ChatGPT and Generative AI in Financial Services: Reality, Hype, What’s Next, and How to Prepare

You can feed it prompts, instructions and examples to inform the tone to some degree, but in many cases, human judgement is still important. Maybe you’ve tested them out yourself – either for fun, to see what they can do, or to investigate the practical applications of AI in your own work. Perhaps you’ve already started using tools like ChatGPT to help you with content creation, generating blog post drafts or social media posts. genrative ai Problems with the input to AI include the legal hurdles to ‘data scraping’ – namely obtaining large amounts of ostensibly public data to train AI programs. Another danger arising from AI input is that the terms and conditions of ‘free to use’ software may allow developers to use information and prompts fed into the system for their own purposes. The risks of amplifying misinformation, bias and inequality are major threats.

Some have sighted research from reputable consultancies whilst others have stamped their feet and made lots of noise but the evidence is hard to refute. When I wrote my initial article on AI & ChatGPT at the start of the year it seemed to have come out of nowhere and there was lots of speculation. In the ensuing months more and more firms have made announcements of their developments in the space but this is all something they have been working on in the background for years. This is not a “lets jump on the bandwagon” and start developing, this is the fruition of development that is now being tested and brought under the spotlight for the world to see. We already know that people are becoming lonelier and suffering more mental illness.

ChatGPT-social: the other side of generative AI

For example, they can generate training data for unstructured documents, or it can help users locate answers to queries based on the data ingested and prompt information. But one important point that ChatGPT has missed from its self-generated autobiography is how
it’s been trained. OpenAI’s training process uses Reinforcement Learning from Human Feedback (RLHF), a technique which involves human trainers providing feedback to the AI model, helping it establish common preferences which influence its future outputs. This is one of the major factors that’s helped ChatGPT become a game changer in generative AI.

• In 2018 I realised that AI was going to change our world dramatically so I completely changed my career by taking a year to study MSc AI and Data Science.
• There is no defence for repeating a false and defamatory statement made by someone else.
• The generated text is mostly original so it can often get around our existing safeguards and detection methods such as originality checkers.
• It will explore the legal concept of duty of care, breach
  of duty, causation, and damages, along with relevant case law.
• The maximum length of prompt, response and the conversation is 4k tokens.

Inaccurate or incomplete responses can be especially problematic in fields that require a high degree of precision, such as the sciences. Generative AI has a massive potential, but so far, it’s mostly gone untapped. Watch the demo below to see how large language models and generative AI can be used in everyday business processes. The rise of generative AI has the potential to be a major game-changer for businesses. This technology, which allows for the creation of original content by learning from existing data, has the power to revolutionize industries and transform the way companies operate. By enabling the automation of many tasks that were previously done by humans, generative AI has the potential to increase efficiency and productivity, reduce costs, and open up new opportunities for growth.

Step 1. You should acknowledge use of generative AI tools

It does this by guessing the best first word, then running that word through its dataset (text from the internet) and model (neural network) to see what the typical word is to follow. It adds that to the sentence, then repeats the process to find the next most likely word and so on. Generative AI also has a tendency to “hallucinate” in certain scenarios, i.e. make up information and make it sound real.

Tim Sargisson (ex CEO of Sandringham) supports his argument that clients will only “trust” tech with insignificant sums through referencing surveys that support this. However, these surveys can only ask people about things they don’t know or understand and are therefore flawed. If you’d asked anyone prior to aircraft development whether they’d fly on a metal plane that weighed hundreds of tonnes they would have all said no. If they turn the other way and pretend it’s not going to happen and therefore don’t embrace technology, they will fall so far behind with their proposition that they are setting themselves up to fail. However, if they embrace technology and utilise its benefits within their business and for their clients then they will be in a much stronger position and stay relevant to client needs. The current rage in the ChatGPT space are so-called chained GPT-4 models like Auto-GPT or BabyAGI.

Many people are excited about the potential of ChatGPT for business efficiency, the time it will save on researching and drafting content, and the speed at which vast amounts of information can be analysed. Others are worried about its ethical challenges, opportunity for misuse, and data security concerns when used by employees. Such requirements are particularly important where AI systems are relied on for operationally critical, regulated or customer-facing processes, especially as it may not be immediately obvious when the operation of an AI system has been hijacked. As the laws governing AI evolve, definitions such as ‘AI system’, ‘AI user’, ‘AI provider’ and ‘AI-generated content’ are being created and negotiated. Some of these definitions may be broadly drafted and could capture companies that have not previously considered themselves to be AI providers or users. Organisations will need to understand the countries and manner in which they intend to roll out the use of generative AI, as well as the scope of potentially relevant laws, in order to identify the laws applicable to their procurement and use of generative AI.

Some sectors, such as the financial services sector, may also have overarching governance and oversight frameworks under which cyber-security and operational resilience considerations may apply to certain uses of generative AI. Ethical, reputational, legal and commercial considerations will need to be addressed holistically when answering these questions. AI oversight principles and robust governance programs increasingly help organisations to centre, and appropriately frame, these transformational discussions. There is Harvard guidance on referencing generative AI on Cite them right.

Since ChatGPT’s
initial launch, several competitor products have emerged, including
Bing (which is based on OpenAI’s GPT-4 with the added
functionality of internet access), Google Bard and Anthropic
Claude. Microsoft has also announced a partnership with OpenAI to
integrate GPT-4 into its Office apps. Given the current pace of
change, genrative ai the AI landscape is likely to have evolved further by the
time you read this article. These platforms can only make use of the information they’ve been given, so they might not be as helpful with very current topics. And their responses are influenced by the same biases and misinformation that colour the internet.

This will open new possibilities for real-time translation, audio dubbing, and automated, real-time voiceovers and narrations. They are trained on massive datasets of text which can contain biases and inaccuracies. So, they are more likely to follow trends which are wide spoken but may contain outdated or incorrect information. This means that particular biases or misconceptions can be reinforced, such as historic gender biases that exist within numerous academic fields, like the prioritisation of male symptoms within health subjects.

First, we have seen the Samsung data leak, where developers asked ChatGPT for help analysing their source code, resulting in Samsung banning generative AI tools. And we’ve seen the Italian government impose, then lift, a ban on ChatGPT when the service added an option for users to request removal of personal data from the service. For the record, OpenAI has gone beyond that now, adding an option to disable chat history (so the data aren’t held for training purposes in the first place). Secondly, although ChatGPT has proven to be very valuable for content generation and translation, I’d advise this is only treated as a first draft.