Generative Artificial Intelligence: beyond deepfake, the new frontiers of innovation

Generative AI systems may be processing legally or commercially sensitive data and may be deployed in the context of regulated or operationally critical processes, with varying degrees of human involvement. As with other software, cyber-security and operational resilience requirements and considerations will apply to the use and procurement of generative AI systems. Furthermore, generative AI can be used to automate insurance claim processes, facilitating faster claims settlements. By utilising algorithms that analyse images or other visual data, insurers can expedite claim processing, minimising the time and effort required from customers. This not only exceeds customer expectations but also reinforces the insurer’s commitment to prompt and efficient service.

• Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interactions between humans and computers using natural language.
• This synthetic data can be used to augment training datasets for other applications, increasing their diversity and enabling more robust training.
• For example, in customer service, AI agents can handle customer queries, provide information, and resolve issues.
• This creates a dangerous situation where an algorithmically biased machine may be viewed by the user as an objective tool that must be correct.
• As AI continues to evolve, it’s important for marketers to stay up-to-date with the latest developments but also use them responsibly.

Generative AI can also assist in risk modeling and forecasting, generating synthetic scenarios to assess potential market risks and optimize investment strategies. These models are trained on massive amounts of data, from which they learn patterns, grammar, context, and even some degree of common sense knowledge. Generative art is art that has been created (generated) by some sort of autonomous system rather than directly by a human artist. Nowadays, the term is commonly used to refer to images created by generative AI tools like Midjourney and DALL-E.

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It is likely therefore that the UK will not make major changes to legislation governing AI and copyright until it is forced to do so, perhaps as a result of case law such as the Getty Images case. With AI capabilities accelerating, it may be that 2023 is the year that such regulatory change will in fact be triggered. Whilst the benefits of AI are extensive, there are nevertheless significant ethical and legal challenges accompanying the technology that must be considered as AI continues to improve and advance. For this reason, it is important that, at least in the near future, AI is monitored by humans. The focus of this article is on the legal issues related to content generating AI such as ChatGPT and Dall-E.

By analysing customer preferences and behaviour, generative AI models can generate personalised recommendations and offers, enhancing the overall customer experience. This can lead to increased customer satisfaction and loyalty, ultimately benefiting insurance companies. Notwithstanding the risks laid out above, it is also clear that Generative AI could create tremendous value for our economy and society.

#3 Enhanced Customer Experience

Although the last few years have seen amazing advances in the AI tools and applications that are available for business, it’s very clear that we’re still in the early stages of the journey. Many of the future developments we will see are likely to arise from combining generative, predictive, and prescriptive elements of AI. A key difference is that while predictive AI forecasts the future based on past (or current, real-time) data, prescriptive AI tells us how we can shape the future according to our own requirements. Bonaci says, «Models that predict the future based on what’s happened in the past … helps businesses enable and anticipate customer behavior, forecast market demands, optimize operations, or any other type of data-driven decision. So many people are talking about generative artificial intelligence (AI) these days that it’s easy to forget it’s just one category of AI application that’s making a difference to businesses today.

Getty Images, known for its historical and stock photos, has sued AI image generation Stability AI, the maker of Stable Diffusion, for copyright infringement. Getty alleges that the company copied over 12 million of its images to train its AI model ‘without permission or compensation’. Metaphysic is also capable of processing live video in real-time, which is at the cutting edge of AI technology. They demonstrate this by replacing the interviewers face with Chris’s in a live video, and even replicating the voice. They can apply this technology to anyone, as demonstrated with Sunny Bates in the audience.

Regulatory developments

Generative AI refers to a class of artificial intelligence that can create content, and it can learn patterns, understand contexts, and apply these learnings to produce new and original content. Whether generating a piece of music, crafting an article, designing a new product, or even creating a piece of software, Generative AI is up for the task. And that’s all before we get to considering emerging regulatory frameworks for AI technology such as the EU’s draft AI Act and sector specific regulations and codes of conduct. Generative AI is a type of artificial intelligence (AI) that is used to create new content, such as images, videos, and text. It is a powerful tool that can be used to generate new ideas, solve problems, and create new products.

Focusing on how exactly AI will reshape the marketing workforce, and the skills we’ll need to thrive now and in the future. The capability of artificial intelligence (AI) – and in particular generative AI – has greatly accelerated in the last few months with advancements in the form of Chat-GPT and AI image generation platforms such as DALL-E, Stable Diffusion and Midjourney. ChatGPT was also refined through a process called reinforcement learning from human feedback (RLHF), which involves “rewarding” the model for providing useful answers and discouraging inappropriate answers – encouraging it to make fewer mistakes. This technology has seen rapid growth in sophistication and popularity in recent years, especially since the release of ChatGPT in November 2022.

Table 1: Examples of foundation model applications

One notable example of generative AI is Large Language Models (LLMs), which are powerful tools that learn from huge amounts of text found in various sources like websites, books, and articles. For HR there will certainly be new policy requirements required governing the intentional and unintentional passing-on of information. Policies will need to dictate when and how AI can and should be used for work purposes. Only by doing this will employees have clarity around the sharing of personal information and IP.

This scalability is particularly beneficial for tasks related to content generation or customer service, where AI can manage increased workload seamlessly. With Generative AI, you can interrogate your business data in natural language, making data genrative ai analysis more accessible and less time-consuming. Instead of writing complex queries or code, you can ask the AI system questions like, «What was our best-selling product last quarter?» or «How many new customers did we gain last month?».

Fake news online is already a huge issue which has led to serious concerns about the authenticity of digital media and its impact on public discourse and democracy. With generative AI this trend will only worsen as new AI tools continue to develop and made available to anyone. Recognizing that trust is an essential factor in encouraging the uptake of AI tools; these use generative methods such as natural language generation to explain genrative ai how and why its decisions have been made in an attempt to eliminate the «black box» problem of AI. Prescriptive AI goes a step further than predictive AI by suggesting the best possible course of action. For example, in a healthcare scenario, computer vision ML algorithms might be used for a predictive AI application in order to determine which of thousands of medical images are likely to signify that a patient has cancer.

Some commentators also suggest AI will lead to a more independent board because decisions are based on the neutral output of information and may give a stronger dissenting voice to independent directors whose positions may be supported by AI. Because foundation models can be built ‘on top of’ to develop different applications for many purposes, this makes them difficult – but important – to regulate. When foundation models act as a base for a range of applications, any errors or issues at the foundation-model level may impact any applications built on top of (or ‘fine-tuned’) from that foundation model. An emerging type of AI system is a ‘foundation model’, sometimes called a ‘general-purpose AI’ or ‘GPAI’ system. These are capable of a range of general tasks (such as text synthesis, image manipulation and audio generation).