Large Language Models (LLMs) like GPT-3.5 and GPT-4 have captivated the world with their ability to answer questions, write essays, and even code. But do these impressive abilities translate into genuine reasoning and planning skills? This question is crucial because it helps us understand the limitations and potential of LLMs
Thanks to the recent advancements of Gen AI human-bot interactions have finally switched from bot-driven to fully user-driven ones. Conversation Designers no longer need to put that much of effort to think multiple steps ahead, predict users’ needs and provide paths to follow.
In the world of AI and data, data architecture plays a critical role in shaping an organization’s ability to manage and leverage its data. For companies launching AI and agentic AI projects, a well-designed data architecture is crucial for scaling initiatives, driving insights, and enabling innovation. This article explores the fundamental components of data architecture, its importance, and the considerations for building a robust data architecture in today’s AI landscape.
That future is already here, thanks to Artificial Intelligence (AI) and the game-changing power of Generative AI (GenAI). These technologies aren’t just buzzwords—they’re revolutionizing how companies operate, interact with customers, and drive growth. Whether you’re looking to automate tedious tasks, boost productivity, or deliver personalized experiences at scale, AI and GenAI are the key to unlocking new possibilities and staying ahead of the competition.
The Minimum Viable Experience (MVE) is a concept that goes beyond the traditional Minimum Viable Product (MVP). MVE focuses on delivering a meaningful, valuable, and coherent experience to users with the least amount of effort and resources. Unlike MVP, which prioritizes functionality and quick market entry, MVE emphasizes creating a delightful end-to-end user journey that captures the essence of the product.
The debate around roles needed in a Conversational AI projects dates back to their beginnings. Yet no ultimate conclusion has been reached so far and the approach keeps evolving together with the field itself.
We have all experienced bad bots. Bots that do not deliver on their promises or looked like half-baked ideas that have gone live.
In fact, a 2021 research paper by Jansen et al, reports that 53% of bots fail within 15 months of launch.
So why does this happen?
Generative AI (GenAI) and Conversational AI (CAI) represent two significant advancements in artificial intelligence, each offering unique functionalities and applications. Here’s a comprehensive narrative on their distinctions, synergies, and how GenAI can enhance CAI, drawing insights from various documents.
Organisations are seeing more and more the revolutionary power of being data-first and AI-first in the ever-evolving business technology ecosystem. Even though this path is different for every company, it covers a crucial progression from preliminary research to complete AI integration.
Recently, “AI agents” and “agentic workflows” have become buzzwords in the tech industry, generating much excitement and curiosity. The purpose of this article is to clarify what AI agents are, explore their strengths, weaknesses, and potential dangers, and provide insights into the future of AI agentic workflows.
