The Future Of Generative AI Beyond ChatGPT

What makes ChatGPT a new iteration in AI is its impressive performance in natural language generation tasks. Compared to earlier language models, ChatGPT is capable of generating much more complex and coherent responses to prompts. It achieves this by using a large number of parameters (175 billion, as of 2021) and being trained on a diverse range of data sources. As a starting point, older neural networks (NN), also called artificial neural networks (ANN), are trained to recognize patterns and make decisions based on input data.

This kind of approach can provide a guide as to how the tools can be used and can reduce the potential risk of liability for IP infringement. Unlike previous algorithmic models used to diagnose pandemics or mitigate disinformation campaigns, ChatGPT enables users and businesses to replicate human tasks and increase operational efficiency. This does not create digital infrastructure out of scratch in remote surroundings, but it provides an opportunity for any user connected to the internet globally to enjoy the perks of AI and its automation capabilities. You should therefore heed ChatGPT when it tells you that it is
generating ‘contextually appropriate responses to user
inputs’.

What is ChatGPT?

Until this can be prevented, consumers and businesses alike need to be wary around the technology. The cyber-security industry has expressed several concerns around the security and privacy of ChatGPT. As the technology is still relatively new and being developed all the time, it’s right to have some concerns.

There are some who think that technology is for simple tasks such as opening and administering a savings account or Isa. However, AdviceBridge already goes way beyond this with technology that analyses cash, ISAs, GIAs and Pensions making recommendations as to how it should be restructured and way, way more. There is no doubt that the use of generative AI / Chat GPT is on the rise. In its first month of release ChatGPT had 57 million users (now at 170M+) and recent numbers show it has in excess of 13 million daily.

Tell students whether or not it is acceptable to use in your context

And when compared to many people’s experiences with chatbots that seem mostly to say, “Sorry, I didn’t understand that,” this experience does seem like alchemy. Before we conclude, how can you tell when an article that has ended up in your hands was written with generative AI? So you can avoid any risk of plagiarism or duplicate content to your brand. But if folk persist in copying and pasting ChatGPT content without combing through it, it’s fair to assume they’re only interested in churning out content and don’t have anything original to add to the discussion. Therefore, companies maintain complete control over their data and can generate less generic content, more aligned with their policies, and style guides optimised with up-to-date information. A private ChatGPT is the low-hanging fruit – the solution to the current risks – and the most natural first milestone on the journey to wider generative AI capabilities.

Generative AI is an umbrella term, which refers to any of the AI models that generate a novel output based on an input, often called a prompt. This broader term encompasses models that produce language, visual imagery, and audio. If you haven’t tuned in yet, ChatGPT is trained to generate responses based on prompts, pulling from diverse online sources, academic journals, books and who-knows-where-else. The platform (developed on Google Cloud Platform and OpenAI) optimises both data cycles and the training of artificial intelligence models.

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

This
article is based on my experience with this novel technology,
chiefly ChatGPT, as at May 2023, and aims to provide some thoughts
on the potential benefits and pitfalls for the legal profession in
its use as well as examples of potential use cases. There are loads of things we (in HE and education more broadly) need to think about and do when it comes to generative AI, both cognitively and practically. I am alert to and concerned about the ethical and practical implications of generative AI tools but here want to focus on ways in which we (teachers and students) might find ways to use these tools productively (as well as ethically and with integrity). My view is that the ‘wow’ (or OMG) moment experienced when you witness tools like chatGPT spouting text needs to be looked beyond and ways in which the mooted idea of AI personal assistants can actually be realised need to be explored and shared. As a compulsive fiddler I am sometimes struck by how little other people have experimented but need to remember that stuff I might do in my spare time may have limited appeal for others (I am, after all, a Spurs supporter). The use of large language models extends well beyond this use case, too.

Founder of the DevEducation project

As such, businesses that are able to effectively leverage the technology are likely to gain a significant competitive advantage. ChatGPT’s terms of use indicate that OpenAI assigns to the user ‘all its right, title and interest’ in the content generated by the chatbot, which indicates that, at least in the case of ChatGPT, the user in fact owns the output. However, OpenAI’s position is not indicative of the approach to ownership of output across the board, nor is the existing law anywhere near to being settled on this point. Current application of AI already stretches beyond chatbots and permeates most industries.

ChatGPT and Artificial Intelligence

Towards the end, the book provides insights into the substantial improvements and advancements in these technologies. It also helps you identify several areas for further research and development that could enhance the capabilities of ChatGPT in the near future. This latest class of generative AI systems has emerged from foundation models—large-scale, deep learning models trained on massive, broad, unstructured data sets (such as text and images) that cover many topics.

This is when vast quantities of text, in this case from the internet, were fed into a complex machine learning (ML) program running on incredibly powerful computers. Such statements certainly look like an attempt by OpenAI to push the responsibility for incorrect answers provided by ChatGPT back onto users (or at least to absolve itself of liability). These caveats and warnings are likely to be noted by courts when assessing defences pleaded by individuals who have re-published inaccurate information originally produced by ChatGPT. However, the authors’ view is that they are less likely to be deemed adequate to remove OpenAI’s responsibility for unlawful content generated via ChatGPT, especially if detriment is caused to the reputation of an individual to whom such content relates.

However, these responses tend to be vague and general, with little real detail and personalisation. They also may not be accurate – citations are likely to be irrelevant or even made-up. Generative AI platforms are large language models, or natural language processers, that draw on huge datasets to respond to questions or prompts. You might like to think of them as highly developed versions of predictive text programs – they respond to prompts by trying to predict what a human responder would most likely say next. ChatGPT runs on a language model named GPT-3.5, and it is powered by RLHF methodology.

Listen to the SalesforceBen webinar to see the future

For example, I would never recommend using it to translate anything deemed confidential or input any data or information specific to your business that isn’t publicly available information. If you’re genrative ai working within financial services, for example, any marketing material needs to meet the requirements of the FCA. This is something that you’re likely accustomed to checking within your organisation.

This chapter will focus on the statutory limitation periods for
professional negligence claims in the context of financial remedy
claims, highlighting the relevant provisions of the Limitation Act
1980. It will also address the pre-action protocols and procedures
applicable to professional negligence claims, including the
Professional Negligence Pre-Action Protocol. This chapter will examine the most frequent allegations of
professional negligence arising in genrative ai financial remedy claims, such as
inadequate advice, failure to disclose relevant information,
incorrect valuation of assets, and errors in drafting agreements. It will also discuss relevant case law and the applicable legal
principles for establishing liability. This chapter will provide a comprehensive overview of the
financial remedy process on divorce and dissolution of civil
partnerships, highlighting the critical stages and procedural
requirements.

Beyond this, NLP provides the ability to respond in natural language too, which comes to the fore in use cases such as chatbots. We would recommend taking a data protection by design approach at the outset of a project involving AI. The launch comes as generative artificial intelligence, based on large language models such as GPT and Bard, aims to transform the way people work, taking companies to a higher level of automation and efficiency. A recent MIT study in which different professionals were asked to use Chat GPT to write reports, press releases and other business documents achieved savings of around 50% in report writing and improve report quality. Additionally, the reliance on generative AI technologies could lead to a reduction in critical thinking and problem-solving skills as well as a lack of creativity in the learning process – all of which are essential for academic success. This is because educators and students may become overly dependent on such tools as automated essays or responses.

• As such, it’s logical to conclude that users and especially developers of generative AI should uphold ethical standards.
• A core concept in the architecture of these models is the
  ‘transformer,’ which excels at processing and understanding
  sequences of information, making it particularly well-suited for
  tasks involving language.
• Before using generative AI in business processes, organisations should consider whether generative AI is the appropriate tool for the relevant task.
• Processes that exist in other contexts regarding procurement, development, implementation, testing and ongoing monitoring of IT systems should be reviewed, adapted and applied as necessary across the roll-out and use lifecycle of a generative AI system.
• Higher education can no longer verify the skills and capabilities of a given student with existing formats of asynchronous assessments such as homework and take-home exams.

Whether this will turn into a battleground for defamation and data protection-based claims remains to be seen. One reason it might not is that ChatGPT is predominantly used by an individual user. However, as these large language models are integrated into larger platforms (eg search engines), so their content will be published more widely and the risk of reputational harm to individuals referred to will increase. We have no doubt this article will need to be updated and developed as the months and years go by. AI systems are trained on large amounts of data, some of which is likely to contain personal data. Any personal data processed by an AI system must be processed in accordance with the requirements of data protection laws including the UK GDPR and EU GDPR.