The rise of Big Data I want to take you back to 2012 . In those early years, our focus was big data, and everyone else was looking at the cloud. We were pioneering, but in a world that was still beginning to understand the value of data.
Think about the last time you spoke to a digital assistant. Now, imagine that this assistant truly understands you and responds naturally in your language. What if it even acts on your behalf? But before you picture a sudden robot takeover, let's clarify: this is not about replacing your human touch. This is the rapidly advancing reality of AI voice agents. Continue reading to find out if your business can leverage voice agents.
The chatbot landscape is evolving from purely scripted flows to dynamically generated AI-driven conversations. This shift not only enhances the customer experience but also minimizes the maintenance effort required to update these flows while maximizing their robustness and flexibility.
Mastering Automated Customer Support: Handling Multiple Intent Conversations with AI Chatbots When designing a chatbot to assist customer support teams, one of the key challenges is accurately identifying and responding to customer intents . While some customers may reach out with a single query, many have multiple concerns within the same conversation. These situations, known as multiple intent conversations, require chatbots to be adaptable and intelligent in handling customer inquiries seamlessly.
The hidden dangers of AI: key risks every business should know
Generative AI is ubiquitous these days, and organizations are rapidly integrating GenAI into their business processes. However, building GenAI applications comes with its own set of specific challenges. The models are often large, meaning inference costs for running these models can quickly get out of hand, and model selection often requires balancing performance against costs and latency. Other common challenges include the misuse of generative models or data leakage. While implementing measures such as rate limiting, monitoring, and guardrailing in your GenAI applications can help overcome these problems, doing so for every individual project brings significant overhead for your engineering teams. It also becomes easy to lose track of global usage of generative AI within your organization and leads to many cases of reinventing the wheel as teams solve the same problems over and over again.
Remember J.A.R.V.I.S. from Iron Man? That intelligent assistant that seemed to have a solution for everything? While we’re not quite there yet, the rapid evolution of Large Language Models (LLMs) like GPT-4, Claude, and Gemini is bringing us closer than ever. Today’s LLMs are impressive. They can generate content, translate languages, and even write code. But let’s be real — they’re still pretty much glorified text processors.
In recent years, Large Language Models (LLMs) have revolutionised natural language processing by enabling machines to understand and generate human-like text with unprecedented accuracy and coherence. Their applications span across diverse fields such as chatbots and content creation, driving significant advancements in automation and AI-driven solutions. As a result, LLMs have become crucial tools in both academic research and commercial innovation, pushing the boundaries of what AI can achieve. Though, I’m sure you already knew this.
An Introduction to AI Governance Since the launch of ChatGPT, AI tools have become much more widely used in many organisations. This technology opens up many new opportunities, such as automating customer service or improving content creation. However, it also introduces significant risks. Headlines in press articles frequently mention concerns such as deep fakes , ethical dilemmas around AI replacing artists , legal disputes over copyright between LLM providers and media companies , and privacy issues .
At ML6, we strongly believe that AI has the potential to do good. However, we recognize that the technology also raises concerns about its impact on society. To unlock AI’s full potential, we need the trust of society, companies and users in the solutions we develop. This trust can only be achieved by aligning the development of AI solutions with ethical values.