The correct answer from Chat GPT-4 shows that the LLMs now provide amazing answers. In order to correctly predict the next word in a sentence, an LLM must understand the text. However, LLMs are not yet able to understand the world as comprehensively and deeply as humans. A major difference lies in how LLMs and humans learn. LLMs are trained exclusively with text data. Humans, on the other hand, learn not only through language, but above all through direct experience. These enable us to understand complex relationships, feel emotions and find creative solutions.
LLMs not only open up opportunities, but also harbour dangers. The AI mastermind you mentioned, Geoffrey Hinton, recently warned that superintelligence will arrive sooner than expected. How do you categorise this?
This warning is all the more remarkable given that Geoffrey Hinton is known as a cautious and thoughtful researcher. His warning is aimed at motivating governments and companies to take a serious look at the development and regulation of artificial intelligence. The main danger Hinton sees lies in the immortal nature of AI models, which can be reproduced at will and copied in seconds. Humans, on the other hand, can only exchange information slowly and their knowledge is lost when they die. This asymmetric dynamic harbours significant risks, as powerful AI systems could be misused without adequate controls, for example by being used to spread disinformation, manipulate opinions, carry out cyberattacks or violate privacy.
Asset management could also benefit from the use of transformers and LLMs. What are application examples?
The transformer architecture of the LLMs can be used to predict time series and future trends. In the Systematic Equity Team in Asset Management at Zürcher Kantonalbank, we are currently working on developing a model for forecasting equity returns with the help of transformers. LLMs can also be used to analyse companies' quarterly reports. By assessing sentiment in company reports, news and conference calls, investors can gain a better understanding of a company's sentiment and potential future developments.
So LLM also supports equity research?
In fact, support for stock analyses is another important area of application for LLMs. LLMs can process and summarise large volumes of data from various reports. This provides analysts with a comprehensive source of information and facilitates the processing and evaluation of the available data. This enables them to make more informed decisions and base their recommendations on a broader database.