8th WIPO Conversation Wraps Up, Igniting Discussions on Generative AI’s Impact on Intellectual Property

Elevating customer experience: The rise of generative AI and conversational data analytics

At IBM we understand the importance of using AI responsibly and we enable our clients to do the same with conversational search. Organizations can enable the functionality if only certain topics are recognized, and/or have the option of utilizing conversational search as a general fallback to long-tail questions. Enterprises can adjust their preference for using search based on their corporate policies for using generative AI. We also offer “trigger words” to automatically escalate to a human agent if certain topics are recognized to ensure conversational search is not used.

However, brands may now build bot flows from only natural language descriptions. Once the developer specifies the purpose of their bot, Kore.ai’s conversational AI platform automatically suggests additional use cases. As a result, contact centers can expand the scope of conversation automation beyond pre-trained chatbots – increasing containment rates.

The two conversations are in separate soundproof distinctive rooms, as it were. I have a bit of heartburn about the phrasing since the tendency would be to infer that ChatGPT has “memory” in some manner akin to how humans have memory, including that you can tell ChatGPT as though it is sentient to remember things. Note that this provides the same foundational precepts and does so without either by design or via happenstance landing into an anthropomorphizing zone. You might recall that at the start I had mentioned my distaste for the use of human-cognitive oriented words as seemingly applied to AI that ergo tends to anthropomorphize AI.

By 2018, major tech companies had begun releasing transformer-based language models that could handle vast amounts of training data (therefore dubbed large language models). Talkmap offers a leading generative AI platform for contact center conversational intelligence and is used by some of the largest mobile operators and financial services providers. Talkmap uses generative AI and LLMs to transform customer conversations into game-changing visibility and actionable business intelligence, securely, continuously, and at scale. Enterprises automatically and dynamically discover new call reasons as they occur (no need to pre-define them) and understand the customer’s reason/intent. In addition, our platform upgrade XO V11.0 enables businesses to put IVAs to work 10 times faster.

Before generative AI, our interactions with computers generally involved minimal linguistic back-and-forth. With generative AI, however, computer systems acknowledge us in a way that seems real. Plus, since generative AI creates unique “original” content, it’s subject to AI hallucinations, which means not all of the answers it gives will be correct. They can pinpoint key action items and discussion trends, automatically classify and triage customer service tickets, and improve the routing process.

LLMs are a type of generative AI that deals specifically with text-based content. Traditional LLMs use deep learning algorithms and rely on massive data sets to understand text input and generate new text output, such as song lyrics, social media blurbs, short stories and summaries. By employing predictive analytics, AI can identify customers at risk of churn, enabling proactive measures like tailored offers to retain them. Sentiment analysis via AI aids in understanding customer emotions toward the brand by analyzing feedback across various platforms, allowing businesses to address issues and reinforce positive aspects quickly. Large language models also display so-called emergent abilities, which are unexpected abilities in tasks for which they haven’t been trained. Researchers have reported new capabilities “emerging” when models reach a specific critical “breakthrough” size.

Assessing Conversational AI Platforms

While conversational AI and generative AI might work together, they have distinct differences and capabilities. Conversational AI is a technology that helps machines interact and engage with humans in a more natural way. Generative AI lets users create new content — such as animation, text, images and sounds — using machine learning algorithms and the data the technology is trained on. Despite their advantages, AI-based CAs carry risks, such as privacy infringement, biases, and safety issues10. Their unpredictable nature may generate flawed, potentially harmful outcomes leading to unexpected negative consequences11.

The event provided insights into how companies are utilizing these platforms to quickly identify opportunities and deliver ROI in customer service for both customers and agents. Part of a comprehensive suite of intelligent cloud tools offered by Google, DialogFlow is a solution for building conversational agents. The system leverages the vendor’s resources for generative AI and machine learning, providing a single development platform for both chatbots and voice bots. Plus, companies can access Dialogflow as part of Google’s Contact Center AI solution. Kore.AI works with businesses to help them unlock the potential of conversational AI solutions.

They want their prior conversations to carry over into their new conversations. The problem they face is that they must essentially reinvent the wheel for each new conversation. They have to expend a lot of the new conversation toward bringing the generative AI up-to-speed. Compared with other types of generative AI models, LLMs are often asked to analyze longer prompts and produce more complex responses. LLMs can generate high-quality short passages and understand concise prompts with relative ease, but the longer the input and desired output, the likelier the model is to struggle with logic and internal consistency.

They achieve this by providing collaborative tools and prebuilt modules that developers can use for common tasks like processing payments or changing passwords. Arguably, the most unique offering in the Forrester report comes from Ada, with its platform eschewing traditional NLU approaches, and instead, choosing to utilize fine-tuned LLMs from Anthropic, Microsoft, and OpenAI. In addition, the report found “holes” in the company’s LLM and GenAI support, with RAG synthesis and summarization currently only available in beta.

Harnessing Natural Language Processing (NLP) Engines

There’s no way to know that except by looking at their previous contact, and we use that to help them route it correctly. We do have something like that in the product, CallMiner GPT, to query your own data using that as an interface. But primarily our view to the AI craze — and I really will use the word ‘craze,’ because people acted irrationally for a while — is that we see it as yet another good user interface tool of automation. More than half (55%) of companies now have an AI board to steer technology purchases toward those that serve business needs and pass risk assessments.

In the new conversation, you mention that you want to plan a birthday party for your toddler. Right away, the mention of your toddler is a means to start bridging back to the snippets and is a kind of helpful clue for finding something that might be relevant. Lo and behold, you ask what kind of gift to get for the toddler, and the link to the toddler’s interest in jellyfish is computationally computed as highly relevant. ChatGPT then generates an indication to make a birthday card for the toddler that incorporates a jellyfish-related element. OpenAI recently posted their blog an announcement entitled “Memory and New Controls for ChatGPT”, OpenAI Blog, February 13, 2024.

Automating Analysis to Improve Productivity

They get a cut of the money that websites spend on improving their online visibility through paid placements, ads, affiliate marketing and the like, collectively known as search engine marketing. For example, approximately 58% of Google’s 2022 revenues – or almost $162.5 billion – came from Google Ads, which provides some of these services. Rather than getting a list of links, both organic and paid, based on whatever keywords or questions a user types in, generative AI will instead simply give you a text result in the form of an answer.

Promising business and contact center leaders an intuitive way to automate sales and support, Yellow.AI offers enterprise level GPT (Generative AI) solutions, and conversational AI toolkits. The organization’s Dynamic Automation Platform is built on multiple LLMs, to help organizations build highly bespoke and unique human-like experiences. Conversational AI solutions are quickly becoming a common part of the modern contact center.

To ensure the safe and effective integration of AI-based CAs into mental health care, it is imperative to comprehensively review the current research landscape on the use of AI-based CAs in mental health support and treatment. This will inform healthcare practitioners, technology designers, policymakers, and the general public about the evidence-based effectiveness of these technologies, while identifying challenges and gaps for further exploration. Tune in to our webinar to learn more about this new feature and how companies are seizing the opportunities of conversational AI to empower agents and elevate customer experiences. When a user asks an assistant a question, watsonx Assistant first determines how to help the user – whether to trigger a prebuilt conversation, conversational search, or escalate to a human agent.

Yet, with businesses and brands realizing AI can transform the customer journey, this is changing. Produced by the CBOT.ai company, the CBOT platform includes access to resources for conversational AI bot building, digital UX solutions and more. The no-code, and secure solution helps companies design bots that address all kinds of use cases, from customer self-service to IT and HR support.

Conversational agents (CAs), or chatbots, have shown substantial promise in the realm of mental health care. These agents can assist with diagnosis, facilitate consultations, provide psychoeducation, and deliver treatment options1,2,3, while also playing a role in offering social support and boosting mental resilience4,5,6. Yet, a majority of these CAs currently operate on rule-based systems, which rely on predefined scripts or decision trees to interact with users7. While effective to a certain degree, these rule-based CAs are somewhat constrained, primarily due to their limited capability to understand user context and intention.

Two years of ChatGPT: the conversation that never ends – Computerworld

Two years of ChatGPT: the conversation that never ends.

Posted: Fri, 29 Nov 2024 08:00:00 GMT [source]

The study selection process, data extraction, and risk of bias assessment were carried out by H.L. The article was revised critically for important intellectual content by all authors. To evaluate the quality of evidence presented in the two primary meta-analyses of RCTs, we used the GRADE approach73, which provides a holistic assessment of the combined evidence from meta-analyses. It incorporates five key considerations, and the quality of evidence may be downgraded if any of these are not adequately met. Conversely, factors like a large magnitude of effect or evidence of a dose-response gradient can lead to upgrades. The overall quality of evidence can be classified as high, moderate, low, or very low.

If ChatGPT were using the new functionality, we can suppose that the remark about the jellyfish would be a potential snippet that has been placed into the datastore. Upon starting a new conversation, all of the numerous snippets are sitting at the ready. Some of them might be pertinent to the new conversation, but many are probably not pertinent.

If we merely had a simple switch that would allow all your generative AI conversations to fully intermingle, which admittedly could be relatively easily done, the odds are that you would have a devil of time with each new conversation. The chances are that the new conversation is going to get overcome and flooded with stuff that is not relevant to whatever you intended the new conversation to be. You’ve worked at this office for two years and thus have amassed around 5,000 conversations with that one person. I am going to return to my human-to-human conversational postulations to get the thought experiment underway. Again, do not overstate this and please do not assign any sentience to AI out of this. Let’s dig into this topic of conversational interlacing and see what we can fruitfully uncover.

Plus, Laiye ensures companies can learn from every interaction, with real-time dashboards showcasing customer and user experience metrics. After each session, the system rates the answers of each bot, allowing them to learn and improve over time. Moreover, Laiye’s offering can interact with tools like Salesforce, Slack, Microsoft 365, and Zendesk.

The RAND report lists many difficulties with generative AI, ranging from high investment requirements in data and AI infrastructure to a lack of needed human talent. However, the unusual nature of GenAI’s limitations represents a critical challenge. Many compelling prototypes of generative AI products have been developed, but adopting them in practice has been less successful. A study published last week by American think tank RAND showed 80% of AI projects fail, more than double the rate for non-AI projects.

For example, text-to-image systems like DALL-E are generative but not conversational. Conversational AI requires specialized language understanding, contextual awareness and interaction capabilities beyond generic generation. Generative AI is a broader category of AI software that can create new content — text, images, audio, video, code, etc. — based on learned patterns in training data.

Collier said he envisions a future where such systems evolve into personalized shopping agents that understand and anticipate individual preferences, creating a “universe of one” for each customer. Together, goals and nouns (or intents and entities as IBM likes to call them) work to build a logical conversation flow based on the user’s needs. If you’re ready to get started building your own conversational AI, you can try IBM’s watsonx Assistant Lite Version for free. Frequently asked questions are the foundation of the conversational AI development process.

It remains to be seen just how much time these tools will save the average user throughout the workday, but Slack says it remains committed to artificial intelligence. To that end, the company is prepping more native AI features, including the ability to generate personalized summaries of channels that users don’t check daily but want to keep an eye on. Additionally, Slack says it’ll soon integrate some of its most-used third-party apps into the AI ecosystem.

But the SEO marketers and consultants who depend on search engines – mostly small- and medium-sized companies – will no longer be needed as they are today, and so the industry is unlikely to survive much longer. When users start ignoring the sponsored and editorial result listings, this will have an adverse impact on the revenues of SEO consultants, search engine marketers consultants and, ultimately, the bottom line of search engines themselves. Over time, as the quality of AI-generated answers improve, users will have less incentive to browse through search result listings. They can save time and effort by reading the AI-generated response to their query. Google, Microsoft and others boast that generative artificial intelligence tools like ChatGPT will make searching the internet better than ever for users.

The key here is that your conversations are usually considered completely distinct and separate from each other. The term generative AI refers to AI systems that can create new content, such as text, images, audio, video, visual art, conversation and code. Wong noted how Thomson Reuters is best positioned to develop professional-grade AI, grounded in fact and data. He emphasized customers’ need for measurable solutions, so they can discern tools’ accuracy rates, as well as the need for security and privacy. For example, generative AI systems can solve some highly complex university admission tests yet fail very simple tasks. This makes it very hard to judge the potential of these technologies, which leads to false confidence.

Don’t miss the opportunity to meet Botwa.ai at Stand No- 21D-35, Hall 21 during GITEX AFRICA 2024. Discover how our advanced AI solutions can drive your business forward today, delivering ROI and operational efficiency within weeks. Join us as we lead the charge in redefining customer experiences and fostering innovation in the Middle East and Africa markets. While these two branches of AI are different, they are not mutually exclusive. In fact, AI programs like ChatGPT involve both — it’s conversational, since it’s a chatbot, yet it is also generative, since it provides users with written content in response to prompts. The application uses AI to guide customers through their wine selections, responding dynamically to informational and product-specific queries.

• In this article, we’ll explore conversational AI, how it works, critical use cases, top platforms and the future of this technology.
• Conversational Search expands the range of user queries handled by your AI Assistant, so you can spend less time training and more time delivering knowledge to those who need.
• In addition, we are working to enhance our goal-oriented conversations feature, making it easier to define and execute a more extensive range of objectives.
• Personalization features within conversational AI also provide chatbots with the ability to provide recommendations to end users, allowing businesses to cross-sell products that customers may not have initially considered.

There are already many generative AI use cases for customer service – and one of the most widely deployed is auto-summarizing customer conversations. Because even if we say all solutions and technologies are created equal, which is a very generous statement to start with, that doesn’t mean they’re all equally applicable to every single business in every single use case. So they really have to understand what they’re looking for as a goal first before they can make sure whatever they purchase or build or partner with is a success. And that’s where I think conversational AI with all of these other CX purpose-built AI models really do work in tandem to make a better experience because it is more than just a very elegant and personalized answer. It’s one that also gets me to the resolution or the outcome that I’m looking for to begin with. That’s where I feel like conversational AI has fallen down in the past because without understanding that intent and that intended and best outcome, it’s very hard to build towards that optimal trajectory.

I’d like to next share some weighty background research and once I’ve done so, we can take a close look at the latest attempt at interlacing of conversational dialogue in generative AI which has prominently been announced by OpenAI for ChatGPT. This foundation that I’ve provided to you herein will allow you to see things clearly and with your eyes wide open. Some prior conversations about oysters that were known to the couple have reared its ugly head. There isn’t a bona fide need in this instance to go down the rocky road of the oysters.

For example, IBM® AskHR gives employees the ability to perform HR tasks with self-serving virtual assistants. IBM employees have found that it’s 75% quicker to perform tasks with AskHR than without, and AskHR is able to contain 90% of inquiries without the need for escalation. IBM watsonx Orchestrate delivers conversational AI and automation capabilities to transform how work gets done in the enterprise, through a unified user management experience.

When a customer submits a help ticket, your NLP model can easily analyze the language used to divert the customer to the best agent for the task, accelerating issue resolution and delivering better service. Let’s explore the various strengths and use cases for two commonly used bot technologies—large language models (LLMs) and natural language processing (NLP)—and how each model is equipped to help you deliver quality customer interactions. Plus, SmartAction’s conversational bots can leverage visual elements, text, and voice, to create personalized experiences for users. The company’s ecosystem can integrate with existing contact center and business apps, and offer excellent data protection and security tools. Laiye promises companies an easy-to-use platform for building conversational AI solutions and bots. The no-code system offered by Laiye can handle thousands of use cases across many channels, and offers intelligent and contextual routing capabilities.

This is no surprise when you consider that to take advantage of AI, organizations require stacks of good data – and for the banking industry, data is plentiful. The development of photorealistic avatars will enable more engaging face-to-face interactions, while deeper personalization based on user profiles and history will tailor conversations to individual needs and preferences. In the coming years, the technology is poised to become even smarter, more contextual and more human-like. With products available today, unlike many competitors still in the development phase, Botwa.ai has already enabled clients to achieve tangible ROI and operational efficiency.