Crypto & Digital Currencies
May 7, 2024
ChatGPT is closing in on 200 million users worldwide. And you’re probably one of them. It has a ton of great use cases. From checking code to getting content ideas, it can make some aspects of your workday much easier. Particularly the boring bits. But how exactly do Large Language Models, aka LLMs, and the tools it powers, e.g. Gemini, ChatGPT, work? And how might that affect you?
Large language models (LLM) are very large deep learning models that are pre-trained on vast amounts of data. It’s also a subset of Artificial Intelligence (AI).
An LLM must first be trained on the amount of data we can’t comprehend. For example, think about just all the English language content on the internet. Now add all the internet data for 50 other languages for ChatGPT. Exactly. And the amount of data increases every second. By using this insane amount of data, the LLM can begin to understand how we communicate via words.
No, we’re not referring to the cartoon or the movie unfortunately. But the technology is still pretty impressive. They enable LLMs to learn on their own through trial and error. For example, when responding to prompts they can figure out what a word is, despite it being spelt wrong. Each time a prompt is made on Chat GPT/Google Assistant/Gemini, the program gets smarter. As it uses information from each prompt to improve its accuracy. LLMs are doing what many motivational speakers tell us to do, get better every day.
This mainly comes in the form of online chatbots. They provide specialised support to customers at a scale not possible with human staff.
Payment companies can use LLMs to recognise fraudulent patterns of activity. This enables businesses to enhance their levels of security in a more efficient manner. Customers can have a greater peace of mind that their information is safe as a result.
LLMs are being used to assess individuals’ creditworthiness. It can quickly analyse large amounts of:
Customers can use fintech platforms to get advice on how to achieve their financial goals.
This includes help with:
Large language models can help fintechs to comply with regulatory requirements. Aspects of this include:
Fintech companies can utilise LLMs to help their marketing efforts. For example, an LLM can be trained to recognise large volumes of textual data. This includes customer feedback, market trends, news articles, and social media interactions. The fintechs can then use this data to gain valuable consumer behaviour insights, which can help business decision-makers. These insights can be used to make more informed business decisions.
Like with anything we humans build, LLMs have flaws. Mainly because they’re trained on data we produce, which is biased. Because we all are. So, it is sometimes biased. It’s not a human, so it won’t always understand the intricacies of human language. The response it gives you will sometimes be inaccurate. However, it’s an exciting area of technology that can do a lot of good in the future – as long as we keep this information front of mind.
LLMs are still under development, but they have the potential to revolutionise the way we interact with technology. Imagine having a virtual assistant that can understand your requests perfectly. Or educational tools that can tailor themselves to your learning style.
The future of LLMs is exciting, but it’s important to remember that they are not perfect. As with any AI, there are challenges to address, like bias in the training data and the potential for misuse.
However, LLMs represent a significant leap forward in AI, and their potential to improve our lives is undeniable. So, the next time you interact with a chatbot or use a smart search engine, remember the power of large language models working behind the scenes.
Fintech’s can utilise Large Language Models to make a wide range of tasks more efficient, saving resources that can be better used elsewhere.
However, it’s important to remember that LLM and AI as a whole aren’t the steering wheel; they are the engine.
It’s able to outdo humans in terms of volume of computation. We still need to tell it where to go and how to do things. It’s a wonderful assistant, but it can’t run a company despite some of the narratives being pushed.
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