Chat GPT, Generative AI and the future of creative work

Despite businesses embracing personalisation as a tactic when using email (89%) and social (73%) channels, only 40% are using personalisation on their website. In my work, I leverage both IT skills and business knowledge to run analytics projects in various industries such as telco, retail, automotive genrative ai or banking. In the summer of 2023, the UN declared that the International Community Must Urgently Confront New Reality of Generative, Artificial Intelligence. The emergence of generative AI as a core issue in global governance also has institutional and logistical import for the humanitarian sector.

• Results from queries can be visualizations or fully formed narratives thanks to Data Stories, which transforms data into natural language stories.
• 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.
• Earlier this year, Kore.ai introduced the zero-shot and few-shot models as part of its efforts to help design conversations and create training and test data to develop truly intelligent and intuitive virtual assistants.

As we continue to explore the immense potential of AI, understanding these differences is crucial. Both generative AI and traditional AI have significant roles to play in shaping our future, each unlocking unique possibilities. Embracing these advanced technologies will be key for businesses and individuals looking to stay ahead of the curve in our rapidly evolving digital landscape. We were promised self-driving cars and cures for cancer, and we ended up with splashy tools for image generation,” wrote Ben Recht, professor of machine learning at the University of California, Berkeley, shortly before the release of ChatGPT. Generative AI is fundamentally a word completion tool that has no understanding of the material it is handling. As The Verge explained, it’s creating “a massive game of AI misinformation telephone, in which chatbots are unable to gauge reliable news sources, misread stories about themselves, and misreport on their own capabilities”.

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Michael is the Chief Science Officer at Prudential plc and the founder and head of Prudential’s Centre of Excellence for Artificial Intelligence (AI CoE). He joined Prudential in 2016 from Silicon Valley based Pivotal Labs where he built and led the Data Science team. His experience lies in the application of artificial intelligence methods to large-scale, multi-structured data sets, in particular neural network based deep learning techniques. Michael previously founded and sold a London-based machine learning startup and prior to that was a partner at a major consulting firm.

Catalina Baincescu is Team Lead at CAI Romania & Technical Lead at E.ON Software Development. She is responsible for development of numerous chat and voice assistants internationally at E.ON, starting from simple FAQ bots to complex transactional assistants deployed on different customer-facing channels. Catalina is supporting E.ON business units in dealing with their demand in the most efficient way, discussing and consulting on their systems architecture and how that can be integrated with the platforms that the E.ON group has. Catalina has a degree in Computer Science and has been volunteering to educate children in Romanian school on the basics of computer science field.

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Personal finance and Health for example constitute two super important areas of life that consumes everyone attention. There is definitely opportunity to build foundation decision models within these domains. In the case of foundation models, as well as many end applications and purposes, there can be multiple developers and deployers in the supply chain. Because of their general capabilities, there may be a much wider range of downstream developers and users of these models than with other technologies, adding to the complexity of understanding and regulating foundation models. It has been used as a tool in many industries including gaming, entertainment, and product design and manufacturing.

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Tessa specialises in the ethical governance of algorithmic and data intensive systems, considering dimensions such as fairness, accountability, transparency and explainability. She has delivered Responsible AI programs covering governance, training and resource development in scientific publication and health domains, and is part of an international research group genrative ai which explores how trust is built in digital environments. Kamal is a technology leader obsessed with customer experience and have expertise in leading world class products including AI / Machine Learning, Cloud Computing, in a Software as a Service (SaaS) model. Currently he is working as Principal Data Engineering Manager for British Telecom (BT), London, UK.

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Perhaps she wanted to wind up the teaching unions, but there’s no evidence generative AI can do marking reliably. Yet even if the hallucinations are fixed, and some productivity gains are finally realised, then the pollution problems remain. We have gifted the world a tool for generating lots of what we don’t want, very cheaply. The Verge, a technology site, warned last week of the damage to “whole swathes of the web that most of us find useful — from product reviews to recipe blogs, hobbyist homepages, news outlets, and wikis”.

In this talk, we will discuss the benefits of using question answering, as well as the challenges and limitations of current technology. With the help of a suite of deep learning algorithms called “large language models,” the Google Bard AI chatbot can offer textual responses to user inquiries. The chatbot was developed using LaMDA and can search the web for the “most current” answers to user queries. Bard AI, an experimental service built by Google that simulates natural conversation, gains knowledge from its interactions with people to improve at its job. Astra DB enables Uniphore to efficiently capture and process about 200 data points per frame on meeting participants’ faces, along with analyzing voice tonality and natural language processing. So, let’s start with the hot topic of the past year, which has undoubtedly been generative AI.

It also entails a need to understand what type of expected and inadvertent projections will arise from the public use of generative AI, including both by populations in crisis and those in donor countries. Finally, continuous post analysis to understand where the risks and opportunities lie will need a data scientist’s attention. What is the feedback from outcomes into improving data, modelling and decision making? Handling bias and explainability is another big emerging theme which will require a data scientist’s focus. For example, Explainability is not just why decisions were made, but also once the decision was made and outcome registered, how do we explain the entire business outcomes chain. The collected dataset could be biased so the AI that is built on it will need to ensure it does not carry (and amplify) these biases.

Python-based projects, such as LlamaIndex and LangChain, provide similar capabilities extending the reach and capabilities of LLMs even further. There are ways to overcome this by giving LLMs access to additional data processing tools and more recent data. The screenshot below shows that the chatbot has picked up the user’s question and added it to the conversation history. However, the documentation is extensive, and several Python libraries (e.g. Streamlit) already exist that can be used to build chatbot interfaces if preferred.