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With Automated Testing's fast execution, developers can run tests and get reports instantly, so if a failure occurs, they can identify it quickly. Start your automation journey now at https://quicktest.ai . . . . . . . . . . . . . . . . . . . #Contactcentretesting #GenesysTesting #Multichannelsupport #Trueautomation #Techservices #Artificialintelligence #Artificialintelligenceai #Automationtesting #AI #Genesystesting #Automatedtesting #Genesyscloud

Yes. AI agents are the (not-so-distant) future of #digitaltransformation and, if done well, a wonderful opportunity for small and mid-size companies to thrive.

🌟 Exciting News! 🌟 Typemock is in a Top Story on SD Times! 🎉 Dive into the latest article on "The Evolution and Future of AI-Driven Testing: Ensuring Quality and Addressing Bias" and discover how we're leading the charge in revolutionizing software testing with cutting-edge AI technologies. 🚀 Check out the full article here: https://lnkd.in/eYh7VpFC #Typemock #SDTimes #AI #SoftwareTesting #Innovation #TechNews #QualityAssurance #AIDrivenTesting

How difficult is it to get a single AI feature working consistently well? It can seem easy enough. Write a prompt, test it, tweak it. The first version of our product that we deployed in Feb/Mar 2023 was just a RAG orchestration. It was simple enough, and we were running it over relatively straightforward data... ...but crikey were the answers sometimes terrible! Little things we'd hardly considered tripped the orchestration up. For example, there was some customer data in there AND some random support docs from Microsoft. Uh oh. The bot couldn't tell the difference between these source types... It only saw chunks and matched chunks and used chunks that seemed relevant even if they were about an entirely different product line or company. Now, everyone who has built a RAG orchestration has been through this learning, and now there are standard ways to approach this challenge. It's a little embarrassing that this tripped us up early on. But this is one of THOUSANDS of hurdles we discovered that trip up chatbots... JUST in the RAG portion of the product. Let me say that again so it sticks... A RAG orchestration has thousands of considerations that need to be dealt with in order to achieve accurate, predictable, and steerable responses. The hardest part is you cannot predict where LLMs will fall over. You have to put them to work thousands and thousands (and thousands) of times, at least. And the challenges you'll face are DIFFERENT depending on the vertical/use case. For some use cases, it's a simple question/answer flow with no conversational component. For others, it's a rich back and forth that relies heavily upon the context of the user (role, location, seniority etc.). How difficult is it to get a single AI feature working consistently well? Very difficult... which is why for now the more vertical products are going to be adopted faster & more successfully than broader platforms.

Danny Major has a wealth of experience in automation, he shares his 13 pillars for success in conversational automation, my key pull is No.13: "The final point of success is how deployment is approached across your enterprise. You will likely start your journey with a single use case or area in which to prove the value delivered to your business, whilst exposing the initial impact of this transformation. In parallel to this initial activity, you should build a pipeline of additional areas in which conversational AI can support in line with your goals and objectives." https://lnkd.in/eJA6Eipf

The question isn’t “Should we adopt #AI?” but rather, “How can AI help us do our jobs better?” 📈 This excellent writeup by Chris Mounce covers how AI can help your #contactcenter... ✅ Move past the manual monotony of spreadsheets ✅ Be augmented, not replaced by robots ✅ Address the unseen 99% of interactions Have a read 👇 https://lnkd.in/eaMPe24p

Trusting AI/ML: The Automated Testing Behind the Tech? by TONI RAMCHANDANI via Generative AI - Medium (GenAI: Healthcare) URL: https://ift.tt/zd4aUNQ People often think automated testing is the Swiss Army knife for AI models handling everything with ease. But in reality, it’s more like bringing a butter knife to a sword fight. There’s no one tool that can cut through every challenge. — Breaking The Swiss Army Knife Myth ? The world around us is changing at an incredible pace, and at the center of this transformation are AI and ML. Every day, these technologies quietly shape the way we live — from the apps that guide our daily routines to the systems that make decisions about our health, finances, and even the relationships we build. As I watch this shift unfold, I can’t help but ask myself: Are we putting too much trust in these AI/ML models? We rely on them so heavily, but are they really tested to ensure they’re doing what we expect? And if they are tested, what exactly goes into that process? What kinds of checks and safeguards are in place to make sure these models are accurate, fair, and secure? These questions linger in my mind as we navigate this ever-changing world, reminding me that while technology advances, the need for trust and reliability remains constant. How to ensure trust in AI ? Imagine you’re working on an AI model that’s going to help doctors diagnose diseases more accurately. It’s an exciting project with the potential to save lives, but there’s one thing that keeps you up at night: How can you be sure that this model will actually work as intended? After all, it’s not just about getting the code right — it’s about making sure the model can handle real-world data, make fair decisions, and remain reliable over time. This is where automated testing comes into play ? So, how do we go about this automated testing I don't know but I have my thoughts ? Let’s dive into the process. It all starts with data validation testing. Data is the lifeblood of any AI model, and if your data is flawed, your model will be too. Imagine trying to build a house on a shaky foundation — it just won’t hold up. Automated data validation checks the data for missing values, outliers, or inconsistencies that could throw off your model. For example, if you’re working with patient records, you need to make sure there are no duplicate entries or impossible values, like a negative age. By catching these issues early, you’re setting your model up for success. Next comes model performance testing. This is where you find out if your model is actually good at what it’s supposed to do. Automated tests measure the model’s accuracy, precision, recall, and other metrics that tell you whether it’s hitting the mark. Think of it like a dress rehearsal before the big show — you want to make sure everything is perfect before you go live. For instance, if you’ve built a sentiment analysis model, you’ll want to test it with a variety of text inputs to see i...

Yellow.ai has introduced Orchestrator LLM, a generative AI agent model designed to facilitate personalized and contextually aware conversations. Orchestrator LLM serves as a customer-facing chatbot that can analyze customer interactions, identify multiple conversational intentions, and guide users in resolving their service or support issues. It leverages past interactions and customer history to add context to current conversations. Orchestrator LLM does not require prior training and can determine the appropriate response, such as retrieving information from a knowledge base, initiating a new conversational flow, or escalating to a live agent. This new AI agent model provides enterprises with greater flexibility and control over customer queries, ensuring dynamic and fluid conversations. https://lnkd.in/gJVhnHw8

AI Agents powered by high-quality data can improve just about any industry. Here’s how: An interesting update from WWDC2024 is Apple's plan to introduce one building block for personalized AI: understanding context by processing relevant personal data. This concept is not new but represents the current maturity of AI. Back in my consulting days, we pitched the concept of connecting conversational AI and Robotic Process Automation (RPA) to automate specific repetitive tasks. To do so, we built a concierge bot that dispatched the work to different agents. However, with the current development in AI, this can be easily done using an LLM.  Large language models can easily understand context and dispatch work to Agents developed for specific tasks or those with access to related data sources. This creates a huge opportunity for automation, cost savings, and increasing customer experience. To paint a clear picture, imagine the possibility (and the time saved) of asking an LLM about the performance of your supply chain. Perhaps kick off specific processes based on a conversation? You would not need to drill down dashboards or switch between core systems. Simply ask your concierge, and it will either dispatch the work, get the relevant information, or act on the required process. AI Agents have substantial practical applications and long-term potential. ChatGPT’s primary function is to assist users by providing information, answering questions, and engaging in dialogue. Agents are LLM models with toolboxes and can be simple rule-based systems or complex autonomous agents capable of making decisions and performing actions in dynamic environments. Industries are successfully implementing AI agents, including customer service and workplace automation. Advanced applications include health care, finance, and mobility. A common thread among agents is the need for structured data embedded in long-term or short-term memory to make fact-based decisions. For example, If we develop a healthcare agent, it  needs to have access to a patient’s medical records and pharmaceutical data to derive the right conclusions. The same case applies to Finance AI Agents in the  fraud detection domain. They require access across various data points  to show why a customer was flagged as a fraud. With their current ability to understand context, I think the increasing development of (AI) Agents for process automation will also take off. So what's needed? A good start is making all the data available across applications, organizations, and industries. At Nuklai, we help businesses take control of their data and build solutions that introduce them to innovative AI technologies and scale the use of agents. Are you curious about how data can help drive your develop of AI Agents and  positively impact your business’s operations? Contact me, and we can look into it. #b2b #ai #data #innovation

🚀 Exciting news! Zendesk Just announced the Early Access Program (EAP) for generative AI-powered call summarization in Zendesk Talk. Boost agent productivity, enhance connections, and optimize ticket management. Join the EAP and revolutionize your customer service! #Zendesk #AI #CustomerSupport #CustomerService #GenerativeAI