights, though the debate around the definition of "high risk" has been raging since last year, with more than 300 organizations weighing in. These AI applications are allowed on the market only if certain safeguards are in place, such as human oversight, transparency and traceability.

The platform leverages AI throughout the buyer's journey, eliminating the guesswork that plagues revenue teams, improving the customer experience and generating high-quality pipelines that are more likely to convert. According to Jason Zintak, CEO of 6sense, the platform offers all-in-one AI and is revenue-obsessed because it has seen what is possible when a complete revenue team has access to AI. Artificial intelligence has the potential to displace hunch-based techniques in favor of real-time, data-driven insights and next best actions. Thus, the key focus of the announcement is artificial intelligence. The results generate consistent revenue growth. "We've experienced the frustration that guesswork causes and how it impedes revenue generation," Zintak told CMSWire.

AI-based marketing enables brands to personalize the customer experience while providing real-time decisioning based on the actionable insights that are obtained through the analysis of massive amounts of historical and current customer data. In fact, according to a report from IBM, 50% of brands that were surveyed are already using AI to quickly access insights, automate campaigns and processes, and they are eager to embed it directly into customer touchpoints. Additionally, the report revealed that executives indicated that they are very interested in AI-enhanced CX, with 70% believing their industry is ready to adopt AI/CX, and 75% predicting that AI will play an important role in the future of their brands. Not surprisingly, 57% stated that responding to customer expectations for more personalized experiences is their number one reason for adopting AI. AI-based marketing uses artificial intelligence to make automated decisions that are based on data collection, data analysis, along with observations of economic trends that may impact marketing campaigns.

Look-alike modeling has been an important part of the media toolkit over the past decade, allowing brands to increase their audience pool by taking a core group of top-performing individuals, grouping them and using data and technology to find other individuals like them. Over the past several years, data management platforms (DMPs), third-party cookies and their associated data are becoming obsolete due to self-regulation by technology providers and legislation like CCPA and GDPR. The movement away from third-party cookies and third-party data overlays on cookies is causing total audience pools to drop in size as individuals have fewer associated identifiers (cookies to connect to). However, look-alike modeling can also help businesses leverage their first-party data to build robust large-scale segments for marketing and advertising purposes. Tealium's regional vice president of strategic partnerships for the Americas, Travis Cameron, explained that the value of being able to expand target populations based on data associated with a high-value segment will take on a different dimension.

Talkdesk's Future of AI in the Contact Center report found that 84% of CX professionals expect their company's total spending on AI and automation to increase in 2025 compared to 2021, with 89% of CX professionals -- including customer service leaders, managers and operational staff -- believing in the importance of using AI in contact centers. However, only 14% of businesses consider themselves transformational with AI. Just having the technology isn't enough to produce positive results. AI users also need to follow these five best CX industry practices. To be useful in CX, it's important that AI can leverage the right data at the right time, said Karl Phenix, Avtex director of sales engineering.

Talkdesk's Future of AI in the Contact Center report found that 84% of CX professionals expect their company's total spending on AI and automation to increase in 2025 compared to 2021, with 89% of CX professionals -- including customer service leaders, managers and operational staff -- believing in the importance of using AI in contact centers. However, only 14% of businesses consider themselves transformational with AI. Just having the technology isn't enough to produce positive results. AI users also need to follow these five best CX industry practices. To be useful in CX, it's important that AI can leverage the right data at the right time, said Karl Phenix, Avtex director of sales engineering.