Observe AI Introduces 30-Billion-Parameter Contact Center LLM with New Generative AI Product Suite
GlobalData research consolidates that the tech sector’s hiring in generative AI increased approximately 600% from March 2023 to June 2023. The deployment of AI has entered uncharted territory as the technology and legal landscape both evolve. Establishing forward-looking frameworks for responsible AI has never been more important. Regulating explicable – or “explainable” – AI models is completely different when it comes to AI models that cannot be explained or interpreted; the regulatory framework will only apply to their inputs and outputs. Organisations will need to consider the level of disclosure they are required to make regarding their use of generative AI, both internally to personnel and more publicly, depending on the AI use cases. A number of existing laws and regulatory requirements, as well as laws that are on the horizon, will require disclosure of certain types of AI use.
Now let’s move on to the next big thing in consumer-oriented AI tools – ChatGPT. While ChatGPT is the term that has dominated the news, it’s been used along with these other terms in a confusing word soup. Contact centre managers can then analyze this data to develop ways to improve customer interactions and improve contact centre KPIs. GPT LLMs, however, are able to process and analyze large amounts of call transcripts, chat logs, and social media interactions.
Recently, the content marketing industry has encountered a significant shift due to the rise of generative artificial intelligence (AI). 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. Although there is not a consistent definition, it is increasingly being used to refer to an undefined group of cutting-edge powerful models, for example, those that may have newer or better capabilities than other foundation models. And as technologies develop, today’s frontier models will no longer be described in those terms.
This means that they predict the likelihood of a character, word or string, based on the preceding or surrounding context. For example, language models can predict the next most likely word in a sentence given the previous paragraph. This is commonly used in applications such as SMS, Google Docs or Microsoft Word, which make suggestions as you are writing. As noted above, some of these, such as generative AI and large language model, are well-established terms to describe kinds of artificial intelligence.
Generative AI Use Cases and Benefits for Enterprises
AI News spoke with Damian Bogunowicz, a machine learning engineer at Neural Magic, to shed light on the company’s innovative approach to deep learning model optimisation and inference on CPUs. In a bid to democratise access to AI technology for climate science, IBM and Hugging Face have announced the release of the watsonx.ai geospatial foundation model. Microsoft Azure users are now able to harness the latest advancements in NVIDIA’s accelerated computing technology, revolutionising the training and deployment of their generative genrative ai AI applications. OpenAI has announced the ability to fine-tune its powerful language models, including both GPT-3.5 Turbo and GPT-4. Another such agent is BabyAGI, which was created by a partner at a venture capitalist firm in order to help him with day-to-day tasks that were just too complex for something like ChatGPT, such as researching new technologies and companies. LLMs are software algorithms trained on huge text datasets, enabling them to understand and respond to human language in a very lifelike way.
For now, marketers leveraging generative AI should monitor legal developments closely and limit training models on copyrighted data if clients are risk-averse. AI-driven code generation is a growing field that can assist developers in writing, debugging, and optimising code. Models like OpenAI’s Codex can understand programming languages and generate code snippets, automate repetitive tasks, and even build entire applications. This not only genrative ai speeds up the development process but also makes programming more accessible to those without formal training. ChatGPT itself recently told me that “generative AI, like any other technology, has the potential to pose risks to data privacy if not used responsibly”. And it doesn’t take too much imagination to see the potential for a company to quickly damage a hard-earned relationship with customers through poor use of generative AI.
Why this policy exists
Generative AI can create tailored training materials and simulations for manufacturing employees. By providing personalized learning experiences, LLMs can help workers acquire new skills and knowledge more rapidly, improving overall workforce efficiency and adaptability. Generative AI can be employed to create dynamic, interactive data visualizations that transform complex datasets into easily understandable formats.
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.
The opportunity for marketers is in the combination of human creative leveraging of these technologies to supercharge outputs and cut down on the time required across mundane tasks. Market Logic is already unlocking the power of trusted insights for some of the world’s biggest consumer brands, including, Dyson, Unilever, Vodafone, Philips, Visa, and Astra Zeneca. Its platform allows companies to centralize all internal and external knowledge assets, conduct new research, and connect with business stakeholders through a single hub. The openness will help mitigate the bias inherent in AI systems, Meta claimed, as it will allow researchers to see the training data and code used to build it. Without safeguards put in to restrict user access if rules are broken – like with OpenAI’s ChatGPT and Google’s Bard – open source AI models could potentially be used to generate limitless spam or disinformation.
We know. It’s a lot to take in.
Because of this, the model is able to accurately capture contextual data and long-range dependencies. They can also be used to analyze data from multiple sources and identify new patterns and trends in customer sentiment. You ask a question about epilepsy and you get a response that could come from a medical researcher.
This can be a quick way of starting some research but requires particular care when using (for example, to ensure you have captured all relevant sources). Another promising avenue of investigation is the pairing of fact-checking models with LLMs. Over the last few years, several AI-powered fact-checking models and benchmarks genrative ai have been developed. Recent work has shown that fact-checking capabilities can be added to LLMs, allowing them to proofread and correct generated outputs on the fly. These technical developments show promise but are still very early, and at Filament, we are starting to experiment with some of these techniques.
- As the model is already fine-tuned to the unique needs of the contact center, it offers higher performance out-of-the-box compared with generic models.
- With the eventual rise of Artificial General Intelligence, most development work will be done by the AGI itself.
- Hallucination still presents an obstacle in this use case, but the effect can be reduced using additional rules and logic as a post-processing step applied to the LLM output.
- Forget having to fumble around for your order number or navigate a generic company home page.
People spend far more time interacting with screens than with real people in real places. It is unsurprising – social media apps from Twitter to TikTok are optimised to grab your attention. We already know that people are becoming lonelier and suffering more mental illness. There are all sorts of explanations, from the decline in local community organisations to the increase in economic insecurity.
Is the future of travel and hospitality data-driven?
Part of ACW involves providing a post-interaction summary so that the next agent is prepared for a follow-up conversation with that customer, but writing these summaries manually is time-consuming. Whether you’re a 10-year-old kid researching a homework assignment or an engineer looking for coding advice, ChatGPT is accessible and easy to use. ChatGPT is a chat application that can hold a human-like conversation about almost any topic. Because of OpenAI’s cozy relationship with Microsoft, these APIs are also available for paid use via Microsoft Azure.
Part of that equation is the routing itself – understanding where to send them and who is available – but it’s also ensuring that the receiving agent has a summary of the information needed to get quickly up to speed on the specific issue. 3 in 4 customers who have interacted with generative AI want and are comfortable with human agents using it to help answer their questions. It’s here – the elimination of manual workloads – where companies will realistically see the biggest gains from generative AI in the short term. Imagine an agent receiving an accurate, customised summary of a customer’s previous issues instead of having to dig up that information on multiple pages or systems.
If ChatGPT were a new employee, you wouldn’t immediately put them in front of a customer on the first day – even if they were great at speaking English. Together, EQ and IQ join forces to ensure that customers reach the right person, issues are escalated when needed and agents can provide better service with the right information (quickly) in hand. Generative AI will also help companies reimagine how customers engage with help centre content. Picture your chatbot receiving a question about how to process a refund, retrieving relevant answers from your help centre and then customising a conversational response.
However, the text manipulation abilities of open source LLMs and their accessibility raises the prospect of a wave of AI-generated text content that threatens to overwhelm content prepared by humans and increase the potency of disinformation campaigns. Google Bard, however, isn’t built on GPT, having been built by Google using their LaMDA family of large language models. But it’s a similar concept, providing a public-facing chatbot to assist in search results. GenAI differs from typical machine learning because it doesn’t rely on labelled data sets or supervised learning techniques but uses generative models to create new ideas or solutions.