Kore ai Unveils Experience Optimization XO Platform V10.1 Equipped with Smart Co-Pilot and Advanced Generative AI Capabilities

Prior to Sharechat, he has worked at Meta, Microsoft Bing, Yahoo! Labs, and Veveo. He has 18+ years of experience in building large scale recommendation systems, natural language understanding/generation, computational advertising, and large scale ML on graphs. Kore.ai is a global leader in the conversational AI-first platform and solutions, helping enterprises automate business interactions to deliver extraordinary experiences for their customers, employees, and contact center agents. More than 350 global 2000 companies trust Kore.ai’s experience optimization (XO) platform and technology to automate their business interactions for over 100 million users worldwide to achieve extraordinary outcomes. Kore.ai has been recognized as a leader and an innovator by top analysts and ensures the success of its customers through a growing team headquartered in Orlando with offices in India, the UK, Japan, South Korea, and Europe.

“New Sprinklr AI+ capabilities are helping the largest and most complex enterprises supercharge agent productivity and automate tasks and processes with AI. As vendors rush to deliver new AI features, Sprinklr is demonstrating the real-world capabilities possible when AI is built across a unified platform and designed to be both open and scalable,” said Sprinklr Chief Technology Officer, Pavitar Singh. In other words, LLMs as a stand-alone model are not business ready for customer-facing conversational interaction. We’re at the beginning of a massive shift in how brands will deliver customer service, and the LLMs behind ChatGPT and other generative AI systems are going to drastically impact contact centre operations. Here are just a few of the exciting new ways that contact centres can use Generative AI, ChatGPT, and LLMs to reduce after-call work (ACW) for agents, improve knowledge base management, and optimize contact centre agent performance. Generative AI is an umbrella term, which refers to any of the AI models that generate a novel output based on an input, often called a prompt.

The Future of AI

By not initiating the conversation on AI, they could inadvertently drive utilisation underground. Instead, progressive leaders are fostering AI conversations through workshops, focus groups, and “town hall” meetings. Open dialogues can identify campus “power users” and AI experimenters who can help inform the university’s AI strategy. This could be a starting point for a more formal strategy, or it could help leaders spotlight campus innovations. Angst over preventing “AI cheating” is also leading universities to ignore the broader impact of AI on the workforce. AI tools are already a staple in many workplaces, and AI’s presence will only grow over time.

While less data-intensive than Conversational AI, chatbots benefit from varied data for accurate handling of user queries. Conversational AI connects with IoT devices and smart speakers, allowing users to control their surroundings using natural language commands. Unlike regular chatbots, Conversational AI remembers past interactions, creating fluid multi-turn conversations that feel human-like.

Generative AI: A Shift in the Global Conversational Paradigm

The blog concludes that the adoption and adaptation of generative AI is a form of humanitarian experimentation and calls for revisiting discussions around humanitarian accountability. The question then arises that why are startups lining up to build generative AI and not so much Decision intelligence. Well, the answer lies partly in the fact that making a large pre-trained foundational language model is more achievable (not easy though) as compared to making foundational decision intelligence models.

To enable IVAs to handle more complex tasks and provide more engaging experiences, Kore.ai has added a feature called AI-Assisted Dialog Node. With the help of AI-Assisted Dialog Nodes, bot developers can create IVAs that utilize contextual information during user interactions and deliver personalized experiences. These dynamic conversations can handle complex tasks and ensure a more natural flow of communication between the IVA and the user. One promising area for retailers to invest in is Conversational AI (CAI) technology. CAI can enable retailers to reposition business models through AI-driven automation without the need for a complete infrastructure overhaul. With CAI, retailers can drive e-commerce interactions, store digitalisation, build customer loyalty, retain talent, and more.

Europe’s video games industry sees growth spurt between 2021 and 2023 – with more to come

The technology is from a class of Artificial Intelligence methods (AI) referred to as ‘generative AI’ that have been investigated over the last ten or so years (Karpathy et al, 2016). Although generative AI is broadly applicable to other areas such as image and music generation, our focus in this special issue is its application to language and models relating to decision making, decision support, and decision support systems (DSS). Companies need to trust their data, and that is so fundamental to what we do at Salesforce. They also need easy solutions that enable curiosity and exploration — not technology that creates more walls, but rather breaks down existing walls and helps people explore and share learnings as they go about their day. The future is all about democratized analytics in the flow of work, and providing everyone with an AI assistant that helps them see and understand their data in new ways.

Yakov Livshits

Expanding outside the claims experience, in this talk, we will explore the other conversational bots HomeServe has created to improve the performance of its marketing campaigns through smarter call routing and rich data insight. Diagnosing an issue in a Customer’s home to deploy an engineer can be a complex query. HomeServe has used conversational genrative ai AI to diagnose a customer’s issue with only two or three questions, resulting in a more efficient conversation. HomeServe has been able to automate 15-20% of its deployed claims and service requests in the UK and USA. Utilizing whispers and screen pops, successful triaging of a customer’s issue has led to over 30 seconds saved in handling time.

DeepPavlov dream: platform for building generative AI assistants

Our content manifests in different ways to suit your consumption preferences, whether that be podcasts, videos, whitepapers, and more. „I really liked how the UI felt like an actual convention. Most other online summits I have attended are just a series of talks I access from a page. This felt more like the in-person experience and I really liked it.“ I run BT’s AI & Data Science research programme with a team of 25 researchers at BT and 15 scientists from our global network of universities and research collaborations. The programme looks at wide spectrum of AI technologies, like NLP, Autonomics, Federated Learning, Ethical AI, AI Safety & Governance, Bias & Fairness Metrics, Anomaly Detection amongst others. He is currently holding the role of Principal Automation architect at BP and instrumental in implementation of conversational AI solutions for a variety of use cases.

An API allows developers and users to access and fine-tune – but not fundamentally modify – the underlying foundation model. Two prominent examples of foundation models distributed via API are OpenAI’s GPT-4 and Anthropic’s Claude. The AI products we use operate within a complex supply chain, which refers to the people, processes and institutions that are involved in their creation and deployment. For example, AI systems are trained using data that has been collected ‘upstream’ in a supply chain (sometimes by the same developer of the AI system, other times by a third party.

Generative AI models can analyse extensive customer profiles and historical data to create personalised insurance policies that match individual needs and preferences. By offering tailored coverage, insurers can resonate with their policyholders on a deeper level, fostering loyalty and customer satisfaction. Moreover, generative AI-powered virtual agents or chatbots can provide personalised support and instant responses to frequently asked questions, enhancing overall customer experiences and streamlining communication channels. The promise of better data collection, management, and analysis is also the promise of analyzing and acting on data flows across humanitarian silos, ultimately achieving the breakdown of these silos.

With its advanced language processing capabilities, ChatGPT can understand and generate human-like responses to text prompts, making it an invaluable tool for improving customer interactions and streamlining insurance communication. Whether it’s answering frequently asked questions or providing personalised support, ChatGPT can enhance customer experiences and improve operational efficiency. While many of these dilemmas, tradeoffs, and balancing acts belong to the mundane world of internal digital capacity building, there are some new challenges. Users are encouraged to familiarize themselves with the technology by ‘asking about anything you are curious about’ to comprehend the potential and limitations of the tool, the workflow, and the impact on work and group dynamics. At present, the reply might regularly be that ‘I am just a language model’ and no answer can be provided.

• By analysing and understanding these patterns, the models can generate new content that is indistinguishable from what a human might create.
• Suddenly that new employee understands the kinds of issues that customers commonly face and knows where to send them or when to escalate a ticket.
• In recent years, misinformation and disinformation have become key challenges for humanitarian field operations, and organizations have set up specific programs to monitor and mitigate misinformation flows, for example in the field of health.

At the same time, any AI-generated actionable recommendation needs to be assessed for possible biases and blind spots due to gaps in data caused by digital divides; function creep of the data – or mission creep of the organization. Ultimately, however, these questions are political questions about power, values, and interest. After a decade of innovation talk and humanitarian technology genrative ai hype cycles and about five to six years of incessant focus on the digital transformation of aid and AI, we are finally faced with what could be a real game changer. Together, these innovations, for the near future in perpetual beta testing phases, look poised to disrupt humanitarian programming, supply chains, and the everyday nature of aid distribution and protection.

We must first understand what is happening in this sector to answer this question. The most apparent enhancement (and probably the simplest) is to allow for further customisation to the responses from ChatGPT; many default settings were chosen for this PoC. Though, several parameters can be passed in with a user’s prompt, such as temperature, which can be used to tune the creativity and randomness of the response.