We have ChatGPT. This is what it takes to get to ‘InvestmentGPT’

Of a google killer until the announcement of an AI-controlled humanity, the new ChatGPT application has been making waves – and even scaring people – since its introduction late last year.

ChatGPT seems like the logical culmination of advanced machine learning technology know almost everything – as well as how to express yourself in an extremely clear and erudite way.

But while ChatGPT knows a lot, it’s probably no better than a human (or any AI system for that matter) at developing a sustainable, profitable investment strategy. This is because ChatGPT is not trained on statistics, but on specific texts, ideas and descriptions. This means it is unable to analyze investment data accurately enough to make reliable decisions about investments, markets or the economy as a whole.

To accurately predict market trends or investment value, AI tools must not only be trained on data, but go a step beyond the current capabilities of most machine learning (ML) systems. That is, developing predictive models trained to self-update in response to new events that were not part of the original training models.

Rehabilitate financial advice

Trained on a huge array of databases, information sources, journalistic sites and social media from around the world, ChatGPT is essentially ML on steroids. With access to so much data, neural network-based algorithms to match data patterns, and advanced natural language processing (NLP) to communicate its findings, ChatGPT can write essays or Articledoing homework, write music, writing and debugging code, take tests (And they cheat), woo potential beaus, and much more. And all this as well, or even better than humans can do.

ChatGPT can also provide general financial advice. Still, according to experts, the advice of the AI ​​system is typical for human asset managers and financial advisors. When requested, “What is the best strategy for making money in the stock market?” ChatGPT replied:

“There is no one-size-fits-all answer to this question, as the best strategy for making money in the stock market depends on a number of factors, such as your financial goals, risk tolerance, and investment timeline.”

In doing so, it stated that there were some basic principles investors should follow: diversify one’s portfolio, invest for the long term, use dollar-cost averaging, and conduct research before investing.

“In general, the key to making money in the stock market is a well-thought-out strategy and patience and discipline in your approach,” the system concluded.

AGI: the upgrade AI needs to “play the market”?

That is a far cry from what we would expect from a sophisticated system that has access to much more information than the average investor and to the most advanced analytical tools in the world. But given the current limitations of ML – especially the fact that learning models can only be built on currently available data – ChatGPT’s financial advice is consistent with what should be expected from such systems.

Until ChatGPT and other ML-based analytic systems get a substantial upgrade, they probably won’t surpass human analysts. That upgrade would require a much more flexible modeling system — one that would allow the system to change its predictive model in response to new events that could skew existing predictions.

Artificial general intelligence (AGI) systems, for example, could provide the upgrade AI needs to “play the market”, not only by providing more human thought processes, but also by enabling those processes to account for a wide range of larger amount of data than humans could handle at one time.

Armed with massive amounts of data and sophisticated, flexible analytic systems designed to tailor predictive models as needed, AGI-based systems would be a much better bet for investment forecasting than current AI systems, including ChatGPT.

“What can (or will) be” possibilities

AGI is still largely in development, but data scientists are working to improve current AI technology to enable better investment predictions. The process is of course incremental, but more sophisticated algorithms are being developed, based on the trading experiences of quantitative funds, that use complex mathematical models to make predictions.

Quantitative funds rely largely on electronic trading, with millions of trades executed simultaneously, providing more data for ML models to develop more accurate predictions. The main difference between these technologies and ChatGPT is that the latter relies on ‘what is’, while AGI and advanced math-based ML analyze datasets to develop models of ‘what can (or will) be’, making them much more suitable for investment purposes .

AGI and math-based advanced ML will — eventually — enable better and more accurate investment predictions; it’s only a matter of time before scientists can build the sophisticated datasets needed to train AI to make accurate investment predictions.

Until then, let’s use current-generation ML-based systems like ChatGPT for the many things it is very good at it. “InvestmentGPT” is still in the future.

Anna Becker is CEO and founder of EndoTech