In a significant move to bolster its fight against AI-generated deepfakes and fraud, AI or Not has successfully completed a seed funding round, raising $5 million to enhance its capabilities. Led by Foundation Capital, the funding round also saw participation from GTMFund, Plug and Play, and several strategic angel investors, underscoring the urgent need for robust technology in this domain.
In the evolving landscape of large language models (LLMs), enterprises are constantly seeking more efficient and effective ways to harness the power of these models. Traditionally, retrieval-augmented generation (RAG) has been a popular method, but it comes with its own set of challenges. Recently, cache-augmented generation (CAG) has emerged as a promising alternative, offering significant advantages in terms of simplicity,
Code migration is a critical process when it comes to maintaining software applications, as it helps improve performance, enhance resilience, keep systems up to date, and eliminate stale or irrelevant code. However, the process can be exceedingly complex and time-consuming because the code is often distributed across a multitude of environments. While artificial intelligence (AI) has already begun assisting with
In the rapidly evolving landscape of artificial intelligence, ensuring the safety and security of AI systems has become a paramount concern. Microsoft, a leader in AI technology, has been at the forefront of these efforts through its AI Red Team (AIRT). Established in 2018, AIRT has been instrumental in identifying and mitigating vulnerabilities in AI systems, particularly as generative AI
Generative artificial intelligence (genAI) is rapidly emerging as a transformative force in the business world. As companies navigate the complexities of the digital age, the potential of genAI to revolutionize business operations and models is becoming increasingly apparent. This article explores the critical role of genAI in driving business transformation, the divide between AI-first companies and traditional firms, and the
Apple recently made headlines by halting its newly launched AI-generated news alert feature, designed to consolidate and summarize push notifications from various media apps, due to repeated inaccuracies. The service, which debuted in December, faced heavy criticism as it generated erroneous headlines, potentially undermining the credibility of reputable news organizations such as the BBC, Sky News, the New York Times,
The remarkable advancements in artificial intelligence (AI) have brought about a significant shift in natural language processing (NLP), with multilingual AI reasoning models emerging as one of the most rapidly evolving and exciting branches of this technology. These models are fundamentally transforming human interactions with technological systems by breaking down language barriers and enabling seamless inter-cultural communication. Multilingual AI models
As businesses increasingly embrace automation to enhance efficiency, reduce errors, and expedite processes, understanding the distinct functionalities and applications of Traditional Robotic Process Automation (RPA) and AI agents becomes imperative. Both technologies are instrumental in automating tasks, yet they differ significantly in scope, capabilities, and suitability for various automation needs. These differences are not only rooted in their technical foundations
Imagine a scenario where businesses in New York can execute repetitive and time-consuming tasks with unwavering accuracy while also gaining insights from complex data patterns to make informed decisions in real-time. This is not a futuristic aspiration but a current reality made possible by Robotics Process Automation (RPA) and its seamless integration with Artificial Intelligence (AI) and Machine Learning (ML).
Big data is revolutionizing the way businesses operate and how consumers interact with products and services. By leveraging vast amounts of data, companies can make more informed decisions, optimize their operations, and provide personalized experiences to their customers. This transformative ability is reshaping industries by enabling businesses to predict customer needs accurately, boost productivity, and enhance personalization. The influence of
The Big Data market is on the cusp of monumental growth, with projections indicating a leap from $230.21 billion in 2023 to $518.55 billion by 2030. This forecasted expansion, driven by advancements in cloud computing, Internet of Things (IoT), and sophisticated analytics, underscores the transition of businesses towards data-centric decision-making. With organizations increasingly recognizing the value of data, this transformation
In today’s rapidly evolving technological landscape, businesses are constantly searching for ways to stay ahead of the competition, and one of the ways they are doing so is through the transformative power of big data. By leveraging the comprehensive analysis capabilities of big data, organizations can make smarter decisions, streamline operations, and significantly enhance efficiency across various sectors. This process
